Handbook of Developmental Cognitive Neuroscience, 2nd edition (Developmental Cognitive Neuroscience)

HANDBOOK OF DEVELOPMENTAL COGNITIVE NEUROSCIENCE Second Edition

Developmental Cognitive Neuroscience Neurodevelopmental Disorders, Helen Tager-Flusberg, ed. (1999) Handbook of Developmental Cognitive Neuroscience, Charles A. Nelson and Monica Luciana, eds. (2001) Modeling Neural Development, Arjen van Ooyen, ed. (2003) Handbook of Developmental Cognitive Neuroscience, second edition, Charles A. Nelson and Monica Luciana, eds. (2008)

HANDBOOK OF DEVELOPMENTAL COGNITIVE NEUROSCIENCE Second Edition

Edited by Charles A. Nelson and Monica Luciana

A BRADFORD BOOK THE MIT PRESS CAMBRIDGE, MASSACHUSETTS LONDON, ENGLAND

© 2008 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. MIT Press books may be purchased at special quantity discounts for business or sales promotional use. For information, please e-mail [email protected] or write to Special Sales Department, The MIT Press, 55 Hayward Street, Cambridge, MA 02142. This book was set in Baskerville by SNP Best-set Typesetter Ltd., Hong Kong and was printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Handbook of developmental cognitive neuroscience / edited by Charles A. Nelson and Monica Luciana.—2nd ed. p. ; cm.—(Developmental cognitive neuroscience) Includes bibliographical references and index. ISBN 978-0-262-14104-8 (hardcover : alk. paper) 1. Developmental neurobiology—Handbooks, manuals, etc. 2. Cognitive neuroscience—Handbooks, manuals, etc. I. Nelson, Charles A. (Charles Alexander) II. Luciana, Monica. III. Series. [DNLM: 1. Nervous System—growth & development. 2. Central Nervous System Diseases—physiopathology. 3. Cognition—physiology. 4. Human Development. 5. Perception—physiology. WL 102 H23535 2008] QP363.5.H365 2008 612.8'2—dc22 2008007886 10 9 8 7 6 5 4 3 2 1

CONTENTS

Preface to the Second Edition

I.

xi

FUNDAMENTALS OF DEVELOPMENTAL NEUROBIOLOGY 1

A. GENERAL PRINCIPLES

3

1.

The Formation of Axons and Dendrites by Developing Neurons Paul Letourneau

2.

Imaging Developmental Changes in Gray and White Matter in the Human Brain 23 Elizabeth D. O’Hare and Elizabeth R. Sowell

3.

Gyrification and Development of the Human Brain Tonya White and Claus C. Hilgetag

4.

Adult Neurogenesis in the Hippocampus 51 Yevgenia Kozorovitskiy and Elizabeth Gould

5.

The LHPA System and Neurobehavioral Development 63 Amanda R. Tarullo, Karina Quevedo, and Megan R. Gunnar

6.

The Effects of Monoamines on the Developing Nervous System Gregg D. Stanwood and Pat Levitt

5

39

83

v

B. STRUCTURAL FOUNDATIONS OF SENSATION, PERCEPTION, AND COGNITION 95 7.

Mechanisms of Auditory Reorganization during Development: From Sounds to Words 97 Richard N. Aslin, Meghan A. Clayards, and Neil P. Bardhan

8.

Brain Correlates of Language Processing during the First Years of Life 117 Angela D. Friederici

9.

Brain-Behavior Relationships in Early Visual Development Bogdan F. Iliescu and James L. Dannemiller

127

10. Motor Systems Development 147 Rosa M. Angulo-Barroso and Chad W. Tiernan 11. Neurodevelopment of Social Cognition 161 Melissa D. Bauman and David G. Amaral 12. Pre- and Postnatal Morphological Development of the Human Hippocampal Formation 187 László Seress and Hajnalka Ábrahám 13. Structural Development of the Human Prefrontal Cortex Ivica Kostovic´, Milosˇ Judasˇ, and Zdravko Petanjek

213

14. White Matter Maturation and Cognitive Development during Childhood 237 Torkel Klingberg

II. METHODOLOGICAL PARADIGMS

245

15. Electrophysiological Methods in Studying Infant Cognitive Development 247 Gergely Csibra, Elena Kushnerenko, and Tobias Grossmann 16. Eye Tracking Studies of Normative and Atypical Development Canan Karatekin

263

17. Diffusion Tensor Imaging 301 Jeffrey R. Wozniak, Bryon A. Mueller, and Kelvin O. Lim 18. Functional MRI Methods in Developmental Cognitive Neuroscience Kathleen M. Thomas and Angela Tseng

311

19. Mechanisms of Language Acquisition: Imaging and Behavioral Evidence 325 Jacques Mehler, Marina Nespor, Judit Gervain, Ansgar Endress, and Mohinish Shukla

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contents

20. Magnetic Resonance Spectroscopy of Developing Brain Balasrinivasa Rao Sajja and Ponnada A. Narayana

337

21. The Integration of Neuroimaging and Molecular Genetics in the Study of Developmental Cognitive Neuroscience 351 Essi Viding, Douglas E. Williamson, Erika E. Forbes, and Ahmad R. Hariri 22. Neural Network Models of Cognitive Development 367 Yuko Munakata, Jennifer Merva Stedron, Christopher H. Chatham, and Maria Kharitonova

III. NEURAL PLASTICITY IN DEVELOPMENT

383

23. Early Brain Injury, Plasticity, and Behavior 385 Bryan Kolb, Wendy Comeau, and Robbin Gibb 24. Developmental Plasticity and Reorganization of Function Following Early Diffuse Brain Injury 399 Linda Ewing-Cobbs, Mary R. Prasad, and Khader M. Hasan 25. Plasticity of the Visual System 415 Daphne Maurer, Terri L. Lewis, and Catherine J. Mondloch 26. Cross-Modal Plasticity in Development: The Case of Deafness Teresa V. Mitchell 27. Plasticity of Speech (Animal Model) Teresa A. Nick

IV. COGNITION

439

453

465

28. The Development and Integration of the Dorsal and Ventral Visual Pathways in Object Processing 467 Mark H. Johnson, Denis Mareschal, and Gergely Csibra 29. Attention in Young Infants: A Developmental Psychophysiological Perspective 479 John E. Richards 30. Nonhuman Primate Models of Memory Development Jocelyne Bachevalier

499

31. Neurocognitive Mechanisms for the Development of Face Processing 509 Michelle de Haan 32. The Development of Visuospatial Processing Joan Stiles, Brianna Paul, and Wendy Ark

521

contents

vii

33. Mechanisms of Change: A Cognitive Neuroscience Approach to Declarative Memory Development 541 Jenny Richmond and Charles A. Nelson 34. The Development of Executive Function in Childhood Philip David Zelazo, Stephanie M. Carlson, and Amanda Kesek

553

35. The Development of Prefrontal Cortex Functions in Adolescence: Theoretical Models and a Possible Dissociation of Dorsal versus Ventral Subregions 575 Elizabeth A. Olson and Monica Luciana 36. Cognition and Aging: Typical Development 591 Jonas Persson and Patricia A. Reuter-Lorenz 37. Cognition and Aging-Dementia 607 Mischa de Rover, Sharon Morein-Zamir, Andrew D. Blackwell, and Barbara J. Sahakian

V. NEURODEVELOPMENTAL ASPECTS OF CLINICAL DISORDERS 621 38. The Role of Nutrition in Cognitive Development 623 Anita J. Fuglestad, Raghavendra Rao, and Michael K. Georgieff 39. Fetal Alcohol Syndrome 643 Sarah N. Mattson, Susanna L. Fryer, Christie L. McGee, and Edward P. Riley 40. Impact of Prenatal Cocaine Exposure on the Developing Nervous System 653 Eric M. Langlois and Linda C. Mayes 41. Neurocognitive Models of Early-Treated Phenylketonuria: Insights from Meta-analysis and New Molecular Genetic Findings 677 Marilyn Welsh, Kathryn DeRoche, and David Gilliam 42. Research into Williams Syndrome: The State of the Art Annette Karmiloff-Smith

691

43. Neurocognitive Development in Autism 701 Mikle South, Sally Ozonoff, and Robert T. Schultz 44. Tics and Compulsions: Disturbances of Self-Regulatory Control in the Development of Habitual Behaviors 717 Rachel Marsh, James F. Leckman, Michael H. Bloch, Yanki Yazgan, and Bradley S. Peterson 45. Developmental Dyslexia 739 Guinevere F. Eden and D. Lynn Flowers

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46. The Development and Cognitive Neuroscience of Anxiety Daniel S. Pine and Christopher S. Monk

755

47. Developmental Neuropsychology of Unipolar Depressions Ian M. Goodyer and Zoë Kyte

771

VI. EMOTION/COGNITION INTERACTIONS

785

48. Toward a Neurobiology of Attachment 787 Myron A. Hofer and Regina M. Sullivan 49. Sleep, Cognition, and Emotion: A Developmental View Oskar G. Jenni and Ronald E. Dahl

807

50. Neural Systems, Gaze Following, and the Development of Joint Attention 819 Peter Mundy and Amy Van Hecke 51. The Biology of Temperament: An Integrative Approach 839 Nathan A. Fox, Heather A. Henderson, Koraly Pérez-Edgar, and Lauren K. White 52. The Developing Adolescent Brain: Molecular Mechanisms Underlying Nicotine Vulnerability 855 Charles F. Landry, Terri L. Schochet, and Ann E. Kelley 53. Environmental Influences on Brain-Behavioral Development: Evidence from Child Abuse and Neglect 869 Jessica E. Shackman, Alison B. Wismer Fries, and Seth D. Pollak 54. Neurocognitive Development of Performance Monitoring and Decision Making 883 Eveline A. Crone and Maurits W. van der Molen

Contributors Index

897

901

contents

ix

Preface to the Second Edition The first edition of this Handbook appeared in 2001, at a time when the field of developmental cognitive neuroscience had only recently taken root. The volume contained fortyone chapters distributed over eight topical areas, including overviews of the fundamentals of developmental neurobiology, a surveying of methodological paradigms, neural plasticity and its expression during development and in the context of disease, sensory and motor system development, language development, cognition (broadly construed), neurodevelopmental aspects of clinical disorders, and emotion-cognition interactions. Seven years have now passed since the first edition was published and the field of developmental cognitive neuroscience has expanded enormously. To illustrate how the field has grown, we recently conducted Medline searches spanning the interval from 1902 to 2007 using the following three search parameters combined: brain, development, and cognition. From 1902 to 2001, there were 972 articles that represented this intersection of topics. From 2002 to 2007 alone, there were 988 articles. Thus, developmental cognitive neuroscience, following the pattern of its parent discipline, cognitive neuroscience, is growing at an exponential rate, with evidence of massive proliferation over the past five years. Many accomplishments within the field have resulted from the application of new methods to developmental samples. This proliferation of activity is also evident through other, more concrete, indices of change, including (a) an exponential increase in the number of developmental papers published in the Journal of Cognitive Neuroscience, (b) the appearance of special issues on this topic in a number of other journals, including Developmental Review, Child Development, Human Development, Neuropsychologia, and Developmental Psychology, (c) authored and edited volumes by a number of senior investigators (e.g., Mark Johnson, Michelle de Haan), and finally (d) the ease with which we were able to expand this volume. We have expanded to fifty-four chapters from the original forty-one. More importantly, we now present a number of areas that in our view represent new inroads made possible by advances in both developmental and cognitive neuroscience. First, there is a greater emphasis on affective and social neuroscience. This offshoot of cognitive neuroscience has firmly taken root in the adult literature and is gradually trickling down to the developmental literature. Second, we have placed a greater emphasis on clinical disorders. We have done so primarily because such work is inherently translational in nature, and translational research is currently receiving a great deal of attention by many working at the interface of brain and behavior. Finally, one chapter exclusively, and several to a lesser degree, discuss the breakthroughs being made in imaging genomics. In our mind the intersection of brain, behavior, and genetics represents an exciting new area of inquiry that will gain considerable traction in coming years, due in large part to advances being made in genetics/genomics and in neuroimaging. We are pleased to bring you this second edition and trust that it will serve as a resource for all those interested in the development of brain-behavior relations in the context of both typical and atypical development. Charles A. Nelson Monica Luciana

xi

I FUNDAMENTALS OF DEVELOPMENTAL NEUROBIOLOGY

A. GENERAL PRINCIPLES

1

The Formation of Axons and Dendrites by Developing Neurons PAUL LETOURNEAU

Introduction The neuronal circuitry that underlies human behavior and other neural functions develops over a prolonged period lasting from the second fetal month through adolescent years. These circuits arise from the extensive development of elaborate neuronal processes, as neurons express intrinsic morphogenetic behaviors, while interacting with other cells and molecules of the developing nervous system. First, immature neurons migrate from their birthplaces to the sites where they are organized into layers, nuclei, and ganglia of neuronal perikarya. Next, immature neurons sprout axons and dendrites that elongate, sometimes for many centimeters, to make synaptic connections with target neurons or other cells. This chapter describes intrinsic mechanisms of morphogenesis of axons and dendrites and the extrinsic environmental features that regulate where and when axons and dendrites grow to create neural circuits. The ability to extend neuronal processes, or neurites, is intrinsic to neurons. This is demonstrated when immature neurons, such as from prenatal hippocampus, are placed into tissue culture. Within a few hours the neurons sprout processes that elongate onto the substrate, each tipped by an adherent motile structure called a growth cone. These neurites mature to become axons and dendrites and form synapses in vitro. These events in a neutral in vitro environment show that the neuronal phenotype defines the intrinsic behaviors that produce neuronal shape. The most significant cellular components in neuronal morphogenesis are the protein polymers of the neuronal cytoskeleton. In the next section the neuronal cytoskeleton and the intrinsic mechanisms of neurite formation and elongation will be discussed. In the following three sections the regulation of axonal and dendritic growth by extrinsic molecules will be discussed.

The dynamic neuronal cytoskeleton Neuronal morphogenesis depends on the organization and dynamic properties of two cytoskeletal polymers, microtubules and actin filaments (Dent and Gertler, 2003; Luo, 2002). These cytoskeletal polymers are present in all cell types, although specific mechanisms determine cytoskeletal functions in neurons.

Microtubules Provide Support and a Means of Transport Microtubules are hollow cylinders 25 nm in diameter that extend through the cytoplasm of neuronal perikarya, axons, and dendrites (figures 1.1, 1.2). The wall of a microtubule consists of subunits of highly conserved proteins, alpha tubulin and beta tubulin. Microtubules have no defined length, and single neuronal microtubules can exceed 100 μm (Letourneau, 1982). Microtubules are rigid and resist compression to support the elaborate extensions of axons and dendrites. Microtubules are also the “rails” along which organelles are transported via linkage to the motor proteins, kinesins, and dynein (Hirokawa and Takemura, 2004). These two functions, providing structural support and being rails for intracellular transport, are the functions of neuronal microtubules. Formation of Microtubules in Cells Tubulin subunits polymerize by endwise addition to form microtubules. Because of inherent asymmetry of the tubulin protein, microtubules are polarized with a distinct molecular face at each end. Tubulin subunits are added more rapidly at one end, called the plus (+) end, while the less likely end for growth is called the minus (−) end (figure 1.1 and plate 1). Microtubules in neurons are formed in the centrosomal region near the nucleus and extend throughout the perikaryon with their minus ends anchored at the centrosome. The plus ends of cytoplasmic microtubules undergo bouts of growing and shrinking called dynamic instability, in which a microtubule end may undergo rapid disassembly, either completely or partially, which is followed by “rescue” and renewed growth (Tanaka, Ho, and Kirschner, 1995). Regulation of Microtubule Organization by MAPs In neurons, microtubule organization is regulated by a group of proteins called MAPs (microtubule-associated proteins). MAPs bind to microtubules and regulate all aspects of their organization, including assembly and disassembly, stability, and binding to neurofilaments, actin filaments, and other microtubules (Dehmelt and Halpain, 2004; Gordon-Weeks, 2000). Motor proteins, such as kinesin, bind to microtubules and move cargo toward microtubule plus ends, while dynein motors move cargo toward microtubule minus ends. The protein katanin binds microtubules and

5

Figure 1.1 Actin filaments and microtubules are polarized polymers. Actin filaments are polarized polymers for which the addition of ATP-actin is more likely at the barbed end than the pointed end. After hydrolysis of ATP-actin to ADP-actin, subunits dissociate at the pointed end. Microtubules are also polarized structures with

GTP-tubulin dimers adding to the plus or growing end and GDPtubulin dimers dissociating from the minus end. Microtubules also exhibit posttranslational modifications (detyrosination shown here) that correlate with the age and stability of the polymer. (From Dent and Gertler, 2003.) (See plate 1.)

severs them, promoting reorganization of microtubules and remodeling of neuronal shape (Baas and Buster, 2004). Some maps, such as MAP2, are localized in dendrites, while other MAPs, such as tau and MAP1B, are localized in axons. Several features distinguish microtubules in axons and dendrites. Unlike most cell types, the minus ends of microtubules in axons and dendrites are not anchored to the centrosome; rather, microtubules lie entirely within these processes. Microtubules are formed at the centrosome and then transported into axons or dendrites. Nearly all axonal microtubules have their plus ends oriented toward the terminal, while microtubules in dendrites have mixed polarity, some with plus ends and some with minus ends oriented toward dendritic termini. Many axonal and dendritic microtubules are highly stable as a result of enzymatic modifications of the tubulin protein and from binding of certain MAPs. Although microtubules must always be present to support neurites, it is uncertain how microtubules and tubulin subunits are advanced as neurites grow (Baas and Buster, 2004). Dynein motor molecules can slide short microtubules along,

depending on microtubule length and connections with other structures. Long microtubules in axons are stationary, although their plus ends undergo considerable dynamic instability of growth and shrinkage. Possibly, tubulin subunits or short microtubules are transported distally via dynein motors and then disassembled to release tubulin for addition to longer, stable microtubules. This dynamic assembly of tubulin onto existing microtubules is a critical event in the morphogenesis of axons and dendrites (Tanaka and Kirschner, 1995).

6

Actin Filaments in Neurons Actin filaments are the other important cytoskeletal components in neuronal morphogenesis (Dent and Gertler, 2003; Luo, 2002). In mature neurons, actin filaments form a cortical meshwork beneath the plasma membrane that organizes ion channels, vesicles, membrane proteins, and neurotransmitter receptors at nodes of Ranvier and at synapses. However, at the ends of growing axons and dendrites, elaborate networks of actin filaments are the organizing component that drives the searching behaviors that are necessary for navigation of

fundamentals of developmental neurobiology

Figure 1.2 The distribution of microtubules and actin filaments in developing neurons and in axonal growth cones. Microtubules (green) are densely packed with the neuronal cell bodies (S) and are bundled in the axons and branches. Actin filaments are arrayed in filament networks and bundles in the peripheral domains (P) of the

growth cones and along the shafts of the axons, where small areas of actin filament dynamics may give rise to collateral branches (B). In a growth cone, the microtubules from the central bundle of the central domain (C) splay apart, and individual microtubules extend into the P domain and into filopodia (arrows). (See plate 2.)

growth cones to their synaptic targets (figure 1.2 and plate 2; Letourneau, 1979, 1983; Yamada, Spooner, and Wessells, 1971).

Like microtubule polymerization, actin filaments polymerize by endwise addition of subunits. Also, like microtubules, the inherent asymmetry of the actin subunit leads to polarity of actin filaments, in which the “barbed” end is favored for polymerization and the “pointed” end is where actin subunits are lost from filaments. Again, like microtubules, neurons contain many proteins, whose function is to regulate the polymerization, stability, and interactions of actin filaments.

Organization of Actin in Cells Actin filaments are polymers of the conserved globular protein actin (figure 1.1). Actin filaments with a diameter of about 6–7 nm are individually not stiff, but bundles of actin filaments have stiffness. Unlike the cortical networks in mature neurons, actin filament arrays in growth cones are extensive, especially at the motile leading margin, where a dynamic actin filament network fills flattened projections, called lamellipodia, and bundles of actin filaments fill the cores of transient, fingerlike projections, called filopodia (figure 1.2; Letourneau, 1983).

Regulation of Actin Filament Organization by ABPs Actin-binding proteins (ABPs) have numerous functions (Dent and Gertler, 2003; Pollard and Borisy, 2003). One class of ABPs binds actin subunits, regulating

letourneau: formation of axons and dendrites by developing neurons

7

their availability for polymerization; other ABPs cross-link actin filaments into meshworks and bundles. ABPs that bind the barbed and pointed ends of actin filaments regulate the addition and loss of actin subunits to filaments. Several ABPs bind actin filaments and sever them, promoting the remodeling of actin filament arrays. In growth cones, actin filament barbed ends face the leading cell margin, where the addition of actin subunits is promoted by several ABPs. Myosins are motor molecules that bind and move cargoes along actin filaments. There are more than 10 myosins, which share common features of their motor activity, but which differ in the direction that they move cargoes along filaments and in cargoes that are moved (Brown and Bridgman, 2004). Myosins in growth cones interact with actin filaments and generate forces to move actin filaments, vesicles, or other cargoes and to exert tensions on cytoskeletal components and associated structures (Rochlin et al., 1995). Myosin II in growth cones is particularly important in

generating developing critical to developing

Figure 1.3 The interwoven network of signaling molecules that link guidance receptors with cytoskeletal dynamics underlying growth cone motility. Membrane receptors for extracellular guidance cues may function either alone or in a complex to activate cytoplasmic adaptors and mediators. The Rho family of GTPases may be pivotal links between guidance signals and actin-associated

proteins, which are responsible for regulating the assembly and disassembly of actin filaments. Similar types of molecules are represented by symbols of similar color and shape. Lines depict activation pathways that have been demonstrated experimentally in different systems. (From Song and Poo, 2001.)

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forces to move components and reshape axons and dendrites. In summary, ABPs are regulating the behaviors of growth cones of axons and dendrites.

Regulation of Microtubule and Actin Organization and Dynamics by Cytoplasmic Signaling Pathways As noted previously, the organization of microtubules and actin filaments is regulated by MAPs and ABPs. The dynamic changes in cytoskeletal organization that drive neuronal morphogenesis reflect the activities of MAPs and ABPs. Certainly, levels of these proteins are regulated by gene transcription and protein synthesis, but in an immediate fashion, MAPs and ABPs are regulated by intracellular signaling and cytoplasmic second-messenger pathways. Cytoskeletal organization can be rapidly changed by fluctuations in levels of small molecules such as Ca++ ions, cAMP, cGMP, and phosphoinositides that bind MAPs and

fundamentals of developmental neurobiology

ABPs and regulate them allosterically (Dent and Gertler, 2003; Song and Poo, 2001; figure 1.3). The addition to and removal of phosphate groups from MAPs and ABPs by protein kinases and phosphates also rapidly regulate their activities. These molecules and pathways are, in turn, regulated by events at the plasma membrane, where adhesive proteins, growth factors, and other ligands bind membrane receptor proteins to trigger events that locally and temporally modulate the levels and activities of these regulatory molecules. Thus cytoplasmic signaling activities that cascade from ligand-receptor interactions at the plasma membrane rapidly and locally regulate cytoskeletal organization during neuronal morphogenesis (Dent and Gertler, 2003; Gallo and Letourneau, 2004). The Rho family of small guanosine triphosphatase (GTPase) proteins, in particular RhoA, Rac1, and Cdc42, are important regulatory proteins that relay signaling from the cell surface intracellularly to the cytoskeleton (Jaffe and Hall, 2005; figure 1.3). Rho GTPases bind to and regulate MAPs and ABPs or their upstream regulators, such as protein kinases and phosphatases. A critical feature of GTPases is that their activity is rapidly switched on or off, depending on whether they are bound to the nucleotides GTP (on) or guanosine diphosphate (GDP) (off). A rich variety of guanine nucleotide exchange factor proteins (GEFs) selectively activate GTPases by exchanging GDP for GTP; GTPase-activating proteins (GAPs) stimulate hydrolysis of GTP to inactive GTPases; and GDP dissociation inhibitors (GDIs) inhibit activation of GTPases by GEFs. These GEFs, GAPs, and GDIs are regulated by cell surface ligand-receptor interactions. Thus by regulating GTPases these membrane events regulate cytoskeletal proteins. Activation of RhoA, Rac1, or Cdc42 has distinct effects on actin filament organization (Jaffe and Hall, 2005). Rac1GTP activates several ABPs to stimulate actin polymerization and formation of lamellipodia, while Cdc42-GTP also stimulates actin polymerization and formation of filopodia. RhoA-GTP activates the kinase ROCK, which phosphorylates several substrates to suppress actin polymerization and activates the motor protein myosin II, increasing mechanical tensions and rearrangements of actin filaments. If RhoA levels are highly elevated, strong contractile forces in the growth cone can cause collapse of microtubule arrays and significant neurite retraction. All three Rho GTPases are present in the growth cone and contribute to growth cone motility. Microtubule organization and polymerization are also regulated by Rho GTPases, although the mechanisms are less well understood than for actin filaments. Microtubule-Actin Interactions Are Important Two particular interactions of actin filaments, which we will describe, are particularly important in neurite elongation and growth cone migration. As mentioned earlier,

microtubules maintain the shapes of axons and dendrites and resist compressive forces that would collapse or withdraw these processes. Proteins that mediate interactions between microtubule plus ends and actin filaments are particularly significant, because these proteins may be important in the initiation of neurites from a spherical perikaryon or in directing the advance of a growth cone (Rodriquez et al., 2003). These microtubule-actin interactions link the microtubule functions of structural support and organelle transport to the dynamic cortical actin filaments and associated membrane receptors that detect extrinsic signals and regulate the cytoskeletal activities that shape the developing neuron (figure 1.4). A Stage 1.1

Stage 1.2

Stage 1.3

Stage 2

B MAPs stabilize microtubules MAPs might promote microtubule bundling MAPs might promote microtubule/actin interactions

MAPs Actin Microtubules

Figure 1.4 A model for cytoskeletal reorganization during neurite initiation. (A) Shortly after plating, cultured hippocampal neurons extend a uniform lamellipodium that surrounds the cell soma (stage 1.1). Preceding the initial neurite outgrowth, the lamellipodium becomes segmented at one or more sites (stage 1.2). Then the lamellipodium migrates away from the cell soma to form a growth cone, concurrent with microtubule advance into the initiation site and formation of an ordered microtubule array (stage 1.3). The newly formed protrusion then elongates, and microtubules become tightly packed into parallel arrays inside the nascent neurite (stage 2). Actin-microtubule interactions are present in lamellipodia at all stages. Panel B depicts a more detailed view of the proposed cytoskeletal organization in stage 1.3. Interestingly, microtubules preferentially grow along actin bundles in filopodia, suggesting that a physical link between the structures exists. Multifunctional MAPs like MAP1B, MAP2, or plakins are candidates to act as such links. (From Dehmelt and Halpain, 2004.)

letourneau: formation of axons and dendrites by developing neurons

9

Actin Filaments and Adhesive Contacts In addition to interactions with microtubules, another key function of actin filaments involves the adhesive interactions of cells that are mediated by membrane receptor proteins that form noncovalent bonds between cells or between cells and extracellular matrices (ECM). The major adhesion receptors are the cadherins and the adhesion proteins of the immunoglobulin-like superfamily, which mediate cell-cell adhesions, and the integrin proteins, which mediate cell adhesion to ECM. As cell-cell contacts are initiated by intercellular binding, receptors cluster within the plasma membrane to form discrete adhesive contacts. By way of transmembrane linkage these clustered adhesion receptors create docking sites for signaling enzymes, kinases, GEFs, GAPs, and a number of ABPs that link actin filaments to the adhesive sites and induce actin polymerization. Thus adhesive sites are loci from which regulatory signals emanate and where actin filament organization and anchorage are regulated (Zamir and Geiger, 2001).

A mechanism for neurite initiation and growth In this section, neuritogenesis, neurite elongation, and growth cone migration by neurons will be described, emphasizing the dynamic cytoskeleton of actin filaments and microtubules. When developing neurons are placed in culture, the neurons settle on the substrate, and extend and withdraw cylindrical filopodia and flattened lamellipodia, like waves lapping on a beach (see figure 1.4). This motility is driven by actin filament polymerization, which pushes the cell margin outward, while simultaneously myosin II, located behind the cell margin, pulls newly formed filaments backward in a retrograde flow. The rearward transported filaments are severed and depolymerized, and if the protrusion and retrograde flow are equal, these activities produce no net change. Initially, microtubules remain in a loose network around the nucleus, and any microtubules that enter the protrusions are swept back with the retrograde flow of actin. However, eventually a filopodium or lamellipodium thickens and moves away from the cell body, tethered by a cylindrical nascent neurite. The critical step that distinguishes neurite formation from the initial protrusive activity occurs when microtubules and associated organelles enter and remain within a filopodial or lamellipodial protrusion and the protrusive motility moves forward ahead of the microtubules and organelles (Da Silva and Dotti, 2002; figure 1.4). Several activities may prompt neurite initiation. An increased expression of MAPs, such as MAP2, tau, and MAP1B, may stabilize microtubules, enhancing their resistance to the myosin-based retrograde forces pulling actin back from the leading margin (Dehmelt and Halpain, 2004). At sites where protrusions make firm adhesive contacts with the substrate, actin filaments become anchored to the adhesive apparatus,

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and retrograde flow stops, creating space into which microtubules can advance. In addition, cytoplasmic signals generated at the adhesive sites may promote microtubule transport and polymerization. Finally, actin filaments linked to adhesive sites can interact with myosin II motors and pull microtubules and organelles toward the adhesive sites in opposition to the retrograde flow of untethered actin filaments (Suter and Forscher, 2000). The significance of these outwardly directed forces in neurite initiation is illustrated by findings that neurites can be pulled out from a neuron by attaching an adhesive bead to a neuronal surface and then pulling the bead and attached elongating neurite away from the nerve cell body (Fass and Odde, 2003). Organization of Growth Cones and Growth Cone Migration A typical neurite has a central bundle of microtubules with associated organelles and a motile terminal expansion, the growth cone (Gordon-Weeks, 2000; figures 1.2, 1.5). At the growth cone’s leading margin, called the P-domain (peripheral), vigorous actin polymerization pushes the cell margin forward, balanced by the myosinpowered rearward sliding of untethered actin filaments. Only when the leading edge forms transient adhesive contacts that link to actin filaments does the retrograde flow attenuate. At the base of a growth cone, microtubulebased motor proteins move microtubules and organelles from the neurite into the central growth cone, comprising the C-domain (central). From the C-domain individual microtubules extend into the P-domain, sliding forward powered by molecular motors and elongating by adding subunits to microtubule plus ends. Retrograde flow pulls most of these microtubules back into the C-domain (Schaefer, Kabir, and Forscher, 2002). Importantly, some microtubules advance into filopodia or lamellipodia, stabilized at adhesive sites (figure 1.2; Letourneau, 1979; Suter and Forscher, 2000). If these microtubules persist and are followed by other microtubules and organelles, the Cdomain advances and the neurite extends. To complete the cycle of growth cone movement, actin filaments and membrane components that are not stabilized by adhesions or associations with microtubules are recycled at the back of the growth cone by the myosin II–powered retrograde flow and by disassembly of actin filaments and endocytosis of plasma membrane. Thus neurite elongation proceeds by three activities (Dent and Gertler, 2003; figure 1.5): (1) the advance, expansion, and adhesion of the leading margin of the growth cone, driven by actin polymerization; (2) the advance of microtubules via polymerization, transport, and linkage to actin and adhesive sites (Letourneau, 1979); and (3) the advance of organelles via microtubule-based transport. The coordination of actin-driven membrane expansion, formation of adhesive contacts, and myosin II–powered exertion of

fundamentals of developmental neurobiology

Figure 1.5 Stages of axon and branch growth. Three stages of axon outgrowth have been termed protrusion, engorgement, and consolidation (Goldberg and Burmeister, 1986). Protrusion occurs with the rapid extension of filopodia and thin lamellar protrusions, often between filopodia. These extensions are primarily composed of bundled and meshlike F-actin networks. Engorgement occurs when microtubules invade protrusions bringing membranous

vesicles and organelles (mitochondria, endoplasmic reticulum). Consolidation occurs when the majority of F-actin depolymerizes in the neck of the growth cone, allowing the membrane to shrink around the bundle of microtubules, forming a cylindrical axon shaft. This process also occurs during the formation of collateral branches off the growth cone or axon shaft. (From Dent and Gertler, 2003.)

tension on these adhesive sites generates a force that pulls the growth cone forward. Thus neurite elongation involves “push” from the advance of microtubules and “pull” from myosin II–powered tension generated at adhesive sites at the growth cone margin (Lamoureux, Buxbaum, and

Heidemann, 1989; Letourneau, 1981; Letourneau et al., 1987). Experimental studies show that the “push” of microtubule advance is necessary for neurite elongation, while the “pull” of actin-based motility in growth cones is neither necessary nor sufficient for neurite elongation. However,

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growth cone “pull” accelerates neurite elongation and, as described later, is necessary for growth cone navigation. Growth Cone Turning Growth cone navigation to synaptic targets occurs by the selective turning, advance, or retreat of a growth cone in response to guidance cues that a growth cone encounters within developing tissues. As described previously, a neurite elongates by the advance of microtubules and organelles from the growth cone Cdomain into the P-domain. In a neutral in vitro environment, this elongation may occur first to one side and then to the other, keeping the growth cone on a straight path. In a complex in vivo environment, however, there are local differences in adhesive surfaces, extrinsic factors, or other ligands that interact with growth cone receptors to generate local differences in the activities of Rho GTPases, protein kinases, protein phosphatases, or second messengers, such as Ca++ or cyclic nucleotides (Gomez and Zheng, 2006; Guan and Rao, 2003; Song and Poo, 2001). On

Figure 1.6 Summary of reorganization of actin filaments and microtubules in the peripheral domain of growth cones that is involved in turning toward an attractant and turning away from a repellent guidance molecule. An attractant promotes actin

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the one hand, if these local variations in regulatory cues are sufficiently strong or persistent, they produce local differences in actin-based motility and microtubule advance that cause growth cone turning. This outcome might occur because the P-domain expands faster on one side as a result of locally enhanced actin polymerization, reduced retrograde actin flow, or linkage of actin filaments to adhesive sites (figure 1.6, upper panels). Localized signals might directly promote microtubule polymerization or stabilization, so that microtubules preferentially advance to one side of the P-domain (Challacombe et al., 1997; Dickson, 2002; Tanaka and Kirschner, 1995). On the other hand (see figure 1.6, lower panels), if local differences in signals triggered by extrinsic cues reduce actin-mediated protrusion on one side of a growth cone or if myosin II– powered retrograde flow of actin filaments increases on one side of a growth cone, microtubule advance on that side will be reduced, and the growth cone will turn toward the other side.

polymerization, adhesion, and microtubule advance, while a repellent inhibits actin polymerization and advance of microtubules. (Adapted from Dickson, 2002.)

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Mechanisms of Branching Branches of neurites, axons, or dendrites are formed in two ways: by a growth cone splitting or by a new branch sprouting from the neurite shaft behind a growth cone. In either case, the acquisition of stable microtubules is key to forming a branch (figures 1.2, 1.5). In a growth cone, part of the P-domain and associated C-domain may separate from the whole and establish an independent growth cone and a new branch of the parent neurite. This result may occur when a growth cone “pulls” in two directions (figure 1.2). Branch formation along a neurite is initiated by localized protrusion of filopodia or lamellipodia (figure 1.2; Gallo and Letourneau, 1998). This mechanism is particularly prevalent in the branching morphogenesis of dendrites. This localized actin-based motility may occur until microtubules enter an actin-filled nascent branch by transport or by polymerization of microtubules from the main neurite (Gallo and Letourneau, 1999). Microtubule ends in the main neurite may become linked to actin filaments of the protrusion and be pulled into the branch. The microtubule-severing protein katanin may promote branch formation by severing microtubules in the neurite shaft to create microtubule ends that can be moved into a nascent branch (Baas and Buster, 2004). Once stable microtubules are established, the advance of microtubules and organelles into the branch sustains its growth. The Differentiation of Axons and Dendrites; Polarization of Neuronal Form A hippocampal neuron in vitro initially sprouts several similar neurites that extend slowly. After 18–24 hours one neurite expands its growth cone and elongates significantly faster than the others. This neurite becomes the axon, and it accumulates proteins typical of axons, such as the MAPs tau and MAP1B, and GAP43, a protein involved in actin motility (Mandell and Banker, 1996). Several molecules and pathways may be critical to axonal specification, including PI3 kinase, the Par complex, and small Rho GTPases (Arimura and Kaibuchi, 2005; Wiggin et al., 2005). These molecules concentrate at the tips of newly specified axons and are implicated in regulating key activities, such as actin filament organization, microtubule polymerization or stability, and transport and addition of plasma membrane components. It is unclear whether axonal specification always begins with the same upstream event, such as concentration of PI3 kinase activity in a neurite tip, or whether concentration of any of the previously mentioned molecules or signals is sufficient to specify axonal character. In vitro manipulations, such as focally pulling on a neurite or presenting adhesive proteins to one neurite will induce a neurite to become the axon. Thus extrinsic signals can influence the intrinsic mechanism of axonal specification, perhaps by locally activating PI3 kinase or other components of the mechanism. After one neurite becomes the axon, the other neurites become

dendrites. Less is known about the mechanisms of dendrite specification. Acquisition of microtubules with mixed polarities may be important, as well as localization of cytoskeletal, membrane, and signaling components that regulate dendritic characteristics.

Regulation of neuronal morphogenesis in vivo The previous section focused on the intrinsic mechanisms of neurite initiation and elongation, growth cone migration and turning, neurite branching, and the specification of axons. This section will discuss the roles of extrinsic molecules and signaling events in regulating neuronal morphogenesis in the developing human brain. The neutral environment of a tissue culture dish facilitates understanding these intrinsic mechanisms. However, the in vivo environment is never neutral, and spatial and temporal patterns of distribution of axonal guidance cues in the environment of the developing brain shape these intrinsic morphogenetic mechanisms to generate neural circuits (Tessier-Lavigne and Goodman, 1996). Neuronal Migration Immature neurons arise from proliferation of neural precursors in the ventricular zone of the developing brain. From their birth immature neurons become polarized by asymmetry in local cues, including the adhesive protein laminin in the underlying extracellular matrix (ECM) of the ventricular layer, as well as growth factors, morphogens, and guidance molecules, such as sonic hedgehog and netrin, produced by the surrounding neuroepithelial cells. These newly born neurons migrate out of the ventricular zone of the telencephalon to establish the cortical plate in a wave of migration between 6 and 18 gestational weeks (Ramakers, 2005). Migrating neurons retain their initial polarization and encounter additional cues as they migrate upward. Neural migration stops at the outer marginal zone, where reelin, produced by CajalRetzius cells of the marginal zone, triggers neurons to cease expressing integrin adhesion receptors. Younger neurons migrate past older neurons to reach the marginal zone, so the upper layer II contains the youngest neurons, while the oldest neurons inhabit the lowest layer VI. Neuronal Polarization and the Initial Growth of Axons and Dendrites Neurons sprout axons soon after ceasing migration, as early as the seventh week in the cortex. In a neutral tissue culture environment, it is a random decision as to which neurite sprouted from a neuron becomes the axon, but cortical neurons in vivo always sprout their axon in the same direction that the axon will grow. In the model organism, Caenorhabditis elegans, a diffusible molecule netrin produced by ventrally located cells causes localized activity of PI3 kinase in young neurons, which then sprout

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their axon toward the netrin source (Adler et al., 2006). PI3 kinase is involved in axonal specification of mammalian neurons, and thus localization of PI3 kinase in response to a local cue may both specify axonal identity and regulate actin motility to control the direction of axonal initiation. Other factors are implicated in regulating the initial direction of cortical axonal growth. Immature cortical pyramidal neurons first extend an axon toward the ventricle, followed by an apical dendrite, which grows toward the pial surface. Unexpectedly, these opposite directions of axonal versus dendritic growth are regulated by the same extracellular molecule, semaphorin 3A (Sema3A), produced by cells near the pial surface and released to create an extracellular gradient (Whitford et al., 2002). Axons are repelled by Sema3A, while the subsequently formed apical dendrites of these neurons are attracted by Sema3A. The difference in directions of these processes lies not in a local difference in expression of membrane receptors for Sema3A, but rather in a local difference in distribution of signaling proteins that modulate levels of the cyclic nucleotide cGMP. The combination of Sema3A signaling and high cGMP levels in the apical dendrite promotes actin polymerization and dendritic growth, while Sema3A signaling in the axon combined with low cGMP activates the GTPase RhoA, which depresses actin dynamics and activated myosin II contractility, so the axonal growth cone migrates away from the Sema3A source. Thus the opposite responses of axons and dendrites to

Sema3A are due to an asymmetric distribution of cytoplasmic signaling components in dendrites versus axons.

Figure 1.7 Summary of the action of guidance cues that are involved in growth cone navigation. Short-range cues on surfaces that growth cones come into contact with act to promote or inhibit growth cone adhesion and migration. Long-range cues are diffus-

ible molecules released from intermediate or synaptic targets that attract or repel migrating growth cones. Growth cones integrate information coming simultaneously from multiple cues during navigation. (From Tessier-LaVigne and Goodman, 1996.)

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Axonal Guidance Once sprouted from neuronal perikarya, axons follow stereotypical routes to their targets. This pathfinding occurs by growth cone navigation; that is, a growth cone detects and responds to physical and chemical features in its environment (figure 1.7). The protrusion of filopodia and lamellipodia from the growth cone of a 1μm-diameter axon allows exploration of an expanded search area 25 μm or more across. When filopodial and lamellipodial protrusion is suppressed, axons grow, but they do not navigate accurately, because without filopodial and lamellipodial protrusions a growth cone’s search area is too small to localize guidance cues. If the path of a growth cone to its target is long, the path is divided into several segments, each ending at an intermediate target to which the growth cone navigates. Often these intermediate targets represent a choice point at which a growth cone turns or changes direction as it enters the next segment of its journey. Pathways for growth cone navigation contain molecules that promote adhesion and growth cone migration. Molecules that repress adhesion or actin dynamics are expressed adjacent to these pathways, acting like “guard rails” to keep growth cones migrating on the proper path. Several proteins have been identified as negative guidance cues, including slit proteins, Sema3A, and several

fundamentals of developmental neurobiology

ephrinA’s. Each negative cue is detected by a different specific receptor with specific signaling mechanisms, although common features of these mechanisms include disruption of growth cone adhesions, suppression of actin polymerization, and activation of RhoA to stimulate myosin II–mediated contraction, leading to growth cone collapse and sometimes retraction of entire axonal branches or segments (Guan and Rao, 2003). Some molecules simply mark a path as positive or negative without providing directional information, while other molecules are soluble, are released by navigation targets, and are distributed in gradients that provide directional information to growth cones. At any instant a growth cone is detecting several guidance molecules, so growth cone migration depends on integrating the intracellular signals simultaneously triggered from multiple receptors. The following section describes specific features of growth cone guidance in the developing CNS. Most of the molecular information about growth cone guidance comes from studies of model vertebrate systems, but the timing of the events in human brain development is included (Ramakers, 2005).

Navigation of Corticofugal Axons As stated earlier, cortical neurons sprout their axons away from the pial sur-

face in response to a gradient of the repellent cue Sema3A. At eight weeks the earliest corticofugal axons reach their first target, the intermediate zone, attracted by Sema3C, expressed in the subventricular zone (figure 1.8 and plate 3; figure 1.9 and plate 4). The intermediate zone is rich in extracellular matrix and contains laminin, an adhesive protein that binds growth cone integrin receptors to form adhesive contacts that promote actin polymerization and give growth cones traction to migrate. The intermediate zone is the first choice point for corticofugal axons, as they encounter the repellent Sema3A, expressed by the underlying ventricular zone. Growth cones of corticothalamic and corticospinal axons turn laterally to exit the dorsal telencephalon through the internal capsule, while growth cones of corticocortical axons turn medially. The molecules or cells that mediate this first decision are unknown. The internal capsule contains the attractant netrin-1, which along with laminin promotes growth through the internal capsule. As these axons traverse the internal capsule, they are prevented from moving medially by expression of the repellent cues, slit-1 and slit-2, in the ganglionic eminence (Bagri et al., 2002). At the telecephalic-diencephalic boundary these corticofugal axons reach another choice point and split into two groups. Corticothalamic axons turn toward the thalamus, while corticospinal axons continue caudally, avoiding slit proteins expressed in ventromedial diencephalon.

Figure 1.8 The trajectory of growing thalamocortical and corticothalamic fibers involves multiple steps and both attractive and repulsive guidance cues. The expression of guidance molecules is related to each of these steps: Slit is a repellent that steers thalamic axons emerging from the diencephalon and in the ventral telencephalon. Ephrin-A5 is involved in sorting thalamocortical axons in the ventral telencephalon. Netrin-1 is an attractive factor for

both populations of fibers in the internal capsule. Semaphorins 3A and 3C steer cortical fibers to penetrate the intermediate zone and then turn. EphA4 in the thalamus and ephrin-A5 in the cortex are involved in the establishment of topographic connections. Th, thalamus; Hyp, hypothalamus; IC, internal capsule; GE, ganglionic eminence; Ncx, neocortex. (From Uziel et al., 2006.) (See plate 3.)

Growth cone navigation along major pathways during cerebral cortical development

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Figure 1.9 Schematic diagrams of coronal sections through the developing forebrain showing the trajectory of corticospinal (red), corticothalamic (blue), and thalamocortical (purple) axons in relation to regions that express slit-1 (blue) and slit-2 (yellow) at selected

levels. Regions depicted in green express both slit-1 and slit-2. CGE, caudal ganglionic eminence; H, hippocampus; ic, internal capsule; NCx, neocortex. (From Bagri et al., 2002.) (See plate 4.)

Growth cones of corticothalamic axons may turn in response to attractants released from their thalamic target, or they may recognize early thalamocortical axons and grow along them to reach the thalamus. Axons express several adhesion molecules, including L1 and N-cadherin, that bind homophilically to the same molecules on growth cones to form adhesive contacts that promote growth cone migration. Growth cone migration along previously extended axons is a major means of axonal growth in many tracts, and it is common that the first axons that establish a path become “pioneer fibers” that are followed by subsequent growth cones. Corticospinal axons continue toward the hindbrain until they reach the decussation area at 10 gestational weeks in humans (Ramakers, 2005; Ten Donkelaar et al., 2004). Although corticospinal axons were previously repelled from the midline by slit proteins and other negative cues, these repellents are not expressed in the decussation area, and corticospinal axons now respond to attractants such as netrin, to cross the midline, completing the decussation by week 17. Renewed expression of midline repellents, slit and ephrin3B, caudal to the decussation, prevents corticospinal axons from recrossing as they grow down the spinal cord. The lumbrosacral area is reached by 29 weeks, but growth cones do not enter spinal cord gray matter for several weeks. Innervation of target areas of gray matter by corticospinal axons occurs in an interesting manner. Corticospinal axons

initially extend beyond their target areas. Eventually, target cells release attractants and express adhesive ligands that specifically activate local regions along the afferent axons. Activation of Rho GTPases and ABPs induces localized actin-based protrusive activity from the axonal shafts, followed by collateral branches that grow into the targets. The axonal segments that extend beyond the innervated target are then eliminated via retraction involving myosin II. This exuberant growth of axons followed by retraction of mistargeted axonal segments is a common feature in the development of many cortical circuits (Innocenti and Price, 2005). The corticocortical fibers that form the corpus callosum make several guidance decisions after their first decision to turn medially in the intermediate zone (Richards, 2002). The molecules that guide these decisions are unknown, although the callosal path may be “pioneered” by axons from the cingulated cortex, creating an axonal path that is followed by neocortical axons to the midline. The growth cones of corticocortical axons are attracted by netrin-1, produced by midline cells, and channeled to cross the midline by repulsion from slit proteins expressed by cells above and below the developing corpus callosum. In humans the corpus callosum begins forming by 11–12 weeks and is well developed by 18–20 weeks. After navigating dorsally and into the contralateral hemisphere, the axons reach the cortical subplate where they extend and branch, remaining for several

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fundamentals of developmental neurobiology

Figure 1.10 Mechanisms and molecules controlling retinotopic mapping in chicks and rodents. The names and/or distributions of molecules known, or potentially able, to control the dominant mechanisms at each stage are listed. The gradients represent the consensus distribution for a combination of related molecules (i.e.,

ephrin-A’s), which are not listed individually owing to distinctions in the individual members expressed and the precise distributions between species. Molecules other than those listed are likely to participate. (From McLaughlin and O’Leary, 2005.) (See plate 5.)

weeks before sprouting collateral branches at 28 weeks into their appropriate final target regions of the cortex.

signaling activated by guidance cues, and temporal and spatial differences in the expression of guidance cues and their receptors by developing tissues and neuronal populations. An interesting recent finding is that growth cone responses to guidance cues may depend on bursts of local protein synthesis of receptors or signaling components within a growth cone. For example, Sema3A rapidly stimulates synthesis of the GTPase RhoA from mRNA within growth cones (Wu et al., 2005). RhoA activity is necessary for Sema3A induction of growth cone collapse. Some growth cones cross the ventral spinal cord and only then synthesize and express EphA receptors that mediate a repulsive response to midline ephrins, preventing recrossing the midline (Brittis, Lu, and Flanagan, 2002). Much remains to be learned about how growth cones detect guidance cues and integrate complex signals to navigate to their intermediate and final targets.

Navigation of Thalamocortical Growth Cones Thalamocortical afferent axons begin their navigation by growing ventrally until they are stopped by repulsion from slit proteins expressed by the underlying hypothalamus (Lopez-Bendito and Molnar, 2003; Uziel et al., 2006; figure 1.8). Then the growth cones turn laterally, being attracted by netrin-1 expressed by cells in the internal capsule. The growth cones turn dorsally and migrate toward the cortex within the internal capsule, keeping lateral in response to slit proteins expressed by the ganglionic eminence (Bagri et al., 2002). Within the internal capsule, thalamocortical axons meet corticofugal fibers, which they follow toward their cortical targets. Thalamocortical axons penetrate the cortical subplate between 9 and 18 weeks in developing humans. By 24 weeks they fill the upper subplate and extend branches exploring for their correct cortical targets. Thalamocortical axons finally enter the cortex between 26 and 28 weeks, prior to the entry of callosal axons. The preceding paragraphs have described how axons navigate to their targets by detecting and responding to guidance molecules that regulate growth cone motility. It may seem that the relatively limited numbers of guidance molecules, laminins, ephrins, semaphorins, netrins, slits, and immunoglobulin-like adhesion molecules are too few to account for the complexity of neural circuitry (Yu and Bargmann, 2001). However, this diversity of axonal pathways arises from cell-type–specific differences in expression of receptors for guidance cues, in downstream cytoplasmic

Patterning Axonal Distribution within Targets Once a group of axons reach their synaptic target, they become organized into patterns that represent physiologically relevant topography or sensory parameters. The distribution of retinal ganglion cells’ axons in their midbrain target (optic tectum or superior colliculus) is a model system in understanding this process (McLaughlin and O’Leary, 2005). Gradients in the distribution of ephrins and their Eph receptors on cells across the optic tectum (or colliculus) and the incoming retinal axons and growth cones are key features that determine the topography of retinal inputs to the tectum (figure 1.10 and plate 5). Ephrin-A2 and -A5 are expressed in an increasing gradient from the anterior to posterior

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tectum. EphA receptors bind ephrin-A ligands and trigger decreased Rac1 and Cdc42 activities and increased RhoA activity, stimulating growth cone repulsion. Growth cones of temporal retinal axons express high levels of EphA receptors, so they stop and innervate the anterior tectum, while nasal retinal growth cones, expressing lower levels of EphA receptors, extend to the posterior tectum, because they are less repelled by the ephrin-A gradient. The distances that retinal growth cones migrate along the anterior-posterior tectal gradient of increasing ephrin-A expression are determined by the relative levels of EphA expression by growth cones. Retinal mapping along the medial-lateral tectal axis involves gradients in the distribution of ephrin-Bs and their EphB receptors among retinal axons and tectal cells. The identification of signaling activity from the cytoplasmic domain of ephrin-B ligands indicates that both ephrins and EphB receptors can activate cytoplasmic signaling to regulate axonal targeting along the mediallateral tectal axis. This mechanism for topographic mapping of connections by gradients of cell surface ligands and receptors was proposed by Roger Sperry (1963) as the chemoaffinity hypothesis. The discovery of gradients in expression of ephrins-A2 and -A5 confirmed Sperry’s hypothesis. It has become clear that the initial distributions of axons, as regulated by these gradients, is not final, and that subsequent remodeling of axons due to further cellular interactions and physiological activities is necessary to create more precise neural circuits. The patterning of inputs to a target depends on activities distributed along the afferent axons, in addition to the growth cones. Local signaling by guidance cues or other physiological events along axonal shafts can rapidly regulate activities of RhoA or Rac1 and Cdc42 to regulate actin dynamics and myosin II activity to induce retraction or addition of collateral or terminal branches along developing axonal shafts (Gallo and Letourneau, 1998). The accessibility and simple anatomy of the retinotectal projection have allowed much progress in understanding the patterning of developing neural circuits. The discovery of gradients in the distributions of ephrin-A and EphA receptors in the neocortex and thalamus, respectively, indicates that gradients of interacting ephrins and their receptors have similar roles in regulating axonal guidance and patterning of thalamocortical connections to their targets in the primary sensory regions of the cerebral cortex (Uziel et al., 2006; figure 1.8). Similar mechanisms may operate in patterning the development of circuits in other regions (Flanagan, 2006). In addition to the development of the correct distribution of axons within a target, axons must recognize the target neurons with which they make synapses. Several cell surface and extracellular molecules are expressed in a lamina-specific manner in the developing cortex, including

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cadherins, Eph receptors, ephrin ligands, proteoglycans, and neurotrophins (Lopez-Bendito and Molnar, 2003). These molecular differences may provide cues for thalamocortical and corticocortical axons to terminate in the correct layer.

Development of dendrites The dendritic arborization of a neuron contains the synaptic inputs to the neuron and is where synaptic inputs are integrated before the initiation of action potentials. Thus dendritic arbors are critical to the processing of neural information for behavior and other neural activities. Like the formation of axons, dendrite formation is intrinsic to the neuronal phenotype. In fact, different neuronal types in a neutral tissue culture environment will form dendritic arbors that are reminiscent of their characteristic in vivo morphologies. As described earlier, the same basic mechanisms of actin filament and microtubule dynamics operate to drive the formation of dendrites, although dendrites are more numerous, shorter, and more elaborately branched than axons, due to expression of dendritic-specific cytoskeletal, membrane, and signaling proteins. Generally, a neuron initiates dendrites after it is actively engaged in axonal elongation. This lag may be several days, and may be due to both environmental factors and intrinsic factors, such as changes in expression of specific cytoskeletal proteins. The sites of dendrite initiation from a neuron may be determined by previous cell interactions; for example, the apical dendrites of cerebral cortical neurons are formed from the leading process with which immature neurons had migrated from the ventricular lining of the cortex. As described previously, the apical dendrites of cortical neurons are oriented by an attractive response to Sema3A, produced at the pial surface. Other extrinsic proteins produced by neighboring cells or afferent axons promote the formation of dendrites, including osteogenic protein-1 (BMP7) and neurotrophins BDNF and NT-3 (Whitford et al., 2002). Thus intrinsic regulation of cytoskeletal and membrane components combined with availability of extrinsic factors, such as osteogenic protein-1 and neurotrophins, orchestrates the initiation and elongation of branched dendritic arbors. However, as described in the following paragraphs, the formation of dendrites is a prolonged activity, and the final shaping of dendritic arbors depends heavily on afferent inputs and interactions with axon terminals (Van Aelst and Cline, 2004). Visualization of the morphogenesis of individual dendrites in developing brains of living frogs and zebra fish has revealed rapidly changing addition and loss of small branches and arbors as dendrites interact with afferent axons. Filopodia transiently extend from dendritic shafts and termini, and if contacts are made with axonal growth cones, the dendritic filopodium may be stabilized, and nascent synapses may form. However, many of these contacts and

fundamentals of developmental neurobiology

synapses are brief, and the terminal axonal and dendritic branches may be retracted. Synaptic activity is a factor in dendritic morphogenesis, and activation of NMDA receptors at nascent synapses may regulate Rho GTPases to modulate actin filament dynamics that underlie the extension and retraction of dendritic filopodia (Van Aelst and Cline, 2004). The roles of these synapses in regulating dendritic growth may also change as the synapses mature. Postsynaptic activation at early synapses may stimulate formation of more dendritic filopodia and elaboration of dendritic branches, while signaling at more mature synapses may generate stop-growing signals to stabilize dendritic arbors. New excitatory synapses contain NDMA receptors only, and AMPA receptors are added later. Addition of AMPA receptors to synapses may be required for retention of synapses and stabilization of dendritic arborizations. The final shaping of axonal terminals is also dependent on interactions with dendrites and postsynaptic contacts. Retrograde synaptic interactions may signal growth cones to reduce their dynamic activity, stop, and transform to a presynaptic ending. Motor axons growing on muscle fibers of mice that lack the Achreceptor-aggregating protein, agrin, or the agrin receptor component, MUSK, extend abnormally long distances across muscle surfaces, implicating MUSK and agrin in an axonal “stop signal.” The neuromuscular junction contains a laminin isoform, S-laminin, that inhibits axonal growth. Nitric oxide, which is released by dendrites in response to synaptic activity, may be a retrograde signal that stops axonal growth in synaptic regions. Dendritogenesis in the Prenatal and Postnatal Human Brain Neurons begin to form dendrites soon after they initiate axon formation, although dendrites are initially short and slow growing. Apical dendrites are present on cortical pyramidal neurons by 12–13 weeks’ gestation. However, once innervating axons arrive in the cortical plate at 26–28 weeks, dendrite formation accelerates as a result of synaptic contacts, electrical stimulation by axons, and the release of neurotrophins and other factors from axons. In humans, most dendritic growth occurs postnatally in conjunction with synaptogenesis and the increased physiological experience and activity of postnatal life. Dendrite formation in the developing human brain has been examined most thoroughly in the visual cortex and prefrontal cortex (Ramakers, 2005). In the visual cortex most dendritic branches develop prenatally, and postnatal growth involves dendritic lengthening by terminal growth of branches as synapses are added. Total dendrite length of pyramidal neurons in the visual cortex increases rapidly in the first few postnatal months, increasing two- or threefold and reaching the adult levels by 1–2 years. In the prefrontal cortex, synaptogenesis and dendritic growth proceed more slowly than in the visual cortex. During the first postnatal year the

length of dendrites increases 5- to 10-fold by branching and elongating, while after the first year most growth occurs by elongation of branches. By two years of age the total dendritic length per pyramidal neuron is only half the adult level. Yet, at age two the average dendrite length per neuron in the prefrontal cortex is longer than dendritic length in the visual cortex, consistent with the greater dendritic and synaptic complexity in the more integrative cortical regions, compared to unimodal primary cortical regions. These measures of dendrite elaboration in the developing human brain are mostly based on anatomical studies involving Golgi staining of fixed neurons. These data are static and fail to account for the dynamic activities of dendritic elongation, branching, and retraction that are revealed from realtime visualization of dendrite growth and synaptogenesis in living embryos, as mentioned earlier. Much remains to be learned about how axonal and dendritic shapes are sculpted over a period of years, as the result of interactions between genetically defined mechanisms of neuronal growth and a dynamic flux of intercellular molecular signaling, synaptogenesis, and the unpredictable physiological activity of postnatal experience.

Summary Neural circuits arise by a morphogenetic process in which axons and dendrites are formed according to intrinsic neuronal mechanisms that respond to extrinsic regulatory interactions with molecules, cells, and features of the developing organism. The driving force for axonal and dendritic growth is the advance of microtubules and associated organelles, while the actin-based motility of growth cones at the ends of elongating processes allows exploration of local tissue environments for molecular guidance cues. Binding of guidance cues to their receptors on growth cones triggers cytoplasmic signaling that regulates actin filament organization, mechanical forces, and microtubule advance to locally direct growth cone migration, turning, and branching. Axonal growth cones reach their synaptic targets by navigating to a series of intermediate targets, guided by positive and negative responses to surface-bound and soluble molecular cues. Axonal projections within a target are initially patterned according to gradients in the expression of molecules, such as ephrins and Eph rceptors, on axons and target cells. Synaptogenesis, other cellular interactions, and physiological activities adjust and refine axonal growth and branching within a target to achieve more accurate axonal topography. Formation of dendrites begins before afferent axons arrive and involves interactions of intrinsic and extrinsic mechanisms that regulate the orientation and rates of dendritic growth. Dendritic growth accelerates when axons arrive and initiate synaptogenesis. The final shaping of arborizations of dendrites and axons depends on mutual interactions, and

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physiological activity has a major role in this final phase of the formation of neural circuits. In the developing human brain, axonal navigation to targets begins in the first trimester and continues throughout the second and into the third trimester. Dendrite growth begins in the second trimester, accelerates in the third trimester, and continues most vigorously through the first 2–3 years and then for years afterward, as dendrites and axonal terminal arbors are sculpted and refined by experience. acknowledgments

The author thanks the members of his laboratory who have been dedicated and enthusiastic in research on axonal growth and guidance for 30 years. The author’s research has been supported by the National Institutes of Health, the National Science Foundation, and the Minnesota Medical Foundation. REFERENCES

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2

Imaging Developmental Changes in Gray and White Matter in the Human Brain ELIZABETH D. O’HARE AND ELIZABETH R. SOWELL

Introduction Human brain maturation is a dynamic and complex process that extends well into adulthood. Understanding developmental changes in brain structure is of fundamental importance in the field of developmental cognitive neuroscience, as it may help elucidate the specific neurobiological changes underlying the maturation of a variety of cognitive processes. Much progress has been made in characterizing structural brain development during the past few decades, largely because of the availability of noninvasive imaging tools such as magnetic resonance imaging (MRI). Because of its relative safety, MRI is ideal for use in developmental populations, where studying individuals at multiple time points is necessary to characterize longitudinal maturational changes in brain structure and function. In vivo neuroimaging methods also have the distinct advantage of allowing for investigations into the relationship between brain structure and brain function, an issue of critical importance in developmental cognitive neuroscience. The main focus of this chapter will be on neuroimaging studies of normative brain maturation that have been performed with a variety of brain-mapping techniques. These studies have allowed for the mapping of structural changes throughout the brain and have advanced our understanding of the timing and localization of alterations in gray and white matter that occur throughout development. We start by reviewing the postmortem literature, and then turn our attention to the in vivo literature. We will briefly review the earliest quantitative imaging studies of brain development that used volumetric methods. Although these studies are highly intuitive and critical for assessing global changes in brain morphology, sophisticated mapping techniques, such as voxel-based morphometry (VBM) and cortical pattern matching (CPM), provide certain advantages. Specifically, they allow for visualization of changes occurring at the cortical surface and throughout the brain that are unbiased by the observable sulcal cortical boundaries necessary for making the anatomical delineations required by volumetric studies. Particular emphasis will be given to cortical gray

matter and peripheral white matter differentiation across development, as recent advancements in our laboratory have allowed for the measurement of cortical thickness in millimeters across the entire brain surface with submillimeter accuracy. We conclude with a discussion of potential cognitive correlates of brain structural maturation.

Postmortem studies: Synaptic modification and myelination Prior to the advent of neuroimaging tools, researchers were limited to inferring structural brain changes from postmortem data. Both approaches have distinct advantages and disadvantages. A major concern with postmortem studies is their generalizability, due to the questionable normalcy of participants studied after death and the lack of samples from younger age ranges. In contrast, despite the advantages associated with performing in vivo MRI imaging in developmental populations described earlier, these techniques measure changes in MR signal values that are only indirectly linked to cellular changes in the brain. Nevertheless, while we cannot directly measure structural changes at the cellular level with MRI, the spatial and temporal patterns of maturational change observed in recent imaging studies reflect patterns that were observed postmortem, demonstrating the validity and compatibility of these methods. Brain development can be characterized as a dynamic process of progressive and regressive changes, which are influenced by both complex genetic programs and experience-dependent plasticity. At birth the human brain contains on the order of 100 billion neurons (Kandel, Schwartz, and Jessell, 2000). As the newborn brain grows in size and complexity, these neurons undergo dendritic branching and arborization, synaptogenesis, myelination, and ultimately synaptic pruning. Huttenlocher’s pioneering work in the early 1980s charted the time courses of synaptogenesis and synaptic pruning. His series of histological studies demonstrated that synaptic density is high at birth and continues to increase throughout the first year of postnatal life, reaching its maximum between 12 and 18 months postnatal. Synaptic density then shows a marked decrease

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between late childhood and early adulthood, presumably owing to the effects of experience-associated synaptic pruning, ultimately decreasing to about 60 percent of the maximum value attained shortly after birth (Huttenlocher, 1979; Huttenlocher et al., 1982). Interestingly, this pattern of synaptic formation and elimination shows regional variation. In primary visual cortex, synaptogenesis occurs more rapidly after birth and reaches a maximum at about 4 months postnatal. Synaptic pruning then begins and continues to about 4 years of age, at which point synaptic density in this region reaches adult levels. In contrast, peak synaptic density in the prefrontal cortex is not attained until 3–4 years of age. Synaptic pruning in this region is offset relative to that of primary visual cortex. Furthermore, prefrontal pruning lasts longer, with the most substantial decline occurring in middle and late adolescence (Huttenlocher and Dabholkar, 1997). In addition to the developmental processes of synaptogenesis and synaptic pruning, the neurons of the developing human brain are being myelinated. Early work by Yakovlev and Lecours demonstrated that myelination begins late in the second trimester of fetal development and extends into the second and third decades of life (Yakovlev and Lecours, 1967). Studies examining the patterns of myelination in the developing brain show that this process also follows a specific spatial and temporal pattern (Brody et al., 1987). Myelination is thought to progress from inferior to superior brain regions and from posterior to anterior regions. That is, the cerebellum myelinates prior to the cerebral hemispheres, and the occipital lobes prior to the frontal lobes. Thus myelination of the brain regions responsible for higher cognitive functioning, the dorsal frontal lobes, is still occurring throughout adolescence (Yakovlev and Lecours, 1967; Brody et al., 1987), a maturational period marked by the finetuning of cognitive control and executive function (Cohen et al., 2000; Bunge et al., 2002). The work of Kaes in 1907 was among the first to illustrate this complex relationship between age and the myelination of cortical regions known to subserve distinct cognitive functions. These postmortem studies of cortical width demonstrated that the primary cortices in which the myelination process is completed earlier show little age-related change. In contrast, the association cortices of the frontal and parietal lobes that are characterized by an extended period of myelination show pronounced age-related change (Albert and Knoefel, 1994). Composite maps of cortical thickness in individuals ranging from 3 months to 97 years of age demonstrated the progressive spread of intracortical myelination into frontal and parietal cortices during the first four decades of life. This proliferation of myelination into the cortex appears to result in gray matter “thinning.” Thus Kaes’s work suggests that loss of cortical thickness with age is due, in part, to an increased proliferation of myelin into the

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cortical ribbon, and not just to the effects of synaptic pruning and cell loss. Further evidence for an extended period of myelination in certain brain regions was demonstrated by the work of Benes and colleagues who observed a 95 percent increase in the extent of myelination relative to brain weight between the first and second decades of life within the superior medullary lamina of the parahippocampal gyrus (Benes et al., 1994). The authors describe the connectivity of this region and indicate that some of the axonal myelination occurring here could be on axons originating in the cingulate gyrus, a region known for playing a critical role in the regulation of cognitive control (Carter, Botrinick, and Cohen, 1999). The work of Benes and colleagues provides additional, although indirect, evidence for a possible relationship between improved functionality and increased myelination. As discussed in the sections that follow, a major advantage of in vivo studies is the ability to relate changes in cortical structure with changes in cognition and behavior. Taken together, the postmortem studies that we have described illustrate that the brain is undergoing progressive and regressive age-related changes throughout development. Concomitant reductions in synaptic density and increases in axonal myelination are the hallmarks of experience-based neural plasticity and are consistent with the principle of selective specialization (Edelman, 1993). Postulated to be the basis of the formation of cognitive networks that underlie higher cognitive processes (Post and Weiss, 1997; Fuster, 2002), this process involves the initial overproduction of neurons and synaptic connections during infancy and early childhood followed by activity-dependent fine-tuning of neural activity via synaptic pruning that continues well into adolescence. This then leads to efficient networks of neuronal connections that are in turn continuously changing with experience (Post and Weiss, 1997; Kandel, Schwartz, and Jessell, 2000). Despite the characterization of the processes of synaptic pruning and axonal myelination that are occurring throughout development, the relationship between postmortem findings and those from in vivo studies remains unclear (Sowell, Thompson, and Toga, 2004). Histological studies yield only fragmentary evidence for brain maturational changes in late childhood and adolescence, because of the underrepresentation of postmortem data from this age range. Furthermore, questions remain as to whether synaptic pruning or axonal myelination contributes most to the gross morphological changes observed in neuroimaging studies. Giedd and colleagues have suggested that reductions in synaptic density are unlikely to account for the large volume decreases in gray matter observed throughout development and that the balance between the decreasing number of neurons and the increasing size of glial cells attributable to myelination is primarily responsible for determining the overall size of

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brain structures (Giedd, Snell, et al., 1996). Further, the early work of Kaes described previously illustrates the proliferation of myelin into the cortical ribbon during childhood and adolescence, resulting in what appears to be cortical “thinning” during that time period. The relative contributions of myelination and synaptic pruning to the global changes in brain size or cortical gray and white matter distributions observed across development may in part be elucidated by findings from brain-mapping studies that are the focus of the remainder of this chapter.

Anatomically based parcellation methods: Volumetric studies Some of the earliest quantitative brain imaging studies in children and adolescents used volumetric parcellation methods. In this approach, the brain is subdivided into separate anatomical regions via either stereotaxic coordinates (Jernigan, Archibald, et al., 1991; Reiss et al., 1996), manual definition of regions of interest (Giedd, Vaituzis, et al., 1996; Sowell, Trauner, et al., 2002), or automated lobar region definition (Giedd et al., 1999). Tissue segmentation and volumetric measurements are then used to estimate the volumes of gray matter, white matter, and cerebrospinal fluid (CSF) within each lobe or region of interest. Given postmortem findings of regional differences in the temporal and spatial patterns of synaptic pruning and myelination across the brain, developmentally related changes in gray and white matter volumes would be expected to show similar regional differences. In the first quantitative structural MRI study in normal children, Jernigan and Tallal (1990) reported that children (ages 8–10) had significantly more cortical gray matter than young adults, despite the fact that the young adults had larger total brain volumes. A second study by Jernigan and colleagues extended this finding with the observation that the timing of gray matter loss had different trajectories depending on the brain region. The earliest gray matter loss occurred in the deep motor nuclei around early childhood and then later in the association cortices of the parietal and frontal lobes during early adolescence (Jernigan, Trauner, et al., 1991). Although these studies did not measure synaptic density directly, this was the first in vivo morphological evidence to support the postmortem findings of Huttenlocher (Huttenlocher, 1979; Huttenlocher et al., 1982) and of Yakovlev and Lecours (1967), regarding the regional and temporal patterns of cellular maturation. Since the initial work of Jernigan and colleagues, cortical gray matter volume decreases during development have been reported by several other groups (Pfefferbaum et al., 1994; Reiss et al., 1996). As with the histological studies described previously, volumetric studies also suggest regional differences in the extent of gray matter loss during childhood

and adolescence. Taken together, these studies demonstrate highly significant decreases in gray matter along with concomitant increases in white matter in the dorsal association cortices of the parietal and frontal lobes, with increasing age. In contrast, the ventral aspects of the temporal lobes change less dramatically between childhood and adolescence (Giedd et al., 1999; Sowell, Trauner, et al., 2002). Interestingly, these structural changes appear to be related to the changing cognitive capacities of children and adolescents. A significant positive correlation between frontal gray matter volume and performance on a verbal learning task was observed in a study investigating brain-behavior relationships (Sowell, Delis, et al., 2001). It is important to note that some studies have observed nonlinear age effects on gray matter volume in various cortical regions (Giedd et al., 1999; Gogtay et al., 2004). These studies have reported an initial increase in gray matter density that peaks between ages 10 and 12, depending on gender, in frontal and parietal lobes. Gray matter density then declines during the adolescent and postadolescent periods. It may be that this nonlinearity in gray matter volume change is due to methodological differences, as those studies reporting initial increases in gray matter have been longitudinal in design and have had more power to detect individual growth patterns, even in the presence of large between-individual variation (Giedd et al., 1999). Furthermore, there is evidence to suggest that the observation of nonlinear age effects on gray matter is contingent upon the age range examined. Reports of initial gray matter increase have come from studies where the youngest subjects studied were 4 years of age (Giedd et al., 1999; Gogtay et al., 2004), in contrast to those reporting progressive gray matter loss (Jernigan, Archibald, et al., 1991; Sowell, Trauner, et al., 2002), where the youngest subjects were 7 years old. Despite differences in findings between research groups, all seem to agree that regionally specific patterns of gray matter loss occur during late childhood and adolescence. Furthermore, this pattern appears consistent with what would be expected given the results from postmortem studies and given the known pattern of cognitive developmental changes that occur during adolescence.

Whole-brain mapping methods: Voxel-based morphometry (VBM) Although volumetric studies provide further evidence for continued gray matter loss and white matter gain throughout child and adolescent development, these studies are unable to precisely localize maturational changes in the brain, given that only gross lobar structures, thus far, have been reliably identified and manually defined. To address the need for more accurate localization of maturational changes, methods were developed that allowed for the

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assessment of structural changes in brain tissues on a voxelby-voxel basis. Modifying methods initially used to analyze functional imaging data, our laboratory used voxel-based morphometry methods (VBM) (Ashburner and Friston, 2000) to localize age-related gray matter density reductions between childhood and adolescence (Sowell, Thompson, et al., 1999b). The main advantage of VBM over volumetric parcellation methods is that it allows for the automated measurement of developmental changes in gray or white matter throughout the entire brain via spatial normalization of volumes into a standard space and the scaling of images so that each voxel coordinate is anatomically comparable across subjects. Results from these analyses revealed that the frontal and parietal gray matter volume reductions observed in the volumetric studies of brain maturation resulted mostly from gray matter reductions in diffuse dorsal regions of these association cortices (Sowell, Thompson, et al., 1999a). In a similar study, Paus and colleagues used VBM to assess white matter changes in children and adolescents and observed significant age-related increases in white matter density within the left and right internal capsule and the posterior portion of the left arcuate fasciculus (Paus et al., 1999). As these white matter pathways connect regions known to be important for speech and motor functions, this finding suggests a relationship between increased myelination and development of cognitive functions (Paus et al., 1999). A third study elucidated the full spectrum of structural brain maturation by using VBM to examine the pattern of maturation between adolescence and adulthood. As described earlier, between childhood and adolescence, cortical changes were distributed in frontal and parietal regions (Sowell, Thompson, et al., 1999a). In contrast, however, the pattern of cortical changes between adolescents and adults was localized to large regions of the dorsal, mesial, and orbitofrontal cortex, with relatively little gray matter loss in the parietal lobes (Sowell, Thompson, et al., 1999b) (see figure 2.1 and plate 6). This postadolescent gray matter loss in frontal regions makes sense when one considers that the cognitive functions typically ascribed to the frontal lobes, known collectively as executive functions (EF), show protracted courses of development that appear to parallel the lengthy course of structural development characteristic of this region (Fuster, 1997; Diamond, 2000).

Figure 2.1 VBM reveals the full spectrum of gray matter density loss across childhood, adolescence, and adulthood. The top panel shows the child minus adolescent statistical map for negative age effects, the bottom panel shows the same maps for adolescent minus adult. Areas in color represent clusters of gray matter density reduction observed between these age groups. These maps are threedimensional renderings of statistical maps shown inside the transparent cortical surface rendering of one representative subject’s brain. Color coding is applied to each cluster based on its location within the representative brains. Clusters are shown in the frontal lobes (purple), parietal lobes (red), occipital lobes (yellow), temporal lobes (blue), and subcortical regions (green) (Sowell, Thompson, et al., 1999a, 1999b). (Reproduced with permission from Sowell, Thompson, and Toga, 2004.) (See plate 6.)

Cortical mapping methods: Gray matter density Although the VBM methodological approach has advantages over volumetric studies, VBM methods are not without shortcomings. These methods rely on automated image registration techniques to normalize brain volumes across subjects. This is problematic because considerable variability in sulcal patterns exists across individuals and across cortical

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regions. Studies have demonstrated significant cortical sulcal variability in children (Sowell, Thompson, et al., 2002), adults (Narr et al., 2001), and between males and females (Luders et al., 2004). Thus, when brain volumes are normalized without taking sulcal variability into account, cortical anatomical regions are likely not well matched across subjects.

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Cortical pattern matching (CPM) methods were developed to address the problem of intersubject cortical surface variability and to improve spatial normalization. Because CPM allows for cortical anatomy to be matched across subjects, these methods can account for interindividual differences in cortical sulcal patterns. With CPM, sulcal landmarks, manually defined in each individual, are used as anchors to drive fluid-warping algorithms (Thompson et al., 2004). These methods allow for the comparison of cortical features of interest at anatomically matched points across all subjects, and statistical analyses can then be used to help localize the effects of age. The studies to be discussed here quantify local gray matter using a measure termed gray matter density. This measures the proportion of segmented gray matter in a small region of a fixed radius (15 mm) around each matched cortical point (Thompson, Mega, et al., 2001; Sowell, Thompson, et al., 2002) (see figure 2.2 and plate 7). Our colleagues and we have used CPM techniques to measure developmental changes in gray matter density in several different populations. In the first of these, we observed a pattern of gray matter density change that was expected given the results from the volumetric studies in the same subjects described previously. Reporting on a sample of 35 individuals between the ages of 7 and 30 years, we observed gray matter density reductions most prominent in the parietal cortices during childhood, followed by a dramatic acceleration in gray matter density loss in the dorsal frontal lobes during the postadolescent years. Interestingly, this loss in gray matter density was inversely correlated with brain growth; that is, regions that showed gray matter density reductions were also expanding, as shown in the composite maps in figure 2.3 and plate 8 (Sowell, Thompson, et al., 2001). A reduction in the number of cortical synapses could result in the observation of reduced gray matter density. However, these results show local brain growth in the same regions where gray matter density reduction is occurring (rather than brain shrinkage). An increase in the amount of myelin could also result in a reduction in the amount of brain tissue that has a gray matter appearance, as suggested in the postmortem work by Kaes described earlier, and given that nonmyelinated peripheral axonal and dendritic fibers do not have normal white matter signal values on T1-weighted MRI (Barkovich et al., 1988). That is, nonmyelinated axonal fibers in the peripheral cortices would not stain for myelin in postmortem studies, and would thus appear more like gray matter at a gross level in MRI. Furthermore, this tissue would have an MR signal value more similar to that of gray matter. If the loss of gray matter observed in the volumetric, VBM, and CPM studies described earlier was caused only by regressive changes such as synaptic pruning or cell loss, we would not see local brain growth during the same time frame in the same cortical regions. Rather, taken together, these observations suggest that the apparent thinning of

cortex (loss of gray matter density) could also result from increased myelination. In summary, although the precise relationship between brain growth and gray matter density loss remains unclear, the in vivo findings of brain growth spatially and temporally concomitant with cortical thinning highlight the combinatorial nature of regressive (synaptic pruning) and progressive (myelination) changes in gray and white matter structure in the developing human brain. As mentioned previously, in contrast to the pronounced postadolescent decrease in gray matter density characteristic of the dorsal frontal and parietal lobes, small increases in gray matter density between childhood and young adulthood have now been observed in bilateral posterior perisylvian regions in three independent samples of normally developing individuals (Sowell, Thompson, et al., 2002; Sowell et al., 2003; Sowell, Thompson, Leonard, et al., 2004). In a longitudinal study of normally developing children, we observed gray matter thickness increases in the left inferior frontal sulcus (i.e., Broca’s area) (Sowell, Thompson, Leonard, et al., 2004). This unique pattern of gray matter thickening specific to primary language regions leads to the speculation that cortical thickening may be specifically related to gains in language processing. This notion is further supported by studies showing the time between childhood and adolescence as a period of intense learning and modification of language functioning (Sakai, 2005). Later, during the adolescent and postadolescent period, characterized by gains in executive functioning (Luciana and Nelson, 1998; Fuster, 2002; Rosso et al., 2004), changes in gray matter consist of cortical thinning in the frontal regions typically associated with these executive functions. Unfortunately, little work has been done to evaluate relationships between changing brain morphology and changing cognitive functions, but some recent work from our laboratory addressing this issue is discussed later. More recently, CPM studies have used regression analyses to create plots of linear and nonlinear effects of age on gray matter density. In a sample of 176 normal individuals ranging in age from 7 to 87 years studied cross-sectionally, we reported a significant nonlinear decline in gray matter density with age. This decline was most rapid between ages 7 and 60, and was localized to the dorsal frontal and parietal association cortices, on both the lateral and medial surfaces of the hemispheres (Sowell et al., 2003). In contrast, gray matter density gain occurred with increasing age in the left posterior region (perisylvian cortex) until about age 30 (see figure 2.4 and plate 9). The cortices that are known from histological studies to myelinate earliest, namely, the primary visual and auditory cortices and limbic regions, showed a more linear pattern of aging than either the posterior temporal language regions or the associative cortices of the parietal and frontal lobes (Sowell et al., 2003). In addition to evaluating local changes in gray matter over the brain

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Figure 2.2 Cortical pattern matching methods and gray matter density. Top left: Three representative brain image data sets with the original MRI, tissue-segmented images, and surface renderings with sulcal contours shown in pink. Top right: Surface rendering of one representative subject with cutout showing tissue-segmented coronal slice and axial slice superimposed within the surface. Sulcal lines are shown where they would lie on the surface in the cutout region. Note the sample spheres over the right hemisphere inferior frontal sulcus (lower sphere) and on the middle region of the precentral sulcus (upper sphere) that illustrate varying degrees of gray matter density. In the blown-up panel, note that the upper sphere has a higher gray matter density than the lower sphere as it contains only blue pixels (gray matter) within the brain. The lower sphere also contains green pixels (white matter) that would lower the gray matter proportion within it. In actual analyses, gray matter proportion is measured within 15-mm spheres centered across every point over the cortical surface. Bottom: Sulcal anatomical delineations are defined according to color. These are the contours drawn on each individual’s surface rendering according to a reliable, written protocol (see also figure 2.5). (Reproduced with permission from Sowell, Thompson, and Toga, 2004.) (See plate 7.)

Figure 2.3 Reductions in gray matter density are occurring in the same locations as brain growth. Composite statistical maps (top) showing the correspondence in age effects for changes in brain growth (defined here as distance from center, or DFC) and changes in gray matter in the child-to-adolescent contrast (A). Shown in green is the Pearson’s R map of all positive correlation coefficients for DFC, and in blue is the probability map of all regions of significant gray matter loss (surface point significance threshold P = 0.05). In red are regions of overlap in the gray and DFC statistical maps. A similar composite map for the adolescent-to-adult age effects is also shown (B). Note the highly spatially consistent relationship between brain growth and reduction in gray matter density. The shapes of the regions of greatest age-related change for the two maps (gray matter and DFC) are nearly identical in many frontal regions in the adolescent-to-adult contrast. Very few regions of gray matter density reduction fall outside regions of increases in DFC. Shown in images in the lower part of this figure (left, right, and top

views) are the difference between Pearson’s correlation coefficients for the age effects for gray matter density and the age effects for DFC between childhood and adolescence (C) and between adolescence and adulthood (D). The color bar represents corresponding Z scores ranging from −5 to +5 for the difference between correlation coefficients for DFC and gray matter. Highlighted in red are regions of significant negative correlation between DFC and gray matter density (P = 0.05), showing that the relationship between regions of greatest gray matter density reduction are statistically the same as the regions with the greatest brain growth, particularly in the adolescent-to-adulthood years. Highlighted in white are the regions where the difference between correlation coefficients for the gray matter and DFC maps is positive, indicating that the change with age is in the same direction for both variables (i.e., increased DFC change goes with increased gray matter density change). (Reproduced with permission from Sowell, Thompson, et al., 2001.) (See plate 8.)

surface, we also plotted the total volumes of gray matter, white matter, and CSF across the age range. While gray matter volume declined continuously with increasing age, white matter volume first increased, reaching its peak around age 50, and then declined. Despite this comprehensive characterization of the effects of age on both local and global measures of brain structure, cross-sectional studies cannot control for interindividual variance in brain maturation. Longitudinal studies are ideal for this purpose because they limit this variance and increase external validity and generalizability. In one of the first longitudinal mapping studies of normative human brain development, Gogtay and colleagues (2004) reported on a sample of 13 individuals between the ages of 4 and 21 years. A total of 52 images from the 13

subjects were analyzed with approximately 2 years between imaging sessions. Animation of the trajectory of gray matter loss over time revealed a shifting pattern of gray matter change that first appeared in dorsal parietal and primary sensorimotor regions between the ages of 4 and 8 years. Gray matter loss then spread laterally and caudally in temporal cortices, and finally extended anteriorly into dorsal frontal cortex (Gogtay et al., 2004). Similar to our findings (Sowell et al., 2003), the authors note that maturation of lower-order visual and somatosensory cortices occurs before that of the higher-order association cortices. Taken together, these CPM studies of gray matter density change suggest that loss of gray matter is most prominent in the association cortices of the frontal and parietal lobes. While gray matter loss in these regions shows a protracted

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Figure 2.4 Plots of the relationship between age and gray matter density reveal different trajectories of gray matter changes for different brain regions. Shown is a surface rendering of a human brain (left hemisphere; left is anterior, right is posterior) with scatter plots for gray matter density at various points over the brain surface.

The graphs are laid over the brain approximately where the measurements were taken. The axes for every graph are identical, with gray matter density plotted on the x-axis and age (in years) plotted on the y-axis (Sowell et al., 2003). (See plate 9.)

course that extends into the postadolescent years, gray matter loss has a different trajectory in primary sensorimotor cortices, where it begins and ends earlier. Notably, these patterns of gray matter loss are complemented by brain growth, probably because of increased myelination in the same anatomical regions. In contrast to this pattern, the posterior temporal lobes show subtle increases in gray matter with age up until approximately age 30 before a subsequent decline (Sowell et al., 2003). Although the precise nature of the cellular changes underlying these in vivo observations remains unclear, the similarity of these patterns of results to those of postmortem histological studies portends their validity.

three-dimensional Eikonal fire equation (Sapiro, 2001), which automatically determines cortical thickness throughout brain volumes with submillimeter accuracy (see figure 2.5 and plate 10). A recent study by our group combined a longitudinal design with gray matter thickness measurements to map changes in cortical gray matter across development. We measured gray matter thickness in millimeters in a sample of 45 normally developing individuals between the ages of 5 and 11 years. Each subject was studied twice with two years between scans. The group average cortical thickness maps were remarkably similar to those described by von Economo in his postmortem sample (von Economo, 1929). Both maps revealed that cortex was thickest in the most dorsal aspects of the frontal and parietal lobes (approximately 4–5 mm), and thinnest in primary visual cortex along the banks of the calcarine sulcus in the medial aspect of the occipital lobe (less than 2 mm) (see figure 2.6 and plate 11). In addition to the striking correspondence of this in vivo data to that of Von Economo’s postmortem data, our data revealed statistically significant cortical thinning of approximately 0.15–0.30 mm per year, most prominently in right dorsal frontal and bilateral parietal regions. Significant

Cortical mapping methods: Gray matter thickness Studies of gray matter density changes are difficult to interpret because they must be reported as a percentage change between one group and another (Sowell, Thompson, Leonard, et al., 2004). Methodological advances now allow for the measurement of gray matter thickness in millimeters (Fischl and Dale, 2000; Jones, Buchbinder, and Aharon, 2000; Miller et al., 2000; Kabani et al., 2001). Our laboratory has recently developed similar methods by using the

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Figure 2.5 Cortical pattern matching methods and gray matter thickness. The skull-stripped, 3 D, gray-scale image volume is shown in the upper left for one representative subject. Surface renderings (upper right) are automatically rendered for each subject using the signal value that best differentiates cortical surface sulcal CSF from cortical gray matter. Thirty-five sulcal landmarks on the lateral and medial surfaces are identified and manually traced. After sulcal patterns are demarcated, surface renderings are flattened to a 2 D planar format. In the bottom left the flattened sulcally delineated surface renderings are shown for four individual subjects. Note the crosshairs in each map: while at slightly different locations in the image, they represent the same sulcal anatomy in each subject (i.e., homologous surface points). A complex deformation, or warping transform, is then applied that aligns the sulcal anatomy of each subject with an average sulcal pattern derived for

the group. Given that the deformation maps associate cortical locations with the same relation to the primary sulcal pattern across subjects (i.e., the crosshairs in all for subjects illustrated here), a local measurement of cortical thickness can be made in each subject and averaged across equivalent cortical locations in all subjects. This is illustrated in the bottom right panel. Cortical thickness, defined as the 3 D distance (in mm) between the inner gray matter/ white matter border and the closest point on the outer CSF/gray matter boundary, is calculated using the Eikonal fire equation (illustrated in more detail in figure 2.6). Using these methods, the average thickness value within a 15-mm sphere can be calculated and averaged across subjects to estimate cortical thickness within groups of individuals. On the bottom right is a group average map of cortical thickness. (See plate 10.)

increases in cortical thickness were observed in canonical language regions of the temporal and frontal lobes (Wernicke’s and Broca’s areas, respectively). This cortical thickening was on the order of approximately 0.10–0.15 mm per year (see figure 2.7 and plate 12). The correspondence of the changes in gray matter thickness to previously described gray matter density changes is striking. These results highlighted that measurable cortical changes were occurring

within individuals over a relatively brief time period during development, and they validate previous cross-sectional findings. Furthermore, they allowed for the quantitative assessment of the magnitude of change in identifiable units (i.e., millimeters). Finally, these results confirmed cortical thickening in posterior perisylvian regions and for the first time revealed cortical thickening in the left inferior frontal language region.

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Figure 2.6 Cortical thickness maps: (A) original T1-weighted image for one representative subject, (B) tissue segmented image, (C) gray matter thickness image where thickness is progressively coded in millimeters from inner to outer layers of cortex using the 3 D Eikonal fire equation. Note the images were resampled to a voxel size of 0.33 mm cubed, so the thickness measures are at a submillimeter level of precision, according to the color bar on the right (mm). Figures A through C are sliced at the same level in all three image volumes from the same subject. Shown in (D) is an in

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vivo average cortical thickness map created from these 45 subjects at the first scan. The brain surface is color coded according to the color bar where thickness is shown in millimeters. Our average thickness map can be compared to an adapted version of the 1929 cortical thickness map of von Economo (von Economo, 1929) (E). Color coding has been applied over his original stippling pattern, respecting the boundaries of his original work, to highlight the similarities between the two maps. (Reproduced with permission from Sowell, Thompson, Leonard, et al., 2004.) (See plate 11.)

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Figure 2.7 These maps show the statistical significance of annualized change in cortical thickness measurements. Color coding represents t values at each cortical surface point according to the color bar at the near right (ranging from t = −3.00 to t = 3.0). Significant values are overlaid in shades of red (significant thickness

decreases, TD) and white (significant thickness increases, TI), according to the color bar at the far right. Arrows point to three regions of significant increases in gray matter thickness. (Reproduced with permission from Sowell, Thompson, Leonard, et al., 2004.) (See plate 12.)

As noted, these findings represent the third observation of specific age-related increases in gray matter in the primary language cortices, drawn from three independent samples (Sowell, Thompson, et al., 2002; Sowell et al., 2003; Sowell, Thompson, Leonard, et al., 2004). In addition, it appears that these regions have a more protracted course of development than any other cortical region (Sowell et al., 2003). That the structural maturation pattern is markedly different for brain regions critical for language processing is perhaps not surprising, given the complexity and protracted nature of the language-learning process. Future work will combine structural and functional MRI methods to characterize the specific relationship between these observed increases in gray matter thickness in primary language cortices and the functional maturation of language processes.

matter thickness and performance on a test of general verbal intellectual functioning, the vocabulary subtest of the Wechsler Intelligence Scale for children (Wechsler, 1991). Greater gray matter thinning was correlated with better performance on this test in diffuse areas of the left hemisphere (Sowell, Thompson, Leonard, et al., 2004) (see figure 2.8 and plate 13). Although the only regions to survive correction for multiple comparisons were the left lateral dorsal frontal and the left lateral parietal, these findings were consistent with expectations, given that the task represents relatively global verbal intellectual abilities and that the significant correlations were observed in the language-dominant left hemisphere. More recently, we examined more specific cognitive functions—phonological processing and motor speed and dexterity—and their relationships to changes in cortical thickness in these same children (Lu et al., 2007). We expected that gray matter thickening in the left inferior frontal gyrus, the only region showing increases in gray matter thickness in the left hemisphere, would be associated with developmental changes in phonological processing. In order to establish the specificity of the relation between gray matter thickening in the left inferior frontal gyrus and phonological processing, we predicted that thickness change in this region would correlate with improving phonological skills but not with nonlanguage measures such as motor dexterity and strength. In turn, we expected improved hand motor skills to correlate with gray matter thickness values in the hand motor region, but not the left inferior frontal cortex. As predicted, increased gray matter thickness in left inferior frontal regions was

Cognitive correlates of developmental changes in gray matter What is the relationship between the changes in gray and white matter distributions that we have described and the changing cognitive capacities of children and adolescents? Unfortunately, little is known on this topic, but a handful of studies have begun to address this important question. This section contains a brief discussion of these studies, which have largely focused on correlating IQ measures with structural changes in the developing brain. In the same sample of 45 normally developing children studied longitudinally for cortical thickness change (described earlier), we evaluated relationships between changes in gray

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Figure 2.8 Brain-behavior maps showing the relationship between vocabulary scores and cortical thickness. These maps show the p value for negative correlations between change in cortical thickness (time 2 value − time 1 value) and change in vocabulary scores (time 2 score − time 1 score). Regions in color represent

negative p values, that is, regions where greater thinning was associated with greater vocabulary improvement. Regions in white were not significant. No positive correlations reached significance. (Reproduced with permission from Sowell, Thompson, Leonard, et al., 2004.) (See plate 13.)

significantly correlated with improving phonological processing, but not with improving motor processing. In contrast, motor processing improvement, but not phonological processing improvement, was significantly correlated with decreases in gray matter thickness in the hand region of left primary motor cortex (see figure 2.9 and plate 14). This double dissociation illustrates a specific correspondence between gray matter thickness change and cognitive maturation in the left inferior frontal cortex, a brain region known to be important for language processing. Evidence suggests that relationships between intelligence and gray matter structure may show regional differences that vary with age. Studies examining the correlation between gray matter volume or density and IQ found a significant positive correlation in children in the anterior cingulate (Wilke et al., 2003). In adolescents, gray matter density was positively correlated with IQ most prominently in the orbitofrontal cortex (Frangou, Chitins, and Williams, 2004). And finally, in adults, Haier and colleagues (2004) used VBM to assess gray matter and found a significant positive correlation between gray matter density and IQ in the lateral prefrontal cortex (Haier et al., 2004). As described previously, we found significant correlation between frontal (and parietal) gray matter thinning and vocabulary, which is likely reflective of more general verbal IQ and thus is quite similar to the other findings of frontal lobe–IQ correlations (Thompson, Cannon, et al., 2001; Toga and Thompson, 2005). While different measures and methods were used, common to all studies were significant relationships between frontal lobe structure and general intellectual functioning.

In a recent study, Shaw and colleagues used a longitudinal design to examine the relationship between gray matter thickness and intellectual ability in 307 normally developing children and adolescents. When examining all subjects, the authors noted modest positive correlations between IQ and cortical thickness in most of the frontal, parietal, and occipital cortex, and modest negative correlations between these variables in the anterior temporal cortex (Shaw et al., 2006). Apparently surprised by the relatively modest correlations between cortical thickness and IQ with such a large sample, the authors decided to further investigate their data by splitting subjects into average, high, and superior IQ groups. In these analyses, they found a significant interaction between IQ group and age in the prefrontal cortex, suggesting that the relationship between frontal cortical thickness and IQ varied as a function of IQ level. A shift was observed from negative correlations between IQ and frontal lobe cortical thickness in the younger children, to a strong positive correlation in later childhood through adulthood. That is, frontal cortex was thinner in the superior IQ children in the earlier years, and thicker in the superior IQ children in the older years. This result occurred in large part because the superior-intelligence group showed a marked increase in cortical thickness in the medial prefrontal cortex that peaked at age 11. In contrast, the average-intelligence group showed a decline in cortical thickness throughout the ages of 7–19. The high-intelligence group showed a trajectory intermediate to those of the other two intelligence groups. Growth curves demonstrated that the superior-intelligence group had the most rapid rate of cortical thinning, while the high-

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A

B

Figure 2.9 Pearson’s R correlations between changes in gray matter thickness and phonological processing (panel A) and between thickness and motor processing (panel B). Regions in white represent positive correlations with a threshold of p < 0.05. Regions in red represent negative correlations with a threshold of p < 0.05. Increases in gray matter thickness in left inferior

frontal regions significantly correlate with improvement in phonological processing, but not with motor processing, while decreases in gray matter thickness in primary motor cortex significantly correlate with improvement in motor processing, but not with phonological processing. (Reproduced with permission of Oxford University Press from Lu et al., 2007.) (See plate 14.)

and average-intelligence groups had slower rates. These findings suggest that instead of intelligence correlating with total gray matter volume or gray matter thickness across the age span, these correlations depend upon the age range studied. While considerable work is yet to be done in connecting structural brain changes to changes in cognitive functioning, the work to date leaves little doubt that measurable changes in gray (and white) matter tissues are linked to changes in cognitive abilities. The work of Lu and colleagues shows that

the direction of the relationship (i.e., positive or negative), depends on the cognitive function and brain regions evaluated, and the work of Shaw and colleagues demonstrates that the time at which these brain-behavior relationship evaluations are made is also critical. Generally, regions that show cortical thinning with development tend to show negative correlations with cognitive functions subserved by those regions. That is, improved functioning is associated with decreasing cortical thickness in regions that thin with age (which cover a wide area of most of the dorsal and

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ventral frontal and dorsal parietal cortices), and in regions that thicken with development, improved functioning is associated with increasing cortical thickness.

Summary, conclusions, and future directions Postmortem studies have demonstrated that brain maturation is characterized by a combination of regressive and progressive changes in cortical structure. Changes in the relative proportions of gray and white matter observed with MRI are a hallmark of human brain development. The most prominent finding appears to be decreases in cortical gray matter along with concomitant increases in cortical white matter across the dorsal aspects of the higher-order associative cortices in the frontal and parietal lobes. This dynamic process may continue well into adulthood and appears to be related to functional increases in cognitive capacities associated with these regions. While we cannot determine the cellular etiology of gray and white matter changes with MRI, it is becoming more apparent that synaptic pruning and increased myelination contribute to cortical thinning during development. Again, during aging, cortical thinning continues, though it is likely more degenerative in nature, and not due to the same progressive changes that occur during development. As we discussed, while most developmental structural MRI studies have focused on gray matter, increased myelination into the inner layers of cortex likely results in observations of “cortical thinning” on MRI. That is, tissue that has gray matter signal on MRI in young subjects may actually be unmyelinated axonal and dendritic fibers. This issue may be largely semantic in nature, but does suggest that we not interpret findings of “gray matter” changes on MRI to strictly refer to changes in the cortical neuropil (i.e., cell bodies and their synaptic processes). New and converging findings from several independent samples suggest that the cortex may actually continue to increase in thickness throughout adolescence and young adulthood in some brain regions, namely, those associated with primary expressive and receptive language functioning. It is possible that there are unique aspects of language functioning that require extended stages of plasticity into adulthood, where decreased plasticity and increased efficiency may be occurring simultaneously in regions of the brain that show cortical thinning. It is not at all clear from the postmortem literature what kinds of cellular processes could lead to the thickening of cortex, but some recent controversial work suggests adult neurogenesis in animal models (discussed by Kozorovitskiy and Gould in chapter 4 of this volume; Gould et al., 1999). While we are keenly interested in the cellular etiology of structural changes observed with MRI, the only definitive way to evaluate this would be to combine MRI with postmortem material in the same individuals. That is, we could measure cortical thickness using MRI

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within numerous individuals, and then correlate measures of cortical thickness in the same cortical locations with postmortem material from the same individuals. Unfortunately, this is not likely to be highly fruitful given the paucity of postmortem material in the child-to-adolescent age range. Animal models could also provide potential answers to these interesting and important questions. Relationships between changes in brain structure and changes in cognitive function have been unfortunately sparse. The few studies that have been conducted suggest that changing brain structure is related to changing cognitive function. The direction and pattern of these relationships varies depending on the typical developmental pattern of brain region subserving the cognitive function being measured, the age range of the subjects studied, and perhaps the overall intellectual capacity of the cohort under investigation. Future work should combine the quantification of brain structural changes with detailed analyses of changing cognitive abilities as assessed with functional MRI in an effort to provide an even greater understanding of the brain changes subserving cognitive development. acknowledgments

Support was provided by the National Institutes of Health (NIMH K01 MH01733 and NIDA R21 DA015878 and R01 DA017830 awarded to ERS, and NIAAA F31AA16039 awarded to EDO’H). Further funding was provided by NIH/NCRR resource grant P41 RR013642 and NIH Roadmap for Medical Research Grant U54 RR021813. REFERENCES

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changes in brain structure between childhood and adolescence using statistical parametric mapping. NeuroImage 9:587–597. Sowell, E. R., P. M. Thompson, C. J. Holmes, T. L. Jernigan, and A. W. Toga, 1999b. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature Neurosci. 2:859–861. Sowell, E. R., P. M. Thompson, C. M. Leonard, S. E. Welcome, E. Kan, and A. W. Toga, 2004. Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24(38):8223–8231. Sowell, E. R., P. M. Thompson, D. Rex, D. Kornsand, K. D. Tessner, T. L. Jernigan, and A. W. Toga, 2002. Mapping sulcal pattern asymmetry and local cortical surface gray matter distribution in vivo: Maturation in perisylvian cortices. Cerebral Cortex 12:17–26. Sowell, E. R., P. M. Thompson, K. D. Tessner, and A. W. Toga, 2001. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation. J. Neurosci. 21:8819– 8829. Sowell, E. R., P. M. Thompson, and A. W. Toga, 2004. Mapping changes in the human cortex throughout the span of life. The Neuroscientist 10:372–392. Sowell, E. R., D. A. Trauner, A. Gamst, and T. L. Jernigan, 2002. Development of cortical and subcortical brain structures in childhood and adolescence: A structural MRI study. Dev. Med. Child. Neurol. 44:4–16. Thompson, P. M., T. D. Cannon, K. L. Narr, T. van Erp, V. P. Poutanen, M. Huttunen, J. Lonnqvist, C. G.

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Standertskjold-Nordenstam, J. Kaprio, M. Khaledy, R. Dail, C. I. Zoumalan, and A. W. Toga, 2001. Genetic influences on brain structure. Nature Neurosci. 4:1253–1258. Thompson, P. M., K. M. Hayashi, E. R. Sowell, N. Gogtay, J. N. Giedd, J. L. Rapoport, G. I. de Zubicaray, A. L. Janke, S. E. Rose, J. Semple, D. M. Doddrell, Y. Wang, T. G. van Erp, T. D. Cannon, and A. W. Toga, 2004. Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia. NeuroImage 23 Suppl 1:S2–18. Thompson, P. M., M. S. Mega, C. Vidal, J. L. Rapoport, and A. W. Toga, 2001. Detecting disease-specific patterns of brain structure using cortical pattern matching and a population-based probabilistic brain atlas. In M. Insana and R. Leahy, eds., IEEE Conference on Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), 488–501. New York: Springer-Verlag. Toga, A. W., and P. M. Thompson, 2005. Genetics of brain structure and intelligence. Annu. Rev. Neurosci. 28:1–23. von Economo, C. V., 1929. The Cytoarchitectonics of the Human Cerebral Cortex. London: Oxford Medical Publications. Wechsler, D., 1991. Manual for the Wechsler Intelligence Scale for Children, 3rd ed. San Antonio, TX: Psychological Corporation. Wilke, M., J. H. Sohn, A. W. Byars, and S. K. Holland, 2003. Bright spots: Correlations of gray matter volume with IQ in a normal pediatric population. NeuroImage 20:202–215. Yakovlev, P. I., and A. R. Lecours, 1967. The myelogenetic cycles of regional maturation of the brain. In A. Minkowski (ed.), Regional Development of the Brain in Early Life. Oxford, UK: 3–70. Blackwell Scientific.

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Gyrification and Development of the Human Brain TONYA WHITE AND CLAUS C. HILGETAG

Introduction The cerebral cortex of the human brain has a readily identified characteristic pattern of grooves and folds. Little is known about the mechanisms behind the emergence of these grooves and folds, known as gyri and sulci. The formation of the characteristic gyri and sulci mainly occurs before birth in a process known as gyrification. Many researchers who study the brain utilize gyri as landmarks to define specific anatomic or functional brain patterns, with little thought given to how they develop. However, the theories surrounding gyrification are fascinating, and, as biological form is closely linked to function, they are potentially relevant for understanding the development of brain function and its localization. It is the primary goal of this chapter to describe the stages and mechanisms of gyrification of the human brain. However, these processes are nested and dependent upon the other major neurodevelopmental processes, which will also be outlined here. The major components of gyrification take place primarily during the third trimester of fetal life, or between approximately 26 and 40 weeks’ gestational age.

Brain development prior to gyrification Formation of the Neural Tube (Neurulation) The mating of egg and sperm is followed by rapid stem cell growth and division. It is not until the second week of uterine life that the dividing cells begin to differentiate into one of three different layers. These layers, known as the endoderm, mesoderm, and ectoderm continue to undergo rapid cell division and differentiate into the different organs of the body. It is the differentiation of the ectodermal layer that forms both the skin and the central nervous system. The neural plate is the first structure of the CNS to develop within the ectodermal layer (Kandel, Schwartz, and Jessell, 2000; Pomeroy and Kim, 2000). A groove is created in the midline of the neural plate, forming perhaps the first short-lived sulcus in the brain. This neural groove rapidly closes to form a neural tube. The caudal region of the neural tube will later become the spinal cord, and the cortical and subcortical structures will spring forth from the rostral region. The cells within the ectoderm that are not involved

with the formation of the neural tube will differentiate into the epidermal layer of the skin. Finally, the neural crest cells between the neural tube and the ectodermal wall differentiate into the peripheral nervous system. During the fifth week of fetal life, the cerebral vesicles begin to take form. The walls surrounding the cerebral vesicles become thick and form the lamina terminalis by the seventh week of fetal life (Destrieux, Velut, and Kakou, 1998). The rostral region of the neural tube undergoes exuberant growth involving an overabundance of neural and glial cells. Clusters of neurons along the midline of the ventral region of the neural tube differentiate further into the thalamus, basal ganglia, hypothalamus, and brain stem (Pomeroy and Kim, 2000). The dorsal region of the lamina terminalis, known as the lamina reunions, folds to become the commissural plate (Rakic and Yakovlev, 1968). The classic concept of a “fate map” has been invoked to describe the differentiation of embryonic precursor structures, which are frequently separated by early limiting sulci, into specific adult brain structures, such as the brain stem or the cerebral lobes (His, 1874; Swanson, 2003). In general, a remarkable variety of morphogenetic and particularly folding processes occur even at this early stage of brain development. These processes are likely driven by differential growth rates of the different embryonic territories. Neuronal Migration Neurons migrate out from the ventral regions of the neural tube in a specific pattern. This mechanism, which has been described as the radial unit hypothesis (Rakic 1988, 1995, 2000), involves neurons formed via mitosis at the ventricular zone migrating along radial glial guide cells in the outer layers of the brain. This outer layer becomes the cortical rim of gray matter (GM) in the adult brain. Before six weeks of fetal life, the neural progenitor cells located in the ventricular zone begin symmetric cell division, with each stem cell producing two identical stem cells with each mitotic cycle (Rakic, 1988). Thus this period results in an exponential growth in neuronal progenitor cells. Then, at approximately six weeks gestational age, the progenitor cells gradually initiate asymmetric division, with one daughter cell remaining as an undifferentiated stem cell, while the other daughter cell matures into a neuron that migrates outward to the cortex.

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The migration forms an inside-out pattern, with later generations passing through the previously developed cells before reaching their ultimate migratory position in the gray matter of the cortex (Sidman and Rakic, 1973). The cortical GM rim consists of six layers of cells that have migrated in this inside-out pattern. During these two phases of symmetric (before six weeks) and asymmetric (six weeks to 12 weeks) cell division, small perturbations can influence the thickness or the surface area of the cortex. In turn, these events can influence gyrification. Before six weeks of age, one additional mitotic cycle would potentially double the number of neural progenitor cells. This point can be illustrated in the story of the father who told his son at the beginning of the month, “I will either give you $20 now, or I will give you a penny now, and double the amount each day for the next month (that is, 1 cent, 2 cents, 4 cents, 16 cents, and so on).” If the son were to choose the penny, he would have more than 5.3 million dollars for a 30-day month or 10.6 million for a month with 31 days. The surface area of the brain has a close association with the number of radial units formed by symmetrical division along the ventricular zone (Rakic 1988, 1995). A larger number of radial units equates with a larger number of lined projections to the cortical plate and thus a greater surface area of the brain (Rakic, 1974; Sidman and Rakic, 1973). Since each round of mitosis results in an exponential increase in the number of progenitor cells, small changes affecting the duration of symmetric growth will have a dramatic impact on surface area (Rakic, 1995). This developmental principle has been called “late equals large,” as neurons migrating into late-developing brain structures undergo a longer period of symmetrical division, resulting in a larger size of these structures. The principle has been verified for the developmental time table and corresponding size of brain structures in a large number of different species (Finlay and Darlington, 1995; Striedter, 2005). When the embryos of monkeys are irradiated during the symmetric phase of progenitor cell division, there is a decrease in total surface area of the brain. However, when radiation is applied after six weeks, during the phase of asymmetric cell division, it results in a deletion of cortical cell layers and, in turn, a decrement in cortical thickness (Rakic, 1995), as well as disrupting the development of gyrification (Stewart et al., 1975). Thus the thickness of the sixlayered cortex is influenced by the asymmetric period of cell division. It has been found recently that the migration of neurons into the cortex is not as straightforward as initially thought; for example, several neuronal subpopulations show different migratory patterns (Nadarajah et al., 2003). In particular, the mode of radial migration is mostly followed by pyramidal cells, while different types of cortical interneurons pursue a tangential migratory path into their target layers

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(Kriegstein and Noctor, 2004). The effect of the tangential mode of migration on cortical morphology is likely small or has only a local influence on the development of the cortical layer. The vast majority of cortical neurons are pyramidal cells that migrate along the predominant radial path. These pyramidal cells form connections with interneurons that are located within the vicinity of their migratory path. Another factor affecting brain morphology is cell death. This process can lead to the elimination of up to 50 percent of the initially formed neurons. However, cell death is a regressive event that occurs early in development (Cowan et al., 1984; Levitt, 2003) and thus may not be directly involved in cortical gyrification, which is a later process. Nonetheless, there are potential indirect consequences of cell death for gyrification and brain morphology that are as yet poorly understood. Development of Connectivity between Cortical and Subcortical Structures As the radial glial cells migrate toward the cortical plate, they meet with afferent cell projections from the thalamus (Rakic, 1988). These projections migrate primarily into layer IV of the cortex (Wise and Jones, 1978). The connection of these nerve cells allows for communication between the subcortical and cortical structures. Such a direct relation between cortical GM and the thalamus would presume a volumetric relation between the two structures. Studies have demonstrated high correlations between volumes of the thalamus and cortical GM, even when controlling for intracranial volume (White, Andreasen, and Nopoulos, 2002). This relation between the thalamus and cortical gray matter meshes well with the present neurodevelopmental and neuroanatomic understanding of thalamic/cortical GM connectivity. Studies that demonstrate volume decreases in both the thalamus and cortical regions (i.e., reductions in volume of both the prefrontal cortex and mediodorsal nucleus of the thalamus) may reflect aberrant patterns of connectivity between the two regions. (White, Andreasen, and Nopoulos, 2002). As the neurons migrate into the cortical plate, they extend apical and basilar dendrites (Juraska and Fifkova, 1979). The apical dendrites subsequently extend additional branches, with increasing complexity that continues during postnatal development (Conel, 1939). Chemical signals guide these developing dendrites toward their ultimate location, where synapses are formed (Sperry, 1963). While these connections form in the absence of the release of neuronal neurotransmitter, their maintenance, once formed, is activity dependent (Verhage, Maia, and Plomp, 2000). Connections are strengthened through activity, and those connections that see little traffic are eventually pruned (Luo and O’Leary, 2005). Thus a certain level of growth and pruning allows for inherent plasticity during neurodevelopment and explains

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why children with large sections of the brain removed may meet with minimal consequences as compared with the same resection in an adult patient. The development of the cerebral cortex requires an orchestration between the processes of neuronal migration, subcortical and cortical connectivity, interneuron development, and gyrification, all of which are temporally overlapping processes (Darlington, Dunlop, and Finlay, 1999). The formation of cortical and thalamocortical connectivity is not fully complete before gyrification, and a complex interplay exists between these processes that is as yet not completely understood.

The phylogeny of gyrification The elephant brain is four times larger, and the brain of the sperm whale is five to six times larger, than the human brain. Although these mammals certainly have a considerable level of intelligence when viewed within an environmentally adaptive context, larger absolute brain size does not necessarily translate into greater intelligence. A larger animal will generally possess a larger brain, owing to the larger surface area of the body and need for a greater number of neurons to cover the sensory and motor domains of the body. One can account for this relationship by normalizing brain mass by the weight of the animal. Brain weight scales to the ¾ power of body weight across both primates and nonprimates (Hofman, 1982). In addition, humans, porpoises, and dolphins have a disproportionately large brain compared to their body mass (Allman, 1998). Since humans, porpoises, and dolphins have the greatest amount of cortical folding, the relationship between body weight and surface area is considerably greater in these mammals. Finally, since the brain is a costly organ with respect to energy metabolism, there is evidence that one limitation in the enlargement of the brain is related to the balance between brain and body energy demands (Armstrong, 1982). The most conspicuous feature of the human brain is the disproportionately large size of its cerebral cortex, which is due to an extension of the developmental period of this latedeveloping structure (Finlay and Darlington, 1995). The result is a related increase in the surface area of the brain. Greater surface area equates to a larger amount of cortical gray matter and thus greater computational power. The phylogenetic increase in the surface area of the human brain has far exceeded growth in the cortical thickness (Welker, 1990). For example, in humans the surface area of the brain is 1,700 times larger than in shrews, yet the thickness of the cortex is only six times greater (Hofman, 1989). Compared to macaque monkeys, the surface area of the human brain is approximately ten times greater, whereas the human cortex is only twice as thick (Rakic, 1995). These comparisons indicate that during evolution the cortex

expanded laterally rather than vertically (Chenn and Walsh, 2002), resulting in a convoluted human cortical sheet that is about three times as large as the inner surface of the skull (Hilgetag and Barbas 2005, 2006; Richman et al., 1975; Toro and Burnod, 2005; Van Essen, 1997; Welker, 1990). Theoretically, the number of neurons in the cortex could also be increased by adding to cortical thickness rather than cortical surface. In this way, tripling cortical thickness, from about 5 to 15 mm, should allow a smooth human cortex with seemingly only a minor increase in brain volume. However, modeling studies (Murre and Sturdy, 1995; Ruppin, Schwartz, and Yeshurun, 1993) have demonstrated that this idea is ill-fated. Given the formidable degree of connectivity among cortical neurons (each forming, on average, a thousand or more connections with other neurons [Braitenberg and Schüz, 1998], the volume of wire grows exponentially with the number of neurons. Thus the extra projections required to link neurons in the additional cortical layers would lead to very uneconomical wiring in the thickened cortex, since connections within the cortex would need to take detours around the additional wiring volume (Chklovskii, Schikorski, and Stevens, 2002). These theoretical studies support the idea that the segregation of brain tissue into components of cell bodies within the GM and connections within the white matter (WM), in concert with the volume-saving folding of the cortical sheet, reflects an optimal wiring and volume arrangement for the very dense connectivity found in the primate cerebral cortex (Murre and Sturdy, 1995; Ruppin, Schwartz, and Yeshurun, 1993). Therefore, the computational prowess of the human brain appears linked to the overproportional size of its cerebral cortex and the intricate architecture of the cerebral cortex. This architecture includes the segregation of WM and GM and limiting the thickness of the gray matter sheet, which is enacted through a lateral increase of the cerebral cortex during evolution. At what point, however, did cortical folding become necessary? It appears that gyrification is strongly related to absolute increases in brain size. Prosimian and primate brains of up to 10 cm3 volume are generally smooth, while for larger volumes there is a close correlation between the degree of gyrification and absolute brain size (figure 10.1 in Striedter, 2005; Zilles et al., 1989). This critical threshold in brain size is likely related to constraints on other body systems and developmental events, such as the maximum birth size of the embryonic skull and its relation to the size of the pelvis and the birth process in humans (Striedter, 2005).

Theories of the ontogeny of gyrification What mechanisms produced the convoluted human cortex? Was it merely additional symmetric cell division with

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subsequent squashing of the excess gray matter into the given space? Or, alternatively, were there additional neurodevelopmental changes that allowed not only for the increase in surface area, but also a mechanism to allow the newfound gray matter to be compacted within the given volume in a characteristic pattern? One need only study several brains within a species to notice the dramatic similarity in the folding patterns. Indeed, studies of monozygotic twins have demonstrated a strong genetic contribution to the development of brain topology (Bartley et al., 1993; Bartley, Jones, and Weinberger, 1997; Lohmann et al., 1999; White et al., 2002). In addition, specific genes have been found that are related to disorders affecting gyrification (Piao et al., 2004). Finally, as we explore the mechanistic theories of gyrification, is it possible that the folding patterns of the brain not only provide for increased surface area of the cortex, but also that gyrification enhances the efficiency of neuronal processing within the brain? During the third trimester of fetal life, the brain develops from a lissencephalic, or smooth, structure to a brain with convolutions that are characteristic of an adult brain (Armstrong et al., 1995; Naidich, Grant, and Altman, 1994; Retzius, 1891; Welker, 1990). Zilles and colleagues (1988) described a “gyrification index” (GI) that was applied to measure the developmental trajectory of gyrification in humans (Armstrong et al., 1995). The GI was calculated on coronal slices of the brain by calculating the ratio between the outline of the cortical surface and a lissencephalic outline of the brain (i.e., excluding the traces into the sulci). From this work on postmortem samples, they found that the gyrification index increases dramatically during the third trimester of fetal life, then remains relatively constant throughout development (Armstrong et al., 1995; Dareste, 1862). Since the brain nearly triples its volume from birth to adulthood, the process of gyrification continues to develop, maintaining this constant ratio. There is a sexual dimorphism in gyrification, with the female cerebral cortex more strongly convoluted than the male cortex (Luders et al., 2004). This finding implies that the volume difference between female and male brains is partly offset by a more efficient packing of the cortical sheet in the brains of females. The exact mechanisms underlying the gyrification of the brain are as yet unknown, although two overarching theories have emerged. These include the theory of gyrogenesis, or the theory that the gyri form as a result of active growth to specific regions of the brain (Le Gros Clark, 1945) and mechanical theories of gyrification based on the physical self-organization of the brain (Hilgetag and Barbas, 2005, 2006; Richman et al., 1975; Toro and Burnod, 2005; Van Essen, 1997; Welker, 1990). The mechanical theories can be further subdivided into those that involve differential growth patterns within the cortex (Richman

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et al., 1975) and axonal-tension-based theories (Van Essen, 1997). A stimulating mechanical hypothesis was proposed by David Van Essen in 1997. This concept, known as the axonal-tension-based morphogenesis of the cerebral cortex, postulated that neuronal connectivity during early neurodevelopment is involved in producing fiber tension that draws interconnected regions closer together. If tension produced by the neuronal connections is involved in the mechanisms of gyrification, then changes in the patterns of the gyri and sulci are an expected outcome of brain changes that alter the connectivity between brain regions. Indeed, local and remote changes of gyrification have been observed after experimental white matter lesions in the developing primate brain (Goldman-Rakic, 1980; Goldman-Rakic and Rakic, 1984). The axonal-tension concept has also been supported by recent experimental findings in the primate brain as well as modeling studies (Hilgetag and Barbas, 2006; Toro and Burnod, 2005), and these studies support a link between brain surface morphology and regional neuronal connectivity within a developmental framework. Implications of the Mechanical Folding Hypothesis Combining the age-related differences in the morphology of the cerebral cortex with changes in neural connectivity is intriguing. The age-related decrease in synaptic and dendritic arborization may result in decreasing the tensile forces that are involved in the morphogenesis of the cerebral cortex. Histological studies of the neuronal pathways have found that the neural fibers on average tend to traverse horizontal to the surface in the sulci, whereas fiber pathways course on average more tangential in the gyri (Welker, 1990). From a mechanical perspective, a release of tension along the line of average tensile would result in widening of the sulci and greater curvature of the gyri. These changes have been found in a group of healthy adults (Magnotta et al., 1999). It is a well-known neuroscience concept that brain regions that wire together, fire together, that is, cooperate in brain function (Hilgetag et al., 2000). Thus another advantage of the tension-based morphogensis hypothesis is that regions that have strong interconnections and form functional circuits are maintained in relatively close proximity. This arrangement contributes to greater compactness of the brain and decreases the conduction time of neuronal signals along axonal fibers, thus enhancing overall efficiency (Van Essen, 1997). However, it needs to be kept in mind that cortical folding due to the balancing of axonal tension is played out as a global tug of war. Thus individual projections may deviate from the general shortening and straightening of fibers during development, so that some projections may remain relatively long and connect distant regions of the brain (Hilgetag and Barbas, 2006). This

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process would account for the long tail in the distance distribution of cortical projections (Kaiser and Hilgetag, 2006). While an admixture of long projections to mostly short cortical connectivity increases the global wiring length of the brain, it also contributes to a reduction of neural processing steps by providing long-distance shortcuts across the system (Kaiser and Hilgetag, 2006). Thus the overall efficiency of brain structure and function is determined by the simultaneous adaptation to multiple, partly opposing constraints. Generally, aspects of physical self-organization may make a substantial contribution to many morphogenetic processes. For instance, stretching and compression forces produced during the folding of the cortex influence the relative laminar thickness of cortical layers, resulting in a larger ratio of thickness of upper to deep layers in sulcal regions compared to gyral regions (Hilgetag and Barbas, 2006). Moreover, neuronal migration itself may be affected by the mechanical forces produced during cortical folding, as there is potential overlap in the developmental timetables of migration and folding. For example, the radial migration of late-maturing neurons through the already existing deep layers in budding gyri may be affected by lateral compression from the ongoing folding process, resulting in additional mechanical resistance to the neurons’ migration, and potentially in an additional accumulation of cells in the deep layers. In agreement with this idea, there are more pyramidal neurons and glia in the deep layers of gyri than in sulci or straight portions of cortex (Hilgetag and Barbas, 2006, unpublished observations); by contrast, there is no such clear trend in the data from monkey prefrontal cortex for different types of inhibitory interneurons (unpublished observations), which are predominantly migrating tangentially. Heritability of Gyrification Numerous studies have capitalized on nature’s clones, monozygotic (MZ) or identical twins, studied with and without dizygotic (fraternal twins) to study the interplay between genetic and environmental influences in neurodevelopment. Since parents and close friends are able to tell one identical twin from another, they are not completely identical (Machin, 1996). Considering the complexity of neurodevelopment, the role of stochastic processes (i.e., there are not enough genes to program all the specific connections of all neurons and their synapses), and the observation that some processes (i.e., pruning) are influenced by environmental factors, it should not be surprising that certain aspects of brain development are different in identical twins. Interestingly, the surface morphology of the brain has much greater variability than midline, volume, or subcortical regions of the brain (Bartley, Jones, and Weinberger, 1997; White et al., 2002). Studies evaluating the gyral patterns in monozygotic twins demonstrate that the deeper and developmentally earlier

sulci of the brain are more highly correlated than the tertiary sulci (Lohmann, von Cramon, and Steinmetz, 1999). In addition, there are regional differences between more conserved and more variable parts of the cortical landscape (Thompson et al., 2001). Nongenetic factors tend to have a greater influence on the tertiary sulci, which develop mainly after birth. Measures of gyral and sulcal curvature of the brain are significantly less correlated in twins than is the thickness of the cortex (White, Andreasen, and Nopoulos, 2002), supporting the phylogenetic differences between cortical thickness and surface area. It is possible that the greater nonshared environmental influences that are present for postnatal twins, coupled with the pronounced cortical plasticity inherent in early development, modulate the changes in cortical surface morphology. The strongest evidence for surface pattern dissimilarities between monozygotic twins came from a twin study by Bartley and colleagues (1997) that included both monozygotic and dizygotic twins. This study, which utilized a crosscorrelation algorithm to compare structural brain images, demonstrated that the majority of the morphologic variance between the brain surface morphology of MZ twins was a result of random environmental effects, while brain size, however, appeared to be strongly determined by genetic factors. Steinmetz and colleagues (1995) also demonstrated that MZ twins discordant for handedness exhibited differing degrees of asymmetry of the planum temporale. Epigenetic factors are probable explanations, not only for asymmetries of the planum temporale, but also for other surface measures of the brain, which show significantly lower correlations than the volumetric brain measures. It needs to be stressed, however, that genetic factors and physical self-organization are not mutually exclusive processes, but interact at all stages of development. For instance, genetic factors may underlie the timing of development of different cortical layers and areas, and mechanical factors may come into play as the cortex grows and cortical regions are interconnected by axonal fibers, resulting in the selforganization of migrating neurons, cortical layers, and cortical convolutions. Thus a dynamic interplay of genetics and simple physical principles underlies the formation of cortical convolutions as well as cortical architecture. Postnatal Changes in Brain Morphology and Gyrification During the first year of life, neurodevelopment changes take place at a rapid pace (Huttenlocher, 1979, 1990; Huttenlocher and Dabholkar, 1997; Huttenlocher and de Courten, 1987; Huttenlocher et al., 1982; Reiss et al., 1996; Yakovlev and Lecours, 1967). Synaptic density peaks by approximately the fourth postnatal month in the striate cortex (Huttenlocher and de Courten, 1987) and by approximately 12 postnatal months in the prefrontal cortex (Huttenlocher, 1979). Brain weight peaks at approximately

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ten years of age, and MRI studies have demonstrated only a minor increase in mass after four to five years of age (Pfefferbaum et al., 1994; Reiss et al., 1996). Studies of gyrification during postnatal development have differed, depending on whether the sample was from postmortem brains or subjects who received magnetic resonance (MR) scans. Armstrong and colleagues (1995) demonstrated that postmortem brains maintained a constant gyrification index from birth through late adulthood. Alternatively, Magnotta and colleagues (1999) showed gradual changes in brain curvature, with the gyri developing greater curvature and the sulci developing less curvature, or becoming more broadened with increasing age. Since Armstrong and colleagues did not specifically study differences between the gyri and the sulci, and Magnotta and colleagues did not measure the gyrification index, it is possible that the opposite differences between the sulcal and gyral findings on MR, which were opposite in direction, canceled each other out, thus maintaining a constant gyrification index. Mixed results have resulted from evaluating age-related changes in total cell counts of the cortex (for review see Peters et al., 1998). Since the original studies of Brody (1955), who found that as many as half of the neurons in regions of the frontal and temporal lobes are lost, there have been conflicting reports of the extent of neuronal loss in the cortex (Anderson et al., 1983; Cragg, 1975; Devaney and Johnson, 1980; Haug, 1986; Haug et al., 1984; Henderson, Tomlinson, and Gibson, 1980; Shefer, 1973). The earlier studies that reported dramatic cell death apparently did not account for methodological techniques that resulted in a greater shrinkage of younger brains. This age-dependent shrinkage resulted in the appearance of a greater compaction of neurons within the brain (Haug, 1986; Haug et al., 1984). More recent studies that utilized techniques to reduce or control for brain shrinkage have not replicated the early findings, although the debate continues (Leuba and Kraftsik, 1994; Peters et al., 1998; Terry, De Teresa, and Hansen, 1987). The current consensus is that cortical neurons are generally preserved during adolescence and adulthood with at most a 10 percent reduction in neuronal numbers (Peters et al., 1998). Since this loss is mainly in older adults, little cell death appears to occur during adolescence, at least in the absence of the use of illicit substances. Even in the absence of considerable neuronal cell loss, developmental differences in brain structure and function have been described during adolescence and adult life (Huttenlocher, 1979; Huttenlocher et al., 1982; Giedd et al., 1996; Sowell et al., 2003, 2004; Sowell, Thompson, et al., 2002; Sowell, Trauner, et al., 2002). Huttenlocher and colleagues (Huttenlocher, 1979; Huttenlocher et al., 1982) have reported substantial decreases in synaptic density in the middle frontal gyrus during adolescence and early adulthood, this decrease being without considerable

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neuronal loss. This decrease in synaptic density is associated with decreased plasticity and is likely related to the pruning of connections that have less functional efficiency in the developing neuropil (Easter et al., 1985). Histological studies have also demonstrated an age-related decrease in cortical thickness associated with the changes taking place in the neuropil (Jacobs et al., 1997). The thinning of the cortical GM during late childhood and adolescent development involves primarily modulations within the neuropil, namely, synaptic pruning and dendritic arborization. The age-related findings identified histologically have also been shown using magnetic resonance imaging (MRI) techniques (Giedd et al., 1997, 1996; Sowell et al., 2003, 2004; Sowell, Thompson, et al., 2002; Sowell, Trauner, et al., 2002; Thompson and Nelson, 2001). Cross-sectional MRI studies of development have consistently demonstrated a decrease in gray matter volume starting in late childhood or early adolescence and progressing into late adulthood (Caviness et al., 1996; De Bellis et al., 2001; Giedd, 2004; Giedd et al., 1996; Gogtay et al., 2004; Jernigan and Tallal, 1990; Jernigan et al., 1991; Lenroot and Giedd, 2006; Pfefferbaum et al., 1994; Reiss et al., 1996; Sowell et al., 2003; Sowell et al., 2004; Sowell, Trauner, et al., 2002). The age of peak GM volume varies between studies and ranges from 4 years (Pfefferbaum et al., 1994) to early adolescence (Giedd et al., 1996), and the cerebellum appears to be one of the later-developing brain structures. There appear to be age-related differences in volume loss, such that between childhood and adolescence there is greater gray matter loss in the parietal lobes (Sowell et al., 1999), whereas comparing adolescents to adults, the gray matter loss occurs more in the frontal and subcortical regions (Giedd et al., 1996; Sowell et al., 1999). MRI studies have demonstrated reductions in the thickness of the cortex that correspond with the decreases in gray matter volume (Sowell, Thompson, et al., 2002).

Clinical conditions associated with abnormalities in gyrification Using the analogy of an archaeologist piecing together specific clues about past events that are based on current finds, so too is it conceivable to piece together the timing of specific clinical aberrations of development based on current data. For example, based on what is known regarding neurodevelopment and gyrification—e.g., the recent reports of disruption of neuronal migration in rats exposed to long durations of ultrasound (Ang et al., 2006)—both changes in the thickness of the cortex and alterations in gyrification would be expected. Clinical conditions can be broken down into primary disorders of gyrification (those that cause gross disruptions in the gyrification that are readily identified on imaging or

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postmortem samples) and secondary alterations in gyrification (those that may influence gyrification through downstream processes). These distinctions define an arbitrary line, as the difference between a primary and secondary disorder of gyrification may only relate to the timing or the extent of the specific neurodevelopmental insult. Primary Disorders of Gyrification Lissencephaly (stemming from the Greek word lissos, meaning smooth, and enkephalos, meaning brain) is an umbrella term that describes a number of rare developmental malformations characterized by an absence or reduction of the gyri and sulci. Children with lissencephaly have severe developmental delays and mental retardation and are typically diagnosed within the first six months of life (Ross and Walsh, 2001). Malformations that are closely related to lissencephaly include pachygyria (pachy stemming from the Greek word pachys, meaning “thick”), a malformation in which the gyri are thicker and the sulci are less deep, and polymicrogyria, which results in a cortex with multiple small gyri and shallow sulci. Evidence shows that individuals with lissencephaly and related disorders have an arrest of normal neuronal migration during the third to fourth month of uterine life (Bielschowsky, 1923; Stewart, Richman, and Caviness Jr., 1975). This is evidenced by histological findings of heterotopic clumps of neurons in subcortical brain regions (Bielschowsky, 1923; Stewart, Richman, and Caviness Jr., 1975). As would be expected from the disruption of neuronal migration, the cortex is reduced from six to four layers, although the actual thickness of the cortex is increased (Bielschowsky, 1923; Josephy, 1944; Stewart, Richman, and Caviness Jr., 1975). This increase in thickness is related to the disruption in the migration and the subsequent spreading of the neurons within the cortex. Since the surface area of lissencephalic brains is reduced substantially and the gyri and sulci fail to form, neuronal migration plays an integral role in gyrification. It is possible that the dilution of neuronal fibers within the thicker cortex, coupled with the increased failure of tangential neuronal fibers to adequately produce neural networks, results in a lack of tension within the cortical surface. This then disrupts the formation of sulci and gryi during brain growth. Interestingly, it is postulated that when the disruption in the neuronal migration takes place earlier (before 18 weeks), there is a greater likelihood that the brain will show fewer gyri and sulci (i.e., lissencephaly), whereas between 18 and 24 weeks the gross morphological defects of gyrification will be less pronounced (pachygyria). Radial and horizontal migration of the deeper layers, prior to an insult to the CNS, may produce enough tension within these deeper layers to result in the development of some, albeit aberrant, gyrification.

Neuropsychiatric Disorders Associated with Altered Gyrification With the emerging evidence supporting a tension-based hypothesis for cortical morphogenesis (Hilgetag and Barbas, 2005, 2006; Van Essen, 1997), there is a direct translation between changes in neuronal connectivity and gyral and sulcal morphology (White et al., 2003; Zilles et al., 1988). Whereas the gyrification index described by Zilles and colleagues (1988) remains constant from birth through adulthood (Armstrong et al., 1995), it is well equipped to determine alterations that occurred during uterine life. Thus intrauterine events that alter gyrification would be evidenced in an alteration in the GI, and thus would support neurodevelopmental hypotheses for specific disorders (Kulynych et al., 1997; Vogeley et al., 2000). But because the gyrification index does not differentiate between sulcal and gyral changes, subtle changes in gyral and sulcal morphology that counteract each other would go undetected during normal aging and development using the GI (Kulynych et al., 1997). Thus Magnotta and colleagues (1999) developed an image analysis algorithm that is able to independently differentiate between sulcal and gyral changes in cortical surface morphology. Most studies of neuropsychiatric disorders utilize the GI to measure patient control differences. Studies in schizophrenia have been mixed, with several studies demonstrating a global decrease in the GI of the left cortex (Kulynych et al., 1997; Sallet et al., 2003) and the right cortex (Sallet et al., 2003; Vogeley et al., 2000). Alternatively, studies of gyrification in schizophrenia have also shown no patient/ control differences (Highley et al., 2003) and, interestingly, an increased GI in the right temporal lobe (Harris, Yates, et al., 2004). Given the variability of the findings of these studies and the clinical heterogeneity that is well known in schizophrenia, a full consensus has not been achieved regarding the specificity of neurodevelopmental abnormalities in gyrification in schizophrenia. Utilizing techniques developed by Magnotta and colleagues (1999) that are able to differentiate alterations in the gyri and sulci independently, White and colleagues (2003) found opposing changes in the gyri and sulci in the frontal and temporal regions in a group of children and adolescents with schizophrenia. The sulci developed greater curvature (i.e., became more broad), whereas the gyri became more peaked, demonstrating an increased curvature. Based on the histological differences between the sulci and the gyri (Welker, 1990), it was postulated that these changes were consistent with aberrant pruning under the tension-based morphogenesis hypothesis of gyrification. Several studies have emerged evaluating those at an increased risk for developing schizophrenia. Harris, Whalley, and colleagues (2004) found an increase in GI in the right prefrontal cortex of patients with schizophrenia, whereas Jou and colleagues (2005) found a decrease in the left frontal

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lobe. Finally, individuals with velocardiofacial syndrome (VCFS), who have a nearly 30 times greater risk of developing schizophrenia than the general population, showed a significant decrease in the GI in both the frontal and parietal lobes (Schaer et al., 2006). Autism, which has been shown to have an altered developmental trajectory in brain volume (Courchesne, Redcay, and Kennedy, 2004; Piven et al., 1996), has only one study evaluating gyrification. In a cross-sectional study of brain gyrification in patients with autism, Hardan and colleagues (2004) found an increased GI in the frontal brain region. One hypothesis that may explain an increased GI in patients with autism, in addition to the reported decrease in GI in individuals with dyslexia (Casanova et al., 2004), involves abnormalities in the minicolumns, or the white matter fibers that course through the gyri (Casanova et al., 2002, 2006). Alterations in brain size or gyrification may alter the volume of white matter passing through these gyral columns, resulting in altered communication between distant brain regions and affecting processing speed and the integration of neuronal signals. Interestingly, we found a narrowing of the gyral alterations in our young patients with schizophrenia. Finally, an increase in GI has been demonstrated in patients with Williams syndrome (Schmitt et al., 2002). Although the increases were global, they were more pronounced in the left frontal, right parietal, and right occipital lobes. An increased GI may reflect a regionally diminished rate of pruning of synaptic and neuronal connections. The direct relationship between alterations in gyrification and subsequent behavioral changes for each of these disorders is an evolving area of research. However, a better understanding of the processes associated with gyrification of the brain may help us achieve a better understanding of factors associated with the pathology within the onset and course of these disorders.

Conclusions The development of the central nervous system and formation of the gyri and sulci in the brain involve an orchestration of highly complex processes. These processes begin in utero and progress throughout the life span. During the first two weeks of fetal life, there is a differentiation of cells within the ectoderm into cell lines that are destined to become the cells of the central nervous system. These cells divide and multiply rapidly, forming first the neural plate, then the neural tube. Cells that line the ventricular zone change from symmetric division to asymmetric division at approximately six weeks of gestational age. This time point is important, since alterations during the period of symmetric cell division are more likely to influence the surface area of the brain, and thus changes in the gyrification patterns.

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Gyrification of the deepest, primary sulci begins near the end of the second trimester, although the brain has a predominant lissencephalic character at the beginning of the third trimester. The major changes in gyrification of the human brain take place during the third trimester with the necessary scaffolding likely being constructed prior to this time (Rakic, 2004). Although the mechanisms underlying gyrification are unknown, an interesting theory proposes that regions of greater neural connectivity apply greater tension, such that as the brain grows, areas of greater connectivity tend to be drawn closer together (Van Essen, 1997). This hypothesis would account for gyri being functional units with more efficient neural communication. Importantly, there are significant temporal overlaps between different developmental events, such as neuronal migration, formation of connections, and gyrification (Darlington, Dunlop, and Finlay, 1999), so that the processes of neuronal migration, formation of connections, and gyrification likely interact and affect each other. The most visible changes in brain structures occur during uterine life and the first few early years following birth. However, more subtle brain changes can be seen throughout childhood and adolescence, even into early to middle adulthood. Structural brain changes during adolescence and young adulthood include a decrease in gray matter, density changes of cells within the neuropil, and an increase in myelination. The brain surface morphology continues to change as well during adolescence and adult life, with the gyri becoming more steep and the sulci developing a broader appearance. These latter findings are associated with an increase of cerebrospinal fluid bathing the outer layer of the brain. With the continued evolution of electrophysiological and neuroimaging tools that are able to increase our knowledge of the complex processes involved with neurodevelopment, the upcoming years will continue to exponentially add to our understanding of brain development and gyrification. At the moment, however, many questions remain about the mechanisms and implications of the process of gyrification in the human brain. For example, what are the exact developmental mechanisms of gyrification, and which aberrations from typical development lead to the observable differences in brain shape in different patient groups? Answers to these questions will depend on a greater understanding of developmental events. (For example, when do specific cortical areas and their interconnections form?) Another important open question concerns the relationship between the amount, distribution, and development of white matter in the brain and the global and local degree of gyrification. Morphometric studies, as reviewed here, have demonstrated systematic differences in WM as well as gyrification between normal subjects and patients with a variety of neuropathologic conditions. While the axonal-tension hypothesis sug-

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gests that the two variables are linked through brain development, it is not yet clear how their relationship can be formalized in the context of given morphometric data. For instance, does a relative regional decrease in WM always lead to a regional increase or a regional decrease of gyrification, or can such relations only be addressed at the level of the whole brain? The recent advances in diffusion tensor imaging applied to studies of gyrification may help to address this question. Finally, processes of gyrification lead to systematic morphologic differences in different parts of the cortical landscape, affecting overall thickness and laminar morphology, as well as morphology at the cellular level (Hilgetag and Barbas, 2005, 2006; Richman et al., 1975; Toro and Burnod, 2005; Van Essen, 1997; Welker, 1990). Are there resulting functional differences between gyri and sulci as well? Such ideas may be tested by electrophysiology or high magnetic field functional imaging of cortical areas that extend across different cortical terrains at high resolution. These methodologies are likely to further our understanding of the relationship between brain structure and function in the developing brain. acknowledgments

The writing of this chapter was made possible by NIMH grant (MH068540). Correspondence concerning this chapter should be directed to Tonya White, M.D., at the Division of Child and Adolescent Psychiatry F256/2B, University of Minnesota, 2450 Riverside Avenue, Minneapolis, MN 55401, USA. We would like to acknowledge Dr. Canan Karatekin for her careful review of the manuscript and also thank Ms. Roxana Voitcu for her helpful comments. REFERENCES

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and Pick’s and Alzheimer’s diseases. Neurosci. Behav. Physiol. 6:319–324. Sidman, R. L., and P. Rakic, 1973. Neuronal migration, with special reference to developing human brain: A review. Brain Res. 62:1–35. Sowell, E. R., B. S. Peterson, P. M. Thompson, S. E. Welcome, A. L. Henkenius, and A. W. Toga, 2003. Mapping cortical change across the human life span. Nature Neurosci. 6:309–315. Sowell, E. R., P. M. Thompson, C. J. Holmes, T. L. Jernigan, and A. W. Toga, 1999. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature Neurosci. 2:859–861. Sowell, E. R., P. M. Thompson, C. M. Leonard, S. E. Welcome, E. Kan, and A. W. Toga, 2004. Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24:8223–8231. Sowell, E. R., P. M. Thompson, D. Rex, et al., 2002. Mapping sulcal pattern asymmetry and local cortical surface gray matter distribution in vivo: Maturation in perisylvian cortices. Cerebral Cortex 12:17–26. Sowell, E. R., D. A. Trauner, A. Gamst, and T. L. Jernigan, 2002. Development of cortical and subcortical brain structures in childhood and adolescence: A structural MRI study. Dev. Med. Child Neurol. 44:4–16. Sperry, R. W., 1963. Chemoaffinity in the orderly growth of nerve fiber patterns and connections. Proc. Natl. Acad. Sci. USA 50:703– 710. Steinmetz, H., A. Herzog, G. Schlaug, Y. Huang, and L. Jancke, 1995. Brain (A) symmetry in monozygotic twins. Cerebral Cortex 5:296–300. Stewart, R. M., D. P. Richman, and V. S. Caviness, Jr., 1975. Lissencephaly and pachygyria: An architectonic and topographical analysis. Acta. Neuropathol. (Berl.) 31:1–12. Striedter, G., 2005. Principles of Brain Evolution. Sunderland, MA: Sinauer Associates. Swanson, L., 2003. Brain Architecture. Oxford, UK: Oxford University Press. Terry, R. D., R. DeTeresa, and L. A. Hansen, 1987. Neocortical cell counts in normal human adult aging. Ann. Neurol. 21:530– 539. Thompson, P. M., T. D. Cannon, K. L. Narr, et al., 2001. Genetic influences on brain structure. Nature Neurosci. 4:1253–1258. Thompson, R. A., and C. A. Nelson, 2001. Developmental science and the media: Early brain development. Am. Psychol. 56:5–15. Toro, R., and Y. Burnod, 2005. A morphogenetic model for the development of cortical convolutions. Cerebral Cortex 15:1900– 1913. Van Essen, D. C., 1997. A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385:313–318. Verhage, M., A. S. Maia, J. J. Plomp, et al., 2000. Synaptic assembly of the brain in the absence of neurotransmitter secretion. Science 287:864–869. Vogeley, K., T. Schneider-Axmann, U. Pfeiffer, et al., 2000. Disturbed gyrification of the prefrontal region in male schizophrenic patients: A morphometric postmortem study. Am. J. Psychiatry 157:34–39. Welker, W., 1990. Why does cerebral cortex fissure and fold? In E. G. Jones and A. Peters (eds.), Cerebral Cortex, vol. 8B, 3–136. New York: Plenum Press. White, T., N. C. Andreasen, and P. Nopoulos, 2002. Brain volumes and surface morphology in monozygotic twins. Cerebral Cortex 12:486–493.

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Regional Development of the Brain in Early Life, 3–70. Oxford, UK: Blackwell. Zilles, K., E. Armstrong, K. H. Moser, A. Schleicher, and H. Stephan, 1989. Gyrification in the cerebral cortex of primates. Brain Behav. Evol. 34:143–150. Zilles, K., E. Armstrong, A. Schleicher, and H. J. Kretschmann, 1988. The human pattern of gyrification in the cerebral cortex. Anat. Embryol. (Berl.) 179:173–179.

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4

Adult Neurogenesis in the Hippocampus YEVGENIA KOZOROVITSKIY AND ELIZABETH GOULD

Introduction Most neurons in the adult mammalian brain are produced during the embryonic period, but a substantial number of granule cells are born in the cerebellum, olfactory bulb, and hippocampus during the postnatal period (Altman and Das, 1966; Altman, 1969). In the olfactory bulb and the dentate gyrus of the hippocampus, neurogenesis continues throughout adulthood at relatively high levels (Altman and Das, 1965). Adult neurogenesis under normal conditions has also been reported in multiple other brain regions, including neocortex, striatum, amygdala, hypothalamus, and substantia nigra (Altman and Das, 1965; Kaplan, 1981; Huang, De Vries, and Bittman, 1998; Gould, Reeves, et al., 1999, 2001; Zhao et al., 2003; Bernier et al., 2002; Fowler et al., 2002; Fowler, Johnson, and Wang, 2005; Dayer et al., 2005; Xu et al., 2005; Bedard, Gravel, and Parent, 2006; Luzzati et al., 2006; Runyan, Weickert, and Saunders, 2006), but these findings remain controversial. In contrast, the idea that neurogenesis persists in the adult hippocampus has gained widespread acceptance in the neuroscience community, although the role of adult-generated neurons in the function of neural systems and in behavior is still a matter of debate. Adult neurogenesis in the hippocampus has been demonstrated in the brains of all vertebrate species investigated thus far, including humans (Altman and Das, 1965; Barnea and Nottebohm, 1994; Polenov and Chetverukhin, 1993; PerezCanellas and Garcia-Verdugo, 1996; Gould, Reeves, et al., 1998, 1999; Eriksson et al., 1998; Zupanc, 1999), suggesting that it is a highly conserved process. Quantitative estimates indicate that approximately 10,000 new cells are added to the young adult rat dentate gyrus every day (Cameron and McKay, 2001); a substantial but apparently smaller amount of neurogenesis occurs in the adult primate hippocampus (Eriksson et al., 1998; Gould, Reeves, et al., 1998, 1999). The discovery of adult neurogenesis has already altered the way the adult brain is viewed in terms of its potential for structural change. Studying adult neurogenesis also opens the possibility of therapeutic application in cases of developmental abnormalities, neurodegenerative disease, or traumatic brain injury. To further these goals, the role that adult-generated neurons play in normal functional circuitry must be elucidated, and the factors that regulate the produc-

tion, differentiation, survival, and integration of newly generated neurons need to be identified. In this chapter, we consider some of the key experimental findings that led to the discovery of adult neurogenesis, discussing the techniques used to quantify neurogenesis in the adult brain. We also examine several factors and conditions that regulate neurogenesis in the hippocampus and consider the possible role of adult-generated cells in hippocampal function and behavior. Adult neurogenesis is an important example of a developmental neural process that persists throughout life, with postnatal experience substantially modifying both brain and behavior.

The discovery of neurogenesis in the adult mammalian brain The study of adult neurogenesis depends on methods that can selectively label newly generated neurons. Early histologists relied on methods which, despite excellent spatial resolution, were not adequately cell-type specific and thus severely limited the investigation of developmental or adult neurogenesis. In 1965, Altman used 3H-thymidine autoradiography and suggested the possibility that neurogenesis occurs in the olfactory bulb and dentate gyrus of adult rats (Altman and Das, 1965). This technique takes advantage of the fact that cells undergoing DNA synthesis incorporate thymidine. When injected into an animal, 3H-thymidine is taken up by cells in the DNA synthetic phase of the cell cycle, radioactively tagging the dividing cell and all its subsequent progeny (although the signal becomes diluted with each subsequent division). Stable and specific, this technique proved useful for mapping mitotically active cell populations at the single-cell level. While it was clear from the work of Altman that new cells are added to the adult rodent olfactory bulb and hippocampus, the identity and fate of these cells remained in question, because light microscopic examination was not sufficient to unequivocally identify them as neurons. Kaplan and Hinds partially resolved this issue by using electron microscopy to demonstrate, at the ultrastructural level, that adultgenerated neurons in the dentate gyrus of the rat receive synaptic input on their cell bodies and dendrites (Kaplan and Hinds, 1977). However, since some glial cells receive

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synaptic input (Oppenheim, Chu-Wang, and Maderdrut, 1978), immunohistochemical advances that enable staining for cell-type-specific antigens and combined thymidine labeling/retrograde axon tracing were required to generate definitive evidence in support of adult neurogenesis in the dentate gyrus. These studies showed that 3H-thymidine-labeled cells expressed markers of mature neuronal phenotype, such as neuron-specific enolase (Cameron et al., 1993; Okano, Pfaff, and Gibbs, 1993). In addition, 3H-thymidine-labeled cells were shown to incorporate retrograde tracers injected into the CA3 region (Stanfield and Trice, 1988), suggesting that they direct axons to the major target site of developmentally generated granule cells. Altogether, these studies provided convincing evidence that the adult mammalian dentate gyrus produces cells with neuronal characteristics, which are incorporated into the hippocampal circuitry. Although the existence of adult neurogenesis in the brains of rodents had been firmly established, early discoveries did not succeed in encouraging the in-depth study of this phenomenon. The observation that the adult brains of less complex vertebrates, such as fish and amphibians, undergo a remarkable degree of regeneration, whereas the adult mammalian brain does not, seemed incongruous with the concept of persistent neurogenesis in the adult mammalian brain. Brain development in mammals was thought to be a temporally defined process, completed long before adulthood. The lack of interest in adult neurogenesis within the neuroscience community may have been partially attributable to the inability to find adult neurogenesis in primates. For example, 3H-thymidine-labeled cells in the dentate gyrus of adult macaques were few in number and lacked the morphological characteristics of neurons; it was therefore concluded that they were most likely to be glia (Rakic, 1985; Eckenhoff and Rakic, 1988). As a result, adult neurogenesis was discounted as being specific to only certain vertebrate species and therefore of relatively little importance. Methodological Advances Lead to the Discovery of Hippocampal Neurogenesis in Adult Primates The development of 5-bromo-2′-deoxyuridine (BrdU) labeling was a major breakthrough in the study of adult neurogenesis. This method provides a sensitive means for assessing the number of adult-generated cells. BrdU is an analogue of thymidine, which is incorporated into DNA and can be immunohistochemically detected using specific antibodies. This technique has several advantages over 3H-thymidine autoradiography: (1) BrdU-labeled cells throughout an entire brain section (40 μm thick or even thicker) can easily be visualized, while 3H-thymidine autoradiography only allows for the detection of labeled cells in the top 1–3 μm of a tissue section; (2) the sensitivity of BrdU labeling can be amplified, since it is an immunocytochemical method; and (3) the technique is highly compatible with the immunolabeling of

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cell-specific antigens used to determine the phenotype of cells. These advantages of the BrdU labeling method allowed researchers to reinvestigate the issue of adult neurogenesis in primates, establishing its occurrence in adult monkeys and humans (Gould, Reeves, et al., 1998, 1999; Eriksson et al., 1998; Kornack and Rakic, 1999). BrdU labeling combined with stereological techniques allowed for the quantification of neurogenesis in the adult brain. These studies have revealed that several thousand new granule neurons are generated daily in the dentate gyrus of adult rats (Tanapat et al., 1999; Cameron and McKay, 2001). The majority of these adult-generated cells express immature and mature neuronal markers, such as beta III–tubulin (TUJ1) and neuronal nuclei (NeuN). Since the advent of BrdU labeling, additional methods for tagging new neurons have been developed. One promising method involves infecting the new neurons with a retrovirus that drives expression of a fluorescent protein (van Praag et al., 2002; Zhao et al., 2006). Retroviral tools have enabled a detailed morphological characterization of dendrites and axons from new neurons, as well as electrophysiological recordings from the new neurons.

Development of the dentate gyrus During embryonic development, granule neurons arise from progenitor cells located in a discrete part of the neuroepithelium in the wall of the lateral ventricle, migrate across the incipient hippocampus and come to reside in the granule cell layer (Altman and Bayer, 1990). Some progenitor cells remain in the hilus and the subgranular zone of the dentate gyrus without undergoing final cell division. These progenitors continue to divide, giving rise to daughter cells that differentiate into granule neurons in the adult brain (figure 4.1). In the rat, most dentate gyrus neurons are produced during the first two postnatal weeks; by the end of the second week, the granule cell layer has been formed, and cell proliferation and migration decrease significantly. In contrast, the granule cell layer in the macaque forms during the prenatal period (Nowakowski and Rakic, 1981). Yet new granule neurons continue to be produced in the adult dentate gyrus of both rodents and primates from progenitors located in the hilus and subgranular zone. The addition of these new neurons to the dentate gyrus in adulthood reflects a turnover of the adult-generated neuron population, because available evidence suggests that many of the new cells die within weeks of their birth (Cameron et al., 1993).

Hormones regulate adult neurogenesis Steroid hormones play a well-known role in the organization and activation of certain behaviors. Numerous studies

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Figure 4.1 Granule cell neurogenesis in the adult dentate gyrus of the hippocampus. Precursor cells residing in the hilus and the subgranular zone of the dentate gyrus divide throughout adult life, giving rise to daughter cells that migrate into the granule cell layer and differentiate into granule neurons.

suggest that these hormones affect behavior by altering the structural development of the brain. Over the past few decades, studies have demonstrated that steroid hormones also alter the structure of the adult brain by influencing dendrites, synapses, and cell survival (Sapolsky, Krey, and MeEwen, 1985; Woolley et al., 1990). More recently, adrenal and ovarian steroids were observed to alter the proliferation of granule cell precursors in the adult brain (Cameron and Gould, 1994; Tanapat et al., 1999). Some evidence also supports the involvement of thyroid hormones in the modulation of adult hippocampal neurogenesis (Desouza et al., 2005; Ambrogini et al., 2005), but this chapter concentrates on the effects of adrenal and ovarian steroids, which have been studied in depth. Adrenal Steroids Several lines of evidence point to an inverse relationship between adrenal steroid levels and the proliferation of granule cell precursors. First, a negative correlation between levels of circulating adrenal steroids and hippocampal granule neuron production is observed across the life span. In the rat, during the first two postnatal weeks known as the stress hyporesponsive period, low levels of circulating adrenal steroids coincide with maximal granule neuron production (Schlessinger et al., 1975; Sapolsky and Meaney, 1986). During adulthood, when levels of circulating adrenal steroids are higher, the rate of granule neuron production decreases. With aging, the production of new cells is further diminished in aged rats (Kuhn et al., 1996) and macaques (Gould, Reeves, et al., 1999), coincident with increases in the levels of circulating glucocorticoids (Sapolsky and Altmann, 1991; Sapolsky, 1992). Experimental manipulations of glucocorticoids have confirmed this negative relationship between the levels of adrenal steroids and granule neuron production. Increases in glucocorticoids during the stress hyporesponsive period diminish the rate of granule cell production in the dentate gyrus (Gould et al., 1991). Removal of adrenal steroids by means of

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Figure 4.2 Stereological estimates of the number of BrdU-labeled cells in the dentate gyrus of adult ovariectomized (Ovx) rats that were treated with vehicle estrogen, estrogen, and progesterone. Treatment with progesterone 48 hours following the last two daily injections with estradiol rapidly reverses an estrogen-induced increase in cell proliferation. Error bars represent SEM, p < 0.05 relative to controls.

adrenalectomy in adult animals leads to an increase in cell proliferation and neuronal production, whereas treatment with the adrenal glucocorticoid corticosterone results in a decrease in these measures (Gould et al., 1992). Removal of circulating glucocorticoids in aged rats returns cell proliferation in the dentate gyrus to the levels observed in adrenalectomized young adults (Cameron and McKay, 1999). Collectively, these observations illustrate that adrenal steroids are potent mediators of adult neurogenesis and suggest that stressful psychological and physical experiences may act to suppress adult neurogenesis. Ovarian Steroids In contrast to the suppressive action of adrenal steroids on adult neurogenesis, the ovarian steroid hormone estrogen has been shown to stimulate the production of new granule neurons in the dentate gyrus (Tanapat et al., 1999; Tanapat, Hastings, and Gould, 2005; Ormerod et al., 2003). In the rat, a natural fluctuation in cell proliferation is observed across the estrous cycle—the production of new granule cells is greatest during proestrus, the stage of maximal estrogen levels. The removal of estrogen by means of ovariectomy results in a decrease in the proliferation of granule cell precursors, while replenishing estrogen levels rapidly reverses this effect (figure 4.2). In addition to increasing the production of new cells, estrogen affects the survival of adult-generated cells in the dentate gyrus (Tanapat et al., 1999). These effects account for a sex difference in adult neurogenesis, such that female rats produce more new granule cells than males (Tanapat et al., 1999). This effect is temporary, since under standard laboratory conditions,

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many of these new cells degenerate over time. Estrogeninduced increases in the pool of immature granule neurons may still exert an impact on hippocampal function, as will be examined further later on.

Experience regulates adult neurogenesis Because the majority of neurons in the dentate gyrus are produced postnatally, this brain region has the potential to undergo functionally significant experience-dependent structural changes during postnatal development and adulthood. The remainder of this chapter focuses on several experiences currently known to regulate adult neurogenesis in the hippocampus and the implications of these findings for the function of the hippocampus, as well as behavior. Stress Numerous studies have demonstrated that stressful experiences alter adult neurogenesis in the dentate gyrus by decreasing cell proliferation (see Mirescu and Gould, 2006, for review). During the stress hyporesponsive period, most stressors that normally activate the hypothalamic-pituitaryadrenal (HPA) axis fail to elevate circulating glucocorticoids. However, exposure to the odors of natural predators is sufficient to increase levels of circulating glucocorticoids in male rat pups (Tanapat, Galea, and Gould, 1998). Given that adrenal steroids are known to suppress the production of new neurons, it is not surprising that exposure to the odors of an unfamiliar adult male rat also suppresses the proliferation of granule cell precursors in male rat pups (Tanapat, Galea, and Gould, 1998). Stressful experiences during the early postnatal period can have a very longlasting impact on hippocampal neurogenesis. For example, daily maternal separation is associated with reduced levels of neurogenesis in the offspring when they grow up (figure 4.3), long after the basal and stress levels of the main rat stress hormone corticosterone return to normal (Mirescu, Peters, and Gould, 2004). Stress suppresses cell proliferation in the dentate gyrus of adult animals as well. In adult male rats, exposure to trimethylthiazoline, the main component of fox feces, results in an activation of the HPA axis accompanied by an inhibition of the proliferation of granule cell precursors (Mirescu, Peters, and Gould, 2004; Galea, Tanapat, and Gould, 2006). The effect of stress on neurogenesis is observed across many mammalian species, using a variety of stress-inducing paradigms. For example, adult marmosets demonstrate significant decreases in the proliferation of granule cell precursors when exposed to the social stress of a resident intruder paradigm (Gould et al., 1998); adult tree shrews show decreased neurogenesis after exposure to subordination stress (Gould et al., 1997); and a similar picture is observed in adult rats following sleep deprivation (Guzman-Marin et al., 2003; Roman et al., 2005; Mirescu et al., 2006; van der Borght

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Figure 4.3 Reduced cell proliferation and adult neurogenesis after prolonged sleep deprivation. Rats subjected to 72 hours of sleep deprivation (small platform, large platform controls, cage controls) received a single injection of BrdU and were perfused 2 hours, 1 week, or 3 weeks thereafter. Sleep-deprived rats had lower numbers of BrdU-labeled cells in the subgranular zone/granule cell layer of the dentate gyrus. Error bars represent SEM, *p < 0.05 relative to LP and CC controls, ♦ p < 0.05 relative to LP controls.

et al., 2006). The impact of stress on adult neurogenesis can be additive over time. Chronic exposure to subordination stress, which does not evoke adaptation of the HPA axis, results in continual suppression of cell proliferation and a decrease in the volume of the granule cell layer (Fuchs et al., 1997). Collectively, these studies indicate that stress inhibits the proliferation of granule cell precursors during development, as well as in adulthood, in a variety of mammals. The factors that underlie the effect of stress on cell proliferation are not fully understood; however, glucocorticoid-induced reduction in cell proliferation through an NMDA receptor– dependent mechanism is likely to play a role (Cameron and Gould, 1994; Gould et al., 1997). While the stress-induced rise in glucocorticoid levels downregulates these measures, a stress hormone–independent rebound in cell proliferation has been reported following stressor cessation (figure 4.4) (Mirescu et al., 2006). The relationship between stressful experiences and adult neurogenesis reductions is not always simple. For example, wheel running increases the levels of circulating glucocorticoids in adult rodents (Droste et al., 2003), while at the same time enhancing neurogenesis by affecting cell proliferation (van Praag, Kempermann, and Gage, 1999; Stranahan, Khalil, and Gould, 2006). Since running is a positive stressor, in the sense that rats seek access to a running wheel, one possibility is that positive and negative stressors affect adult neurogenesis differently. In addition, the effect of running on the number of adult-generated neurons is mediated by social context—after a brief period of running, socially housed rats have increased levels of neurogenesis, while singly housed animals show a decrement in this measure

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Figure 4.5 Social context mediates the influence of short-term running on adult neurogenesis. Rats received daily injection of BrdU and were sacrificed on day 12. The number of BrdU-labeled cells was greater in group-housed runners compared to controls, but lower in single-housed runners compared to controls. Error bars represent SEM, p < 0.05 relative to controls.

(figure 4.5); rats living in isolation eventually manifest the running-related increase in adult neurogenesis, but this effect requires long-term exercise (figure 4.6) (Stranahan, Khalil, and Gould, 2006). Stressors appear to regulate adult neurogenesis in distinct ways that may depend on the valence of the stressor and the social context of the experience. Learning Studies have reported changes in the number of new hippocampal granule neurons in adult animals living in conditions associated with enhanced learning opportunities.

Figure 4.6 A longer duration of physical activity is required to enhance cell proliferation in socially isolated rats. Separate cohorts of individually housed rats ran for 3–48 days before being injected once with BrdU. The number of BrdU-labeled cells in the dentate gyrus was increased only after 48 days of running. Error bars represent SEM, p < 0.05 relative to controls.

One example is the striking relationship between experience and hippocampal neurogenesis in black-capped chickadees (Barnea and Nottebohm, 1994). New neurons persist for longer periods of time during parts of the year when these birds engage in seed storage and retrieval, behaviors that are likely to involve spatial navigation learning and, thus, the hippocampal region. Additionally, black-capped chickadees that live in the wild retain more new hippocampal neurons than those that live in captivity. Others have shown that mice living in an enriched laboratory environment maintain more new hippocampal granule neurons than those living in standard laboratory control cages. In rodents, the influence of environmental complexity on the number of new granule neurons appears to be maintained throughout the life span, affecting juvenile, young adult, and aged rodents (Kempermann, Kuhn, and Gage, 1997; 1998). While many variables such as stress, social interaction, nutrition, and activity levels differ between animals living in the wild and in a laboratory setting, these findings present the possibility that increased learning opportunities alter the survival of new neurons. It is possible that the positive impact of social dominance on adult neurogenesis in the dentate gyrus (figure 4.7) (Kozorovitskiy and Gould, 2004) might also relate to the differences in the opportunities for social learning that are available to dominant and subordinate animals living in a seminaturalistic environment. Multiple studies have addressed the link between learning and adult neurogenesis in rodents, and the overall results have been mixed (see Leuner, Gould, and Shors, 2006, for review). We and others have found that the types of learning that require the hippocampus tend to increase the number of new neurons in the dentate gyrus of adult rats (Leuner et al., 2004; Olariu et al., 2005). In animals living in standard laboratory conditions, many adult-born cells degenerate over

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2004). In contrast, training on tasks that do not require the hippocampus does not affect the number of new neurons. It is unclear what specific aspects of hippocampal-dependent tasks are necessary and sufficient for the enhanced survival of adult-generated cells. Electrophysiological studies have shown that both hippocampal-dependent and hippocampalindependent tasks activate hippocampal neurons (Weisz, Clark, and Thompson, 1984). One possible difference between the two sets of tasks is their difficulty. Several traditionally used learning tasks that require the hippocampus are more difficult to acquire than those that do not rely on this brain region. Although attempts have been made to determine whether task difficulty and hippocampal dependence are critical for the influence on adult-generated cells (Leuner et al., in press), these issues remain unresolved. In addition to studies finding enhanced adult neurogenesis with certain types of learning, other reports have either failed to find such increases or instead have found decreases in the numbers of new neurons (see Leuner, Gould, and Shors, 2006, for review). Some of these discrepancies can be attributed to methodological variations, such as differences in schedules of BrdU injection and survival times. A recent study has verified that the learning-induced enhancement of neurogenesis in the dentate gyrus alters only those cells that are produced within a specific time period prior to the learning (Epp et al., 2006). Thus studies that have utilized different paradigms, BrdU doses, or time points could easily miss such an effect. It is additionally possible that other types of learning tasks, particularly those that are stressful, might diminish the number of new neurons in the dentate gyrus, obscuring the enhancing effect of learning on this brain measure. Clearly, the effects of learning on adult neurogen-

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Figure 4.7 Status in the social dominance hierarchy influences adult neurogenesis. Dominant rats (Dom) had more BrdU-labeled cells in the dentate gyrus compared with subordinate animals (Sub) and cage controls (Con), 2 weeks after a single BrdU injection. This difference was maintained in animals whether or not they had access to an enriched environment during the survival time after BrdU injection. Error bars represent SEM, p < 0.05 relative to controls.

time (Cameron and McKay, 2001; Dayer et al., 2005). Training on hippocampal-dependent tasks during the time period when many newly generated granule neurons die significantly enhances the rate of their survival, although not their production, in adult rats. This increase in survival persists for months (figure 4.8), long after the time that task performance becomes independent of the hippocampus (Leuner et al., 6000

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increased the number of BrdU-labeled cells when compared with exposure to unpaired stimuli at all survival times. Error bars represent SEM, p < 0.05 relative to controls.

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esis in the hippocampus are complex and likely dependent on the age of the neurons and the particular learning task examined. Although the exact manner in which learning alters adult neurogenesis remains unresolved, the new neurons are likely altered by learning. Indeed, recent studies suggesting that new neurons are activated by exploration and learning experiences (Ramirez-Amaya et al., 2006; Snyder and Cameron, 2006), confirm this hypothesis.

Functional significance of adult neurogenesis The functional significance of neurons generated in the dentate gyrus of adult animals remains unknown. Given their incorporation into the hippocampus, which is important for learning and memory, and the effect of learning on the number and activation of these new cells, it seems reasonable to consider a potential role for these new neurons in learning. Altman was the first to propose that neurons generated during the postnatal developmental period might be important for forming associations (Altman and Das, 1965, 1967; Altman et al., 1973), suggesting that the granule “microneurons” are morphologically well suited for the development of learning processes. Subsequently, Nottebohm suggested that new hippocampal neurons may be a cellular substrate for learning in the adult (Nottebohm, 1989). Electrophysiological studies of adult-born fluorescently labeled neurons indicate that the new cells eventually develop passive membrane properties, action potentials, and synaptic inputs similar to the surrounding granule neurons (van Praag et al., 2002; Zhao et al., 2006), although in their response to the neurotransmitter GABA they seem to resemble immature granule cells generated during early development (Overstreet et al., 2005). It has been suggested that new neurons positively contribute to the amount of synaptic plasticity shown by the hippocampus in adult animals (Snyder, Kee, and Wojtowicz, 2001), which may imply a functional utility for adult neurogenesis. A continually rejuvenating population of new neurons seems well suited for the proposed transient role of the hippocampus in information storage (Squire, 1992; Squire and Zola, 1998). Aside from the data showing that learning alters new neuron number and activation, additional lines of evidence suggest that new neurons might be involved in learning. First, several studies report a positive correlation between the number of new neurons and learning performance (see Leuner, Gould, and Shors, 2006, for review, as well as for consideration of the studies that do not reach similar conclusions). Second, multiple studies attempting to interfere with adult neurogenesis have found impairments in certain types of learning and memory tasks.

Parallel Changes in Adult Neurogenesis and Learning Since new granule cells require time to differentiate and become integrated into circuitry, changes in cell proliferation are not likely to result in immediate functional consequences, although adult-generated cells may be capable of exerting an impact on hippocampal function prior to complete maturation. In addition, it is important to note that acute changes in cell proliferation and neuronal survival may not be of sufficient magnitude to produce an observable functional impact. As a result, conditions under which cell proliferation and survival are chronically enhanced or diminished are of particular interest, because they are most likely to elucidate the functional consequences of changes in adult neuron production and survival. In general, chronic increases in the factors that negatively regulate adult granule neuron production are associated with poor performance on hippocampal-dependent tasks, whereas chronic increases in the factors that positively regulate adult granule neuron production are associated with enhanced cognitive function. Several studies have demonstrated that chronic stress results in an impairment of hippocampaldependent learning (Luine et al., 1994, Bodnoff et al., 1995; Luine et al., 1996; Krugers et al., 1997). These stress-induced impairments are not permanent; the performance of animals tested on a spatial task long after the termination of stress is similar to unstressed controls (Luine et al., 1994, 1996). This observation is consistent with a possible role for adultgenerated cells in hippocampal function: the deficit may only last as long as neurogenesis is impaired. It should be noted that previous work has reported that brief stress enhances hippocampal-dependent learning (Shors, Weiss, and Thompson, 1992). However, these behavioral changes were observed shortly after stress and may likely involve other cellular mechanisms, such as changes in synaptic plasticity. Several studies have demonstrated that estrogen has a positive effect on the acquisition of hippocampal-dependent tasks. Although estrogen treatment has generally been found to enhance learning, some studies report decreases in performance during times of high levels of circulating estrogen. As in the case of acute stress studies, the time frame examined in many studies is likely to be too early to involve changes in neuron production. Chronic treatment with estrogen for more than four days results in enhanced performance on a hippocampal-dependent task (Luine et al., 1998). This finding is consistent with the observation that adult-generated cells do not extend axons prior to four days after their production (Hastings and Gould, 1999). It is likely that sufficient time for adultgenerated cells to extend axons is required for estrogen-induced increases in cell production to have a functional effect. Consistent with studies that have demonstrated a positive effect of estrogen on the performance of hippocampaldependent tasks, a sex difference, favoring females, in spatial

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navigation learning in rats has been reported (Perrot-Sinal et al., 1996). Previous studies of sex differences in spatial navigation learning in rodents on this task have yielded conflicting data (Bucci, Chiba, and Gallagher, 1995; Galea, Kavaliers, and Ossenkopp, 1996). However, the results of the former study demonstrate that although male rats initially perform better than female rats on the task, females learn better than males once the animals have been acclimated to the testing apparatus. Thus a sex difference in reaction to the novelty of this task prevents females from performing well. After this performance confound is removed, females learn certain aspects of this task better than males. Similarly, another study demonstrated a sex difference favoring females in hippocampal-dependent learning as well. This study reported that females learn trace eyeblink conditioning faster than males (Wood and Shors, 1998). Taken together with the observation that females produce more new granule neurons than males (Tanapat et al., 1999), these data indicate an additional positive relationship between new granule neurons and certain types of learning. Studies have also demonstrated a positive correlation between the number of new granule neurons and performance on hippocampal-dependent learning tasks following enriched environment living (Kempermann, Kuhn, and Gage, 1997, 1998). However, it should be noted that many of the factors and conditions known to alter the number of new neurons, either by affecting the proliferation of precursor cells or by altering the survival of new neurons, also affect other measures in the hippocampus—for example, synaptogenesis, dendritic architecture, and dendritic spines. Finally, the hippocampus is not the sole brain region affected by experiential and hormonal treatments that affect adult neurogenesis. For example, the amygdala and prefrontal cortex have been identified as brain regions sensitive to experience and hormones. Thus any behavioral changes observed after experiences that impact adult neurogenesis cannot be attributed solely to the alterations in the number of new neurons. Blockade of Neurogenesis and Learning Several studies have attempted to determine the function of adult neurogenesis in the hippocampus by inhibiting the process and examining the behavioral consequences. Collectively, these experiments suggest that adult-generated neurons are important for certain types of learning and memory. However, the specific conclusions of these studies differ. Some have shown that decreasing adult neurogenesis impairs trace eyeblink conditioning, trace fear conditioning, context fear conditioning, spatial navigation memory, and object recognition, while others have observed no effect on context fear conditioning and spatial navigation learning (see Leuner, Gould, and Shors, 2006, for review). One reason for these discrepancies involves the different methods used to

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reduce adult neurogenesis, as well as the different paradigms employed to assess learning. Some of the techniques that have been used to study the connection between learning and adult neurogenesis are treatment with the antimitotic drug MAM, global or focal irradiation, and transgenic models with impairments in cell proliferation induced in adulthood (Shors et al., 2002; Achanta, Fuss, and Martinez, 2006; Fuller et al., 2006). Methods for decreasing adult neurogenesis work on different timescales and vary in their effectiveness. The possibility that they affect other aspects of brain function also remains an important potential confound in interpretation of these studies. In addition to assessing whether learning and memory are affected by ablation of adult neurogenesis, studies have examined another class of behaviors involving the hippocampus related to anxiety regulation. Santarelli and colleagues (2003) have shown that focal irradiation of the hippocampus inhibits adult neurogenesis and prevents the anxiolytic action of antidepressants in an animal model of chronic stress. Although these findings are also open to the same criticisms made for similar studies of learning and memory, they suggest the possibility that structural plasticity in the hippocampus is also involved in the therapeutic response of antidepressants, at least with regard to anxiety. Thus the definitive answer to the question of whether adult neurogenesis is necessary for hippocampal function in learning and/or anxiety regulation awaits further investigation, including the development of new methodologies for selectively depleting the hippocampus of new neurons without causing unrelated changes in other cell populations.

Conclusions It is evident that new neurons are generated in the hippocampus of adult mammals, including humans. The number of new granule neurons produced in adulthood and the variety of species in which this process has been reported suggest that new neurons are important for the function of this brain region. Studies carried out over the past several years have demonstrated that the production of new granule cells in the hippocampus can be modulated by hormones. The production of new neurons can be inhibited by adrenal steroids and stimulated by ovarian steroids, both of which act by altering cell proliferation. Furthermore, experiences, such as stress and learning, control the production of new neurons, by affecting either cell proliferation or cell survival. Collectively, these observations suggest that newly generated granule cells may provide an important cellular substrate by which hormones and experience alter hippocampal function. Cellular phenomena such as neurogenesis, axon extension, dendritic development, synaptogenesis, and cell death are traditionally viewed as developmental processes. The

fundamentals of developmental neurobiology

continual addition and replacement of new neurons that integrate into hippocampal circuitry during adulthood indicates that these developmental processes continue throughout life in certain brain regions. The extent to which adult neurogenesis and other forms of structural plasticity contribute to brain function under normal and pathological conditions remains to be elucidated.

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5

The LHPA System and Neurobehavioral Development AMANDA R. TARULLO, KARINA QUEVEDO, AND MEGAN R. GUNNAR

It has long been known that stress has significant impacts on the developing brain (Levine, Alpert, and Lewis, 1957; for review see Levine, 2005). Stressors in the form of abuse, neglect, parental loss, and poverty increase the risk of emotional disorders and poor academic outcomes (for review see Cicchetti and Cohen, 2006). As we will describe, research on animals and adults supports the hypothesis that activity of the limbic-hypothalamic-pituitary-adrenocortical (LHPA) system plays a role in mediating the impact of adverse experiences on the developing brain. Based on this research, studies of children are increasingly employing measures of cortisol, the major hormonal product of the LHPA system in humans, both to understand individual differences in stress vulnerability and resilience and to identify pathways through which adverse experiences affect brain development. Despite the ease of measuring cortisol, which can now be done using noninvasive sampling of saliva, interpretation of cortisol-behavior findings is often fraught with ambiguity. For instance, sometimes both positive and negative associations are obtained for the same target behaviors. In this chapter, we describe the anatomy and physiology of the LHPA system, mechanisms through which this system may impact brain development, developmental changes in animals and children in the reactivity of this system to stressors, and the critical role that psychosocial processes play in regulating its activity during infancy and childhood. Finally, we provide a very brief introduction to work on genes that may be important in stress vulnerability and resilience. While the information in this chapter will not reduce the complexity of interpreting cortisol-brainbehavior relations in studies of human development, it should help researchers new to this area understand why these relations are complex and often dependent on context and age.

LHPA anatomy and physiology The LHPA system is one of the two primary systems regulating mammalian stress responses (Stratakis and Chrousos, 1995; see table 5.1). The other mammalian stress system, the sympathetic adrenomedullary (SAM) system, will not be covered extensively in this chapter because of space limita-

tions. The LHPA stress response pathway begins with the secretion of corticotropin-releasing hormone (CRH) and arginine vasopressin (AVP) from the medial parvocellular region of the paraventricular nucleus (mpPVN) of the hypothalamus, and culminates in increased adrenocortical production of stress hormones called glucocorticoids (cortisol in humans and primates, corticosterone in rodents). As depicted in figure 5.1, the initiation of this cascade is modulated by an intricate network of both excitatory and inhibitory limbic inputs to the mpPVN (Gunnar and Davis, 2003; Herman and Cullinan, 1997). The excitatory inputs include the central amygdala and lateral bed nucleus of the stria terminalis (BNST), while the inhibitory inputs include the medial BNST, prefrontal cortex, preoptic area, and ventral subiculum (Herman et al., 2002, 2004; Shekhar et al., 2005). For systemic stressors—that is, context-independent physiological stressors to the body such as hypoxia or infection—the mpPVN is activated by means of signals relayed from the brain stem, often through limbic circuits, and the response would occur even if the animal were unconscious. Processive stressors, in contrast, such as being chased by a predator or separated from a caregiver, require interpretation (processing) by the animal: assessing environmental threat and emotional significance by comparing the current situation to past experience (Gunnar and Vazquez, 2006; Herman and Cullinan, 1997). Such psychological processing, occurring at the level of the limbic system and prefrontal cortex, influences the signals sent to the mpPVN. Once released by the mpPVN, CRH and AVP travel to the pituitary, where CRH binds to the anterior pituitary CRH receptor 1 (CRH r1), stimulating synthesis of the proopiomelanocortin (POMC) molecule. In turn, adrenocorticotropic hormone (ACTH) is derived from the POMC molecule and released in pulses from the anterior pituitary. AVP potentiates this process. ACTH then enters the bloodstream and binds to receptors on the cortex of the adrenal gland, stimulating the synthesis and release of cortisol. This cascade is a rather slow process, such that peak cortisol levels in the bloodstream occur approximately 20–30 minutes after activation of the mpPVN. Cortisol binds to intracellular receptors throughout the brain and periphery, influencing gene transcription and thus having long-lasting effects on

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Cortex of the adrenal gland (adrenal cortex)

Anterior pituitary (AP)

Hippocampus

Amygdala

Regions Associated with LHPA Stress Response Hypothalamus (mp PVN) Neurobiological Effects Stimulates the release of ACTH by modulating the actions of CRH 1. Stimulates production of propiomelanocortin (POMC) in the anterior pituitary 2. Stimulates the release of norepinephrine via the locus coeruleus 3. Enhances neuronal excitability 4. Mediates mechanisms of synaptic plasticity involved in memory formation and learning

Some impacts via retrograde passage from pituitary to CNS; see work by de Wied (e.g., de Wied and Jolles, 1982) Glucocorticoids: Basal levels cortisol in humans 1. Regulation of and other primates, metabolism and corticosterone in energy utilization rodents 2. Enhances synaptic plasticity underlying learning Chronic stress levels 1. Inhibits growth and reproduction 2. Contains immune and inflammatory responses 3. Dendritic atrophy and apoptosis

1. Adenocorticotropic hormone (ACTH) (derived from POMC)

Corticotropinreleasing hormone (CRH)

Secreted Agent Arginine Vasopressin (AVP)—just in PVN

Sense of increased vigor followed, if elevations prolonged, by increased negative emotionality

1. Augments arousal, alertness and readiness to respond 2. Increases startle 3. Elevations in amygdala trigger fear and anxiety 4. Increase is related to avoidance and decreased social behavior

Stress-Related Behavioral Symptoms

Acute: increases release Chronic: increases or decreases release

Acute or chronic: increases or sensitizes its release

Acute or chronic: increases its release

Effects of Stress

Mineralocorticoid (MR) Higher affinity for cortisol, binds at basal levels in the central nervous system Outside the brain, MR binds aldosterone Glucocorticoid (GR) Lower affinity for cortisol, binds at high concentrations

Maintains electrical activity in neurons, blood pressure, HPA rhythm Facilitates cerebral glucose availability, synaptic plasticity, and SAM system’s immediate stress response

Induces the termination of the HPA activation Reduces cerebral glucose availability, thus increasing risk of neuronal death Impairs synaptic plasticity and memory formation

Hippocampus Frontal cortex (in primates)

Pituitary Hypothalamus Hippocampus Amygdala Medial frontal cortex and other limbic regions

Stimulates the synthesis and release of glucocorticoids

Cortex of the adrenal gland ACTH-R

1. Evokes anxiolitic and antidepressive responses 2. May promote recovery from stress and adaptation 3. Increases vasodilatation and diminishes blood pressure

Subcortical areas Lateral septal nuclei Choroid plexus Olfactory bulb Amygdala Hippocampus

CRH-2R: lower affinity for CRH. Binds preferentially with urocortins (II and III)

1. Mediates changes in ACTH release 2. Mediates the fast fight-flight response 3. Mediates defensive responses to processive stressors 4. Anxiogenic and depressogenic effects

Effects of Eeceptor Activations Increases sensitivity of AP to CRH

Neocortical areas Cerebellum Hippocampus Pituitary Hypothalamus Amygdala

Predominant Expression of Relevant Receptors Pituitary

CRH-1R: higher affinity for CRH

Receptors of Interest V1b

Table 5.1 Regions involved in the LHPA system: their hormonal products, neurobiological and behavioral effects, response to stress, and associated receptors

NE Locus coeruleus

Anterior cingulate

Spinal Cord

Brain stem Amygdala

Hippocampus

ACh NE

Hypothalamus Adrenal Orbital PFC

BNST CRH

POA

Anterior pituitary

ACTH

Medulla

EPI

Cortex

GC

Ventral subiculum Cortico-Limbic Level

Hypothalamic–Brain Stem Level

Neural-to-Adrenal Level

Figure 5.1 Schematic of activation pathways for the LHPA system. For processive stressors, activation of the LHPA cascade at the level of the medial parvocellular region of the paraventricular nucleus (mpPVN) of the hypothalamus depends on cortico-limbiclevel excitatory and inhibitory inputs. The anterior cingulate (ACC), orbital frontal cortex (OFC), amygdala, bed nucleus of the stria terminalis (BNST), hippocampus, and preoptic area (POA) all have direct inputs to the hypothalamus. The ACC, OFC, and amygdala are also reciprocally interconnected. The amygdala and ventral subiculum also have pathways to the hypothalamus by way of the BNST. The hippocampus and the amygdala are connected to the locus coeruleus (LC), which releases norepinephrine to brain areas involved in alerting. For systemic stressors, the mpPVN is activated by the brain stem, either directly or by way of limbic circuits. Once activated in response to a systemic or processive stressor,

the mpPVN produces corticotropin-releasing hormone (CRH) and arginine vasopressin (AVP), which travel through the hypophysial portal system to the anterior pituitary gland, stimulating the production and release of adrenocorticotropic hormone (ACTH). ACTH stimulates cells in the adrenal cortex to produce glucocorticoids (cortisol in humans). Cortico-limbic regions including the hippocampus and ACC maintain feedback control of the PVN. The hypothalamus is also a key region in the sympathetic adrenomedullary system. The lateral hypothalamus activates nuclei in the brain stem, including the parabrachial nuclei, that regulate release of sympathetic hormones (norepinephrine, NE, and epinephrine, EPI) and parasympathetic hormones (acetylcholine, ACH), with pathways traveling from the spinal cord to preganglionic nuclei or to target organs (e.g., the adrenal medulla). (Adapted from Gunnar and Davis, 2003.)

physiology and behavior (de Kloet, 1991; Sapolsky, Romero, and Munck, 2000). Modulation and eventual termination of the LHPA stress response is achieved by means of multiple negative feedback loops, with varying time courses and mechanisms. Within minutes, cortisol binds to receptors in the anterior pituitary, hypothalamus, and hippocampus, inhibiting the LHPA axis at multiple levels by means of this signaling (see Dallman et al., 1992). Over the course of hours to days, cortisol also down-regulates CRH gene expression in the hypothalamus and POMC gene expression in the anterior pituitary, thereby exerting a prolonged suppressive effect on ACTH secretion (Gunnar and Vazquez, 2006). The LHPA system does not lie dormant between stress responses. Rather, the stress response is superimposed on an LHPA circadian rhythm. Basal cortisol levels peak about 30 minutes after waking and gradually decline across the day to reach their nadir in the late evening, near the onset of sleep (Daly and Evans, 1974; Kwak et al., 1993). In humans, this circadian rhythm is evident by 6 weeks of age (Larson et al., 1998), and the decline across the day becomes more

stable by 4–6 years of age as children give up daytime naps and develop a more adultlike sleep schedule (Watamura et al., 2004). Thus developmental researchers are frequently interested in assessing LHPA rhythmicity and reactivity as a means of studying the influence of stressful experience on the developing brain, as well as individual differences in stress vulnerability and resilience. Over the past several decades of research, a number of methodological considerations have come to light.

LHPA measurement in developmental research Interrogating the LHPA axis in the context of human development presents a host of challenges. Because of ethical and practical considerations, human development researchers are generally limited to measuring cortisol levels and must rely on animal models to draw tentative inferences about what is occurring at the levels of the pituitary and hypothalamus (Gunnar and Vazquez, 2006). Ethical concerns also restrict the paradigms available to human development researchers for assessing LHPA reactivity. Pharmacologic

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probes are rarely used with children, and often ethically approved psychosocial stressors do not reliably activate the LHPA axis. For infants, separation from a caregiver generally elevates cortisol levels, although elevations in response to brief separations are not always observed among infants over 12 months of age (e.g., Spangler and Schieche, 1998). For older children and adults, manipulations that involve a threat to the social self (such as critical social evaluations by authority figures) sometimes are effective in provoking a stress response (Dickerson and Kemeny, 2004). However, the inconsistency of these mild manipulations in raising cortisol levels in children necessitates a heavy reliance on animal models to inform our understanding of cortisol reactivity to more severe stressors. Collecting salivary cortisol samples is a noninvasive, relatively well defined procedure. In contrast, interpreting the significance of the cortisol values obtained from those samples is a complicated, ambiguous undertaking requiring consideration of many variables that affect the LHPA system, such as collection context and time of day. Therefore, at the design phase of a study, careful attention must be paid to deciding when, where, and how frequently to sample cortisol (Gunnar and Talge, 2007). Studies measuring cortisol reactivity in children have often compared a cortisol sample collected upon arrival at the laboratory to samples collected following a psychosocial stressor. However, this approach is misleading because the first sample is not a typical baseline measure: It reflects an LHPA response to coming to the laboratory. For reasons that remain mysterious, infants and preschoolers often show cortisol levels that are suppressed upon arrival at the laboratory compared to samples collected at home at the same time of day (Goldberg et al., 2003; Gunnar et al., 1989; Larson, Gunnar, and Hertsgaard, 1991; Legendre and Trudel, 1996; Lundberg, Westermark, and Rasch, 1993). Several studies have found that older children (ages 9 and up) had higher cortisol levels upon arrival at the laboratory than time-matched home samples (Gunnar et al., in press; Tottenham et al., 2001), indicating that there may be developmental differences in response to the laboratory collection context between early and later childhood. Because of the multifaceted dynamic processes affecting cortisol levels, no one sample can prudently be considered “baseline.” Researchers commonly use one or both of the following methods to address the baseline problem. First, they collect home samples at the same time of day as the laboratory visit, which is equivalent to animal researchers’ definition of baseline levels as those observed when the animal has been left undisturbed in the home cage. Second, when children are assessed in the laboratory, researchers allow for an extended period of acclimation to the laboratory context prior to collecting baseline samples (Gunnar and Talge, 2007; van Goozen et al., 2007).

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Because of the LHPA diurnal rhythm, time of day is another key design consideration, with the best time to sample varying depending on the question of interest. Latent state-trait modeling has demonstrated that the maximum trait component occurs shortly after waking, at the diurnal peak, making this a good sampling time for researchers interested in stable individual differences in basal cortisol levels (Kirschbaum et al., 1990; Shirtcliff et al., 2005). Conversely, researchers interested in stress reactivity may choose to sample in the late afternoon or evening, when state components are higher and the system is more responsive to stressors (Dallman et al., 1992; Kirschbaum et al., 1990). Variability in cortisol levels from day to day presents another interpretive challenge. Sampling on a single day is unlikely to be sufficient to detect stable individual differences. Aggregating time-matched samples across three or more days provides a more reliable measure (Gunnar and Talge, 2007). However, the day-to-day variation itself may be informative, particularly in identifying abnormal LHPA function and risk for psychopathology. In a recent study by Halligan and colleagues (2004), children whose mothers were clinically depressed during their first year of life, at 13 exhibited higher and more variable cortisol levels soon after awakening than did children of nondepressed mothers, even after controlling for maternal depression postinfancy. Goodyer and colleagues (2000) noted that among adolescents at high risk for depression, obtaining one or more abnormally high morning cortisol levels across four days of sampling predicted onset of depression in the ensuing year. Given these findings, the use of statistical methods such as hierarchical linear modeling to isolate variability may yield a richer picture of LHPA function than can be obtained by simply aggregating sample values. Thus, while collection of salivary cortisol samples is quite straightforward, meaningful assessment of LHPA function in children is a far more problematical and ambiguous undertaking than it might at first appear. Ethical and practical issues constrain the techniques available to human development researchers to probe the LHPA axis. Research design and the interpretation of cortisol results must take into account the time of day, collection context, developmental differences in LHPA reactivity, and variability in cortisol from day to day. On the positive side, consideration of all these variables may yield a richer, more refined picture of the effects of early adverse experience on cortisol rhythm and reactivity. However, human development researchers owe a substantial debt to the adult and animal literatures for informing our understanding of the LHPA system. While interpretation of adult and animal studies is also challenging, there are somewhat less severe constraints on researchers’ ability to probe the axis. The next section provides an overview of the influence of cortisol and CRH on the brain,

fundamentals of developmental neurobiology

relying heavily on the human adult literature and on animal models.

Influence of cortisol and CRH on the brain Cortisol targets tissues throughout the brain and periphery, making adjustments in the allocation of the body’s resources in response to stressors in the environment that threaten homeostasis. McEwen (1998) referred to this maintenance of stability through change as allostasis. Cortisol readily permeates the blood-brain barrier, and its activity influences multiple brain regions including (but not limited to) the hippocampus, amygdala, hypothalamus, prefrontal cortex, and orbital and medial cortical regions. Cortisol operates on tissues through well-understood genomic mechanisms and relatively little understood nongenomic mechanisms (de Kloet, 1991). Cortisol mediates genomic effects by binding to its receptors, after which the hormone-receptor complex is translocated to the cell nucleus, where cortisol interacts with glucocorticoid receptive elements (GREs) on the genome and modulates gene expression. One reason for interest in the LHPA system among developmental researchers is that cortisol is a gene transcription factor that plays complex roles in modulating genes involved in brain development as well as brain function (Gunnar and Vazquez, 2006). Nongenomic effects of cortisol will be briefly described later; here, we discuss the better-understood genomic effects that occur when cortisol binds to its receptors. There are two receptors that bind cortisol in the brain, mineralocorticoid receptors (MR) and glucocorticoid receptors (GR). These receptors mediate different types of effects (de Kloet, 1991). It may seem odd that MRs mediate cortisol effects in the brain, because outside the brain, these receptors bind aldosterone, a hormone involved in salt-water balance. However, the enzyme 11 beta hydroxysteroid dehydrogenase, which protects MRs from cortisol in the periphery, is not sufficiently present in the brain. As a result, MRs bind cortisol in the central nervous system. An inverted U function characterizes the relationship between cortisol and physical and behavioral health, such that moderate levels of cortisol are most adaptive, while both chronically low and chronically high levels yield differential but similarly deleterious effects. Two characteristics of MR and GR account for this paradox: cortisol’s differential affinity for these two types of receptors and the different types of effects they generate when bound and activated (Sapolsky, 1997). Cortisol has more than 10 times higher affinity for binding with MR than with GR (de Kloet, Oitzl, and Joels, 1993). Accordingly, at typical basal levels, 80–90 percent of MRs are bound with cortisol while very few GRs in the brain are bound (de Kloet, 1991). Thus, in this basal range, MRmediated cortisol effects predominate. These effects include maintaining a steady electrical current in the brain so that

neurons will be able to respond to neurotransmitters, maintaining the LHPA circadian rhythm, and facilitating cerebral glucose availability (Bradbury, Akana, and Dallman, 1994; see for review Gunnar and Vazquez, 2006). These MR-mediated effects are classified as permissive because they support the ability to immediately respond to stressors by means of the other stress system (i.e., the fast-acting sympathetic adrenomedullary system; Ingle, 1952; Sapolsky, Romero, and Munck, 2000). So, while stress-related increases in cortisol are much too slow to be helpful in responding to imminent physical threats, basal levels do play a crucial role in facilitating rapid responding to stressors (e.g., fight/flight responses; Sapolsky, Romero, and Munck, 2000). As cortisol levels increase in response to a stressor or at the morning peak of the circadian rhythm, cortisol molecules in the brain will occupy the remaining MRs and begin to occupy GRs. In the brain, GR-mediated effects tend to be suppressive, in that they counteract the responses of other stress-reactive systems (e.g., turning off stress-induced immune system responses and opposing impacts of stressinduced catecholamine actions on neural systems). GRmediated activity in the hippocampus, hypothalamus, and pituitary also serves to contain stress-induced activations of the LHPA axis in a process termed negative feedback. The suppressive effects of these stress-induced cortisol elevations are thought to serve the function of restoring homeostasis in the aftermath of a challenge (Sapolsky, Romero, and Munck, 2000). Many GR-mediated effects counteract MR-mediated effects. For example, MRs increase cerebral glucose availability, while GRs reduce cerebral glucose transport (Sapolsky, Romero, and Munck, 2000). Similarly, while GRs inhibit hippocampal neurons, impairing synaptic plasticity and memory formation, MRs boost synaptic plasticity and facilitate memory formation by lowering the refractory period of hippocampal neurons (for review, see Gunnar and Quevedo, 2007). When MRs and GRs act in opposition to one another, the effect of cortisol depends on the ratio of MR to GR occupation (de Kloet, 1991). If cortisol levels are chronically elevated, this ratio will tilt toward GR-mediated effects, with deleterious consequences including dendritic atrophy, cell death, and impaired learning and memory. In addition to suppressive effects, GRs also mediate preparatory effects, that is, long-term changes in gene expression that influence the LHPA response to future stressors (Sapolsky, Romero, and Munck, 2000). For example, chronically high levels of GR occupation can lower the amygdala’s threshold for responding to threatening stimuli and activating the mpPVN, with the result that LHPA responses may become more frequent and prolonged (Rosen and Schulkin, 1998). However, preparatory impacts of cortisol on CRHproduction in the mpPVN may oppose those in the amygdala, resulting in blunted cortisol responses to mpPVN stimulation (Rosen and Schulkin, 1998). These opposing

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effects make it very difficult to predict, a priori, whether exposure to chronic or frequent high levels of cortisol will increase or decrease cortisol reactions to subsequent stressors (Friese et al., 2005). Additionally, chronically high cortisol levels, acting through GRs, can put the body’s physical health at risk because of their immunosuppressive effects (inhibiting cytokine transcription). However, chronically low cortisol levels can also be deleterious to physical and behavioral health because not enough MRs will be occupied to maintain the body in a state of preparedness to cope with the stressors it will inevitably encounter (de Kloet, 1991). The relation of both very high and very low cortisol activity to poor physical and mental health reflects the inverted Ushaped relation between this neuroendocrine system and healthy adaptation. The effects of LHPA activity also depend upon the location of the MR or GR receptors within the brain. Differences among species have been identified in the pattern of distribution of MR and GR receptors. In rats, MRs are mainly confined to the hippocampus and lateral septum. GRs, while also plentiful in the hippocampus and lateral septum, are more broadly expressed, appearing in the mpPVN, central amygdala, and nucleus tractus solitarius, among other locations (Reul and de Kloet, 1985; Sanchez, 2006). Evidence from nonhuman primates indicates relatively fewer GRs in the hippocampus compared to rodents (Sanchez et al., 2000). In primates, high concentrations of MRs and GRs have also been observed in the prefrontal cortex and other cortical regions, implicating cortisol in cognitive and emotional regulatory function (López, Akil, and Watson, 1999; Patel et al., 2000; Sanchez, 2006; Sanchez et al., 2000). Effects of cortisol produced by impacts on gene transcription take many minutes to hours (Sapolsky, Romero, and Munck, 2000). Recent evidence indicates that cortisol can have impacts on behavior and neural activity that occur too quickly to operate through genomic mechanisms and that are observed even in the presence of drugs that block MR and GR binding (see review by Makara and Haller, 2001). Nongenomic, rapid impacts of cortisol are not well understood, but they hold promise for a much better appreciation of the role of cortisol in adaptation. Notably, the picture that is emerging for these nongenomic effects includes evidence that they involve specific binding sites in the synaptic membrane, are involved in modulating neurotransmitter interactions with their receptors, and may have particularly marked effects on neurons that are active in response to the stressor event. These effects do not appear to be specific; rather, they seem to enhance activity that is already occurring. As a consequence, rapid nongenomic effects may exacerbate individual differences in adaptive responses to threat. In other cases, rapid nongenomic effects have been found to be the opposite of more slowly emerging genomic effects

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(Makara and Haller, 2001). In sum, the more that is learned about rapid nongenomic and slower genomic impacts of cortisol (and corticosterone), the more apparent it becomes that the HPA system has multiple and often contradictory roles in adaptation, roles that depend on timing, context, and the specific neural system being affected. The complexity and context-dependent findings of cortisol-behavior studies mirror the complexity and subtlety of the neurophysiology of this system. Although studies with children must rely on measures of cortisol, many of the effects attributed to the LHPA system may have more to do with its releasing hormone, corticotropin-releasing hormone (CRH), than with cortisol itself. CRH and its associated receptors and ligands have a prominent role in the organization of behavioral, autonomic, and neuroendocrine responses to processive stressors. CRH is produced not only by the hypothalamus, but also in the central nucleus of the amygdala and other fear- and anxietyrelated regions, and the CRH involved in behavioral impacts is likely produced by these extrahypothalamic sites (Heinrichs and Koob, 2004; Rosen and Schulkin, 1998). Similarly, CRH receptors are located in regions involved in appraisal and processing of psychological threat (e.g., cingulate cortex, orbital/medial prefrontal cortex, amygdala, bed nucleus of the stria terminalis, and locus coeruleus; Bale and Vale, 2004). As with cortisol, CRH has two primary receptors (1 and 2) that have distinct distributions in the brain and appear to have opposing effects (Dautzenberg and Hauger, 2002; de Kloet, 2004; Vermetten and Bremner, 2002). CRH-1 receptors are believed to mediate many of the fear- and anxiety-like effects of CRH (M. Davis, 1997; M. Davis et al., 1993; LeDoux and Phelps, 2000), while CRH-2 receptors are believed to mediate many of the vegetative effects observed in chronic stress (e.g., stress-induced suppression of eating; Vermetten and Bremner, 2002). Notably, impacts of altered amygdala-produced CRH can be observed in the absence of robust activation of the LHPA axis or elevations in glucocorticoids by the adrenal (Makino et al., 1999). To summarize, the LHPA system affects the brain through multiple, often contradictory mechanisms to promote allostasis, the maintenance of stability through change. Slow, long-lasting genomic effects of cortisol in the brain are mediated by two receptors, MR and GR, with opposing influences and differential affinities for cortisol, such that an inverted U-shaped function characterizes the relationship between cortisol levels and healthy adaptation. In an emerging research area, evidence is accumulating for faster-acting nongenomic mechanisms of cortisol activity as well. In the context of processive stressors, CRH also appears to have widespread impact on the brain. This multilayered system of checks and balances is not fully mature at birth, yet cortisol and CRH have critical roles in brain development.

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We turn now to consider how these hormones shape the developing brain.

Influence of cortisol and CRH during brain development Prenatally and postnatally, cortisol is required for neurogenesis, synaptogenesis, gliogenesis, neural apoptosis, myelination, and development of neurotransmitter systems (Duman, Malberg, and Thome, 1999; for review see Gunnar and Vazquez, 2006). As noted, cortisol is a gene transcription factor, which allows even small variations in cortisol levels to have prolonged and widespread influences on neural development. These influences depend, of course, on the timing of cortisol variations in relation to periods of brain development. Several lines of evidence document the effects of elevated cortisol on the developing brain. First, in human fetuses at risk for premature birth, the medically prescribed administration of a synthetic form of cortisol called dexamethasone (DEX) creates a natural experiment to examine the effects of elevated cortisol on the pre- and perinatal brain. Also, 11β-HSD in the placenta helps to buffer the fetus from natural maternal cortisol, but DEX is unaffected by this placental barrier (Seckl, Cleasby, and Nyirenda, 2000). Consequently, DEX, which is administered to accelerate fetal lung maturation, has the significant side effect of flooding the fetal brain with an active and long-acting form of cortisol. In human infants, it is difficult to disentangle the effects of DEX exposure from the effects of being born prematurely. Animal models are helpful in isolating these effects and have demonstrated long-term consequences of prenatal exposure to excess glucocorticoids. In fetal rats, DEXinduced elevations inhibit neurogenesis, gliogenesis, cell division, and myelination. Fetal DEX exposure has been linked to permanent blunting of norepinephrine expression in the hippocampus and cerebral cortex; elevated serotonin expression in the hypothalamus, hippocampus, and brain stem; and elevated LHPA activity with impaired negative feedback (see for reviews Matthews, 2000; Whitelaw and Thoresen, 2000). Animal studies also suggest a link between prenatal glucocorticoids and long-term emotional and behavioral functioning. Prenatal DEX exposure is associated in adulthood both with impaired coping in adverse situations in adulthood and with elevated CRH in the central nucleus of the amygdala, a region instrumental in fear and anxiety (Welberg et al., 2001). Seckl and Meaney (2004) suggest that the long-term consequences of excess glucocorticoid exposure reflect prenatal programming of the HPA system, including permanent effects on GR gene expression. Cortisol also affects myelination in the developing brain because GRs are expressed in oligodendrocytes, the glial cells that manufacture myelin sheaths in the CNS (Cintra et al., 1994; Huang et al., 2001). Huang and associates (2001) examined the effect of repeated prenatal administration of

exogenous cortisol to sheep on the corpus callosum, a major white matter tract critical to cognitive and attentional processes. The corpus callosum was selected as the focus of this study because it was hypothesized to be particularly vulnerable to the effects of repeated exogenous cortisol administration as a result of its immaturity at the time of cortisol administration and its prolonged period of myelination. Indeed, they found that myelination was delayed in this region, suggesting another potential side effect of high levels of prenatal cortisol. Therapeutic administration of synthetic glucocorticoids to human adults to treat brain tumors also results in white matter abnormalities, specifically a widespread decrease in extracellular fluid concentrations in white matter (Minamikawa et al., 2004). The hippocampus appears to be particularly vulnerable to chronically elevated cortisol. Its structure and function are sensitive to environmental influence throughout its extended period of postnatal development, and in the dentate gyrus of the hippocampus, neurogenesis continues even in adulthood (Gould and Tanapat, 1999). Exposure to chronic stress has been associated with decreased dendritic arborization in the CA3 layer of the hippocampus (Watanabe et al., 1992), a prolonged suppressive effect on neurogenesis in the dentate gyrus (Fuchs, Uno, and Flugge, 1995; Gould and Tanapat, 1999), and impaired performance on hippocampally mediated learning and memory tasks (see for review Gould and Tanapat, 1999). Across the life span, granule neuron proliferation in the dentate gyrus is inversely related to cortisol levels (Sapolsky and Meaney, 1986; Schlessinger, Cowan, and Gottlieb, 1975). In rats, granule neuron proliferation peaks in the postnatal stress hyporesponsive period (SHRP), when corticosterone levels are low, and slows down when basal corticosterone levels rise near the end of the SHRP (Schlessinger, Cowan, and Gottlieb, 1975). Elevating corticosterone during the SHRP by exogenous administration or by exposure to an intense stressor (odor of a predator) decreases granule neuron proliferation in the developing dentate gyrus (Gould et al., 1991; Tanapat, Galea, and Gould, 1998). Most granule neuron precursors do not have MRs or GRs (Cameron, Wooley, and Gould, 1993), so cortisol impedes granule neuron proliferation indirectly through an NMDA-receptor-mediated pathway. Specifically, elevated cortisol (or corticosterone) levels stimulate the hippocampus to release glutamate, which inhibits granule neuron proliferation in the dentate gyrus (Gould and Tanapat, 1999). The deleterious effects on the hippocampus of chronic exposure to cortisol may be partially mediated by alterations in the expression of neurotrophic factors. Neurotrophic factors are proteins that facilitate neurogenesis and synaptogenesis. They also preserve existing neurons by preventing apoptosis. In the dentate gyrus and other hippocampal regions of rats subjected to chronic stress (immobilization),

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Smith and colleagues (1995a, 1995b) found greatly decreased brain-derived neurotrophic factor (BDNF) mRNA, paired with increased neurotrophin-3 (NT-3) mRNA. The elevation in NT-3 expression was mediated by corticosterone. The mechanism for BDNF down-regulation was less clear, as a decrement in BDNF expression also was observed in the dentate gyrus of adrenalectomized rats exposed to chronic stress. The authors suggest that corticosterone likely plays some role in the suppression of BDNF, but other elements of the stress response must also be involved (Smith et al., 1995a, 1995b). While this study was conducted with adult rats, it has implications for the effects of glucocorticoids on the dentate gyrus and other hippocampal regions during their extended period of postnatal development. By altering expression of these neurotrophic factors, chronically elevated cortisol (in primates) and corticosterone (in rodents) could disrupt neurogenesis, synaptogenesis, and selective neuronal survival, perhaps inducing long-term effects on learning and memory. Because glucocorticoid effects can vary depending on species and developmental status, however, it will be important to replicate these findings in nonhuman primate infants whose postnatal neural development is more comparable to human development. Notably, there is already evidence that exposing the fetal rhesus monkey to dexamethasone impairs the development of the hippocampus, although to our knowledge this has not been studied in relation to alterations in BDNF activity as a potential mechanism in this process (Uno et al., 1990). In addition to these cortisol effects, CRH also affects the developing brain. The mechanisms through which CRH influences development depend on the timing of exposure, likely related to maturational changes in neurotransmitter production as well as receptor density and distribution. In the rodent hippocampus, the number of CRH-expressing neurons and CRH-1 receptors peaks on postnatal days 11– 18 and then declines drastically to adult levels (Chen et al., 2001). This abundance of CRH neurons is believed to enhance synaptic transmission, thus contributing to postnatal processes of learning and memory. Excessive CRH during this period of particular hippocampal excitability could make rodents prone to pathological outcomes. Similar windows of vulnerability may be present in the development of the human nervous system, and processes of priming involving CRH-1 receptors may be related to the development of human stress-related disorders. Experiments with rodents illustrate how CRH mediates mechanisms of neural plasticity that provoke long-term changes in limbic pathways involved in emotional disorders. For example, activation of CRH receptors facilitated longterm potentiation in the mouse hippocampus and enhanced context-dependent fear conditioning in rats subjected to acute stress (Blank et al., 2002). As further evidence of CRHinduced priming effects, the enhanced learning that rodents

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typically show under stress did not occur in the context of pharmacological inhibition of hippocampal CRH receptors (Blank et al., 2002). A recent study demonstrated that CRH induces long-lasting cellular changes in the amygdala, which increase the rodent’s stress vulnerability (Rainnie et al., 2004). A potent CRH receptor agonist was chronically infused into the basolateral amygdala. The anxiety-like syndrome these rodents developed was correlated with cellular mechanisms of neural plasticity known to mediate long-term learning. The repeated activation of the basolateral amygdala resulted in long-term changes in the sensitivity of its neurons, including reduction of spontaneous inhibitory synaptic potentials and reduced expression of the inhibitory neurotransmitter GABA (Rainnie et al., 2004). This downregulation of inhibitory mechanisms resulted in chronic amygdalar hyperexcitability. These studies implicate cortisol and CRH in a variety of crucial neurodevelopmental processes and demonstrate that distortions in levels of these hormones can have persistent adverse effects in shaping the developing brain. For the developmentalist, such findings immediately raise the issue of the neurodevelopmental consequences of early stressful experiences. In the animal literature, research on this issue has a long history (reviewed in Levine, 2005). Here we review current findings from the animal literature on effects of early experience, to lay the groundwork for discussing the findings on this topic from studies of human development.

Early experience effects in animal models Studies in both rats and nonhuman primates indicate that early experiences, particularly those involving variations in parental care, can have long-term effects on reactivity and regulation of the LHPA system (reviewed in Sanchez, 2006). In nature, variations in parental care occur on a continuum. Experimental manipulations have yielded evidence that the developing LHPA system is sensitive not only to gross deviations in care such as peer rearing (e.g., Champoux et al., 1989), but also to species-typical variations in parenting quality. For instance, Francis and colleagues (1999) crossfostered the offspring of mothers who were at the high and low ends of the continuum of licking/grooming and archback nursing, key indicators of parenting quality in the rat. These variations within the normal range of maternal behavior influenced stress reactivity in the foster offspring. Many of these effects appear to involve regulation of MR and GR development by means of alterations in GR gene expression. This has been convincingly demonstrated in studies of infant rats. In rats, hippocampal GR expression increases throughout development (Suchecki, Rosenfeld, and Levine, 1993; M. Schmidt et al., 2003). Quality of parental care permanently and radically affects the density of hippocampal GR expression, thereby shaping LHPA reactivity in adulthood

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(Meaney and Szyf, 2005). Highly nurturant parental care increases hippocampal GR density and leads to more efficient feedback regulation of the LHPA axis, while extended parental separation and low nurturant parental care result in fewer hippocampal GRs and prolonged LHPA reactivity to processive stressors (for review see Gunnar and Vazquez, 2006). In elegant work, Meaney and colleagues (see Meaney and Szyf, 2005) traced these effects to the role of parental care in regulating methylation of the GR gene. Although long-term impacts of variations in parental care and parental separation have been observed in nonhuman primates, it is not clear whether these are mediated in the same way as in rodents (see Sanchez, Ladd, and Plotsky, 2001). For example, in nonhuman primates, postnatal levels of GR expression do not appear to vary developmentally, and therefore do not appear sensitive to social experience. In rats, MR density is highest during the early postnatal period, peaking in the hippocampus around postnatal day 10, and is sensitive to early social experience (Vazquez et al., 1998, 1993). One species of New World monkey shows a developmental peak in MR expression coinciding with weaning. Pryce and colleagues (2005) posit that the weaning phase could therefore represent a sensitive period for social experience to shape MR expression in primates, with potential long-term consequences for LHPA basal levels in the wake of early social adversity. It is not yet known whether humans share this MR-sensitive period. It would be informative to replicate the study with Old World monkeys, who are more closely evolutionarily related to humans. These findings exemplify the ubiquitous challenge in translational research of determining whether observed phenomena are species-specific or more broadly applicable (Sanchez, 2006). At the same time, they illustrate the rich potential of preclinical models to explicate the mechanisms of social influence on the LHPA system. Notably, some of the effects of early experience on brain development may operate by means of CRH rather than through modulations of glucocorticoids. During the stress hyporesponsive period in the rodent, perturbations that do not elevate corticosterone do increase CRH activity in the brain (Smith et al., 1997). Disturbances in parental care also produce persistent increases in CRH activity that, even in the absence of elevated corticosterone, would be capable of the deleterious impacts on hippocampal development noted to co-occur with disturbances in parental care (Baram et al., 1997, 2001; Brunson et al., 2001). Furthermore, although long-term changes in basal cortisol levels have been difficult to observe in nonhuman primate studies of disturbances in parental care, there is evidence of chronic increases in cerebral spinal levels of CRH in rhesus monkeys who had been reared by mothers foraging under unpredictable conditions compared to those whose mothers enjoyed predictable foraging conditions (e.g., Coplan et al., 1996). Finally, early

disturbances in parental care shift the balance in CRH receptors toward those that mediate fearful or anxious behaviors and heightened reactivity of the LHPA and sympathetic adrenomedullary (SAM) system (see review, Sanchez, Ladd, and Plotzky, 2001). Thus early rearing experiences in rodents and monkeys may produce long-term impacts on brain and behavior through multiple LHPArelated mechanisms. These animal models have helped to inform hypotheses about LHPA regulation in the complex realm of human social experience.

Social experience and LHPA regulation in human development The LHPA system is immature at birth, and LHPA basal function and reactivity continue to evolve throughout childhood (for review see Gunnar and Donzella, 2001). During this extended period of development, social experiences contribute in important ways to shaping these brain circuits. The sensitivity of the developing LHPA axis to social experience is both an asset and a liability. On the positive side, sensitive, responsive caregiving buffers the LHPA system during the first few years of life, preventing or mitigating cortisol elevations. However, social experience is also one of the major sources of stress that challenges the immature LHPA axis. In the absence of sensitive, responsive care, young children faced with psychosocial stressors such as maternal separation, child maltreatment, or social deprivation are highly vulnerable to cortisol elevations and longterm disturbances in LHPA regulation. Human newborns have a highly reactive LHPA system, exhibiting marked elevations in cortisol and ACTH in response to stressors such as well-baby physical examinations or childhood inoculations (for review see Gunnar, 1992). Over the course of the first year of life, LHPA reactivity to mild stressors declines precipitously, despite the fact that behavioral distress in response to these same stressors continues to be observed (Gunnar, Broderson, Krueger, et al., 1996; Larson et al., 1998; Lewis and Ramsay, 1995). By 12 months of age, it is difficult to observe mean increases in cortisol to mild stressors such as childhood inoculation injections, and this resistance to producing elevations in cortisol at least at the mean or group level persists through the toddler and preschool years (de Haan et al., 1998; Gunnar et al., 1997; Nachmias et al., 1996). This period of blunted LHPA reactivity in young humans appears to be functionally equivalent to the stress hyporesponsive period (SHRP) that occurs in rat pups from postnatal days 4 to 14, which is hypothesized to shield the developing brain from potentially damaging elevations in corticosterone. In rats and humans alike, the SHRP is mediated by sensitive, responsive caregiving. For rat pups, if maternal caregiving is sufficiently disorganized, marked increases in

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corticosterone are observed even during the SHRP (Suchecki, Rosenfeld, and Levine, 1993). In humans, sensitive, responsive caregiving by the attachment figure is believed to result in formation of a secure attachment bond (Sroufe, 1983), and securely attached toddlers do not exhibit cortisol elevations to mild stressors while in the presence of their attachment figure (Ahnert et al., 2004; Gunnar, Broderson, Nachmias, et al., 1996; Nachmias et al., 1996; Spangler and Grossman, 1993; Spangler and Schieche, 1998). In contrast, toddlers with a disorganized/disoriented attachment status may be particularly stress vulnerable. Unlike securely attached toddlers, disorganized/disoriented toddlers show high cortisol levels in response to repeated maternal separations (Hertsgaard et al., 1995; Spangler and Grossman, 1993). Disorganized/disordered attachment behavior is a pattern of behavior often associated with maltreatment in which toddlers are ambivalent about whether to approach or avoid the primary caregiver and are unable to use their caregivers as a coping resource in stressful situations (Main and Solomon, 1990; van Ijzendoorn et al., 1999). Studies with children have shown that sensitive, responsive alternate caregivers, such as child care providers and babysitters, are also able to buffer infants and toddlers from cortisol elevations to mild stressors even when the mother is absent (Dettling et al., 2000; Gunnar et al., 1992). However, in a laboratory manipulation, maternal separation did result in significant increases in cortisol for 9-month-olds when the babysitter was instructed to be distant and perfunctory (Gunnar et al., 1992). These findings reveal the major drawback of the immature LHPA system’s dependence on social regulation: When deprived of a sensitive, responsive caregiver, toddlers become highly vulnerable to activation of the LHPA axis with significant, and sometimes large, increases in cortisol to even mild stressors (for review see Gunnar and Donzella, 2001). Findings from animal research indicate long-term consequences of chronic cortisol elevations in the context of disrupted care. These findings raise the specter of increased risk for psychopathology in genetically vulnerable children deprived of sensitive, response care during the toddler years. However, prospective research on the functional consequences of disrupted care for the developing LHPA axis has yet to be conducted in humans. Short- and long-term functional consequences would likely depend on the frequency and chronicity of LHPA elevations. Young children chronically subjected to inadequate or disrupted care tend to show dysregulation of basal LHPA function. Specifically, children exposed to deprived rearing environments (such as orphanages or neglectful homes) have blunted early morning cortisol levels and do not show the typical decline in cortisol levels over the course of the day (Bruce et al., under review; Carlson and Earls, 1997; Fisher et al., 2000; Kroupina, Gunnar, and Johnson, 1997). This flattening of the diurnal rhythm is also observed in chroni-

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cally stressed and neglected rhesus monkey infants (Boyce et al., 1995; McCormack et al., 2003; Sanchez, 2006). These blunted or low early-morning levels may reflect changes in the LHPA axis and in stress-related neural activity in limbic and cortical regions that will, over time, result in increases in basal cortisol levels. For example, toddlers recently adopted from an orphanage or other institution and those studied soon after placement in foster care after being removed from maltreating parents often exhibit extremely low or blunted early-morning cortisol levels (Bruce et al., 2000; Dozier et al., 2006; Gunnar et al., 2006). However, studies of such children many years after adoption from orphanages or rescue from abusive parental care have often yielded evidence of elevated basal cortisol levels for at least some of the children. For example, elevated basal cortisol levels have been reported for physically or sexually abused children who have internalizing disorders when studied months or years after rescue (Carrion et al., 2002; Cicchetti and Rogosch, 2001a, 2001b; De Bellis et al., 1994, 1999). In two studies conducted at day camps, depressed maltreated children showed a rise in cortisol levels across the day instead of the expected decline (Hart, Gunnar, and Cicchetti, 1996; Kaufman, 1991). Finally, orphanageadopted children who were the most severely affected by their preadoption experiences as evidenced by severe growth delays at adoption had elevated early morning cortisol levels when studied an average of 6 years postadoption (Kertes et al., in press). Studies of adults maltreated as young children also tend to confirm the long-term consequences of maltreatment on activity of the LHPA system. However, the precise effects observed depend on the methodology employed—that is, pharmacological probes that activate specific levels of the axis versus processive stressors that rely on cortico-limbic circuits to activate the axis. In addition, particularly for processive stressors, whether hyper- or hypoactivity of the LHPA axis is observed depends on whether or not the adult is suffering concurrently from a clinical affective disorder and on the nature of that disorder (posttraumatic stress disorder or depression, or both). Among adults, PTSD is associated, anomalously, with low basal cortisol levels and blunted cortisol responses to many psychosocial stressors (Yehuda et al., 2001), while depression is associated with elevated basal cortisol levels, a blunted diurnal rhythm in cortisol, and hyperreactivity of the LHPA system to stressors (McEwen, 2005). Childhood maltreatment effects do not precisely mirror the effects observed for adults with these disorders who do not have a history of maltreatment during childhood, although conclusions remain tentative because not all studies of adults with childhood maltreatment histories have been careful to employ a nonmaltreated affective disorder comparison group (see for discussion, Heim, Plotsky, and Nemeroff, 2004). On the whole, however, as in the

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studies of children, a history of maltreatment increases the likelihood of abnormalities in the cortisol daily rhythm, atypically low basal ACTH levels (suggestive of down-regulation in response to chronic CRH drive on the pituitary), and problems in regulating ACTH and cortisol responses to psychosocial threat (see Heim et al., 2000, 2001; reviewed in Tarullo and Gunnar, 2006). These data tend to support the hypothesis that chronic stress during development sensitizes brain circuits to perceived environmental threat, influencing the frequency, amplitude, and duration of future LHPA stress responses and resulting in increased stress vulnerability and elevated risk of internalizing disorders (Heim and Nemeroff, 2001). Even for typically developing children not exposed to the extreme adversity of maltreatment or deprivation, social experience remains one of the most significant sources of stress. Many two- to four-year-old children show a rise in cortisol levels over the course of the day when attending full-day child care, but not on the days they are at home (Dettling, Gunnar, and Donzella, 1999, 2000; Tout et al., 1998; Watamura et al., 2003, 2004). For children of this age, peer interactions are becoming increasingly salient, but they are still in the process of acquiring the social skills to negotiate those interactions. Long hours in the challenging peer environment of child care may strain the emerging regulatory capacities of the LHPA system (reviewed in Gunnar and Donzella, 2001; Gunnar and Quevedo, 2007). Indeed, the magnitude of cortisol increases over the course of the childcare day is greatest for the children who are least skilled at negotiating social interactions: those rejected by peers and those rated as less socially competent and less capable of emotion regulation (Dettling et al., 1999, 2000; Gunnar et al., 1997, 2003). As further evidence that the developing LHPA axis is under strong social regulation by adults, children who receive sensitive, responsive, individualized care from child-care providers show little or no evidence of daycare cortisol elevations, even when their behavior is such that it would provoke negative or hostile responses from peers (Dettling et al., 2000). While we do not yet understand the significance for later development of the small but frequent cortisol elevations observed in less sensitive and responsive day-care settings, this important question is currently under active investigation. A key consideration in this research area will be the possible interaction between variations in parental care and variations in child care. Another challenge will be determining whether these cortisol elevations specifically affect development over and above the impact of being in a lower quality child-care setting. LHPA sensitivity to child care is age specific, such that, as a group, children aged 5 and older do not show these increases in cortisol across the day in school or other group care settings (for review see Gunnar and Quevedo, 2007). This developmental change may reflect the improved social skills of older

children which facilitate successful peer interactions, or improved LHPA self-regulatory capacities, or some combination of these factors. While increases in cortisol across the child-care day have been observed in a number of studies, it is often challenging to find stressors that reliably increase cortisol in laboratorybased studies. As noted, the problem of identifying ethical and effective laboratory stressors emerges around 12 to 18 months, at around the period when an adult with whom the child has a secure relationship history or even a sensitive, responsive unfamiliar adult can buffer reactivity of this neuroendocrine system. Researchers have employed a variety of stressors in studies with preschool and school-aged children, but with little success in provoking a mean or average increase in cortisol. While some of these putative stressors, such as exposure to challenging cognitive tasks, would not be effective with adults either (see review, Dickerson and Kemeny, 2004), others, such as the threat of speaking publicly about one’s most embarrassing moment, fit criteria for effective stressors among adult subjects (L. Schmidt et al., 1999). This group difference between children and adults in LHPA reactivity to laboratory tasks raises the question of what accounts for the transition from child to adult reactivity patterns. Several researchers have suggested that with puberty the LHPA system becomes more responsive, and that this heightened responsiveness may help explain the rise in prevalence of affective disorders around midpuberty (Spear, 2000; Walker, Walder, and Reynolds, 2001). When one takes a longer developmental perspective, including infancy, early and middle childhood, and adolescence, an alternative but not inconsistent hypothesis is that as children move into adolescence, the period of relative stress hyporesponsivity of the LHPA system in humans slowly draws to a close. The association of puberty with heightened LHPA responsivity may reflect maturational changes in the biology of the LHPA axis, experience-driven alterations in how the adolescent perceives and processes threatening situations, or an interaction between biological maturation and experience. There is increasing evidence that reactivity of the LHPA system and its relation to social experience change during the transition to adolescence, likely in connection with pubertal changes. Basal cortisol levels, particularly morning levels, increase from ages 6 to 17 (Kiess et al., 1995; Legro et al., 2003; Netherton et al., 2004; Shirtcliff et al., in press). Two studies have found that the timing of this increase in morning levels is linked to pubertal status, occurring around Tanner stage 3 (Halligan et al., 2004; Netherton et al., 2004). Gonadal steroids influence the LHPA system, and animal models indicate that estrogen stimulates LHPA activity (Netherton et al., 2004). Thus it is not surprising that sex differences in LHPA functioning also emerge around Tanner stage 3. In one study, girls who had reached midpuberty had

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higher cortisol morning levels than either midpubertal boys or prepubescent girls and boys (Netherton et al., 2004). Several studies have also noted that cortisol reactivity to laboratory stressors increases with age and pubertal status (Klimes-Dougan et al., 2001; Walker, Walder, and Reynolds, 2001; Wewerka et al., 2007). Notably, in the one study that employed the Trier Social Stress Test (the stressor shown to be most capable of elevating cortisol in studies of adults, see Dickerson and Kemeny, 2004), 9-, 11- and prepubertal 13-year-olds failed to exhibit increases in cortisol to this stressor task, although they all reported being stressed or anxious during testing. In contrast, both pubertal 13-yearolds and 15-year-olds showed significant elevations in the range typically noted in studies of adults (Wewerka et al., 2007). These changes in the LHPA axis in human children parallel changes seen in rodents at the end of the relative stress hyporesponsive period (Vazquez, 1998). At this point, it is not clear whether these changes reflect alterations in the axis and its regulation by cortico-limbic circuits or alterations in psychosocial regulation of the axis, that is, a decrease in the capacity of parental presence and availability to buffer its activation. Both psychological and physiological changes associated with the transition to adolescence may be involved. Furthermore, it is unlikely that these changes involve only the LHPA system. Other systems involved in reactivity and regulation of stress may also undergo developmental changes around the same period of time (see Gunnar and Quevedo, 2007). For example, there are well-known changes in sleep around the pubertal transition that may also increase vulnerability to stressors by reducing the child’s ability to use sleep as a stress regulator (Dahl and Lewin, 2002). Thus, at the group level, we have some understanding of the relation between social experience and LHPA development. The LHPA system is under strong social regulation in the first years of life, a fact which can be considered a double-edged sword. Sensitive, responsive care buffers LHPA reactivity, but inadequate care leaves young children vulnerable to LHPA dysregulation. Elevated levels of cortisol and CRH can have long-term detrimental effects on developing neural circuits, including increased stress vulnerability. However, at the individual level, LHPA outcomes of exposure to early social adversity are heterogeneous and difficult to predict. In the final section of the chapter, we explore individual factors that may interact with experience in shaping LHPA development.

Individual differences in LHPA function: Temperament and genes In considering individual variability, it can be useful to employ the developmental psychopathology concepts of multifinality—that is, multiple outcomes from the same starting point—and equifinality, multiple paths to the same

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end point (see Cicchetti and Tucker, 1994, for further discussion of these concepts). For instance, in different individuals, exposure to chronic, severe early social adversity may lead to LHPA hyperreactivity, LHPA hyporeactivity, or apparently normal LHPA function (multifinality). Conversely, a postinstitutionalized child several years after adoption may have LHPA function that closely resembles that of a child who has experienced low social adversity throughout life, but these two children may have arrived at that outcome by very different pathways (equifinality). Thus, while early social experience exerts a profound influence on the developmental path of the LHPA system, that influence is filtered through the lenses of an individual’s genetic predispositions and current neurodevelopmental status. The developmental implications of social adversity also depend in part on the broader context of concurrent and subsequent social experiences. To explore individual differences in LHPA development, we will likely need to learn a great deal more about how genetic predispositions and social experience interact at different points in development. A starting point in this analysis may be to consider interactions between stable individual differences in temperament and children’s social experience in predicting differences in basal and stress responses of the LHPA axis at different points in development. Here we use behavioral inhibition as an example. Behavioral inhibition has received extensive research attention because the 5–10 percent of children who fall in this temperamental category are at increased risk of developing internalizing disorders (Kagan, Reznick, and Snidman, 1987). Behavioral inhibition—characterized by acute shyness, avoidance of novelty, fearfulness, and vigilance— demonstrates moderate temporal stability and a significant heritable component. As further evidence of the biological basis of behavioral inhibition, extremely inhibited children exhibit elevated heart rate, low vagal tone, and greater relative right frontal EEG activation (Fox et al., 2001; Kagan, Reznick, and Snidman, 1988). This physiological profile is consistent with elevated amygdala activity: The amygdala activates the sympathetic nervous system (raising heart rate), inhibits the parasympathetic nervous system (lowering vagal tone), and sends projections to the right frontal cortex. It has been hypothesized that in inhibited children, LHPA hyperactivity increases CRH activity in the amygdala, which would lower the threshold for perception of threat and result in more frequent, prolonged LHPA stress responses, paving the way to development of an anxiety disorder (Rosen and Schulkin, 1998). The body of evidence on this topic indicates that behavioral inhibition interacts with social context in predicting activity of the LHPA axis. For instance, there are no differences in cortisol levels for behaviorally inhibited and noninhibited children in the presence of a sensitive, responsive child-care provider, but if the child-care provider is not

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sensitive and responsive, more temperamentally inhibited children do exhibit higher cortisol levels (Dettling et al., 2000). Similarly, while exposure to a frightening toy does not provoke cortisol increases in securely attached infants or in bold infants regardless of attachment status, the combination of insecure attachment and behavioral inhibition is associated with an elevation in cortisol (Nachmias et al., 1996). Generally, main effects of temperament are not observed in the proximate LHPA response to novel stressors, such as laboratory manipulations, entrance into a new playgroup, or adjusting to a new school year (reviewed in Gunnar and Vazquez, 2006). In these situations, a rise in cortisol may be considered an adaptive response to provide resources to cope with the challenge. However, early in a new school year, inhibited temperament predicts higher home levels of cortisol (Bruce et al., 2002; E. Davis et al., 1999; de Haan et al., 1998), suggesting difficulty in terminating the LHPA response following removal from the novel context. Inhibited children also have elevated basal cortisol levels, especially levels obtained early in the morning (Buss et al., 2003; Kagan, Reznick, and Snidman, 1987; L. Schmidt and Fox, 1998). Inhibited children may be less able to regulate LHPA responses to familiar stressors (for review see Gunnar and Vazquez, 2006). This pattern would be consistent with studies of adults conducted by van Eck and colleagues (1996a, 1996b), in which highly anxious adults were more likely to show a cortisol response to familiar daily hassles, but did not differ from other adults in response to a novel laboratory stressor. The interaction of behavioral inhibition with social context in predicting LHPA activity cautions against expecting that genetic predispositions will exert main effects on activity of the LHPA system. Rather, we might expect that individuals with particular genetic polymorphisms may be at risk for altered LHPA activity under certain conditions and given certain rearing histories. The question is, Which genetic polymorphisms may be relevant? Here we will review several that are receiving attention in research on stress and stress vulnerability: genes involved in regulation of brain-derived neurotrophic factor (BDNF), those involved in regulation of serotonin (i.e., the serotonin transporter gene), and several genes that operate within the LHPA system. We do not intend this discussion to be exhaustive, but merely to point to the type of gene research that will likely alter the nature of developmental research on stress and stress vulnerability in the near future. BDNF is a protein involved in neural plasticity and produced in high levels in the hippocampus and cortex. Neural activity, such as that involved in the encoding of new information, increases gene transcription of BDNF (Hartmann and Lessmann, 2001). BDNF increases neuronal excitability and synaptic transmission, and thus plays a critical role in facilitating neural plasticity (Alder et al., 2003). Chronic

exposure to corticosterone decreases BDNF expression in the rodent hippocampus, and its reduced expression may contribute to dendritic atrophy (Smith et al., 1995a, 1995b). Thus glucocorticoid-induced decreases in hippocampal BDNF have been related to stress-induced memory deficits. Additionally, BDNF down-regulation has been linked to the pathogenesis of depression, and some antidepressants ameliorate stress-induced reductions of hippocampal BDNF (Duman, 2002; Shirayama et al., 2002). In humans, a common single nucleotide polymorphism (SNP) in the BDNF gene has been identified that results in a valine (Val) to methionine (Met) substitution. The Met allele appears to reduce the efficiency of BDNF regulation (less trafficking), with impacts on hippocampal and cortical morphology (Cheng et al., 2005). Theoretically, the lowfrequency Val/Met and Met/Met genotypes should be associated with greater vulnerability to chronic stress than the predominant Val/Val genotype. This hypothesis is consistent with evidence that bipolar disorders, substance abuse problems, and mood disorders—all disorders that are increased in probability by histories of stress during development—appear to be associated with this BDNF polymorphism (Cheng et al., 2005; Green et al., 2006; Parsian et al., 2004; Rybakowski et al., 2006; Tsai et al., 2006). To our knowledge, there have been no studies of the effects of glucocorticoid infusions in human adults with the Val/Met genotype versus the more efficient BDNF allele. There have been studies using mice with homo- and heterozygous knockouts of the BDNF gene. On the whole, there is little evidence that BDNF knockouts are more stress reactive (e.g., Chourbaji et al., 2004). In contrast, there is evidence that BDNF knockout mice that also lack the serotonin transporter gene are hyperfearful and exhibit exaggerated increases in corticosterone to processive stressors (RenPatterson et al., 2005). While it is difficult to extrapolate from knockout mice studies to human development, this latter study does suggest that both the serotonin system and neurotrophic factors may be important in regulating the development of stress reactivity and regulation. Indeed, there is increasing evidence that genetic polymorphisms affecting the efficiency of serotonin regulation do influence reactivity and regulation of the LHPA system and moderate the developmental impact of adverse experiences. In both human and nonhuman primates, a functional polymorphism has been identified in the length of the serotonin transporter (5HTT) gene (Bennett et al., 2002). Its short version is associated with decreased serotonergic function (Holden, 2003). In nonhuman primates, parental deprivation in the form of peer rearing as compared to mother rearing has been shown to reduce cerebrospinal levels of serotonin. However, these effects were expressed only in those peer-reared animals with at least one short copy of the serotonin transporter gene (Bennett et al., 2002). Likewise,

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relative to mother-reared monkeys, peer-reared monkeys exhibit greater increases in LHPA reactivity to the stress of separation and single-case housing for several days. Again, this effect is exacerbated in monkeys carrying at least one short serotonin transporter allele (Barr et al., 2004). Importantly, cortisol and corticosterone are known to affect serotonin regulation, and there is growing evidence that the effects of elevated glucocorticoids operate, at least in part, through promoting synthesis of the serotonin transporter (Tafet, Toister-Achitiv, and Shinitzky, 2001). These findings may partially account for a report by Caspi and colleagues (2003) that the increase in risk of depression among individuals who were maltreated as children was moderated by the serotonin transporter polymorphism. Risk of depression by age 25 increased among those who were maltreated as children, but the increase in risk depended on whether the individual carried one or two short copies of the serotonin transporter gene. As noted, children who have been severely maltreated may exhibit chronic increases in cortisol, which in turn may influence serotonin regulation and expression of the serotonin transporter (Cicchetti and Toth, 2005). These effects may be amplified in children with one or two short copies of the serotonin transporter gene, significantly increasing their risk for developing affective disorders (Caspi et al., 2003). As noted earlier, in knockout mice models, impairment in both serotonin and BDNF regulation interacted to enhance stress reactivity and fearful behavior (RenPatterson et al., 2005). This statement may also be true for human development; in at least one study, depressive symptoms were most evident in children who carried both the MET allele of the BDNF polymorphism and the short copy of the serotonin transporter gene. However, the impact of these two genes in combination increased depressive symptoms in maltreated but not in nonmaltreated children (Kaufman et al., 2006). Unfortunately, none of the studies examining gene-by-experience interactions for maltreated children included measures of LHPA activity, so we can only speculate that activity of this system may have helped to mediate the gene-by-experience impacts on the development of depression. Another class of genetic polymorphisms that may play a role in individual differences in stress reactivity and the impact of early stressful life events involves variations in genes directly involved in activity of the LHPA system. Several polymorphisms have been described for the GR gene, including the BclI and the ER22/23EK. The BclI polymorphism has been associated with hypersensitivity to glucocorticoids and consists of a single nucleotide change (C to G) in the GR gene, the effects of which are not yet fully understood (van Rossum et al., 2006). The ER22/23EK polymorphism is related to glucocorticoid resistance and consists of two linked nucleotide changes resulting in GR receptors that have less affinity for cortisol. Recent research

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has found that homozygous carriers of both these polymorphisms had an increased risk of developing a major depressive episode, but the ER22/23EK carriers had a faster clinical response to antidepressants (van Rossum et al., 2006). Several polymorphisms in the CRH gene have also been identified, and at least one recent study associated variations in the region of the CRH gene with panic disorder in adults and behavioral inhibition in children (Smoller et al., 2005). These are only a few of the genetic polymorphisms that are currently being explored in LHPA-axisrelevant genes. Results to date, however, suggest that many of these polymorphisms will affect the development and regulation of the LHPA system, and thus children’s vulnerability to stressors. As repeatedly noted in this section on individual differences, neither temperament nor genetic variations appear to have deterministic effects on development. In many cases, their effects are moderated by the care and experiences children have during childhood. Some of these genetic differences may affect reactivity and regulation of the LHPA system, while others may moderate the impact of LHPA reactivity on other neural systems. Regardless, their impact on the brain likely depends on whether the child experiences significant stressors during periods of rapid brain development. Introducing genetics and candidate gene analyses into the study of stress and development may help us understand the development of individual differences in vulnerability to stress, but it is not likely to reduce the importance of research on childhood experiences. Rather, it may provide new avenues through which to comprehend the role of experiences in shaping the development of individual differences in stress vulnerability and resilience.

Conclusions It is well established that stress has considerable influence on the developing brain. Stressful life events including abuse, neglect, and parental loss are associated with emotional disorders and lower academic achievement. In the last several decades, researchers studying child development have increasingly turned to measuring cortisol as a means of examining the potential role of the LHPA system in mediating these impacts and in helping to explain individual differences in stress vulnerability and resilience. Notwithstanding the ease of introducing measures of salivary cortisol into studies in child development, the complexity of the neurophysiology and developmental psychobiology of the LHPA system belies simple interpretation of cortisol findings. Nevertheless, based on the accumulation of both animal and human studies, there is ample reason to expect that this neuroendocrine system plays important roles in brain development and functioning and in mediating the impact of adverse experiences on the developing brain. In addition,

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adverse care during development appears to shape reactivity and regulation of this system in humans in ways that complement those observed in more detailed and controlled animal studies. Individual differences abound, both in vulnerability to adversity and in reactivity of the LHPA system. Genetic differences among individuals may impact LHPA activity and interact with effects of glucocorticoid- and CRH-induced effects on brain targets. Increased understanding of this genetic variability will likely help explain why some children are more stress resilient than others. However, it is unlikely that the growing infusion of gene analyses into our studies of stress reactivity, regulation, and impacts will relegate the role of experience to the background. Rather, the inclusion of molecular genetic information may provide us with a better understanding of the processes through which experiences impact stress vulnerability and resilience during development. acknowledgments

Preparation of this manuscript was supported by a National Science Foundation predoctoral fellowship to the first author, a National Institute of Mental Health predoctoral fellowship (T32 MH15755) to the second author, and a National Institute of Mental Health Senior Scientist Award (K05 MH66208) to the third author. Correspondence regarding this article should be addressed to Megan R. Gunnar, University of Minnesota Institute of Child Development, 51 E. River Rd, Minneapolis, MN 55455. E-mail: [email protected].

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The Effects of Monoamines on the Developing Nervous System GREGG D. STANWOOD AND PAT LEVITT

Introduction The development of the brain relies on the spatial and temporal regulation of cell-cell interactions that are controlled by contact-mediated and diffusible effector substances. Both the availability of molecular signals and the ability of developing cells to respond to those signals, by expression of specific receptors, are essential to ontogenesis. Transcription factors, cell adhesion and guidance molecules, and neurotrophic factors all have been established as mediators of tissue patterning, histotypic organization, and circuit formation. It is clear, however, that many molecules exhibit pleiotropic activities, serving as regulators of distinct cellular functions at different times in development and adulthood. Neurotransmitters and neuromodulators are now recognized as exhibiting multiple activities, performing very different roles in cellular communication in the mature brain and during development (Lauder, 1993; Levitt et al., 1997; Herlenius and Lagercrantz, 2004). Here, we focus on several features of brain development to understand the potential impact that an altered neurochemical environment may have on histogenesis. In this chapter we describe research findings that implicate monoamine systems in the regulation of neural development. These neuromodulators are particularly susceptible to modifications by exposure to drugs of abuse and psychotherapeutics during pre- and postnatal development.

Fundamentals of cerebral cortical development The cerebral cortex mediates higher cognitive functions and is responsible for the integration of complex sensory, motor, and homeostatic information. Defects in cortical development, therefore, can have a profound impact on mature brain functions. It has been suggested that developmental anomalies in cortical development underlie certain types of psychopathology, such as schizophrenia (Weinberger, 1995; D. Lewis and Levitt, 2002), and forms of mental retardation and autism (Charman, 1999; Levitt, Eagleson, and Powell, 2004). The molecular and cellular bases that tie developmental defects to cortical dysfunction in these disorders remain unknown, but we know that influences on cell-cell interactions that mediate specific developmental events are

likely targets. This also seems to be true for nongenetic alterations in development, such as prenatal exposure to toxicants, stress, or drugs of abuse (Trask and Kosofsky, 2000; Stanwood and Levitt, 2004; Andersen, 2005). The Basics of Brain Development Temporally overlapping events, grouped in five major categories, contribute to the formation of all brain structures from the neural tube. Progenitor cells of the germinal matrix, situated along the forerunner of the ventricular system, give rise to all neurons and macroglia in a well-controlled proliferative process. Recent evidence suggests that specialized radial neuroepithelial cells serve two purposes: precursor cells for both neurons and glia (Campbell and Gotz, 2002; Gotz and Huttner, 2005), and a radial scaffold to guide the directed migration of postmitotic neurons from their place of origin to their final resting position (Noctor et al., 2001; Kriegstein, 2005). Neuronal differentiation involves the expression of specific gene products that, together with the appearance of polarized structural features (axons and dendrites), contribute to the remarkable phenotypic diversity of the nervous system. Glial cells differentiate early to form specialized migration guides, radial glia, and later to form astrocytes and oligodendrocytes. Progenitor cells, neurons, and glia also undergo naturally occurring cell death, a complex process that appears to be a normal developmental mechanism to establish appropriate quantitative relationships between projection and target neurons and between neurons and glia. Synaptogenesis is a temporally extended developmental event, beginning prenatally and lasting through adolescence in the central nervous system (CNS) of all mammals, which is critical in the formation of synapses between specific target populations of neurons. These developmental milestones are described schematically in figure 6.1. Cerebral Cortical Histogenesis The neocortex is a sixlayered structure that exhibits very similar features across its tangential extent. Thus all functional areas have repeated laminar and columnar organization that is assembled during development in well-defined temporal and spatial patterns. Very similar temporally ordered patterns of cortical development occur in all mammalian species. In the rat, total gestation is approximately 21 days, with the peak of

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Figure 6.1 Schematic representation of developmental milestones in the forebrain, depicting proliferation and migration, aggregation, circuit formation, and synaptogenesis. The approximate times of each event in the human brain are labeled on the left, and corresponding days in the rodent brain are on the right. Neurons and glia are produced from proliferative zones, and a variety of physical and chemical cues contribute to their migration to specific locations in the brain. Transient structures, such as the subplate in the cerebral cortex, provide cues that assist in the gen-

eration of topographic axon projections. Some elements of cell differentiation and synaptogenesis occur over protracted periods of time and peak after birth. Abbreviations: E, embryonic day; P, postnatal day; W, age in gestational weeks; M, age in months after birth; PZ, proliferative zone; MZ, marginal zone; Bf, basal forebrain; Th, thalamus; SP, subplate; CP, cortical plate; CC, corpus callosum; TC, thalamocortical projections; CTX, cerebral cortex. (Figure is modeled on Rakic, 1995.) (See plate 15.)

cortical neuronogenesis occurring at about embryonic day (E) 16. In the mouse, total gestation is about 19 days, and E15 corresponds to the peak of neuron proliferation in the cerebral cortex. In humans, neuron production, migration, and differentiation occur prenatally in the cerebral cortex, beginning by the end of the second month of gestation and peaking prior to midgestation. By the third trimester, basic organization and a minimum of connections are already formed, and myelination begins (figure 6.1 and plate 15; Levitt, 2003). The first neurons produced are actually not located in the forerunner of the cortex, the cortical plate, but rather form a structure called the preplate, which eventually is split into a subplate and a supraplate in the marginal zone by the first neurons destined for the cortical plate. The process of splitting the preplate is an important first step in establishing

appropriate migration patterns of neurons. In the mutant mouse reeler, in which expression of the reelin gene is defective, this process is abnormal and the cortex is disorganized. Subplate neurons are transient cells that serve as temporary targets for subcortical axons from the thalamus. These neurons aid in the guidance of axons to correct target regions of the cerebral cortex, and in the formation of precise sensory maps within specific layers of the cerebral cortex (Kanold et al., 2003). Neurons arising from the proliferative zone along the ventricular surface of the dorsal telencephalon (dorsal pallium) attain a bipolar morphology and migrate along radial glia to reach the cortical plate, with neurons born early residing deep and those born subsequently more superficial. This so-called inside-out settling pattern is a hallmark of cerebral cortical formation and allows one to define the

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time of origin of neurons residing in specific layers of the cortex. Migrating cells exhibit polarized features, with the trailing process of migrating cells generally forming the axon, which can be recognized as neurons migrate. These axons grow into the intermediate zone, the forerunner of cortical white matter, which resides between the subplate and the germinal matrix during development. Following the production of neurons destined for layers 6–4, a new, superficial zone of progenitor cells appears, termed the subventricular zone (SVZ). These progenitor cells maintain a position away from the luminal surface and produce the neurons of the superficial cortical layers and most of the macroglial cells (astrocytes and oligodendrocytes). The SVZ is maintained in adults in a restricted region along the rostro-ventral end of the lateral ventricle and can produce new neurons and glia in the mature forebrain. Recent studies have demonstrated that not all neurons of the cerebral cortex arise from the dorsal telencephalon. Rather, it appears that most of the GABA interneurons of the cerebral cortex are generated in the ganglionic eminence of the ventral telencephalon and migrate tangentially to reach all areas of the cerebral cortex (Anderson et al., 1997; Nadarajah and Parnavelas, 2002; Levitt, Eagleson, and Powell, 2004). These interneurons comprise 12–18 percent of all neurons in the cerebral cortex and exhibit many phenotypic properties of neurons as they migrate, including the formation of axons and dendrites and the synthesis of neurotransmitters. Neurotransmitters themselves can influence neuronal migration, and it is therefore possible that unique regulatory interactions between amino-acid-transmitter– synthesizing neurons occur during this migratory process (Behar et al., 1999). Neuronal polarity reflects the asymmetry of information processing and the molecular and structural differences in the accumulation of proteins and organelles. As noted previously, polarity is seen first when a neuron becomes postmitotic, with dendrite- and axon-specific proteins expressed during migration. Dendrites grow slowly over many weeks and months, whereas axons grow several orders of magnitude more rapidly, reaching target areas in some instances prior to neurons reaching their final resting position. Complex molecular signaling is responsible for the regulation of axonal growth. Diffusible and membrane proteins that comprise different families, including cadherins, semaphorins, the receptor tyrosine kinase Ephs and their membrane ligands, the ephrins, members of the Ig superfamily, and netrins are responsible both for chemoattraction and chemorepulsion. These signals can act locally, or at a distance to direct extension of axons along specific pathways to reach their proper targets. In the cerebral cortex, transcription factors control the specification of different functional areas that initially facilitate the correct targeting of axons from the dorsal thalamus prenatally, before extensive den-

dritic development of projection neurons. Downstream effectors of axon guidance are thus distributed in unique patterns to produce a basic blueprint of long projections. This process appears to be independent of specific activity in early developing circuits. In contrast, activity-dependent processes appear to be essential for mediating synaptogenesis. Whereas axon targeting occurs rapidly, quantitative analysis of synaptogenesis in the cortex indicates that only a small fraction of adult synapses are present by birth. In all mammalian species, the process of synapse formation and pruning occurs over a relatively long time period, beyond puberty. For example, in primates synapse number peaks early postnatally and through adolescence, plateaus for several years, and ultimately is reduced by almost 40 percent through a normal process of retraction and remodeling (Caviness et al., 1997; Bourgeois, Goldman-Rakic, and Rakic, 1999; Levitt, 2003). Recent studies indicate that regulation of neuronal excitability can have dramatic effects on synapse number (Burrone, O’Byrne, and Murthy, 2002) by controlling the expression of intracellular signaling molecules and transcription factors, such as Mef2, which in turn influence gene transcription (Flavell et al., 2006; Shalizi et al., 2006). Moreover, other molecules, some of which heretofore were considered irrelevant to neuronal function and development, and which can be modulated by physiological activity, including major histocompatibility complex class I receptors and complement proteins, have important roles in regulating synaptogenesis (Boulanger and Shatz, 2004; Bjartmar et al., 2006). The control of dendritic growth is complex, with neurotransmitters, neurotrophins, tyrosine kinases, small GTPases, and afferent-driven physiological activity among the key regulators (McAllister, 2000). In the visual system, neurotrophins are transported in a retrograde manner by developing thalamic neurons from the cortex and increase the complexity of dendritic branching. Ephrins, Eph receptors, semaphorins, and small GTPase signaling proteins also have profound influences on axonal and dendritic development. In vitro studies have shown that neurotransmitters, in the absence of synapses, also can modulate cell migration and dendritic growth. For example, the amino acids glutamate and GABA have complex effects on cell survival and neurite growth. Similarly, monoamines can serve as negative or positive regulators, depending upon which subtypes of receptor proteins are activated (see next section).

Developmental neuropharmacology of brain monoamines Dopamine (DA) In order to place the developmental roles of monoamines in a proper context, it is necessary to understand their normal ontogeny, pharmacology, and functions. Dopamine (DA) has been implicated in a variety of functions in the mature CNS, including motor control,

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cognition, and endocrine, emotional, and cardiovascular regulation. Abnormalities in brain DA systems are thought to contribute to several major neurological and psychiatric disorders including schizophrenia, Parkinson’s disease, attention-deficit hyperactivity disorder (ADHD), and drug addiction. Many exogenously administered drugs, including psychostimulants, act directly on DA systems and can produce long-lasting alterations in endogenous dopaminergic and nondopaminergic functions. The catecholamine DA is synthesized from the amino acid tyrosine through the actions of tyrosine hydroxylase (TH), the rate-limiting enzyme in the process, and followed by aromatic amino acid decarboxylase. The rate at which DA is synthesized is controlled by several mechanisms, including end-product inhibition, changes in the number or structure of TH molecules, and changes in the availability of necessary cofactors for tyrosine hydroxylation. Because of these regulatory processes, neurons usually are able to match the rate of DA synthesis to the rate of DA utilization, thereby avoiding either the buildup or the depletion of the transmitter. As is the case for all neurotransmitters, DA release occurs in response to an influx of calcium into the nerve terminal, which is triggered by the arrival of an action potential. Like DA synthesis, several processes regulate DA release. For example, DA can act back on the terminal from which it was released to inhibit subsequent release. Such influences represent negative feedback loops and act to maintain the rate of DA utilization within relatively narrow limits. DA release can also be potentiated or attenuated by both local and distal actions of other neurotransmitters. Dopamine induces a wide range of cellular and biochemical effects in neurons by way of its interactions with specific receptor proteins. These effects include relatively rapid (seconds) modulation of biochemical events in the target cell, resulting in changes in the responsiveness of the cell to other neuronal inputs, as well as more gradual (minutes-hours) alterations in gene expression. DA receptors belong to a large superfamily of neurotransmitter and hormone receptors that are characterized by an extracellular N-terminus, intracellular C-terminus, seven transmembrane domains, and coupling to specific effector functions through guanine nucleotide binding proteins (G proteins). DA receptors are classified into two subfamilies according to pharmacological profiles and sequence homology: the D1-like receptor subtypes (D1, D5) and the D2-like receptor subtypes (D2, D3, D4) (Sibley and Monsma, 1992). The neurotransmitter actions of released DA and other monoamines are typically terminated by transport back into presynaptic terminals by plasma transporter proteins that form high-affinity uptake sites. Once taken up, monoamines can be metabolized in the nerve terminal by monoamine oxidase (MAO) or further sequestered into storage vesicles by vesicular transporters for later reuse. In human prefrontal

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cortex, nonneuronal catechol-o-methyl transferase (COMT) is primarily responsible for inactivation, because the DA transporter is not present in these synapses (Tunbridge, Harrison, and Weinberger, 2006). There are several major dopaminergic pathways. Axons of DA-containing cells in the substantia nigra (SN) pars compacta form the nigrostriatal tract, which provides the dopaminergic innervation of the caudate and putamen, or striatum. The striatum is the major output component of the basal ganglia, a group of nuclei involved in motor and cognitive functions. Degeneration of nigrostriatal DA neurons is the primary pathology in Parkinson’s disease. The mesolimbic and mesocortical DA systems largely arise from the ventral tegmental area (VTA), which lies medially to the SN in the midbrain. The mesolimbic DA system innervates ventrally located subcortical regions such as the nucleus accumbens, olfactory tubercle, and amygdala. The nucleus accumbens is thought to be a site of interface between limbic and motor systems and is a critical substrate for the development and regulation of goal-directed behaviors. The mesocortical system provides dopaminergic afferents to the medial prefrontal (mPFC) and anterior cingulate (ACC) cortices. These regions have been implicated in cognitive, emotional, and attentional processes (Goldman-Rakic, 1998; Elston, 2003; Dalley, Cardinal, and Robbins, 2004), and disruption of mesocortical DA neurotransmission has been associated with disease states including schizophrenia, ADHD, and depression. Furthermore, polysynaptic circuits through glutamatergic pathways produce anatomical substrates by which mesocortical DA dysregulation can secondarily alter dopaminergic activity in nigrostriatal and mesoaccumbens neurons, a process that may contribute to pathophysiology. The rate-limiting enzyme in DA synthesis, TH, is first apparent at E12–13 in the rat midbrain, and is present by E14 in the rabbit. Axons of dopaminergic cells reach the cortex a few days later. Limbic cortical regions, such as the ACC and mPFC, receive the densest dopaminergic innervation. This input is thus already present in the cortex even while more superficial cortical layers (II–IV) are beginning to form, consistent with a morphogenic role of DA. In the monkey, DA neurons of the SN/VTA are produced by E30 of a 165-day gestation period (Levitt and Rakic, 1982). In humans, midbrain DA neurons appear in the second month of gestation (Olson and Seiger, 1972). The mechanisms responsible for the proper guidance of dopaminergic afferents to the cortex and the morphogenic properties of these afferents on cortical neurons are not well understood, but netrins and ephrins have been implicated. Transcripts for the D1, D2, and D3 receptors can be detected in the striatum and cortex by E14 in the rat. In addition, D1 and D2 receptor proteins are measurable

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prenatally, and they increase throughout prenatal and early postnatal development to reach adult levels of expression between P14 and P21 in rodents. DA receptors functionally couple to G proteins very soon after their appearance. DA also is likely to have early biological activity in the primate brain. In the monkey, for example, DA receptors appear in target regions of DA input by the first quarter of gestation. In the human fetus, D1-like and D2-like receptor binding sites have been detected at gestational week six. Therefore, in all species examined, DA receptors are present very early in prenatal development, consistent with a role for DA in regulating neuronal differentiation and circuit formation. In vitro studies have supported a role for DA as both a promoter and an inhibitor of neurite growth. The actions of DA on outgrowth are modified by the complement of receptors that are activated, and as a function of the neuronal cell type being modulated. In cortical neurons, for example, selective D1 receptor activation decreases neurite outgrowth in a dose-dependent manner, whereas D2 receptor activation increases outgrowth. In striatal neurons, however, these effects are reversed, with D1 receptor activation serving to promote neuronal differentiation and process outgrowth. DA signaling also appears to be involved in prenatal neurogenesis itself within the neuroepithelial precursors of the striatum and cerebral cortex, by way of influences on cell cycle length (Ohtani et al., 2003; Zhang et al., 2005). The phenotypic differentiation of inhibitory GABAergic interneurons may also be modulated by dopaminergic stimulation. Studies from our laboratory and others investigating the effects of prenatal cocaine exposure suggest that modification of DA D1 receptor signaling during a sensitive period of prenatal development induces permanent effects on circuit formation and function (discussed in a later section of this chapter). Recent data also suggest that transient overexpression of the D2 receptor in the developing striatum can cause lifelong changes in the activity of D1 receptor systems in the prefrontal cortex (Kellendonk et al., 2006). Finally, DAdependent processes also alter postnatal development of brain circuits, especially during the periods of synaptic maturation and refinement. Serotonin (5-HT) The indoleamine 5-HT is found in cardiovascular tissue, blood cells, and the nervous system. In the CNS, the cell bodies for serotonergic neurons reside in the midline raphe nuclei in the brain stem. In addition, 5-HT serves as a precursor for melatonin production in the pineal gland. It is formed by the hydroxylation and decarboxylation of tryptophan by the actions of tryptophan hydroxylase and aromatic amino acid decarboxylase, respectively. Similarly to the catecholamines, 5-HT synthesis is controlled by several regulatory mechanisms, including end-product inhibition, changes in the availability of tryptophan hydroxylase, and the activation of autoreceptors.

Receptor proteins for 5-HT are diverse and numerous, as more than 15 different 5-HT receptors have been cloned. These are grouped into seven families (5-HT1–5-HT7). Moreover, alternative mRNA splicing and mRNA editing create at least 20 additional 5-HT receptors with different binding affinities and physiological functions. Similar to the other monoamines, most 5-HT receptors are G-protein– coupled receptors; a notable exception is the ligand-gated ion channel 5-HT3 receptor. The indoleamine 5-HT is removed from the synapse by the actions of a high affinity 5-HT transporter, followed by intracellular metabolism by MAO. Important roles for 5-HT have been implicated in a wide variety of behaviors and conditions including anxiety, appetite, aggression, schizophrenia, migraine, sexual behavior, and drug abuse. The indoleamine 5-HT is one of the earliest developing and most widely distributed neurotransmitter systems in the mammalian brain (Whitaker-Azmitia, 2001; Gaspar, Cases, and Maroteaux, 2003; Luo, Persico, and Lauder, 2003). Serotonergic neurons are first evident by E12 in the rat midbrain (Lauder and Bloom, 1974), by the end of the first month of gestation in the monkey (Levitt and Rakic, 1982), and by the fifth week in humans (Olson and Seiger, 1972). Serotonergic axons reach their forebrain targets prenatally in the rodent (E16–17), but terminal arborization and peak 5-HT levels occur during the second and third postnatal weeks, during synaptogenesis and circuit refinement. The influence of 5-HT occurs at specific sensitive periods. For example, removal of 5-HT during very early fetal development in rats can cause a permanent reduction in the number of cortical neurons in the adult brain. Also, 5-HT-releasing fibers influence Cajal-Retzius cells within the marginal zone of the cerebral cortex. Later in prenatal development, 5-HT receptor activation modifies specific aspects of dendritic development in differentiating neurons. Neurite outgrowth by dorsal thalamic neurons is modulated in a hierarchical fashion by different 5-HT receptors (Persico, Di Pino, and Levitt, 2006). Furthermore, 5-HT plays a negative feedback role on its own neurons. Unfortunately, there are large gaps in our current understanding of where and when distinct 5-HT receptor subtypes are expressed during prenatal brain development. Recent studies from our laboratory on the expression patterns in mice indicate highly regulated patterns of 5-HT receptor expression in the cortex, basal forebrain, and dorsal thalamus (Bonnin et al., 2006). A particularly striking example of a role for 5-HT in circuit formation has been demonstrated in mice where the gene encoding MAO-A has been disrupted (Cases et al., 1996, 1998). Loss of MAO-A results in increases in 5-HT and NE levels, the former being directly responsible for the failure of the development of barrellike structures related to topographic vibrissae representation in the somatosensory cortex. This pattern of cytoarchitectonic organization,

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unique to rodent somatosensory cortex, normally forms during the first postnatal week of life. Barrel formation in the MAO-A null mice can be restored with early intervention, using a drug to inhibit 5-HT synthesis during the critical period for barrel formation. Pharmacological inhibition of MAO-A activity in wild type mice or total absence of 5HT also leads to a loss of barrel formation, demonstrating the remarkable sensitivity of somatosensory pattern formation to 5-HT. Although barrels are absent in the somatosensory cortex in the MAO-A mutant line, thalamic and brain stem barrellike patterns are still evident. Cortical barrel formation is dependent upon activity arising from peripheral whisker barrels, transmitted through several synapses in the brain stem and thalamus. So why do non-monoaminergic afferents arising from regions with seemingly normal barrel formation produce abnormal barrel formation at the next synapse, in the somatosensory cortex? During normal development, the ventrobasal thalamic projections to somatosensory cortex express transiently the presynaptic 5-HT transporter, the vesicular monoamine transporter, and accumulate 5-HT during the critical period in the first postnatal week. The modulation of cortical barrel formation by 5-HT is shown further in the study in which genetic deletion of presynaptic 5-HT1B receptors, in the context of excessive 5HT produced by MAO-A disruption, results in normal barrel development (Salichon et al., 2001). Collectively, these studies have provided the strongest evidence to date that alterations in monoamine levels during development can lead to aberrant neuronal projection patterns and target organization. These data also have led to the hypothesis that exposure to selective 5-HT reuptake inhibitors (SSRIs) during thalamocortical synaptic formation may have deleterious consequences. Recent studies in rodents have suggested that this may be the case (Xu, Sari, and Zhou, 2004). In addition, 5-HT plays a role in the establishment of adult anxiety behavior through a mechanism in which activity need only be disrupted during the early postnatal period (Gross et al., 2002). Mutation of the 5-HT1A receptor in mice causes increases in anxiety-related behavior. This defect can be rescued by expression of the receptor in specific regions of the forebrain using a conditional transactivation genetic system. This conditional knockout strategy was used to show that, whereas repression of receptor expression in the adult is ineffective, repression of receptor expression until three weeks of age is sufficient to produce adult mice with increased anxiety-related behavior. Forebrain 5-HT1A receptor expression during the early postnatal period, but not in the adult, is thus necessary for the expression of normal anxiety responses. Norepinephrine (ne) Norepinephrine is involved in attention, anxiety, arousal, and learning and memory. The cell bodies of NE neurons are concentrated in the brain

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stem, particularly in the locus coeruleus. From this structure, five major NE tracts originate that innervate the whole brain. DA serves as a metabolic intermediate within NE neurons, with conversion of DA to NE occurring through the action of the enzyme dopamine β-hydroxylase. There are at least ten identified NE receptors, which are segregated into two families (α and β). NE neurons in the pons (locus coeruleus) and brain stem first appear at relatively early stages in the CNS, approximately E13 in the rat, E30 in the monkey (Levitt and Rakic, 1982), and by 5–6 weeks in humans (Olson and Seiger, 1972). Despite the fact that many types of psychotropic medications have actions on the noradrenergic system, very little research has been conducted on the developmental roles of NE during pre- and postnatal development. One developmental function that has been described for NE during embryogenesis is on the development of Cajal-Retzius cells in the cerebral cortex (Naqui et al., 1999). These cells are the first neurons to be born in the cortex and are instrumental in neuronal migration and laminar formation (figure 6.1). Furthermore, the α2a NE receptor (as well as other monoamine receptors) is expressed by migrating neurons in the intermediate zone in close association with radial glia (Lidow and Wang, 1995). Thus NE may be involved in regulating the generation, migration, and maturation of cerebral cortical cells, but directed studies are needed in this area. Postnatally, the best characterized effect of NE has been in studies examining the role of neuromodulators in criticalperiod plasticity in sensory systems (Manunta and Edeline, 2004).

An example: Alterations in brain development and function due to prenatal cocaine exposure Given the modulatory influence of monoamines on specific aspects of neural development, it is understandable that prenatal exposure to drugs that affect these systems, such as drugs of abuse and psychotherapeutics, can have pronounced effects on the development of the cerebral cortex. Because of space limitations, our discussion will concentrate specifically on the effects of in utero cocaine, and we direct the reader to other sources for discussions of other psychopharmacological insults (Slotkin, 1998; Trask and Kosofsky, 2000; Thadani, 2002; Carlezon and Konradi, 2004; Olney et al., 2004; Andersen, 2005). The primary pharmacological sites of action of cocaine and other psychostimulants in the brain are the high-affinity transporters for DA, 5-HT, and NE. Cocaine binds to these transport proteins and blocks the reuptake of the neurotransmitters, thus prolonging their time in the extracellular space. This action permits the monoamine to bind to its receptor proteins for more sustained periods, resulting in excessive activation of these receptors, particularly those located

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extrasynaptically. Cocaine is a drug of abuse in adolescents and adults, produces a host of neuroadaptations in the brain of the user that are associated with addiction (Hyman and Malenka, 2001), and can potently modulate monoaminergic systems during prenatal development if the drug is used during pregnancy. Clinical reports of the impact of prenatal cocaine exposure have been diverse, as some suggest gross physical malformations, others observe specific deficits in cognitive and emotional development, and yet others indicate no effects. The variable outcomes are at least in part the result of important covariates such as the timing and amount of cocaine use during pregnancy, polydrug use, and the quality of pre- and postnatal care (Singer et al., 2004; Mayes, 2003). Severe physical abnormalities in cocaine-exposed infants typically occur following very prolonged and high levels of cocaine intake by the mother. However, there are measurable deficits in cognition and emotional regulation in children exposed to relatively low doses of cocaine in the womb. These disturbances are due primarily to alterations in attention, language development, and regulation of arousal, and are likely caused by improper functioning of limbic cortices and monoamine systems. Prospective longitudinal studies of cocaine-exposed infants and school-age children (Mayes et al., 1995; Richardson, Conroy, and Day, 1996; Singer et al., 2002; Arendt et al., 2004) have produced the least confounded and most compelling reports. For example, Singer and colleagues have assessed a prospective cohort where the relationship between prenatal cocaine exposure and developmental and cognitive outcomes has been assessed in 218 cocaine-exposed and 197 unexposed infants. The rate of developmental delay was nearly doubled (13.7% versus 7.1%, respectively). Importantly, this still ongoing study has controlled for prenatal exposure to other drugs, gestational age at birth, weight at birth, and caregiver characteristics. Disturbances reminiscent of children diagnosed with ADHD are consistently found in children exposed to cocaine prenatally (Leech et al., 1999; Mayes, 2002; B. Lewis et al., 2004; M. Lewis et al., 2004; Singer et al., 2004). These changes include deficits in recognition memory, task persistence, distractibility, and stress responsiveness, and may be worsening as these children continue to age. Interestingly, increased risk of ADHD also has been documented in the offspring of women who smoke or consume alcohol heavily during their pregnancy. Reports also have suggested that prenatal cocaine exposure delays language development during infancy and early childhood. There are numerous inconsistencies in the basic science literature regarding the biological effects of prenatal cocaine exposure, likely stemming from differences in dosing, route of administration, time of exposure, age of assessment, and species (see Stanwood and Levitt, 2001, for review of this

issue). Many models have utilized subcutaneous or intraperitoneal injections of high doses of cocaine (20–80 mg/kg). However, cocaine has complicated and diverse pharmacological effects on biological targets in the brain and the periphery of both the mother and fetus, and can exhibit pronounced teratological effects at high doses. We have suggested that a low-dose model, using a route of administration that closely mimics the pharmacokinetic response in human cocaine abusers, provides a reproducible system that facilitates the assessment of specific effects on the organization of cocaine’s targets in the CNS, the biogenic amine systems. A low-dose (2–4 mg/kg), intravenous rabbit model that resulted in highly reproducible and selective brain defects was thus initiated by a group of investigators. There are specific, dose-dependent, and permanent effects on behavioral functioning and on the structure and function of cortices receiving a rich DA innervation (Levitt et al., 1997; Harvey, 2004; Stanwood and Levitt, 2004). The effects are produced either by exposure for the majority of pregnancy or, perhaps more interestingly, during a short sensitive period during the human equivalent of the second trimester (Stanwood, Washington, and Levitt, 2001). The anatomical defects include aberrant growth of dendrites of cortical projection and interneurons, suggesting disruption of local circuitry, and behavioral abnormalities that involve Pavlovian learning and stereotypic motor behavior (figure 6.2 and plate 16). Selective deficits in aspects of cognition and/or attention also have been noted (Thompson, Levitt, and Stanwood, 2005). Possibly the most remarkable demonstration of the impact of prenatal cocaine exposure, and perhaps the point of origin for the structural and behavioral disturbances, is the striking reduction in coupling of the DA D1 receptor to its G protein (Jones et al., 2000; Zhen et al., 2001), which is initiated prenatally and sustained into adulthood (figure 6.2). This effect appears to be specific for D1-Gs coupling, because D2 and muscarinic cholinergic receptor coupling to Gi/Go proteins is normal. We have viewed the D1 receptor signaling defect as a manifestation of a cellular strategy, distinct from that in adults, for adapting to disrupted balance of DA signaling during development. Strikingly, the D1 receptor knockout mouse exhibits similar structural alterations in cortical development to rabbits in which prenatal cocaine reduces D1-Gs coupling (Stanwood, Parlaman, and Levitt, 2005). The neuroadaptive changes that occur in response to repeated exposure to cocaine vary with the maturational state during which the exposure occurs. The offspring of rabbits treated with cocaine show near complete loss of an amphetamine-induced stereotyped behavior (head bobbing) when tested as young adults (Stanwood and Levitt, 2003). In contrast, the mothers of these offspring, receiving cocaine at the same dose and duration, and with the same period of withdrawal, exhibit profound sensitization to the behavioral

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Figure 6.2 Schematic representation of cellular effects of in utero cocaine exposure in the rabbit. Rabbits exposed to low doses of cocaine intravenously exhibit alterations in the structure and function of neurons in the anterior cingulate cortex. The apical dendrites of pyramidal neurons (red) exhibit an undulating trajectory. The number of interneurons in which immunoreactivity for GABA is detectable (black) is increased. There also is an increase in parvalbumin (green) immunostaining in the dendrites of a subset of

these neurons. D1 dopamine receptor coupling to Gs protein (blue) is reduced, whereas coupling of the D2 receptor to Gi (orange) is unaffected. These changes likely influence the balance of excitatory and inhibitory influences in the cingulate cortex and produce the aberrant behavioral phenotypes exhibited by these rabbits, including psychostimulant responsiveness and attentional deficits (see text for more detail). (Adapted from Levitt et al., 1997). (See plate 16.)

effects of amphetamine (Stanwood and Levitt, 2003). Our most recent data suggest a similar double dissociation in the reinforcing effects of cocaine following previous in utero or adult exposure to the drug (B. Thompson, G. Stanwood, and P. Levitt, unpublished observations). Two groups have utilized intravenous administration of cocaine in rats (Mactutus, Herman, and Booze, 1994; Mactutus, 1999; Morrow, Elsworth, and Roth, 2002, 2003). Although the studies have emphasized the analysis of distinct biological measurements and markers, direct parallels between models are beginning to be established. Furthermore, models of higher dose cocaine administration in rodents (Kosofsky, 1998; Rocha, Mead, and Kosofsky, 2002; Melnick and Dow-Edwards, 2003) and nonhuman primates (Lidow, 1998; Chelonis, Gillam, and Paule, 2003) have revealed additional disruptions of CNS development by cocaine, including potential effects on neuronal migration, differentiation, and cell survival. We propose that the loss of dopamine D1 receptor signaling may represent a conserved feature of prenatal cocaine exposure and quite possibly the primary cellular mechanism used to compensate for excessive DA release and receptor stimulation during neuronal differentiation. Given the primarily extrasynaptic location of the D1 receptor, as compared to the intact primarily synaptically located D2 receptor, and the activation of the D1 receptor by phasic, but not tonic, DA release, we predict that the basal functioning of DA-modulated circuits would be minimally disrupted.

Environmental or pharmacological challenges, which would engage both receptors, however, would be influenced substantially, with the outcome likely to produce atypical DA modulation of circuits. Furthermore, the long-lasting decrease in stimulant-induced behavior in the rabbit model suggests that brain pharmacology is permanently altered and “typical” responses to drugs of abuse or psychotherapeutics cannot be assumed to occur in cocaine-exposed children. The data further suggest that some children exposed prenatally to cocaine will respond poorly to mild psychostimulants such as methylphenidate, a disturbing prediction given the high incidence of ADHD in prenatal cocaineexposed children. An alternative strategy might include treatment aimed at restoring the “balance” of D1 and D2 receptor signaling, which could be more efficacious (see next section). Further interactions between DA and the other monoamines are likely to play a role. Cocaine also blocks the reuptake of NE and 5-HT, and, as described earlier, these neuromodulators also influence excitatory and inhibitory activity within the brain. Although no evidence for changes in 5-HT or NE innervation or activity has been identified in the rabbit model, deficits in serotonergic functioning certainly have been described in higher dose models of prenatal cocaine exposure. Intriguingly, the ACC and mPFC, the cortical regions in which we have observed in utero cocaine-induced changes in our rabbit model, contribute to the neural control of

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attention and are sites of brain dysfunction in ADHD. Children exposed in utero to cocaine perform poorly on tasks that depend on the proper functioning of the mPFC and ACC, showing deficits similar to those of children diagnosed with ADHD. Reduced signaling through the DA D1 receptor appears to underlie the structural and functional deficits observed in prenatal cocaine-exposed rabbits, and thus it is intriguing to speculate that similar processes may be occurring in cocaine-exposed children. The development of a therapeutic strategy to restore proper D1 receptor coupling in these children might therefore be efficacious in the treatment of attentional disturbances. Reduced D1 receptor coupling could have other severe consequences throughout their lifetimes for children exposed prenatally to cocaine. D1 receptor activation is an important substrate for reward pathways in the brain. Although not yet studied in the human population directly, one prediction is that drug-exposed children would likely experience defects in endogenous reward systems that could lead to the occurrence of anhedonia and/or depression. In fact, reports

from animal models suggest that the efficacy of reinforcers is reduced following prenatal cocaine exposure.

Figure 6.3 D1- and D2-like DA receptors produce distinct, and sometimes opposite, neuromodulatory effects on cell signaling, electrophysiological responses, and behavior. They are often coexpressed within specific functional brain circuits, and sometimes even by the same neurons. A reduction in D1 receptor signaling could shift the balance of activity to D2 receptor subtypes. Normal physiological responsiveness may not necessarily depend on the

levels of receptor (above a certain minimum), but rather a balance between opposing activities. Thus, in considering strategies for restoring normal DA responsiveness, blockade of D2 receptors may have beneficial effects on the net outflow of DA-dependent neural circuits. (Adapted from Stanwood and Levitt, 2004.) (See plate 17.)

Balance of receptor signaling The amount of receptor stimulation by each of the monoamines, as well as the molecular identity of those receptors, determines the intracellular response to the extracellular cue. These molecules, however, have relatively slow modulatory actions, in contrast to fast-acting neurotransmitters such as glutamate and GABA. It has been hypothesized, therefore, that an even more important determinant of ultimate functional response than the absolute level of stimulation is the balance of receptor subtype activation (figure 6.3 and plate 17). This concept was initially proposed nearly 20 years ago to explain some of the actions of DA D1 and D2 receptor activation, but few studies have been conducted to test this hypothesis. Recently, two phenomena have returned attention to the idea. First, detailed analysis of the

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electrophysiological responses of prefrontal cortical neurons to application of DA and specific agonists have revealed complex “state” changes in firing patterns mediated distinctly by D1-like and D2-like receptors (Seamans and Yang, 2004). Second, a recent biochemical study revealed that interactions between DA receptors and specific G proteins are more complicated than previously believed. In this regard, D1 receptors couple to cAMP through Gs or Golf if expressed alone, and D2 receptors couple best with Gi or Go in the absence of D1 receptors. However, when the two receptors are coactivated in the same cell, an entirely new intracellular cascade, a rise in intracellular calcium mediated by Gq, is activated (Lee et al., 2004). Other monoamine receptors exhibit similar patterns of coupling to G proteins, activate diverse intracellular signaling pathways, and are often coexpressed by the same neurons within the brain. It is therefore likely that combinatorial rules of receptor balance are utilized by monoamines during development, as well as in the mature brain (Stanwood and Levitt, 2004).

Conclusions Alterations in biogenic amine availability can modify neurotransmitter systems and intracellular messengers both in the developing and adult brain, with likely very different outcomes. Monoamines are pleiotropic signaling molecules that serve to regulate specific aspects of central nervous system development. Pharmacological or genetic modulation of their receptors leads to specific, targeted changes in brain structure and circuitry, and results in permanent alterations in neural function. Such effects on the developing nervous system, before homeostatic regulatory mechanisms are properly calibrated, differ from their effects on mature systems. We suggest that mature, dysfunctional states acquired following disruption of monoamine signaling during development are defined in part by altered function of biogenic amines as nontraditional modulators of pre- and postnatal brain maturation, and more traditional influences on neurotransmission in the adult. It is likely that other environmental factors that engage the DA systems, such as other psychotropic drugs or environmental stressors, may contribute to dysregulation of circuit formation and function by altering distinct cell signaling systems in analogous ways. The sensitive periods for these insults will be defined by the relation between the timing of the environmental exposure and the timing of developmental modifications in the neural system upon which it acts. acknowledgments

The authors are supported in part by DA11165, DA017957, and P30HD15052. We thank Drs. Barbara Thompson, Alexandre Bonnin, and Kathie Eagleson for helpful conversations on the topics reviewed in this chapter.

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B. STRUCTURAL FOUNDATIONS OF SENSATION, PERCEPTION, AND COGNITION

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Mechanisms of Auditory Reorganization during Development: From Sounds to Words RICHARD N. ASLIN, MEGHAN A. CLAYARDS, AND NEIL P. BARDHAN

Several weeks before the human newborn is first exposed to visual stimulation, auditory inputs are capable of stimulating hair cells on the basilar membrane, generating neural signals that travel from the auditory nerve to brain-stem, thalamic, and cortical areas of the central nervous system, triggering behavioral responses, and establishing rudimentary representations that can affect behavioral preferences several days or weeks later. Newborns exhibit the ability to orient toward sounds, to discriminate between some acoustic differences, and to show preferences for certain classes of auditory stimuli, only some of which could have been induced by prenatal experience. Given this state of the auditory system at birth, the challenge facing the developing infant is to determine which of the myriad of acoustic cues present in the proximal auditory environment carries information relevant to solving particular ecologically relevant tasks. For example, sound localization requires the extraction of particular spectral and temporal cues, the most important of which involve interaural differences. In contrast, speech perception involves the extraction of different spectral and temporal cues, most of which do not depend on binaural information. In addition to this problem of learning to extract the “right” acoustic cues, the infant is also faced with tremendous variability along the acoustic dimensions that are relevant for solving a particular task. For example, spectral cues are relatively unimportant for sound localization along the horizontal axis but play a crucial role along the vertical axis. Similarly, variations in pitch and duration are less important than spectral cues for the perception of consonants and vowels, but these pitch and duration cues play a crucial role in tone languages and in identifying a particular talker or determining the talker’s emotional state. We begin this chapter with a brief summary of the development of basic auditory capacities in infancy, which, as we will see, play a relatively minor role in constraining most auditory tasks that involve suprathreshold auditory stimuli. We then turn to underlying changes in auditory anatomy and physiology gleaned from invasive studies of nonhuman infants, as well as behavioral evidence for plasticity in these

underlying neural mechanisms in human infants and adults. Next we review how one class of auditory signals produced by the human vocal tract—speech sounds—are perceived by infants and how their underlying discriminative capacities influence the formation and maintenance of speech-sound categories in both infancy and adulthood. Finally, we review how speech sounds are mapped onto words by describing how infants begin to associate sounds with meanings and develop a lexical categorization system that rapidly expands in size and is robust to considerable dialectal variation and other nonphonemic speaker-dependent acoustic variability. We conclude with some speculations about how a cognitive neuroscience approach could reveal the underlying brain mechanisms that make this progression from sounds to words possible.

Fundamental auditory capacities in infancy The goal of studying basic auditory capacities in infancy is not only to document the normative course of auditory development and its underlying neural mechanisms, but also to determine whether higher levels of auditory processing are constrained by lower level immaturities. For example, the speech perception literature (see chapter 8 by Friederici in this volume) has highlighted the sophisticated discriminative skills of very young infants. However, it is unclear whether and how these discriminative capacities are used in a natural language context to form speech categories and organize these categories in auditory memory for use in a lexical (referential) context. Thus basic data on auditory detection and discrimination place a lower bound on when higher level auditory processing can begin and how this higher level processing compares to the exquisite sensitivity of adults to linguistically relevant stimuli. The Onset of Hearing Evidence from the intact fetus and from infants born prematurely suggests that auditory stimuli can elicit changes in heart rate (Lecanuet, GranierDeferre, and Busnel, 1988), eyeblinks (Birnholz and Benacerraf, 1983), gross motor responses (Kisilevsky, Muir,

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and Low, 1992), and auditory brain-stem responses (ABRs; Hecox and Burkard, 1982) as early as the 28th week of gestation (when ABRs can first be recorded). Behavioral evidence using two techniques—conjugate reinforcement of sucking responses and preferential listening while fixating a visual stimulus—have shown that newborns exhibit auditory preferences (Cooper, 1997). Auditory preferences in newborns reveal two important facts about development. First, any evidence of listening preferences confirms the newborn’s ability to discriminate among two or more classes of sounds (of course, the absence of a preference is uninformative about discriminative capacities). Second, listening preferences in newborns could be due to the effects of auditory experience (and learning) in the womb, analogous to the effects of auditory experience in prehatchling birds (Gottlieb, 1976). The plausibility of auditory experience as a significant influence on the human fetus was bolstered by demonstrations that premature infants have a functional auditory system and by hydrophone recordings showing that both internal (maternal) and external sounds of high intensity and low frequency are detectable in the amniotic fluid surrounding the fetus’s ears (Querleu et al., 1988). DeCasper and Fifer (1980) reported that newborns suck differentially when given the opportunity to listen to their own mother’s voice over the voice of an unfamiliar mother. Thus not only are maternal sounds available to a functioning auditory system in utero, but also these sounds are apparently encoded during some portion of the prenatal period and retained across a 2–3 day perinatal period to affect the newborn’s listening preferences. When given a choice between a lowpass-filtered version of their mother’s voice (similar to the spectral content available in utero) and an unfiltered version of their mother’s voice, newborns prefer the low-pass-filtered version (Fifer and Moon, 1985), suggesting that they have learned the proximal characteristics of the most intense and frequent intrauterine sounds. In addition to a specific preference for the mother’s voice, newborns exhibit a general preference for highly familiar auditory materials. DeCasper and Spence (1986) showed that newborns prefer to listen longer to a familiar story that had been read aloud repeatedly by the mother during the final weeks of pregnancy than to a novel story. The stories were chosen to have different rhythmic structures, and the newborns’ preferences were exhibited despite the presentation of the test stories by an unfamiliar female voice. Similarly, Mehler and associates (1988) and Moon and associates (1993) have shown that newborns prefer their native language to a foreign language with different rhythmic properties. Taken together, these results suggest that newborns have extracted a number of prosodic (rhythmic and intonational) characteristics of auditory input during the last few days or weeks of prenatal development.

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Newborns raised in nearly all languages also prefer to listen to a pattern of maternal speech called “motherese,” or infant-directed (ID) speech, over the same sentences spoken by the mother in an adult-directed (AD) register (Cooper and Aslin, 1990). Infant-directed speech is characterized by a slow speaking rate, a small number of words per utterance, and large pitch excursions (Fernald, 1985). In contrast to the early evidence for maternal voice and prosodic preferences, the preference for ID speech is not induced by prenatal experience. In the absence of prenatal exposure to ID speech (Cooper and Aslin, 1990) or ID singing in hearing newborns of deaf parents (Masataka, 1999), newborns show clear preferences for these sounds over AD sounds, and newborns prefer hyperarticulated ID speech (despite never being heard in utero) over normally articulated ID speech (Cooper and Cooper, 1999). Thus there are some acoustic characteristics of sounds that are intrinsically preferred by newborns in the absence of any inducing experience. Newborns also appear to spontaneously form categories for speech sounds along the linguistically relevant dimension of lexical form class: function words versus content words. Function words are generally shorter in duration and unstressed compared to the longer and stressed content words. Shi and associates (1999) showed that newborns who were habituated to a list of function words or a list of content words failed to discriminate a shift to a novel list of words from the same category, but succeeded in discriminating a shift to a list from the opposite category. Thus, when multiple acoustic cues are available to differentiate two lexical categories, even newborns can readily do so. However, as we will see in a later section, there are limits to category formation in infants and a paucity of methods for assessing how sophisticated these speech categories are as compared to adults. An important point in the domain of speech is that maternal input to young infants, although hyperarticulated to enhance some auditory cues (e.g., vowel differences; see Kuhl et al., 1997; Liu, Kuhl, and Tsao, 2003), cannot be exaggerated too much, or the exemplars will extend into a different perceptual category. Thus there are limits to how “simplified” the acoustic properties of maternal speech can be when it is presented to infants. Absolute Thresholds The foregoing evidence confirms that sounds of sufficient intensity are perceived not only by newborns but also prenatally. Thus, as long as sounds are above the infant’s hearing threshold, acoustic information along highly salient auditory dimensions is both detected and discriminated. However, if auditory sensitivity is poorer in infants than in adults, then many sounds that are easily detected by adults will not be available to infants for further processing. The available evidence from studies using complex sounds like speech suggests that auditory

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thresholds do not impose severe constraints on higher level processing. Three techniques have been used to assess sound thresholds in human infants: the conditioned head-turning procedure (CHP; Moore et al., 1975), the observer-based procedure (OBP; Olsho, Koch, Halpin, and Carter, 1987), and the auditory brain-stem response (ABR; Hecox, 1975). It is beyond the scope of this review to discuss these methodologies in detail, but the interested reader is referred to the works we have cited. The latter two techniques have documented significant improvements in absolute thresholds between birth and 6 months of age, and the conditioned head-turning technique has documented further improvements between 6 months and 2 years of age. The ABR in newborns is 10–15 dB less sensitive than in adults (Hecox, 1975), and the OBP in 2-week-olds (Werner and Gillenwater, 1990) is up to 50 dB less sensitive. By 3 months of age, OBP thresholds are 15–30 dB poorer than in adults (Olsho et al., 1988). The CHP has also provided estimates of absolute thresholds in 6- to 18-month-olds for pure-tone and octave-band noise stimuli (Berg and Smith, 1983; Nozza and Wilson, 1984; Schneider, Trehub, and Bull, 1979, 1980; Sinnott, Pisoni, and Aslin, 1983; Trehub, Schneider, and Endman, 1980). Although estimates vary across studies, by 6 months of age, absolute thresholds are approximately 10– 20 dB poorer than in adults, and these estimates do not differ from those obtained using OBP (Olsho et al., 1988). In summary, while there are significant postnatal improvements in absolute thresholds, many signals in the natural environment are accessible to young infants (and to the fetus) because they are well above threshold. Intensity, Frequency, and Duration Discrimination Given a sound above absolute threshold, one can ask whether the infant auditory system can make fine discriminations of intensity, frequency, and duration—the key acoustic parameters that are used to analyze auditory signals. Using the CHP, thresholds to detect an intensity increment improve from 6 dB to 4 dB between 6 and 12 months of age (Schneider, Bull, and Trehub, 1988; Sinnott and Aslin, 1985). Although these infant thresholds are 2–3 times worse than adults’, most of the critical information carried in speech and nonspeech environmental sounds contains intensity differences that are easily detected by infants. The CHP has also provided evidence that 6-month-old infants can discriminate an increment or decrement in frequency of approximately 2 percent for midfrequency tones (Olsho, 1984; Olsho et al., 1982; Sinnott and Aslin, 1985). The OBP has confirmed this 2 percent threshold in 6-montholds and shown that 3-month-olds have a slightly poorer threshold of 3 percent (Olsho, Koch, and Halpin, 1987). Again, it should be noted that while adult thresholds are slightly less than 1 percent, a 2–3 percent difference in fre-

quency is exceeded by nearly all relevant speech and nonspeech stimuli in the infant’s environment. Other studies using a masking technique to assess infants’ thresholds for detecting a signal (either an octave-band noise or a pure tone) in the presence of either a narrow or broadband masking noise (Bull, Schneider, and Trehub, 1981; Nozza and Wilson, 1984; Olsho, 1985; Schneider, Morrongiello, and Trehub, 1990, 1989; Spetner and Olsho, 1990) have shown that thresholds are elevated by the same relative amount in infants and adults, suggesting that frequency resolution is adultlike by 6 months of age. Perhaps more relevant to everyday listening, Bargones and Werner (1994) have shown that infants do not always appear to selectively attend to the frequency range within which signals are presented. How infants choose the specific acoustic cues to which their processing is directed will be discussed in a later section on cue weighting. Duration discrimination is also important for suprasegmental or prosodic aspects of speech (and phonemic discrimination in languages that make duration distinctions), and temporal acuity is important for the processing of binaural cues to sound localization. Morrongiello and Trehub (1987) used the CHP to assess duration discrimination and reported that 6-month-olds’ thresholds were 25 milliseconds (ms) whereas adults’ were 10 ms. Werner and associates (1992) used OBP to assess gap detection in 3- and 6-montholds and, by altering the low-frequency cutoff of the noise, to determine which frequencies were used by infants in this gap-detection task. Trehub and associates (1995) used the CHP to assess two-tone gap-detection thresholds in 6- and 12-month-olds. These two studies show that gap-detection thresholds decline from 4–5 times longer in 3-month-olds than adults to only a twofold difference by 12 months of age. However, several studies (Irwin et al., 1985; Wightman et al., 1989) have shown that gap detection thresholds continue to improve until at least 5 years and perhaps up to 10 years of age. Duration discrimination also continues to improve well into early childhood (Elfenbein, Small, and Davis, 1993; Jensen and Neff, 1993) and may play a role in the discrimination of stop consonants in medial syllable position where closure duration is a primary cue. A final aspect of duration discrimination is the processing of spectrotemporal cues. Aslin (1989) showed using the CHP that 6- to 9-month-olds’ thresholds for discriminating a rising or falling tone from a steady tone were quite good (similar to the 2% thresholds for frequency increments or decrements). However, when these tone sweeps were rapid (50 ms), thresholds increased by a factor of two, and when infants had to discriminate one rising (or falling) tone from another rising (or falling) tone, their thresholds increased by an additional factor of 3–4. Thus, in the context of broadband spectral information whose components are undergoing rapid changes over time, as in the time-varying

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formants of speech, infants are quite poor at discriminating these spectrotemporal cues. This poor performance may not prevent infants from discriminating the major acoustic/phonetic cues that are used to make phonemic distinctions in natural languages, but it may reduce the robustness of speech discrimination under the less than ideal listening conditions outside the laboratory. In addition, many of the subtle timevarying cues in fluent speech may be inaccessible to the infant until spectrotemporal sensitivity and working memory have matured. Summary Fundamental aspects of auditory development undergo substantial improvements in the first few postnatal months. The processing of intensity and frequency information appears to reach values similar to those of adults by 6 months of age. However, temporal processing appears to have a more protracted development continuing at least into the preschool years, and spectrotemporal processing shows similar immaturities. How these basic capacities affect the processing of more complex sounds like speech remains unclear.

Neural specializations and reorganization The foregoing summary of basic acoustic sensitivities and preferences in newborns suggests that a quite sophisticated auditory system, albeit less mature than in adults, is functioning at birth. However, it is unclear whether newborns are analyzing auditory stimuli with the same neural subsystems used by the adult brain. The classic view of how the neocortex is organized in the adult brain is one of functional specialization; that is, separate and discrete regions are devoted to different sensory modalities and to the analysis of specialized features of sensory inputs. To some extent this classic view is correct, with separate pathways from the sensory periphery to modality-specific regions of primary cortex. However, the interaction among cortical areas is both extensive and far-reaching, suggesting that simple models of neural specialization are at best incomplete and perhaps misleading, particularly models that fail to consider the multitude of direct and indirect inputs to any given cortical area. A key developmental question in this debate about neural specialization is one of origins. Where do modality-specific and neural processing specializations come from? The two extremes of this question, of course, comprise the naturenurture debate, in which neural specializations are either an intrinsic property that arose from evolutionary processes or an experience-dependent property that emerged during ontogeny. Neither of these extremes is likely to be correct, and the debate has shifted to understanding whether neural specializations can be modified by interventions during development and perhaps even in adulthood.

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Auditory Localization in Nonhumans A classic example of specialization in the auditory domain is localization. Most mammals that have a well-developed visual system combine information from both their visual and auditory systems to localize a sound source. Visual stimuli are essentially twodimensional projections of the external world onto a sensory surface (the retina), with very high acuity along the line of central sight for some species (e.g., predatory mammals and birds). Auditory stimuli do not have this spatiotopic representation at the sensory periphery (the cochlea), but rather compute sound location from intensity (sound level) and time of arrival differences at the two ears. In binaural animals, a sound originating from anywhere other than the midline will strike one ear before the other because the ears are separated on the head, producing an interaural time difference (ITD), which is the main cue for azimuthal localization (right-left along the horizontal plane). In addition, because the head sits between the ears of many binaural creatures, one ear lies in the sound shadow of the head, reducing the intensity of the sound at that ear and resulting in an interaural level difference (ILD). These differences, in combination with monaural influences on sound quality (spectral cues) produced by the shape of the external ears (pinnae) and head, provide the information used in sound localization. Perturbing either the ITD, ILD, or spectral cues (see Parsons et al., 1999) can disrupt the ability to localize a sound source. The visual and auditory systems form a useful combination of contrasting characteristics: the auditory system detects sound sources regardless of the orientation of the ears, and the visual system provides detailed information about a stimulus and its surrounding environment in the visual field. A major concern is how these two perceptual systems are integrated so that information from one is able to inform the other for stimulus localization. Visual and auditory information come together for the purpose of stimulus localization in the optic tectum in vertebrates and the superior colliculus in mammals. The tectum contains topographic maps of both visual and auditory space, as well as bimodal neurons that respond to both visual and auditory stimuli (Knudsen and Brainard, 1995). Thus the tectum provides a locus for crossmodal integration of auditory and visual information concerning the location of stimuli in the external world. An example of auditory localization that has clear parallels with humans comes from research on barn owls. In a series of perturbation studies, Knudsen and colleagues altered the correspondence between visual and auditory localization cues in barn owls, either by placing prisms in front of the eyes (Knudsen, 1988) or by monaural occlusion with an earplug (Knudsen, Esterly, and Knudsen, 1984; Knudsen, Knudsen, and Esterly, 1984). Wearing displacing prisms or plugging one of the animal’s ears produces significant alterations in the tectal alignment of receptive fields for

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locations in space using auditory (ITD and ILD) and visual cues. If the prism or plug is in place during early development, then a remapping occurs and spatial localization is recalibrated. However, altered experience during adulthood does not lead to a successful remapping. Thus there is a sensitive period for the alignment of auditory and visual spatial cues in the tectum that directly mediates spatial localization responses. If the prism or plug is removed prior to the end of the sensitive period, then a return to “normal” is achieved. However, if the prism or plug is removed after the end of the sensitive period, then realignment is not observed and a permanent mismatch results. Perhaps most importantly, Knudsen (1998) found that owls that had adapted to prisms during the sensitive period and subsequently readapted to normal vision before the close of the sensitive period were able as adults to show subsequent shifts in the tuning of tectal neurons after the reintroduction of the original prismatic displacement. Similar evidence was not found in control animals that had never experienced prismatic displacement. These results suggest that there is a trace of the former adaptive experience that remains in the nervous system long after a different set of adaptive parameters has been adopted, and that the neural pathways that were shaped originally by experience during the sensitive period can be resurrected during adulthood. Auditory Localization in Human Infants Auditory localization in human infants has been studied almost exclusively by noting the presence of overt head-turning responses to discrete sound sources (see review by Clifton, 1992). Despite early anecdotal evidence of auditory localization in a newborn (Wertheimer, 1961), Muir and Field (1979) were the first to report definitive evidence that newborns, under special circumstances, could reliably orient their head toward a sound. Critical factors included supporting the newborn’s head to compensate for poor neck muscle strength and using broadband (preferably high-pass) stimuli of at least 1 second in duration (Clarkson, Clifton, and Morrongiello, 1985, 1989; Morrongiello and Clifton, 1984). These localization responses in the newborn are only crudely spatial; that is, the sound source was located 90 degrees to the right or left of the head along the interaural axis, and the criterion response was any head turn in the direction of the stimulus. However, the presence of bidirectional head turns in newborns, and even in premature infants at 32 weeks of gestation (Muir, 1985), suggests that this crude localization response is not learned from visual feedback. The subsequent postnatal improvement in the accuracy of head turns (Muir, 1985; Muir, Clifton, and Clarkson, 1989) and their greater accuracy in the light (4 degrees) than in the dark (16 degrees) (Morrongiello and Rocca, 1987a) suggests

that visual information serves to improve localization accuracy. However, poor control of the motor system controlling head orientation could mask superior sensitivity to the sensory cues for auditory localization. Studies of infants’ ability to discriminate a change in the spatial location of a sound source (the minimum audible angle, or MAA) reveal a substantial postnatal improvement in the spatial resolution of the auditory system. Studies using both the OBP and conditioned head turning have shown that the MAA in the horizontal plane improves from nearly 30 degrees in 2-month-olds to 9 degrees in 11-month-olds (Ashmead et al., 1991; Morrongiello, 1988; Morrongiello, Fenwick, and Chance, 1990). Further improvements to adult levels (1–2 degrees) occur by 5 years of age (Litovsky, 1991). The MAA in the vertical plane, which is much greater in adults than in the horizontal plane because binaural cues are absent along the midline, is approximately the same as the horizontal MAA in 6-month-olds. However, like adults, the MAA in infants relies on the high-frequency spectral information that comes from the shape of the pinnae (Morrongiello and Rocca, 1987b; Morrongiello, 1987). Importantly, improvements in the horizontal MAA in 4- to 7-month-olds are not the result of improvements in detecting interaural timing differences (Ashmead et al., 1991), because the ability to resolve temporal cues is a factor of two better than the temporal cues that are present in infant MAAs. Rather it appears that the MAA improves because of a tighter sensorimotor mapping, perhaps because in early infancy, motor control of the head is so poor that the associated temporal cues cannot be learned with great reliability. Auditory localization in humans is susceptible to the effects of early deprivation, though detailed occlusion experiments like those conducted with barn owls cannot be performed with human infants. However, Morrongiello (1989) was able to test infants who had unilateral ear infections, both during the infection and after it had been successfully treated. As expected, the accuracy of auditory localization was systematically biased while the infant had a hearing loss in one ear. There was no long-term recalibration of auditory localization, presumably because these episodes of unilateral hearing loss were quite brief. Recalibration of Auditory Localization in Human Adults Although the studies of barn owls support a sensitive period for the calibration (and recalibration) of spatial localization using visual and auditory cues, there are no experimental studies to determine whether a similar sensitive period is present in human infants. However, two recent studies have addressed the question of auditory-visual recalibration in normal adults (Alais and Burr, 2004; Battaglia, Jacobs, and Aslin, 2003). Both these studies

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compared the ability of adults to localize a bimodal stimulus consisting of a visual object and a briefly presented auditory noise. Under normal conditions, the spatial location of a visual object and the sound it generates are coincident in space. However, when these two sensory components are spatially dissociated, subjects judge the combined stimulus to be located in the direction of its visual component—a phenomenon called visual capture. The accepted explanation of visual capture is based on the higher spatial resolution of the visual system as compared to the auditory system, which renders judgments of spatial location more reliable, since they are, on average, based primarily on visual information. Alais and Burr (2004) and Battaglia and associates (2003) asked whether this visual dominance was fixed or whether it was susceptible, even in adults, to recalibration. They could not enhance the spatial resolution of the auditory system, so they degraded the visual stimulus so that its spatial location was rendered less reliable. Then they presented the combined visual-auditory stimulus to adults in a cue-conflict situation—the visual stimulus and the auditory stimulus were not located in the same position in space. The question of interest was whether the degree of visual capture was reduced. Both studies reported a significant reduction in the magnitude of visual capture after less than an hour of experience with a visual stimulus whose location in space was made less reliable. Although some of the quantitative aspects of the data from these two studies differed, the important point in the present context is that the weighting of auditory and visual cues for object location in space is still plastic and can be recalibrated in adulthood, at least over short time periods. Cortical Plasticity in Nonhumans The foregoing review of auditory localization provided substantial evidence for plasticity in both infants and adults, even though there may well be a sensitive period for recalibration in infancy. However, auditory localization is largely mediated by midbrain and thalamic neural areas. Because the discrimination and identification of more complex sounds, such as speech, are mediated by cortical areas, it is important to examine their developmental properties and their ability to undergo reorganization. A key question in this regard is whether the mapping of frequency from the auditory periphery to the cortex is invariant during development. The basilar membrane (BM) and the outer hair cells undergo a normal developmental process of stiffening and elongation that results in a shift of the best frequency that is represented at a given locus along the BM. As a result, a pure tone of a given frequency will be represented at a different location along the BM as the animal matures. Hyson and Rudy (1987) showed that rats conditioned to a pure tone respond selectively to that tone immediately after training, but they

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respond selectively to a frequency-shifted tone several days after training. Thus the cortical representation of the tone is dependent not on its absolute frequency but on the location along the BM from which it originated. The shift in the frequency map between BM and cortex occurs very early in postnatal development and is unlikely to play an important role in humans because their auditory periphery is quite mature at birth. However, other studies have shown that the frequency map in auditory cortex is susceptible to the effects of altered inputs. Robertson and Irvine (1989) showed that the tonotopic map in the primary auditory cortex (A1) of adult guinea pigs undergoes reorganization in response to lesions to restricted areas of the cochlea. In particular, the area of cortex that represents the lesioned region of the cochlea was negatively affected. Immediately after the cochlea was lesioned, there was an increase in the minimum intensity needed to trigger firing in the cortical cells in that area. After several weeks, the area was found to have cells with normal thresholds that responded to frequencies adjacent to those represented in the lesioned area of the cochlea. This reorganization is similar to that found in the somatosensory cortex following amputation (Merzenich et al., 1984; Calford and Tweedale, 1988). Recanzone and associates (1993) investigated changes in the organization of the tonotopic map in primary auditory cortex of adult owl monkeys after extensive frequencydiscrimination training. Monkeys were trained on an auditory frequency discrimination task for several weeks and demonstrated an improvement in their performance with training. Compared to untrained controls and passively stimulated monkeys engaged in a tactile discrimination task, electrophysiological recordings in primary auditory cortex of trained monkeys revealed that the number of recording sites as well as the cortical area representing the frequencies used in training were larger than for frequencies not used for training. This increase in cortical area of representation was significantly correlated with behavioral improvement. However, it is unclear whether these changes in cortical organization reflect changes at the cortical level, the subcortical level, or both. Nevertheless, it seems that some aspects of the auditory system remain plastic into adulthood and that changes in behavior based on auditory experience may reflect changes at the neural level rather than simple response biases. Early acoustic environments play a major role in the organization of auditory cortex. Zhang and associates (2001) found in rat pups that repeated exposure to a pure tone led to a greater representation of frequencies near that tone, as well as a compensatory lack of sharpening in A1-neuron sensitivity at other frequencies. Exposure to a train of pulsed noise (Zhang, Bao, and Merzenich, 2002) yielded similarly broad tuning curves at all frequencies. Noise may also effectively delay the sensitive period for A1 sharpening. Chang

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and Merzenich (2003) exposed rat pups to continuous white noise in the time period during which the neurons would normally sharpen their tuning curves in response to relevant frequencies in the environment. When frequency-specific input followed noise exposure, sharpening occurred in adult rats as in naive younger rats. Thus the sensitive period can be extended by broadband early exposure. Another contributor to auditory plasticity is the presence of differential reward. Rutkowski and Weinberger (2005) showed that a conditioned tone stimulus (CS) paired with a water reward led to an expansion in representation of the CS tone within primary auditory cortex of adult rats. Control animals who received the tone but not paired with reward showed no increase in the A1 representation. Not only did the CS tone itself elicit changes in cortical mapping, but there was also an increase in representation for frequencies emitted by the reward delivery equipment. The measurable area of auditory cortex did not differ between trained rats and controls; only the distribution of representations differed. Similarly, Polley and associates (2006) found that the task-dependent properties of the stimulus determine the changes that occur in the cortical representation. In their study two groups of adult rats were exposed to identical stimuli and were trained to attend to either the intensity or the frequency of the sounds. The rats in the frequency condition experienced an expansion of neuronal representation for the frequency range of interest. Similarly, those in the intensity condition had enhanced representation for the particular intensities presented in training. Moreover, the degree of neural retuning was well predicted by each animal’s magnitude of perceptual learning performance. In summary, there is ample evidence from research with nonhumans that the auditory cortex has considerable plasticity, and that some plasticity is present even in adult cortex despite greater plasticity in infancy. We turn now to related findings from humans. Cortical Plasticity in Humans A number of recent studies using noninvasive neuroimaging have confirmed the presence of cortical plasticity in humans. These studies have approached the question of cortical plasticity by examining subjects who have experienced a variety of sensory deprivations, some beginning at birth and others acquired later in life, or by exposing normal adults to a specific auditory training regimen. Lessard and associates (1998) reported evidence of auditory compensation in humans similar to that found in binocularly deprived cats (Korte and Rauschecker, 1993; Rauschecker, 1995). They compared the performance of subjects with normal vision, residual peripheral vision, or total blindness in an auditory localization task. In the binaural condition, totally blind subjects were able to localize at least as well as normals,

and better than subjects with residual vision. In the monaural condition, the totally blind subjects fell into two different categories: those biased to judge a stimulus presented to the occluded ear as originating from the side of the unoccluded ear (a bias shared by normal subjects and subjects with residual vision) and those not. Biased blind subjects performed similar to normals, but showed increased variability for stimuli presented to the occluded ear. However, unlike sighted subjects and those with residual peripheral vision, biased blind subjects reported qualitative differences in sounds presented to the occluded ear as compared to those presented to the unoccluded ear. Unbiased blind subjects performed better with stimuli presented to the occluded ear than both normal subjects and subjects with residual peripheral vision. The fact that unbiased blind subjects outperformed sighted subjects in this condition, and that biased blind subjects reported differences in sound quality even though they did not localize to the correct side, suggests that people blind from birth are able to use monaural cues better than these other two groups in analyzing their auditory environment, perhaps because of neural changes similar to those Korte and Rauschecker (1993) found in binocularly deprived cats. Röder and associates (1999) combined behavioral and electrophysiological methods to investigate auditory spatial tuning in blind and sighted humans and found better auditory localization in blind subjects, but only in peripheral (lateral) auditory space. Normally sighted adults, after a period of recalibration to artificially altered external ears (pinnae), can also adjust their reliance on those spectral cues that are now most reliable for sound localization (Hofman, Van Riswick, and Van Opstal, 1998). Perhaps the most direct measure of auditory cortical plasticity in humans comes from the use of cochlear implants to restore hearing in the deaf. Although this topic is beyond the scope of the present chapter, and the literature on the outcomes of hearing loss are covered in chapter 26 of this volume by Mitchell, a number of key points about the impact of cochlear implants are clear. First, the success of cochlear implants is much higher in children and adults who have already acquired their native language and are then faced with a hearing loss. This result occurs because the implant introduces two types of changes in the mapping of external sound frequencies onto the hair cells that send their signals to the brain. These changes are caused by the inability of the multielectrode wire that is inserted into the cochlea to be positioned all the way to the tip (low-frequency end) of the basilar membrane. As a result, external sound frequencies below approximately 1000 Hz are unmapped, and all higher frequencies stimulate regions of the basilar membrane that would ordinarily be triggered by lower frequency sounds. As a result, the low-frequency sounds that carry crucial information for speech are missing, and the frequen-

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cies that the brain does receive are frequency shifted. Thus a postlingual listener fitted with a cochlear implant can attempt to match the frequency-shifted input to stored representations of lexical items. In contrast, the prelingual listener fitted with an implant has no remapping task, but must make sense of stimuli that are missing crucial pieces of lowfrequency speech information. Most adults and postlingual children are able, after weeks or months of listening experience, to adapt to the frequencyshifted sounds and to understand speech. But some implant users never adapt. The situation is made much more complicated for prelingual children because they have never acquired their native language and so have no lexical representations to which they can compare the distorted speech sounds. Although some prelingual deaf children learn to perceive speech, these children are relatively rare (see Pisoni, 2005). More relevant to the present discussion about cortical plasticity is how normal hearing adults adapt to speech that is frequency shifted in a manner that mimics what an implant user would experience. Dorman and associates (1997) reported considerable difficulty in adapting to frequency-shifted speech, and Fitzgerald and associates (2006) showed that the sudden introduction of the frequency shift results in a longer period of adaptation than a gradual introduction. This latter result suggests that cortical mechanisms for adaptation to frequency-shifted speech can better handle small and progressive frequency shifts (much like prism adaptation in the visual-motor domain). What remains unclear is which regions of cortex are involved in this adaptation. There could be a remapping in primary auditory cortex, where detailed maps have been revealed by fMRI in both adult humans (Talavage et al., 2004) and monkeys (Petkov et al., 2006), or to changes in higher-level auditory areas as revealed in a pitch-memory task after five days of training (Gaab, Gaser, and Schlaug, 2006). We will return to this question of levels in the section on adaptation to speech dialects. Taken together, these studies of blind and deaf adults, as well as simulated frequency shifts in normal adults, suggest that both auditory and visual cortex can adjust to altered sensory input either by utilizing regions of cortex that would normally process inputs from a sensory periphery that is now silent or by remapping the sensory or lexical representations. Similar examples have been reported for the activation of visual cortex during tactile discrimination tasks by blind subjects (Sadato et al., 1996) and for the filling-in of primary visual cortex by patients with a congenital absence of three of the four classes of retinal photoreceptors (Baseler et al., 2002). In summary, we have reviewed evidence of behaviorally relevant adult cortical plasticity both on a large scale in humans (recruitment for a new modality) and on a small

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scale in rats and primates (changes in frequency sensitivity in A1). It is not currently possible to study small-scale cortical plasticity at a neuroanatomical level in humans because of the invasive procedures required. However, we can study changes in human behavioral sensitivity to relevant stimuli in the environment. Changes in phonetic organization throughout the lifetime provide an excellent venue to study small-scale changes in sensitivity because (1) the stimuli are highly behaviorally relevant, (2) the variety of languages provides us with a natural way to contrast the role of the environment with the role of maturation, and (3) the processing of speech stimuli requires the listener to make finegrained distinctions, thereby allowing us to observe very small-scale changes.

Phonetic reorganization Universal Inventories (Human and Nonhuman) The voluminous literature on phonetic discrimination and identification in infants and adults is beyond the scope of this chapter, but it is covered by several excellent recent reviews (Jusczyk, 1997; Kuhl, 2004; Saffran, Werker, and Werner, 2006; Werker and Yeung, 2005), as well as chapter 8 by Friederici in this volume. We wish to make only two major points by way of introduction to the topic of phonetic reorganization. First, infants in all language environments, as well as several species of nonhuman animals exposed to English, show discontinuities in discriminability along many phonetic continua, including continua that are not used contrastively in their language input. This fact has been interpreted as evidence for an innate system of categorization—categorical perception (CP). However, none of these findings from infants include the requisite labeling data that would show a close correspondence between peaks in discriminability and labeling boundaries that defines CP. Moreover, when both discrimination and labeling data are available from nonhumans, they suggest that CP is not unique to humans, and therefore not unique to a linguistic analysis of the speech stimuli (because animals do not have a phonetic system). Second, an innate system of categorization could not match every native language and, therefore, must be adjusted to conform to the details of the native-language phonetic categories. This adjustment process requires exposure to the distributional properties of linguistic input carried by the words to which the infant is exposed. The earliest evidence of such adjustments is present by 6 months of age for vowel perception (Kuhl et al., 1992; Polka and Werker, 1994), followed by language-specific consonant perception at 10–12 months of age (Werker and Tees, 1984). Thus early phonetic reorganization is traditionally characterized as a shift from the language-universal sensitivities shared by all infants, and at least some animals, to language-specific sensitivities.

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Distributional Modifications We now turn to the question of what can account for the changes in auditory sensitivity that are observed in the first year of life and the language-specific sensitivities that are observed in adulthood. These changes have been described as a warping of the perceptual space by experience with a particular language environment (Kuhl, 1994; Iverson and Kuhl, 2000). The result is that listeners become more sensitive to particular dimensions of the speech stream (acoustic cues) and less sensitive to others. For example, a comparison of the distributions of acoustic cues from many tokens (instances of words) in English and Japanese reveals that in English, the F3 dimension is relevant for identifying the approximates /l/ and /r/, while in Japanese the F2 dimension is the more relevant (Lotto, Sato, and Diehl, 2004). Iverson and Kuhl (1996) found that speakers of English, German, and Japanese showed different sensitivities in an F2–F3 space for distinguishing English /r/ and /l/. The Japanese speakers exhibited expanded sensitivity in the F2 dimension and reduced sensitivity in the F3 dimension, while English and German speakers exhibited the opposite pattern of perceptual warping. There is still debate about whether the perceptual warping happens at the level of auditory processing or at higher levels that influence behavior but not perception. Evidence that adults can show sensitivity to nonnative contrasts under certain testing conditions that reduce memory load (e.g., Tees and Werker, 1984) suggests it is not a matter of auditory processing. There may be several mechanisms that could cause perceptual warping through exposure. In general they should direct the attention of the listener toward dimensions that are informative and away from dimensions that are not. This allows the listener to optimize his or her use of the acoustic information in identifying the target utterance of the speaker. One way of formalizing this optimization is to apply Bayes’ theorem: listeners should base their judgments on dimensions (cues) to the extent that they are reliable, and this reliability will depend on the variability of those cues in the environment (for a general overview of this Bayesian approach, see Ernst and Bulthoff, 2004). It has been shown in the visual-motor domain that the relative reliabilities of different cues influence how heavily they are weighted by adults (Atkins, Fiser, and Jacobs, 2001; Ernst and Banks, 2002). A similar differential weighting of cues has been shown in the perception of novel nonspeech sounds (Lotto and Holt, 2006; see Smits, Sereno, and Jongman, 2006, for a different categorization theory). Such an account predicts that in learning a native language, the task of the listener is to track the variability of multiple dimensions and develop a cue-weighting strategy that relies most heavily on the dimensions that are the least noisy. Since cue-weighting strategies are language specific, they must be learned. For

example, vowel length plays a greater role in the /i/ versus /I/ distinction in Scottish English than it does in British English (Escudera and Boersma, 2003), and the F3 dimension is more relevant for English approximates such as /r/ and /l/ than for Japanese (Lotto, Sato, and Diehl, 2004). When a language-specific cue-weighting strategy is then applied to speech from a new language that does not utilize the same dimensions, as in the case of native Japanese speakers listening to English /l/ and /r/, listeners may have great difficulty in making accurate judgments. Their cue-weighting strategy has led them to put the most weight on a dimension that is not informative (F2 in this case) and to pay relatively less attention to the dimension that is informative (F3 in this case). Distributional Sensitivity The foregoing cue-weighting account depends on sensitivity to the distributional frequency of tokens, a source of information that has long been known to be informative. Lisker and Abramson (1964) noted that the major cue distinguishing stop consonants along the voicing dimension was voice onset time (VOT). They found that the frequency distributions of VOT across any given language tended to group into 1–3 roughly normal distributions. It is also known that there is a close relationship between the distributional frequency of tokens and phonetic sensitivities. For any given language, the locations along the VOT continuum where very few tokens occur also correspond to category boundaries in that language. It is precisely in these regions of low token probability that we find increased discrimination sensitivity and labeling boundaries (Lisker and Abramson, 1964). The sensitivity of infants to distributional frequency was presumed to account for how innate phonetic categories were adjusted to a specific native language. The empirical demonstration by Saffran and associates (1996) that 8month-olds can perform distributional learning on the sequential properties of speech streams set the stage for a direct test of distributional learning of phonetic categories. Maye and associates (2002) familiarized 6- and 8-month-olds to a range of values along a /d/–/t/ continuum that were either distributed unimodally: with no region of low token probability, or bimodally: with a region of low token probability in the middle of the range of values. After exposing infants to passive listening to these distributions, the researchers found that infants who had been exposed to the bimodal distribution discriminated the phonetic contrast, whereas infants exposed to the unimodal distribution did not. Since all infants heard the same tokens during training, it can only be the frequency of occurrence of particular tokens that formed a distribution along the phonetic continuum that led one group of infants to show evidence of discrimination. Maye and associates (2008) extended these findings to a more difficult phonetic contrast. These results provide

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empirical evidence for the theory of distributional learning suggested by Lisker and Abramson (1964). Furthermore, a recent study by Werker and associates (2007) found that Japanese- and English-speaking mothers addressing their 12-month-old infants produced just the kind of languagespecific distributional patterns used in the studies by Maye and associates. It is important to emphasize a key assumption of a distributional theory of phonetic category learning or adjustment: Infants must be sensitive to small (within-category) acoustic differences. If they were only sensitive to the category labels themselves, as presumed by a strong theory of CP, then there would be no distributional information available to the learning mechanism except for the frequency of each category. While this assumption of a distributional theory seems to fly in the face of empirical evidence that infants’ discriminative capacities for withincategory differences are poor, much as they are in adults, recent evidence from both adults and infants provides strong support for within-category sensitivity. McMurray and associates (2002) used an eye-tracking measure to confirm earlier research using rating scales and reaction times and showed that adults are sensitive to within-category differences along a /b/–/p/ continuum. Moreover, the likelihood of falsely labeling a given token along the continuum was monotonically related to the distance from the category boundary; that is, sensitivity was gradient within the phonetic category. Thus adults are sensitive to where along the phonetic dimension a given token resides. McMurray and Aslin (2005) found similar evidence for gradiency in the listening preferences of 8-month-olds exposed to words whose initial /b/ or /p/ consonant was in the center of the phonetic category (i.e., prototypical) versus near the category boundary. Further evidence of sensitivity to within-category differences comes from a set of ERP studies by Rivera-Gaxiola and colleagues showing early or preattentive sensitivity (mismatch negativity, late positive deflections, or N1/P2 components) to within-category differences as well as nonnative contrasts in adults (Rivera-Gaxiola, Johnson et al., 2000; Rivera-Gaxiola, Csibra et al., 2000) and infants at 7 and 11 months (Rivera-Gaxiola et al., 2005) using an oddball paradigm. The result with 11-month-old infants is particularly surprising because this is an age when behavioral sensitivity to nonnative contrasts is reduced and because previous work (Cheour et al., 1998) had not found evidence of nonnative contrast detection. These empirical results on gradient sensitivity provide the requisite evidence that a distributional theory of phonetic category learning and adjustment is plausible. These properties of gradient sensitivity and the role of distributional input in forming and adjusting phonetic categories have recently been captured in computational models by McMurray and associates (in press) and Vallabha and colleagues (2007).

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Despite the evidence that infants are sensitive to the distributional frequency of tokens, it has not been shown experimentally that infants use distributional frequency to learn to weight a particular acoustic dimension more heavily than another in order to make a phonetic judgment. Little is known about how infants combine multiple acoustic cues or whether they show the same patterns of differential cue-weightings as adults. More is known about young children. Like adults, young children often weight some acoustic cues more heavily than others, but their cueweighting strategies are not always the same as adults and will change throughout childhood (Krause, 1982; Nittrouer and Studdert-Kennedy, 1987; Mayo and Turk, 2004; Morrongiello et al., 1984; Wadrip-Fruin and Peach, 1984). The cause of this developmental shift in cue weighting is unknown. One theory proposes that children process speech more globally and thus place more weight on cues such as formant transitions that occur over larger segments of the speech stream and less weight on cues such as release bursts that could be considered more local phenomena (Nittrouer et al., 2000). However, a study by Mayo and Turk (2004) found that children’s strategies (like adults) differ according to the particular phonetic context of the segment (e.g., /ta/–/da/ versus /ti/–/di/) and do not always favor formant transitions over other cues. Instead they may start out placing more weight than adults on strong (i.e., intrinsically salient) cues and less weight on weak cues, achieving adultlike strategies over time. Further investigation is necessary to determine whether distributional frequencies may be part of the developmental process. Distributional Sensitivity in a Second Language The dramatic changes seen in early infancy are by far the strongest cases of auditory reorganization by exposure to a specific language environment. Moreover, infants raised in a bilingual environment are able to develop and maintain two different underlying phonological systems (Bosch and Sebastian-Galles, 2003). In contrast, research on secondlanguage (L2) acquisition in adults shows that although some modification of sensitivities is possible, it fails to result in native-language proficiency and is very difficult to achieve. It is a well-established observation in second-language learning that some contrasts are hard to learn by adults whereas others are relatively easy (see Jusczyk, 1997, for a review) and that the phonological similarity of L1 and L2 is a major predictor of success on L2. A well-known example is the difficulty that speakers of Japanese have in learning to distinguish between English /r/ and /l/. Speakers of German (which does not have a sound like English /r/) find this contrast relatively easy. Similarly, speakers of English have little difficulty distinguishing between different Zulu clicks (Best et al., 1988) but find it relatively difficult to do the same task with Hindi dental and alveolar stops (Werker et al., 1981).

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A variety of models have been proposed to account for this complex pattern of results (Best et al., 1988; Flege, Schirru, and MacKay, 2003; Kuhl, 2004; Werker and Curtin, 2005), but all these models propose that the phonetic categories of the native language are established early in development and constrain the ability of older children and adults to acquire native phonetic proficiency in L2. Crucially, it is thought to be the particular acoustic dimensions involved in the native and nonnative contrasts that determine the amount of L1 interference on L2 (Iverson and Kuhl, 1995). Although it is difficult to distinguish some foreign contrasts, it is possible to improve on this ability through training. Tees and Werker (1984) found that English listeners could learn to discriminate the difficult Hindi dental alveolar stop contrast with training. Similarly, several studies with native Japanese speakers learning English /l/–/r/, show that intensive training can improve discrimination (Strange and Dittmann, 1984), or labeling performance (Lively, Logan, and Pisoni, 1993), but learning only transfers to acoustically different stimuli when training involves multiple tokens of many speakers (Logan, Lively, and Pisoni, 1991). This training regime (called high variability phonetic training or HVPT) is thought to work because subjects get a range of natural variability that allows them to determine the relevant dimensions. McCandliss and associates (2002) showed that by enhancing an acoustic cue to the /r/–/l/ distinction in a training study of native speakers of Japanese, and then gradually fading out this enhanced cue (coined perceptual fading), there was substantial enhancement of discrimination performance, even when subjects were given no feedback during training. Thus enhanced distributional cues may serve a useful role in drawing attention to otherwise difficult-to-discriminate speech contrasts. A recent study directly comparing these two training regimes found equal improvements in labeling performance for both HVPT and perceptual fading groups (Iverson, Hazan, and Bannister, 2005). Thus it is unclear whether high variability in the irrelevant dimension or increased attention to the meaningful variability in the relevant dimension is the more important factor in learning new contrasts. These studies do not explicitly test whether subjects are learning to attend more to one dimension over another. There is evidence, however, that subjects can learn to change their attentional weights. A series of studies by Escudera and colleagues has found that when learning a second language, listeners will adopt a range of cue-weighting strategies at first and may eventually arrive at a strategy that is more appropriate for the linguistic context (Brasileiro and Escudero, 2005; Escudero and Boersma, 2004). Further evidence comes from cue-weight-training studies. Francis and associates (2000) trained listeners to use either the burst cue or the formant transition cue when distinguishing between /ba/, /da/, and /ga/ or /bi/, /di/, and /gi/ syllables. Training

stimuli were cross-spliced so that one of the cues signaled one place of articulation and the other cue signaled a different place of articulation. During training, listeners were given feedback on the “correct” place of articulation. After training, they found an increase in responses consistent with the trained cue across all stimuli. A similar study replicated this finding using an /i/–/I/ vowel contrast and real words and found that individual variability in training success was correlated with sensitivity to the weaker dimension before training (Clayards, Tanenhaus, and Aslin, 2006). These studies demonstrate that listeners can change their cue weightings with training, but they manipulated the relationship between cues and labels, rather than the distribution of tokens. Maye and Gerken (2000) did study distributional learning of phonetic categories in adults and found that exposure to a bimodal distribution allowed for discrimination while exposure to a unimodal distribution did not, just as in infants. Similarly, Clarke and Luce (2005) found that exposure to a shifted distribution of VOT values in sentence context changed listeners’ categorization boundaries. Although no study has directly tested the role of distributional learning in adjusting cue-weighting strategies (rather than category boundaries), Escudera and Boersma (2003) have shown that the distribution of cues available to listeners in the dialect-learning study (Escudera and Boersma, 2004) could predict the optimal cue-weighting strategies that native listeners use. In summary, early universal auditory sensitivities are altered by language-specific phonetic reorganization in infancy. This reorganization is characterized by a warping of the perceptual space to produce increased sensitivity to some dimensions and reduced sensitivity to others. The likely cause of this cue-reweighting process is exposure to the distributional frequencies of tokens along those dimensions in the ambient language. In this way, children gradually develop an adultlike cue-weighting strategy tailored to their linguistic environment (though developing auditory capabilities may also play a role in the developmental progression). In adulthood, when faced with a new language for which the strategy is unsuitable, there is often initial difficulty in distinguishing contrasts. With enough exposure to the new distributions, however, new cue-weighting strategies can develop that at least partially accommodate the new language.

Reference and learning the lexicon Given that infants by the end of the first year have established their native-language phonetic categories, how do they deploy those categories in the service of learning words? It may seem straightforward to think of the phonetic categories as an acoustic alphabet whose elements are simply strung together into a unique sequence (e.g.,

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the auditory word form ball) and then mapped onto a real-world referent (a spherical object). Two problems confront the infant listener in solving this seemingly simple task. First, speech discrimination has been assessed in isolated word (or syllable) contexts rather than the fluent multiword speech characteristic of natural linguistic input. Thus there are working memory constraints that influence whether the infant’s discriminative capacities are utilized effectively in recognizing maternal utterances. Second, there is substantial acoustic variability in how talkers (mothers and other adults) produce speech, including gender, speaking rate, prosody, and subtle stress patterns used for emphasis or to convey emotional states. Do infants readily solve these two problems? Evidence from Jusczyk and Aslin (1995) suggests that the first problem is solved by 8 months of age. Infants can extract auditory word forms from fluent speech and recognize these words when presented in isolation. Moreover, infants do not falsely recognize subtle acoustic variants of the words, thereby showing robust evidence for specificity in their auditory memory of those words. However, the second problem has proven more challenging for infants. Stager and Werker (1997) showed that 14-month-olds who easily discriminate a phonetic distinction in a nonreferential context (i.e., when only listening to the phonetic contrast) fail to map these same word forms onto two different visual objects. Initially it was thought that the referential context itself caused infants to relax their criterion for what counts as a meaningful phonetic difference. But more recent evidence suggests that 14month-olds are, indeed, sensitive to the relevant phonetic information, but they fail to associate it with the visual referents. Swingley and Aslin (2000, 2002) showed in an eyetracking paradigm that 14–20-month-olds detect mispronunciations of known words when they are presented with visual referents of those words. And Fennell (2006) showed that this word-referent mapping problem can be alleviated by rendering the visual objects familiar. That is, evidence now suggests that the mapping problem is most acute when both the word forms and the visual referents are unfamiliar. By making the word forms and/or the visual referents familiar, even in isolation, the associative mechanism that links sounds to objects can rapidly perform the mapping task. Thus it is not reference per se that is the bottleneck, but rather the facility with which associations can be formed between sounds and objects—unfamiliar sounds and objects are not easily encoded, and resources devoted to this encoding are not available to form associative linkages. Swingley and Aslin (2007) added support to this view of word learning by showing that new words for novel objects are only acquired rapidly if the words come from sparse regions of the infant’s lexicon. That is, a new word that could

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be confused with a familiar word inhibits the lexical learning process. And Graf Estes and associates (2007) have shown that novel auditory word forms that have recently been segmented from fluent speech using distributional information can be mapped onto referents in a word-learning task. Thus there is growing evidence that infants have all the acoustic/ phonetic skills needed to map word forms onto referents, provided that the word forms are familiar to auditory memory and not easily confused with similar words already in the lexicon. Dialects and Multiple Representations One interpretation of the foregoing review of word learning is that candidate auditory word forms map transparently onto referents, provided that the word forms and the referents are both familiar perceptual objects. However, such a model presumes that word forms have little acoustic variability, an assumption that we know to be incorrect. Adult listeners are able to cope with widely varying acoustic information and normalize the signal to map it onto their phonological representations in the lexicon in order to access meaning. These nonphonemic acoustic parameters, although not used for decoding the speech signal itself, can provide so-called indexical cues to the speaker’s gender, identity, and accent. How does the infant determine which acoustic cues are phonemically relevant and which cues are indexical? Moreover, do indexical cues interfere with the extraction of phonemic cues? The indexical cue that has received the most attention in both adult and infant speech perception is talker variability. The voice quality of a specific talker is defined by subtle variations in pitch, harmonic structure, rhythm, and vowel color. These are the kinds of indexical cues that play little or no role in decoding the phonemes from the speech signal. However, Allen and Miller (2004) showed that listeners can learn a particular talker’s distribution of voice onset times and use that to identify the talker, provided that there is sufficient experience with a talker and a task that demands tracking VOT as a cue. This ability transferred to novel words they had not heard the speaker produce. McLennan and Luce (2005) discovered that indexical information (talker identity and speaking rate) was used late in processing, after early stages of phonetic processing. Creel and associates (2008) found that talker identity was processed earlier, but only for highly overlearned words and not for newly learned words. And Houston and Jusczyk (2000, 2003) found that infants have long-term memory for the indexical properties of word lists and are negatively affected by talker variability in a word-segmentation task. Thus indexical factors play a role in speech processing both early in development and in adulthood. Presumably, as in the case of cue weighting in phoneme identification, indexical cues are up- or down-weighted depending on their reli-

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ability in providing information about a specific property of speech. Talker-specific effects in speech perception are, by definition, linked to particular vocal tracts. A more pervasive indexical variable is the regional dialect to which the infant (or adult) is exposed (Labov, 1998). The ability of one American English listener to understand another from a different dialect implies that a listener can impose a shift, or rotation, of their perceptual vowel space to accommodate the speaker’s productions to arrive at the intended meaning. Maye and associates (in press) exposed listeners to a 20minute passage of an artificial, but plausible, American English dialect wherein front vowels were lowered (e.g., “witch” becomes “wetch”). After exposure, subjects were more likely to classify items within the dialect as words than they had before experiencing the dialect. This was true of items that they had heard in the exposure phase as well as other dialect items that they had not heard. Importantly, subjects learned only the specific direction and magnitude of the vowel shift rather than simply accepting any unusual vowel. Bardhan and associates (2006) found the same results when the spoken items were heard in isolation but paired with visual images, and this paradigm may be applicable to infants and children in the future. In related work, Clarke and Garrett (2004) studied adult listeners’ adaptation to foreign-accented speech. Native speakers of Chinese and Spanish spoke English sentences to which English listeners had to match the sentence-final word. Within a minute of exposure, listeners were as accurate at identifying the target word as those who listened to unaccented speech. These studies suggest that listeners are remarkably efficient in adapting to unfamiliar speech. Unfortunately, to date there are no studies of dialect adaptation in infants or young children, and the mechanism by which adaptation occurs is unknown. Sine-Wave and Noise-Band Speech An extreme form of dialect adaptation involves natural speech that has been degraded in various ways. One technique involves eliminating all the fine spectral detail in speech and replacing it with a parallel series of amplitude-modulated noise bands (Shannon et al., 1995). Adults are able to perform at nearly asymptotic levels in an identification task with these stimuli, provided that there are at least four noise bands. Another technique involves retaining the changing spectral information from the formants of natural speech but replacing these formants with a pure tone that follows the frequency contour of the center frequency of each formant (Remez et al., 1981). Again, adults are able to reliably identify these so-called sine-wave speech stimuli at high levels of accuracy, despite the fact that nearly all the spectral cues of speech have been removed.

An important fact about adults’ perception of sine-wave or noise-band speech is that they are being asked to make judgments of lexical identity from stimuli that are not good exemplars of natural speech. But as in the case of postlingually deafened adults who are fitted with a cochlear implant, these adults have a robust representation of the phonological system of their native language and a lexicon to which nonprototypical speech can be compared. Although adults are also able to recognize sine-wave speech versions of nonwords (Remez, Fellowes, and Nagel, 2007), this ability surely exists, in part, because of their overlearned phonological system. Importantly, many adults, upon first hearing an exemplar of sine-wave speech, perceive it as a set of varying tones and clicks that lack any language-like or phonetic quality. However, when the same exemplar is repeated or when subjects are given instructions that the stimuli are degraded speech, most listeners will suddenly hear them as speech. Again, this repetition effect is undoubtedly due, in part, to the top-down knowledge that adults bring to any speech perception task. In fact, Davis and associates (2005) have shown for noise-band speech that improvement in identification is driven by activation of lexical candidates. These studies of adults with a robust phonological and lexical system raise the question of what qualifies as phonetic to an infant, as well as the question of whether phonetic qualities differ fundamentally from indexical qualities. Vouloumanos and Werker (2004) found that infants as young as 2 months of age preferred to listen to natural speech over sine-wave speech. This finding suggests an early bias toward natural speech that could be based on an innate species-specific preference, a learned preference from early exposure to maternal speech, or a simple preference for broadband stimuli over narrowband stimuli.

Brain correlates of auditory plasticity and learning One of the most seductive, yet vexing, questions in cognitive neuroscience is what brain regions are responsible for language processing. This question will not be answered here, in part because no definitive studies have yet been published and in part because the topic of brain and language is so multifaceted. For example, in mature users of a natural language it is impossible to isolate only one of the many levels of language processing (from phonetics to pragmatics) using either spoken or written materials because even the simplest stimulus (phoneme or letter) is associated with each of these levels in natural language materials (Price, Thierry, and Griffiths, 2005; McCandliss, Cohen, and Dehaene, 2003). Thus activation of a particular brain area, at least in adults, is undoubtedly the outcome of a cascade of processing steps that are triggered by a simple stimulus. Four paradigms have been used in an attempt to gain a foothold on this seemingly

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intractable problem. The first is to direct the subject’s attention to a particular level of processing. The second is to elicit repetition effects to passive exposure. The third is to examine brain responses in bilinguals to L1 and L2 speech or in monolinguals to native and nonnative speech. And the fourth is to study infants who have not yet attained higher levels of language processing. Each of these paradigms has advantages and disadvantages. Directing attention to a particular level of processing is fraught with difficulties because it depends on the ability of the subject to control attention and exclude other levels of processing. Moreover, the typical paradigm of using two different classes of stimuli (e.g., speech and music) can never eliminate the possibility that differential brain responses are due to the physical differences between the classes. A more fruitful type of stimulus for controlling the level of processing is one that is perceptually bistable. This paradigm has been used to great effect in the visual modality by eliciting binocular rivalry—a spontaneous fluctuation of two perceptual states—and asking the subject to report changes from one state to the other. Brain responses correlated with that perceptual shift are taken as evidence of separate (in fact, incompatible) attentional states (see Tong, Meng, and Blake, 2006). The sine-wave speech stimuli discussed earlier serve a similar role in the auditory domain. These stimuli do not oscillate between two perceptual states, as in the case of binocular rivalry, but for most listeners they begin as nonspeech and transition (typically rapidly) to intelligible but nonprototypical speech. Vouloumanos and associates (2001) showed that adults have greater activation in several portions of the left temporal lobe and right frontal lobe in response to natural speech than to sine-wave speech, with no areas responding more to sine-wave speech. This finding suggests that these stimuli are both being processed by the same brain regions. Unfortunately, none of the subjects interpreted the sine-wave tokens phonetically. Dehaene-Lambertz and associates (2005) found that the posterior superior temporal sulcus (pSTS) was indeed sensitive to mode of perception (speech versus nonspeech) while listening to sine-wave speech and that the supramarginal gyrus responded to phonemic changes. They suggest that while phonemic and nonphonemic auditory stimuli may be processed simultaneously, the phonemic processing system may inhibit purely acoustic processing. This inhibition presumably can happen only after subjects are aware of the speechlike quality of the stimuli. Möttönen and associates (2004) not only found a greater BOLD response to sine-wave speech in left pSTS in informed subjects (i.e., in speech mode), but also found that these subjects were able to integrate the phonetic information with a visual stimulus, demonstrating the McGurk effect. Liebenthal and associates (2003) found categorical percep-

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tion for phonetic continua created with sine-wave speech, but only when subjects perceived them phonetically, with left medial and anterior STS activation to this phonetic difference in the speech mode. Finally, Benson and associates (2006) contrasted sine-wave speech with sine-wave nonspeech stimuli, and Uppenkamp and associates (2006) contrasted normal vowels with spectrally matched nonvowels. Again, left STS was differentially activated by the sine-wave speech and normal vowels. Thus the evidence from adults points fairly clearly to the left STS as the locus of phonetic processing, although many other brain regions are activated above baseline to speech and nonspeech stimuli. Repetition effects (decrements and recovery to novelty) are the hallmark of the infant habituation paradigm. Under the assumption of neural adaptation in brain regions involved in stimulus processing, repetition effects have been adapted for use in fMRI (Grill-Spector, Henson, and Martin, 2006). Two recent studies have used this paradigm to study speech perception (Joanisse, Zevin, and McCandliss, 2007; Zevin and McCandliss, 2005). Both these studies used a short-term habituation design in which subjects heard either four identical speech tokens (AAAA) or three identical tokens followed by a novel fourth token (BBBA). This design was based on the mismatch negativity response gathered from scalp electrodes (Dehaene-Lambertz, 1997; Naatanen et al., 1997). Zevin and McCandliss (2005) reported fMRI activations in a broad array of temporal cortex to these four-stimulus epochs, and a left hemisphere bias for the novel BBBA epochs. Novelty responses were also observed in the right frontal cortex and hippocampus, with the former most likely a general novelty response and not a speechspecific response. Joanisse and associates (2007) extended this paradigm to study between-category and withincategory phonetic contrasts. Greater activation was elicited by between- than by within-category contrasts in the BBBA epochs, and these activations were located in the left temporal cortex. This paradigm has great potential for use with infants and young children because it does not require the listener to perform a task and appears to tap basic discriminative capacities. Brain responses in bilinguals have been used to determine whether language fluency is predictive of differential patterns of activation. Kim and associates (1997) gathered fMRI data from both early and late bilinguals, with fluency in L2 greater in the early group. Activations to L1 and L2 speech did not differ in temporal cortex between the two groups, with both languages activating common areas. But activations in frontal cortex differed, with early bilinguals showing an overlapping region of activation to L1 and L2, but late bilinguals showing two separate but partially overlapping regions of activation. The Kim and associates report suggested a sensitive period for the formation

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of subcomponents of frontal brain regions for processing speech in different languages. However, Pallier and associates (2003) found no such segregation of L1 and L2 in adults who had acquired Korean as L1 and then were adopted into French homes and acquired L2 without the maintenance of L1. It is not clear if traces of L1 were present but below detection thresholds (as in the case of barn owl sound localization), or whether L2 in the absence of maintaining L1 simply used the same area of frontal cortex. Two other studies have reported that native speech contrasts elicit greater fMRI activations than nonnative contrasts (Callan et al., 2004; Jacquemot et al., 2003). Thus, when one phonological system dominates the other, brain activations may swamp the smaller nonnative (or L2) activations, rendering them invisible as a spatially separate region of activation. Finally, there are structural aspects of the brain, both in terms of white matter density and volumetric properties, that predict the facility with which adults improve their discrimination of nonnative speech contrasts during training (Golestani et al., 2007) and infants progress through the vocabulary spurt (Pujol et al., 2006). These and other structural measures, such as diffusion tensor imaging that estimates the organizational complexity of white matter tracts (see Dubois et al., 2006, and chapter 17 in this volume by Wozniak, Mueller, and Lim), offer the prospect of accounting for individual differences in language proficiency. Finally, ERP, fMRI, and near-infrared spectroscopy (NIRS) responses have been measured from normal infants (see recent review by Dehaene-Lambertz, Hertz-Pannier, and Dubois, 2006, and chapter 8 by Friederici in this volume). Two fMRI studies (Dehaene-Lambertz et al., 2002; Dehaene-Lambertz, Hertz-Pannier et al., 2006) are particularly impressive because they obtained data from normal 3-month-old infants in a 1.5T scanner, which entails solving problems of scanner noise (and hearing safety), movement artifacts, and data analysis in uncooperative subjects. Dehaene-Lambertz and associates (2002) reported greater activation to forward then to backward speech in left temporal areas and right frontal areas. Dehaene-Lambertz, Hertz-Pannier, and associates (2006) presented short sentences and recorded activations that unfolded over time in both an anterior and a posterior direction, emanating from primary auditory cortex. These results suggest that different levels of speech and language processing can be extracted from the same recording session in young infants and that these patterns of activation are similar to those observed in adults (Dehaene-Lambertz, Dehaene et al., 2006). However, many more studies will be required to map out the functional activity of brain areas that respond to language materials and to answer fundamental questions about how these areas are interconnected and change with experience.

Summary and conclusions Our coverage of auditory development and early language learning has admittedly been selective. We have attempted to outline the major events that enable the auditory system to access sounds, beginning prenatally, and select those acoustic components that are useful for solving a particular task. This process of selecting a subset of the available acoustic cues implies either the operation of innate constraints on learning or the ability to rapidly adjust the weights attached to these cues based on their functional utility. We outlined a Bayesian model of cue weighting in which the relative importance of a given cue is proportional to its reliability. Empirical evidence supports this model of cue weighting in the tasks of sound localization and speech perception. Furthermore, we speculate that as infants acquire the referential properties of their native language, they map sounds onto meanings using a similar Bayesian cue-weighting scheme. We also reviewed a number of neural mechanisms that are available to support auditory development. These mechanisms have been studied invasively in animals, but of course studies of language development must be limited to humans. Recent neuroimaging techniques have provided insights about where in the brain activity is present as speech stimuli are being processed. The challenge for the future is to understand how these neural systems develop and become elaborated as the phonological system matures in infancy and the lexicon expands rapidly in childhood. Finally, the mechanisms of adaptive plasticity that enable the lexicon to continue to expand in adulthood but limit nativelike acquisition of a second phonological system will prove crucial in addressing the role of experience in auditory learning. acknowledgments

Preparation of this chapter was made possible, in part, by grants from NIH (HD-37082 and DC05071) and the McDonnell and Packard Foundations to RNA, and by NIH training grant (DC-000035) to the University of Rochester that supported NB. REFERENCES

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Brain Correlates of Language Processing during the First Years of Life ANGELA D. FRIEDERICI

Introduction Children first experience language by calculating phonological and positional regularities of items in speech input. Keen to extract rules from auditory input, the infant around the age of 7 to 8 months demonstrates the ability to abstract beyond the mere probability of occurrence of particular items in a sequence (for a review see Gomez and Gerken, 2000). With this ability, the foundation for acquisition of syntactic rules in the mother tongue is already partly laid. It has been argued, however, that the acquisition of syntax can only be successful if statistical learning is constrained by innate and domain-specific principles of linguistic structures. Parallel to the task of syntactic rule extraction, infants must learn that certain segments of the speech stream carry meaning that refers to objects and actions in the world around them. Since the relations between words and the objects and actions they refer to are arbitrary, this is not an easy task to accomplish. Infants are helped in this task by a number of cues ranging from extralinguistic ones such as pointing and gaze to innerlinguistic cues provided by functional categories such as function words and inflectional morphology. For example, studies have demonstrated that the learning of object names is eased by social cues (e.g., Baldwin and Moses, 2001; Tomasello, 2003). Also, functional categories help in determining whether a particular name belongs to an object (ball) or an action (rolling). (For example, when preceded by a determiner, the likelihood is high that the word refers to an object, “a ball,” but when preceded by an auxiliary the likelihood is high that the word refers to an action, “is rolling.”) In addition, languages differ in the extent to which the different lexical categories are marked by morphology or position in the sentence. (For example, some languages, such as English, have a strict word order, while others, such as German, have relatively free word order but more morphological

marking.) It has been shown that infants are sensitive to functional categories starting at 6 months of age (e.g., Höhle and Weissenborn, 2003; Höhle et al., 2004). I will not review the behavioral studies supporting the different theories (e.g., Wanner and Gleitman, 1982; Pinker, 1984; Jusczyk, 2000; Gleitman and Gleitman, 2000; Werker and Yeung, 2005), but instead will focus on the neurocognitive literature relevant to the topic of how the infant extracts his or her target language from auditory input. Therefore, the goal of the present chapter is to provide information concerning the brain basis underlying early semantic and syntactic processes. One of the important questions in this context is whether the brain mechanisms supporting prosodic, lexical, semantic, and syntactic processes between 4 months and 4 years of life are similar precursors to those of adults, or whether they are qualitatively different. Qualitative differences would indicate support for the discontinuity hypothesis claiming that processes underlying language performance differ between children and adults (Felix, 1994). In contrast, quantitative differences such as changes in latency and duration of processes would provide support for the continuity hypothesis that assumes that processes are principally the same during development and just change quantitatively with development (Gleitman and Wanner, 1982; Pinker, 1984; Weissenborn et al., 1992). Before reviewing the available neurophysiological literature that is relevant to these theories, the different methods used to investigate the brain bases of language processing in early development will be briefly reviewed.

Measuring brain activity in early development The goal of measuring the brain’s reaction to particular input can be achieved by different tools. The most frequently used measures in infants and young children are eventrelated brain potentials (ERPs) as registered with electro-

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encephalography (EEG). Event-related potentials reflect the brain’s activity in response to a particular stimulus event with a high temporal resolution. Each time-locked average waveform typically shows several positive or negative peaks at particular latencies after stimulus onset, and each peak, or component, has a characteristic scalp distribution. The polarity (negative/positive inflection of the waveform relative to baseline), as well as the latency and scalp distribution of different components, allows us to dissociate cognitive processes associated with them. Changes within the dimensions of the ERP can indicate changes in the cognitive mechanisms they reflect. For example, changes can be interpreted to reflect a slowing down of a particular cognitive process (reflected in the latency), a reduction in the processing demands or efficiency (amplitude) of a positivity or negativity, or a change in the cortical tissue supporting a particular process (topography). A second method is near-infrared spectroscopy (NIRS), also called optical imaging (OI) (Villringer and Chance, 1997). It allows us to examine the cortical hemodynamic response in infants. This method relies on the spectroscopic determination of changes in hemoglobin concentrations in the cerebral cortex resulting from increased regional cerebral blood flow. It assesses the spectroscopic characteristics of the cerebral cortex through the scalp and skull. Changes in light attenuation at different wavelengths greatly depend on the concentration changes in oxygenated and deoxygenated hemoglobin ([oxy-Hb] and [deoxy-Hb]) in the cerebral cortex. This method’s temporal resolution is low as it reflects hemodynamic responses, which are relatively slow. Its spatial resolution depends upon the number of channels measured (Obrig and Villringer, 2003; Okamoto et al., 2004; Schroeter et al., 2004). Another method used to measure the metabolic demand due to neural signaling is functional magnetic resonance imaging (fMRI). The resulting changes in oxygenated hemoglobin, the blood-oxygen-level-dependent (BOLD) contrast is measured in fMRI. This method’s temporal resolution is similar to OT, but its spatial resolution is higher than that of OT. Until now, this measurement has been applied to infants only when they are asleep in the scanner. Thus no measurement tool has really been available for the study of infants and children that combines optimal temporal and spatial resolution as well as feasibility. Results from the respective measures, however, add up to provide insight into the brain bases of language development in its early stages.

Processing sentential prosody Sentential prosody can be defined as the overall intonational pattern of a sentence including the fall and rise of the fundamental frequency in the acoustic input, but also including linguistically relevant pauses inserted in the speech input

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that mark intonational phrases. An infant’s first exposure to language is based on the processing of phonological information. One of the crucial abilities in this first step in language acquisition is the differentiation of speech from nonspeech auditory input. In an experiment using fMRI, DehaeneLambertz and associates (2002) measured the brain activity evoked by normal (forward) speech and reversed (backward) speech in 3-month-old sleeping and awake infants who were exposed to French language. Forward and backward speech elicited stronger activation in the left-temporal lobe compared to silence. The left-hemisphere activation ranged from the superior temporal gyrus, including Heschl’s gyrus extending to the superior temporal sulcus and the temporal pole, but no significant activation was found in the right temporal lobe. Left-hemispheric asymmetry between forward and backward speech was found for the angular gyrus and precuneus. These data suggest that infants as young as 3 months of age show a left-hemispheric dominance for the processing of fast transitions in auditory input such as speech. Separate analyses of awake infants revealed an additional activation for forward speech compared to backward speech in the right frontal cortex, which was interpreted to reflect higher attention for the normal speech condition in awake infants. While the left hemisphere is dominant for speech processing in right-handed adults, the right hemisphere is responsible for suprasegmental prosodic processing (for a review see Friederici and Alter, 2004). A study comparing normal speech and speech in which the intonation (e.g., the pitch contour) of sentences was removed, leaving formant and spectral information intact, found clear right-hemispheric activation in the temporal cortex for the processing of prosodic information in German-speaking adults (Meyer et al., 2004). Using the same paradigm in Japanese, Homae and associates (2006) investigated 3-month-old sleeping infants. In their study, they applied near-infrared optical topography and observed bilateral activation in the temporoparietal and frontal cortex for both normal and flattened speech. A direct comparison between normal and flattened speech revealed the right temporoparietal cortex as the region supporting pitch information processing. This finding suggests that the right-hemispheric dominance in the processing of sentential prosody seen in adults is established in babies as young as 3 months. One crucial aspect of sentential prosody is that it marks intonational phrase boundaries (e.g., “Peter knows # Mary loves her work;” the # indicates the prosodic break). This feature is important, because intonational phrase boundaries signal syntactic phrase boundaries. Every syntactic phrase boundary is not necessarily marked by prosody, but each intonational phrase boundary is a syntactic boundary. Therefore, once infants are able to detect intonational phrase boundaries, they are equipped with the information needed to structure incoming speech into phrases. In adults, a par-

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ticular ERP component has been found to correlate with intonational phrase boundary processing in spoken language—that is, the positive shift. As the intonational phrase boundary signals the end of a phrase, this shift is referred to as closure positive shift (CPS) (Steinhauer, Alter, and Friederici, 1999). The CPS has also been observed for hummed sentences and can, therefore, be taken to reflect purely prosodic processes (Pannekamp et al., 2005). In an ERP study with 8-month-old infants, a CPS similar to what has been observed in adults could be observed for intonational phrase boundaries in spoken language. This finding indicates that the brain mechanism supporting intonational phrase boundary processing is clearly established by the age of 8 months (Pannekamp, Weber, and Friederici, 2006) (figure 8.1). Future studies will have to show whether this mechanism is in place at an even earlier developmental stage.

Stress Patterns of Words Different languages have different rules according to how stress is assigned within multisyllabic words. English, like German, is a stress-based language and has a bias toward a stress-initial pattern for two-syllable words (Cutler and Carter, 1987). French, in

contrast, is a syllable-based language that tends to lengthen the word’s last syllable (Nazzi et al., 2006). Behavioral studies have demonstrated that infants learning English are able to segment disyllabic words with stress on the first syllable from speech input but not those with stress on the second syllable at the age of 7.5 months (Jusczyk, Houston, and Newsome, 1999). This ability was also reported for 9-month-olds learning Dutch (Houston et al., 2000). The ability to segment words with stress on the second syllable in various contexts, however, was only observed by the age of 10.5 months in Englishlearning infants (Jusczyk, Houston, and Newsome, 1999). Therefore, it appears that knowledge about the stress pattern of possible words in a language must be available at about 7 months in order to be used for segmentation. It has been shown behaviorally that infants learning English in particular acquire this knowledge between the ages of 6 and 9 months (Jusczyk, Cutler, and Redanz, 1993). However, as behavioral paradigms require the attention of infants during testing, it may well be that infants have already acquired this knowledge but attentional lapses make this fact undetectable. In contrast, neurophysiological studies using the method of ERPs suggest that infants are indeed sensitive to the preferred stress pattern of their target language as early as 4 to 5 months of age. In these studies, the so-called oddball paradigm or mismatch paradigm was used. In such a paradigm a string of identical stimuli or standard stimuli are presented, and a deviant stimulus is introduced at rare occasions. The brain reacts to the deviant auditory stimulus with a mismatch response that in adults is characterized by a more

Figure 8.1 The closure positive shift (CPS) as an index of processing intonational phrase boundaries. (a) Grand-average ERP for adults at electrode PZ. Vertical line indicates sentence onset. IPh1, IPh2, and IPh3 bars indicate the length of the two intonational phrases and the intervening IPh boundary in sentence type A ([Kevin verspricht Mama zu schlafen] IPh1 [und ganz lange lieb zu sein] IPh2 / [Kevin promises Mom to sleep] [and to be a good boy for a while]) represented as solid line (with one CPS), and the three intonational phrases and two intervening IPh boundaries in

sentence type B ([Kevin verspricht] IPh1 [Mama zu küssen] IPh2 [und ganz lange lieb zu sein] IPh3 / [Kevin promises] IPh1 [to kiss Mom] IPh2 [and to be a good boy for a while] IPh3) represented as dotted line (with CPS1 and CPS2). (b) Grand-average ERP for 8-month-old infants at electrode P4 for sentence type A (solid line) and sentence type B (line) (Modified with permission from Friederici, A. D., 2005. Neurophysiological markers of early language acquisition: From syllables to sentences. Trends Cogn. Sci. 9:481–488.)

Toward identifying the lexical form To achieve lexical knowledge, infants first have to segment words from the auditory stream. Both knowledge about a word’s stress pattern and information about a word’s possible phonotactic structure enable infants to successfully recognize words when they hear the target language.

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negative going wave for the deviant as compared to the standard stimuli (Näätänen et al., 2001) and that in infants is sometimes expressed as a more positive going wave for the deviant stimulus (Weber et al., 2004). Studies that used the mismatch paradigm to investigate the infants’ ability to discriminate different phonemes early during development indicate this general ability very early on and start to show a language-specific discrimination response by the age of 6 months (for a review see Kuhl, 2004). In a recent ERP study (Friederici, Friedrich, and Christophe, 2007), groups of 5-month-old German- or French-learning infants were tested for their ability to discriminate between different stress patterns. A mismatch paradigm was used in which the standard stimuli were disyllabic words with stress on the first syllable (baaba) and the deviant stimuli had the stress on the second syllable (babaa). The data showed that both groups are able to discriminate

between the two types of stress patterns (figure 8.2). However, they differed in the amplitude of the brain response: infants learning German showed a larger effect for the languagenontypical iambic pattern (stress on the second syllable), whereas infants learning French demonstrated a larger effect for the language-nontypical trochaic pattern (stress on the first syllable). This finding suggests that a pattern that is nontypical for a particular language is considered deviant both within the experiment (i.e., a rare stimulus in the set) and with respect to an individual infant’s target language. This finding, in turn, presupposes that infants have already established knowledge about the dominant stress pattern of their target language by the age of 5 months. In an ERP study with infants learning Dutch, Kooijman and associates (2005) found that 10-month-olds recognized two-syllable words with stress on the first syllable when these were presented in continuous speech after they had heard the

Figure 8.2 The mismatch response (MMR) during processing of a word’s stress pattern expressed as a more positive going wave for the deviant stimulus condition (dotted line) as compared to the standard condition (solid line). (a) Grand-average ERPs for trochaic stress pattern (stress on the first syllable) as a deviant

(rarely occurring) stimulus in a train of stimuli with stress on the second syllable. (b) Grand-average ERPs for iambic stress pattern (stress on the second syllable) as a deviant stimulus in a train of stimuli with stress on the first syllable. The MMR is indicated by the arrow.

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words in isolation. Recognition was reflected in a greater negativity between 350 and 500 milliseconds (ms) over the left hemisphere for familiar words than for unfamiliar words. Phonotactic Knowledge and Lexical Form There are only a few ERP studies that have investigated infants’ phonotactic knowledge, that is, knowledge about the legal combinations of phonemes in syllables or words and about legal positions of syllables in words. Behavioral studies have shown that initial phonotactic knowledge is established by 9 months (Friederici and Wessels, 1993; Jusczyk and Luce, 1994). The so-called preferential looking paradigm used in these studies revealed a preference for auditory input containing stimuli that are phonotactically legal (“str” at word onset as in “street”) in the respective target language compared to those that are illegal (“str” at word offset which is not a possible word ending in English or in Dutch, the language in which in infants were tested by Friederici and Wessels, 1993). However, these behavioral studies cannot resolve whether this phonotactic knowledge is considered to be lexically relevant by infants. One way to test phonotactic knowledge and thereby the lexical status of a given stimulus is by means of the electrophysiological N400 component, that is, a negative-going waveform peaking at around 400 ms. In adults, an N400 effect is reflected in a larger amplitude for words that are semantically incongruous to a given context than for congruous words (see figure 8.3). Moreover, the N400 amplitude is larger for pseudowords than for words, whether they are phonotactically legal or not (for reviews, see Kutas and Federmeier, 2000; Kutas and Van Petten, 1994). A paradigm appropriate for both adults and children is one in which the participant is shown the picture of an object and at the same time is presented with an auditory stimulus that is either a word matching the object’s name or not, or one that is a pseudoword that is phonotactically legal or illegal. Using this paradigm, we observed a developmental change between the ages of 12 and 19 months (Friedrich and Friederici, 2005a, 2005b). The ERP effects in 19-month-olds are quite similar to those of adults, that is, an N400 effect for the incongruous words and phonotactically legal pseudowords but not for phonotactically illegal pseudowords. However, no N400 effects were observed in 12-month-olds. As the N400 is taken to reflect mechanisms of lexicalsemantic integration, these data suggest that at the age of 19 months, both real words and phonotactically legal pseudowords are considered as possible word candidates, but that phonotactically illegal pseudowords have already been excluded from the native language lexicon (Friedrich and Friederici, 2005a). The preceding behavioral and electrophysiological data indicate the presence of initial phonotactic knowledge at 9 months, but this knowledge is not actually used in lexical processing until several months later.

Phonological Familiarity of Words There is another ERP effect suggesting that children are already trying to map sounds onto objects (or pictures of objects) at about 11 months of age. This development has been indicated by a negativity around 200 ms reported for 11-month-olds in response to listening to familiar versus unfamiliar words (Thierry et al., 2003). There are some concerns about the statistical techniques used in this study. An ANOVA was performed to cover every millisecond of recording, and no correction for multiple comparisons was applied, so this fact challenges the authors’ interpretation to some extent. Using a picture-word priming paradigm, our group has found an early frontocentral negativity between 100 and 400 ms in 12- and 14-month-olds for auditory word targets that were congruous with a picture compared to incongruous words (Friedrich and Friederici, 2005a). This early effect was interpreted as a familiarity effect reflecting the fulfillment of a phonological (word) expectation after seeing the picture of an object. At this age, infants seem to have some lexical knowledge, but the specific word form referring to a given object might not be sharply defined, so phonetically similar words are still considered as possible word candidates. This interpretation is supported by the finding that 12- and 14month-olds showed an ERP difference between phonetically dissimilar words they knew, but not between words they knew and phonetically similar words (Friedrich and Friederici, 2005a). The available data thus indicate that phonological and semantic knowledge interact at around 12 months of age.

Semantic processes The adult N400 component is taken to reflect the integration of a lexical element into a semantic context (Kutas and Van Petten, 1994). In the study of semantic processes in infants and young children, the adult N400 has been used as an ERP template pattern against which the ERPs for semantic knowledge and processes during early development are compared. Word Level In a study on the processing of words whose meaning infants either knew or did not know, infants between 13 and 17 months old showed a bilateral negativity for unknown words, but 20-month-olds showed a lefthemispheric negativity (Mills, Coffey-Corina, and Neville, 1997). This result was interpreted as a developmental change toward a hemispheric specialization for word processing. In a more recent study, the effects of word experience (training) and vocabulary size (word production) were tested (Mills, Coffey-Corina, and Neville, 1997). In this word-learning paradigm, 20-month-olds acquired novel words either paired with a novel object or without an object. After training, the infants’ ERPs showed a repetition effect indicated by a

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reduced N200–500 amplitude to familiar and novel unpaired words, whereas an increased bilaterally distributed N200– 500 was found for novel paired words. This finding is taken to indicate that the N200–500 is linked to word meaning. However, it is not entirely clear whether the N200–500 reflects semantic processes only or whether phonological familiarity also plays a role. The interpretation of this early effect as semantic is challenged, given that semantic effects in adults are observed later in reference to the N400. It is possible, however, that the early onset of this effect in infants as compared to adults is due to infants’ relatively small vocabularies. A small vocabulary results in a low number of phonologically possible alternative word forms, allowing the brain to react earlier, after hearing a word’s first phonemes (see earlier section on phonological familiarity). A clear semantic-context N400 effect at the word level has been demonstrated for 14- and 19-month-olds (Friedrich and Friederici, 2005b, 2004). The ERP to words in picture context showed a centroparietal, bilaterally distributed negative-going wave between 400 and 1400 ms, which was more negative for words that did not match the picture context than those that did (see figure 8.3). Compared to adults, this N400-like effect reached significance later and lasted longer. There were also small topographic differences

of the effect as children showed a stronger involvement of frontal electrode sites than adults did. The latency differences suggest slower lexical-semantic processes in children than in adults. The more frontal distribution could mean either that children’s semantic processes are still more image based (adults show a frontal distribution when pictures instead of words are processed; West and Holcomb, 2002) or that children may recruit frontal brain regions associated with attention in adults (Courchesne, 1990) in addition to those subserving semantic processing.

Figure 8.3 The N400 as an index of lexical-semantic processes, here showing a picture-word incongruity effect. Top: Grandaverage ERP at electrode PZ for the different age groups. Note the different mircovolt scales for the different age groups. Bottom: N400 effect (difference between congruent and incongruent words)

as a distributional map. Negativity is coded in dark gray. (Modified with permission from Friederici, A. D., 2005. Neurophysiological markers of early language acquisition: From syllables to sentences. Trends Cogn. Sci. 9:481–488.)

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Sentence Level The processing of congruous and incongruous words in sentential context has only recently been investigated in children younger than 4 years of age (Silva-Pereyra, Rivera-Gaxiola, and Kuhl, 2005). Previous studies with 5- to 15-year-olds (Holcomb, Coffey, and Neville, 1992) and 6- to 13-year-olds (Hahne, Eckstein, and Friederici, 2004), as well as the study with 3- and 4-year-olds, reported N400-like negativities for semantically anomalous sentences in children of all age groups. In the study with 3and 4-year-olds, the children listened passively to sentence stimuli that were either semantically correct or anomalous (e.g., My uncle will blow the movie) while watching a puppet show (Silva-Pereyra, Rivera-Gaxiola, and Kuhl, 2005).

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Whereas 3-year-olds showed a first negativity between 300 and 500 ms, a second negativity between 500 and 800 ms, and a third negativity between 800 and 1000 ms, 4-year-olds demonstrated a first negativity peaking at around 400 ms and a second negativity between 500 and 800 ms. These negativities, which were anteriorly distributed, were interpreted to reflect different semantic mechanisms. The later negativities were taken to reflect sentence closure. The negativity in the time window relevant for the adult N400 was not given any specific interpretation but could be taken to reflect semantic processes. Using the same paradigm with slightly younger children at 30 months, a frontally distributed negativity between 300 and 500 ms with a statistically significant difference between semantically anomalous and nonanomalous sentences was reported (Silva-Pereyra, Klarman, et al., 2005). These data suggest that the semantic processes at the sentential level similar to those reflected by the adult N400 are present between the ages of 30 and 36 months. More recently, an N400-like semantic effect at the sentence level has been reported for children at 19 and 24 months (Friedrich and Friederici, 2005b). This study investigated children learning German and used sentences that were either semantically incorrect (e.g., Die Katze trinkt den Ball/The cat drinks the ball) or semantically correct (The cat drinks the milk). For 19-month-olds, a first negativity was observed between 400 and 500 ms followed by a sustained negativity between 600 and 1200 ms (figure 8.4). In 24month-olds, the negativity was found to start as early as 300 ms and lasted until 1200 ms. For adult listeners, the N400 effect was present between 300 and 800 ms. From these data, it is apparent that the N400 effect in children starts at around the same time as the adult N400, but extends longer, until 1200 ms. The longer duration of the children’s N400 effect suggests that the integration of the object noun into the sentence context requires enhanced efforts. The presence of the N400 effect indicates that before the age of two, children possess lexical representations of verbs specified with respect to their lexical restrictions and that brain mechanisms underlying semantic processes at the sentential level are established in an adultlike manner.

Syntactic processes One of the key questions in early syntax acquisition is whether infants rely on statistical cues of the input or whether

Figure 8.4 The N400 as an index of lexical-semantic integration processes at the sentence level, here showing a verb-object noun incongruity effect. Grand-average ERPs of N400 effect across different age groups. Right: Distributional map of the N400 effect (difference between correct and incorrect condition).

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the acquisition of syntax is guided by some innate rule system during early syntax acquisition (for recent reviews see Gomez and Gerken, 2000; Saffran, Aslin, and Newport, 1996). Because ERP studies that focus on whether infants can detect rule deviations are not available in the literature, we cannot answer the question about the innateness of syntax. However, there are ERP studies that have investigated syntactic processing in 2- to 4-year-old children. These groups will be evaluated for whether and to what extent infants’ ERP patterns resemble those of adults. Studies of sentence processing in adults have shown that violations of syntactic rules are associated with two ERP components: a late, centroparietal positivity (P600) and an earlier left anterior negativity (ELAN). While the ELAN is seen as a fast response to violations of local syntactic rules and the LAN is seen in response to morphosyntactic errors, the P600 has been interpreted to reflect later, more controlled processes of syntactic revision that could either be a syntactic reanalysis of a correct but falsely analyzed syntactic structure or the repair of a syntactically incorrect structure (Friederici, 2002). Currently, only a few ERP studies on syntactic processing during early language acquisition are available. One paradigm has been used to study morphosyntactic violations in English, and the other has been used to examine phrase structure violations in German. The morphosyntactic paradigm, using sentences containing a morphosyntactic violation (My uncle will watching the movie), reported a P600-like positivity for 3- and 4-year-olds (Silva-Pereyra, RiveraGaxiola, and Kuhl, 2005). For slightly younger 30-monthold children, the positivity observed between 600 and 1000 ms did not reach significance (Silva-Pereyra, Klarman, et al., 2005). No LAN effects were observed at any of the ages tested.

However, a recent ERP study investigating the processing of phrase structure violations (e.g., Der Löwe im brüllt/The lion in-the roars) in children at 32 months demonstrated a biphasic ERP pattern consisting of a left-hemispheric negativity around 500 ms and a bilaterally distributed centroparietal late P600 for such syntactically incorrect sentences (Oberecker et al., 2005) (figure 8.5). The left lateralization of the children’s negativity suggests that this component can be interpreted as a child precursor to the ELAN observed in adults for phrase structure violations. Both components, the ELAN and the P600, started later and persisted longer than those observed in adults. The appearance of these syntax-related components indicates that the neural mechanisms of syntactic parsing are present in principle at 32 months, although the processes are clearly slower in children than in adults. When applying the same phrase structure violation paradigm to younger 2-year-olds, the ERPs revealed a late P600 but no ELAN component or any other left-lateralized negativity preceding the P600 (Oberecker and Friederici, 2006) (figure 8.5). Thus it appears that the automatic initial phrase structure building reflected in the ELAN is established later than the late integration processes reflected in the P600. The P600 also seems to precede the development of morphosyntactic processing reflected in the LAN (Silva-Pereyra, RiveraGaxiola, and Kuhl, 2005). Moreover, the data from the German and English studies suggest that the neural mechanisms for phrase structure building (ELAN) might be established earlier during development than those for morphosyntactic processes (LAN).

Figure 8.5 The ELAN-P600 pattern as an index of syntactic processes. ELAN stands for early left anterior negativity and P600 for a late centroparietal positivity. Grand-average ERPs at selected electrodes (F7, PZ) across the different age groups. Note the differ-

ent mircovolt scales between children and adults. (Modified with permission from Friederici, A. D., 2005. Neurophysiological markers of early language acquisition: From syllables to sentences. Trends Cogn. Sci. 9:481–488.)

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Conclusions The literature reviewed in this chapter demonstrates that we are beginning to formulate a developmental neuroscience of

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language processing based on electrophysiological data. The methodological approach of recording ERPs while participants process language input of different types is based on the general assumption that different ERP patterns observed for different types of experimental conditions or different age groups can inform us about the underlying processes (Rugg and Coles, 1995). If qualitatively different patterns of brain activity are observeed, then the conclusion can be drawn that different neural structures and therefore different functional processes are involved, whereas pure quantitative differences are more likely to constitute evidence for the same neural structures and functional processes at different levels of engagement. In the case of ERPs, qualitative differences are correlated with either different spatial distributions of electrical activity or with distinct polarities. Quantitative differences, however, are correlated with different magnitudes of electrical activity (i.e., different amplitudes or time courses but an equal topographic distribution and polarity). The ERP studies investigating language development available thus far indicate that language-related ERP components that reflect lexical-semantic processes (N400), syntactic processes (ELAN–P600), and prosodic processes (CPS) appear to change in their latency and duration from childhood to adulthood, but not in their basic morphology, namely, their polarity or their main topographic distribution. According to the assumptions that we have specified, these ERP data suggest that the functional processes underlying lexical-semantic, syntactic, and prosodic aspects of language comprehension change in their level of engagement and timing, but not in their qualitative parameters. These neurophysiological findings may provide additional evidence for the theoretical debate on language development during which two hypotheses have been put forward. The “discontinuity hypothesis” holds that processes underlying language comprehension and production are qualitatively different in childhood and adulthood (Felix, 1994), and the “continuity hypothesis” assumes that language processes are similar during development and adulthood with observable differences only being quantitative in nature (Gleitman and Wanner, 1982; Pinker, 1984). The ERP findings reviewed could be taken to support the continuity hypothesis according to which the underlying principles of language processing develop continuously from childhood to adulthood and change only quantitatively over time. Moreover, they indicate that major parts of the brain system underlying adult language processing are already installed by the age of 2.5 years.

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I. Dan, 2004. Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI. NeuroImage 21: 1275–1288. Pannekamp, A., U. Toepel, K. Alter, A. Hahne, and A. D. Friederici, 2005. Prosody-driven sentence processing: An event-related brain potential study. J. Cogn. Neurosci. 17:407– 421. Pannekamp, A., C. Weber, and A. D. Friederici, 2006. Prosodic processing at sentence level in infants. NeuroReport 17:675–678. Pinker, S., 1984. Language Learnability and Language Development. Cambridge, MA: Harvard University Press. Rugg, M. D., and M. G. H. Coles, 1995. Electrophysiology of Mind: Event-Related Potentials and Cognition. New York: Oxford University Press. Saffran, J. R., R. N. Aslin, and E. L. Newport, 1996. Statistical learning by 8-month-old infants. Science 274:1926–1928. Schroeter, M. L., S. Zysset, M. Wahl, and D. Y. von Cramon, 2004. Prefrontal activation due to Stroop interference increases during development—An event-related fNIRS study. NeuroImage 23:1317–1325. Silva-Pereyra, J., M. Rivera-Gaxiola, and P. K. Kuhl, 2005. An event-related brain potential study of sentence comprehension in preschoolers: Semantic and morphosyntactic processing. Cogn. Brain Res. 23:247–258. Silva-Pereyra, J., L. Klarman, J. L. Lin, and P. Kuhl, 2005b. Sentence processing in 30-month-old children: An event-related potential study. NeuroReport 16:645–648. Steinhauer, K., K. Alter, and A. D. Friederici, 1999. Brain potentials indicate immediate use of prosodic cues in natural speech processing. Nature Neurosci. 2:191–196. Thierry, G., 2003. The affective content of words is primarily processed in the left hemisphere. J. Psychophysiol. 17:237. Thierry, G., M. Vihman, and M. Roberts, 2003. Familiar words capture the attention of 11-month-olds in less than 250 ms. NeuroReport 14:2307–2310. Tomasello, M., 2003. Origins of language. In Constructing a Language: A Usage-Based Theory of Language Acquisition, 8–42. Cambridge, MA: Harvard University Press. Villringer, A., and B. Chance, 1997. Noninvasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 20:435–442. Wanner, E., and L. R. Gleitman, 1982. Language Acquisition: The State of the Art. Cambridge, UK: Cambridge University Press. Weber, C., A. Hahne, M. Friedrich, and A. D. Friederici, 2004. Discrimination of word stress in early infant perception: Electrophysiological evidence. Cogn. Brain Res. 18:149–161. Weissenborn, J., H. Goodluck, and T. Roeper, 1992. Theoretical Issues in Language Acquisition: Continuity and Change in Development. Hillsdale, NJ: Lawrence Erlbaum. Werker, J. F., and H. H. Yeung, 2005. Infant speech perception bootstraps word learning. Trends Cogn. Sci. 9:519–527. West, W. C., and P. J. Holcomb, 2002. Event-related potentials during discourse-level semantic integration of complex pictures. Cogn. Brain Res. 13:363–375.

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Brain-Behavior Relationships in Early Visual Development BOGDAN F. ILIESCU AND JAMES L. DANNEMILLER

Empirical and theoretical work on the relations between neural maturation and perceptual capabilities during infancy has focused on the major visual pathways. This work has addressed development at the retinal, subcortical, and cortical levels and has attempted to link observed data on visual function, whether behavioral or evoked electrical activity, to changes taking place within the underlying neural substrate. Theories of brain-behavior relations in early visual development typically make inferences either from the anatomical or physiological data to observed behavior or from observed behavior to the anatomical and physiological substrate. In the former case, data on the states of various neural elements within the visual system are used to explain observed behavioral data (e.g., acuity, color vision, or contrast sensitivity). In the latter case, observed behavioral data are used to make inferences about the presence and functioning of various subpopulations of feature-selective neurons within the visual pathway (e.g., orientation or direction selectivity). These theories are developmental examples of what Teller (1984) referred to as linking propositions in visual science, that is, formal propositions that link aspects of visual anatomy and physiology with observed visual capacities and behavior. At the neural level, this period is characterized by variable developmental timetables for processes such as neuroarchitectural transformations, physiological activity, biochemical events, and gene expression. Different structures and functional pathways within the visual system (and many other brain systems) develop at different rates. For example, Atkinson (1998) proposed that the magnocellular pathway may lag slightly behind the parvocellular pathway early in postnatal development. Johnson (1990) and Banton and Bertenthal (1997) based their models on data showing that different layers within primary visual cortex develop at different rates. The regions that process primary motor and sensory information mature earliest, whereas parietal and temporal association cortices that will serve as substrates for spatial attention and basic language skills mature later. Higher order association areas, such as prefrontal cortex responsible for integrative behavior

and decision-making processes, mature last (Gogtay et al., 2004). It is important to realize that in these theories, especially those referring to development at the cortical level, vision in the early postnatal human infant is not simply assumed to be a scaled version of adult vision. It is not simply that there is more noise in the young visual system or that the signals are generally weaker, but rather that the sensory and perceptual information processed by the infant brain is of a different quality than that available in normal human adults. The reasoning behind this claim is that some types of information that rely on specific brain structures for their processing or extraction will never be available to the infant because those structures are not yet capable of extracting that information. While it is well known that postnatal experience plays a large role in visual development, we also review important studies on the roles of intrinsic activity in setting up the neural circuits that experience will later mold. In some ways this information addresses two age-old developmental questions: What structure is present innately in the visual system, and how did that structure arise in the absence of visual experience? The important role played by inhibition in visual processing also provides an opportunity to understand how mature visual processing differs both quantitatively and qualitatively from visual processing early in postnatal life. The purpose of this chapter is to review the available data on the early postnatal development of the visual system as an example of how one might think about these complex brain-behavior relations. We will focus on the previously mentioned observation that different structures and processes in the infant brain develop at different times and with different rates. We will look at the progress made at different levels in understanding neural development and its relations with perception. We will review the anatomical, physiological, biochemical, and genetic data from human and animal visual development research without going into the extensive literature on plasticity and deprivation in early visual development. The interested reader might wish to consult chapter 25 in this volume by Maurer, Lewis, and Mondloch for a discussion on visual system plasticity.

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Retinal development The absorption of photons of light by the photoreceptors is the first step in the visual cascade that eventually leads to seeing. Why is it important to focus on this first stage of information transmission in the visual pathway? There is a simple answer to this question. Information in the optical image that is lost at this first stage of processing can never be recovered. The only thing that subsequent stages in the visual pathway can do is to operate on the information that leaves the retina; they cannot recreate information (spatiotemporal contrast) that has been lost in the sampling and capture of light by the photoreceptor mosaic. If the anatomical data on which the models are based are accurate and representative, and if the assumptions are valid, then these models tell us importantly that vision can be quantitatively no better than what has been calculated; it can only be equal to or worse than this. It pays, therefore, to understand these models, because they tell us what spatial information is available to the remainder of the infant’s visual system for important tasks like object recognition and the perception of spatial layout. Two factors make it possible to construct reasonably rigorous, quantitative developmental models at the retinal level. First, the contributions of various structures in the eye to the amount of information in the pattern of photoreceptor absorptions are fairly well understood. For example, the diameter and length of photoreceptor inner and outer segments determine what proportion of the incident photons are likely to be absorbed by the photopigment. This is a purely physical/optical calculation like calculating the optical aperture of a telescope. Second, anatomical data exist on the characteristics of these optical and retinal structures. One can use these data to calculate estimates of information transmission to and including the point at which photons are actually absorbed by the photopigment. Examples of the models that have been proposed at this level are ones covering scotopic (night) vision (Brown, Dobson, and Maier, 1987; Hansen and Fulton, 1999) and photopic (day) vision (Banks and Bennett, 1988; Banks and Crowell, 1993; Brown, 1993; Brown, Dobson, and Maier, 1987; Candy, Crowell, and Banks, 1998; Wilson, 1988, 1993). The Hansen and Fulton model is somewhat simpler, because it is meant to explain only the large differences that exist between the absolute thresholds of infants and adults in the detection of light. Absolute threshold is simply a measure of the minimum amount of light that can be detected reliably under a set of fixed viewing conditions. We will turn next to the Hansen and Fulton (1999) model because it illustrates how anatomical measurements can be used to make reasonably accurate predictions of the development of visual sensitivity.

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Absolute thresholds are significantly higher in young infants than in adults (see Brown, 1990, for a review). Why is this statement true? It would be possible to answer this question in many ways. The answer to this simple question illustrates one of the primary virtues of these models. One could point to the weaker attentional capacities of young infants or to their lack of motivation for behaving like practiced psychophysical observers as explanations for why their thresholds are higher. Instead, these models choose to answer this question by using known anatomical data to constrain best or “ideal” visual performance. In other words, if one is going to explain improvements in absolute threshold, perhaps the place to start is at the very beginning, in the photoreceptors themselves. Only after estimates have been derived using these anatomical data, should one then proceed to attribute any remaining infant/adult differences to higher level attentional or motivational processes. These models have the virtue of simplicity; more complex, psychological processes should only be entertained as explanations for some observed behavior after simpler, more peripheral explanations have been ruled out as being incomplete. With postmortem anatomical measurements Fulton and associates (1999) showed that the amount of rhodopsin—the photosensitive pigment in rods—is much lower in preterm and term infant eyes than it is in adult eyes, and that it increases rapidly during infancy. At birth, rhodopsin content is approximately 35 percent of its adult value, and it reaches 50 percent of its adult value by 5 weeks postnatally. It appears to reach its adult value sometime near 40 weeks of age. These increases in rhodopsin are likely to reflect the increases in the lengths of the rod outer segments (OS) in which the rhodopsin is held (Hendrickson, 1994). Hansen and Fulton (1999) noted that rod outer segment lengths at a peripheral retinal site in adults are approximately 2.3 times longer than they are in 5-day-old infants. Given that the amount of rhodopsin should be directly proportional to the length of the rod OS (assuming equal densities), this finding would predict a threshold increase for infants relative to adults of 0.4 log units. In their study of the development of absolute thresholds, Hansen and Fulton (1999) reported that the thresholds for 10-week-old infants were approximately 0.58 log units higher than they were in adults. At a more parafoveal retinal site, rod OS lengths in adults are approximately 9 times as long as they are in 5-day-old human infants. This figure would predict a difference of approximately one log unit. The 10-week-old absolute threshold at this parafoveal site was approximately 1.06 log units higher than the adult threshold. Finally, at 11 months of age, infant rod OS lengths are approximately 68 percent of the adult value, leading to the prediction that infant thresholds at this age should be only 0.16 log units higher than adult thresholds. Hansen and Fulton (1999) noted that no infant in their study who was 6 months of age

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or older had an absolute threshold that was more than 0.1 log units above the median adult threshold. These data show quite simply how increases in rod OS length and by inference in the amounts of photon-catching rhodopsin (Fulton et al., 1999) can be used to explain some of the differences between young infants and adults in their absolute thresholds and how those differences narrow over the course of postnatal development (see also Nusinowitz et al., 1998). The same strategy has been used by Banks and Bennett (1988), Banks and Crowell (1993), Brown, Dobson, and Maier (1987), Brown (1993), Candy, Crowell, and Banks (1998), and Wilson (1988, 1993) to model the development of visual acuity and contrast sensitivity. These models also include the spatial distribution of the cone photoreceptors because this distribution differs significantly across the retina in adults, and it has strong implications for photon capture and spatial contrast transfer from the retina. We will concentrate here on the models proposed by Banks and colleagues, because Banks and Crowell (1993) showed that many of the conclusions offered by these alternative models regarding front-end constraints on vision in early infancy are very similar. These models start with the optics of the eye. Prior to being absorbed by the photoreceptors, photons must of course pass through the optics of the eye. Several factors contribute to the optical quality of retinal images. For the purposes of this chapter it is sufficient to note two things. First, Candy, Crowell, and Banks (1998) and Banks and Bennett (1988) both assume that the optical transfer function of the neonatal eye is similar to that of the adult eye. More recent evidence suggests that there is a modest degradation of image quality caused by the optics in 5- to 7-week-old human infants (Wang and Candy, 2005). The optical transfer function represents the extent to which contrast is attenuated as it passes through the optics of the eye (cornea, lens, and ocular media) as a function of spatial frequency. Optical systems generally attenuate contrast more at high spatial frequencies than at low spatial frequencies. The second factor that is important for the purposes of this chapter is that the length of the infant’s eye is shorter than the length of the adult’s eye. This fact has two consequences: (1) the retinal image is spread over a smaller area in the infant’s eye, and (2) the number of photons falling on a small patch of retina (photons/degree2) would be higher in the infant’s eye if pupil sizes did not differ. The latter intensity effect is offset by the smaller pupil size of the infant’s eye, so that the actual number of incident photons per patch of retina is probably not very different between infants and adults, except for small differences resulting from the slightly higher media transmittance in the neonate (Candy, Crowell, and Banks, 1998). Candy, Crowell, and Banks (1998) estimated that the image of a small, distant object would be approximately 2/3 as large on the neonatal retina as on the adult

retina. If photoreceptor packing densities were the same at these two ages, and they most certainly are not (see discussion later in this section), then this difference in eye size alone would spread the image over fewer photoreceptors in the neonate’s eye, leading to a less detailed initial encoding. Once the optical properties of the cornea, lens, and media have been factored into the model, the most important remaining factors are (1) the morphologies of individual cone photoreceptors and (2) their spatial arrangements (packing densities). Data on the development of these properties and their mature, adult values are available from several sources (Abramov et al., 1982; Curcio, 1987; Curcio et al., 1990; Hendrickson, 1994; Hendrickson and Drucker, 1992; Yuodelis and Hendrickson, 1986). It should be noted, however, that these anatomical data used to model spatial contrast vision during early infancy come from one 5-dayold human infant (Yuodelis and Hendrickson, 1986), so the issue of the representativeness of this example must always be kept in mind (Candy, Crowell, and Banks, 1998). It is known that in adults there is considerable individual variability in some of the anatomical parameters used in these models (Curcio et al., 1990). The morphologies of individual cones in the neonatal retina are markedly different from those of adults (Youdelis and Hendrickson, 1986). As was true of rods, the outer segments of the cones are much shorter in the neonate than in the adult. Banks and Bennett (1988) modeled this difference as a factor of approximately 16 : 1 for foveal cones. Of course, this length difference implies that the amount of photosensitive material for capturing incident photons is that much less in the neonate, assuming equal densities. Additionally, the effective apertures of these cones through which photons must be funneled to be absorbed was estimated by Banks and Bennett (1988) to differ by a factor of approximately 1.88 (0.48/0.35)2 in favor of infants. The slightly larger aperture of the neonate’s cones means that slightly more photons will be absorbed per infant cone given the same flux, but it also means that higher spatial frequency information will be degraded more because of greater spatial averaging over the cone’s aperture. The cones are also packed much less densely in the newborn’s fovea. This packing density is important because it determines to a large extent the highest spatial frequency that can be reliably signaled from the photoreceptor catches across a small region of the retina. Sampling the image very finely permits information about high spatial frequencies (fine detail) to be transmitted well, while sampling the image very coarsely attenuates this high-spatial-frequency information significantly.1 Based on the spacings of the photoreceptor inner segment spacing, Candy, Crowell, and Banks (1998) calculated an acuity limit of 15 cycles per degree (cpd) for newborns (cf. 60 cpd for adults). Additionally, lower packing densities also imply fewer photon absorptions given

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a fixed nodal distance because more of the photons pass through the retinal space not occupied by photoreceptors. When all of these factors were taken into account, Banks and Bennett (1988) estimated that if identical patches of light were to be presented to newborn and adult central retina, approximately 350 photons would be absorbed by the adult cone lattice for every one photon absorbed by the newborn’s cones. It is hard to overstate the significance of this factor for newborn vision. Fewer photon absorptions mean much noisier signals, because the emission of light is an inherently random process. Because the variance in the number of photons emitted from a source or reflected from a surface is proportional to the mean number of photons emitted or reflected (Poisson process), the signal-to-noise ratio expressed as the ratio of the mean to the standard deviation will increase with the square root of the mean. In other words, the availability of more photons ideally leads to an improvement in sensitivity that is proportional to the square root of the mean. The 350 : 1 ratio of absorbed photons for adults compared to infants means that the signal-to-noise ratio for infants would be at best 1/18 (square root behavior) as strong in newborns as in adults. Imagine trying to estimate a mean with a set of observations that was 18 times as variable as another set. Any visual task (e.g., intensity discrimination at an edge) that depends on pooling and averaging the responses from these isomerizations will surely suffer substantially by having such impoverished information on which to base performance. Recent evidence from the study of the development of retinal circuitry in other species suggests that it is not just the photoreceptors that undergo marked postnatal change. The ganglion cells of the retina collect signals from prior levels in the retina and pass this information to higher levels in the visual system in the form of action potentials. In mice, retinal ganglion cell dendritic arbors are subject to significant remodeling during development, eventually making contact with amacrine cells in very specific strata within the inner plexiform layer. Cholinergic amacrine cells probably guide and shape this remodeling of ganglion cell dendritic processes during postanatal development (Stacy and Wong, 2003), reflecting the postnatal maturation of this aspect of the retinal circuitry. In the mouse this class of ganglion cells corresponds to directionally selective ganglion cells in other species. While human retinal ganglion cells do not exhibit directional selectivity, these studies nonetheless show that intrinsic chemical signals within the retina play a role in the postnatal development of retinal circuitry in at least one mammalian species. Experimental techniques ranging from gene manipulation to two-photon microscopy helped in working out the details of this circuit (He and Masland, 1997; Yoshida et al., 2001). It is clear that the normal development of this functional relationship between these two

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retinal cell types is essential for the appropriate computation of the direction of motion in mouse vision. Stacy and Wong suggest that amacrine–ganglion cell interactions are important in determining the stratification of ganglion cells into on and off laminae in the inner plexiform layer as well. on and off pathways in the visual system are important for encoding the visual signal into positive (increments) and negative (decrements) contrast levels. Models of retinal function based on extant data do not fully explain the poor contrast sensitivities and reduced acuities observed during early postnatal life. In other words, observed values for these measures are worse than would be predicted from a visual system with the optical and retinal characteristics included in these models. Although an obvious possibility is that we lack a comprehensive understanding of the retinal circuitry and its development beyond the photoreceptors, another possibility is that there are probably postretinal sites in the visual pathways where significant information loss occurs. The immaturity of these sites, both subcortical and cortical, could further limit visual function. In the remainder of this chapter we will consider the evidence for how structures in the visual pathway beyond the retina might inform our understanding of early postnatal brain–behavior relations.

A selective look at cortical development Because of the greater complexity of the visual circuitry beyond the retina, theories trying to make a direct connection between the neural substrate and visual behavior tend to be less quantitative than their retinal counterparts. Cortical theories generally make predictions about the presence or absence of a certain perceptual capacity, known or thought to depend on cortical processing (e.g., orientation sensitivity). We will focus mainly on two themes in considering development beyond the retina: (1) While there is incontrovertible evidence for postnatal experience playing an important role in shaping cortical development and instructing specific functional capabilities at least in the visual cortex, there is also clear evidence that intrinsic neural activity in the prenatal period has a crucial influence on the formation of a normal neural architecture and connectivity. (2) There is a very dynamic evolution of the biochemical landscape during this period; the excitatory and inhibitory neural mechanisms in particular that, in light of the latest data, account for important developmental phenomena during this period are particularly malleable. Before discussing these themes, we will review briefly the major visual pathways. For an overall view of the visual pathway up to primary visual cortex please refer to figure 9.1. Figure 9.2 is a schematic of several subcortical and cortical visual pathways, although this is certainly not an exhaustive diagram of such pathways. The major subcortical

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Figure 9.1 Schematic representation of the visual pathway with emphasis on the functional organization of the dorsal lateral geniculate nucleus (upper insert) and of the primary visual cortex. (Lower

inset reproduced from Grinvald et al., 1999, with kind permission of Springer Science and Business Media.)

pathway involves direct projections from the retina to the superior colliculus (SC). The axons of retinal ganglion cells terminate primarily in the superficial layers of the superior colliculus (Kaas and Huerta, 1988). The major cortical pathway runs from the retina to the lateral geniculate nucleus (LGN) to area V1 of visual cortex (VC). There are also numerous descending pathways from VC to subcortical structures (e.g., LGN, SC, pulvinar) that are not shown here. We will discuss selective aspects of these pathways, as they are relevant to the issues that we have raised. A good general discussion of these pathways as they exist in mature primates can be found in Rodiek (1998). The retinocortical pathway is thought to consist of two parallel and quasi-independent streams of processing: parvocellular and magnocellular (Livingstone and Hubel, 1988; Maunsell and Newsome, 1987; Van Essen, Anderson, and

Felleman, 1992). These streams are evident in distinct classes of ganglion cells in the retina, are segregated in layers within the LGN (four parvocellular and two magnocellular layers, figure 9.1, upper insert), and project to distinct layers with the recipient zone of layer 4C of primary visual cortex. The parvocellular stream, postulated to subserve mainly color and form vision and comprising 80 percent of all ganglion cells in the primate retina, projects mainly to layer 4Cβ, while the magnocellular stream projects to layer 4Cα. This division is important because, as noted earlier, several models advance the hypothesis that the differential maturation of cortical layers explains corresponding differences in certain aspects of visual performance. The division of this pathway into magnocellular and parvocellular streams persists to some extent into other cortical areas. The magnocellular or parietal-directed stream is

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frontal

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retina Figure 9.2 Various subcortical and cortical visual pathways: M, magnocellular; P, parvocellular. Notice the retinal projections both to the superior colliculus and to the LGN. There are also extensive reciprocal connections between various extrastriate cortical areas

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and the pulvinar not shown here. The K (koniocellular) pathway is not discussed in this chapter. (From Casagrande, 1994, by permission.)

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associated with areas V3, MT, and MST, and posterior parietal areas. The parvocellular or temporal-directed stream is associated with areas V4 and inferotemporal cortex. It is important to keep in mind that this division into two primary streams does not imply complete independence between the processing of information within these streams (Nassi, Lyon, and Callaway, 2006). Even in area V1, there is evidence for cross talk between these two streams. Many of the visual processes that are important in the mature brain only begin to appear postnatally. More importantly, these processes start at different postnatal ages, progress at different rates, and terminate at different ages (figure 9.3A). The latest relevant research shows that although the foundations of a healthy, normal cerebral circuitry are laid down before birth, evoked activity of neural circuits and certain biochemical transformations that take place after birth ensure the formation of highly specialized and essential cortical areas and connections. The idea that neuronal activity has a profound impact on the normal development of the visual system connectivity has its roots in the pioneering deprivation studies of Hubel, Wiesel, and later Shatz. They showed that closing one eye during a critical period in postnatal life rendered that eye permanently incapable of driving cortical cells (Wiesel and Hubel, 1965). This effect was accompanied by a marked reduction in the cortical area innervated by that eye and by a significant increase in the cortical territory allocated to the geniculocortical axons serving the nondeprived eye—that is, a massive reorganization of the brain due to the lack of input from one eye (Hubel, Wiesel, and LeVay, 1977; Shatz and Stryker, 1978). What factors are responsible for this postnatal reorganization of visual cortex? The mark of the adult lateral geniculate nucleus (LGN) and of the primary visual cortex is the presence of a highly organized structure. The LGN is segregated into eye-specific layers, while the visual cortex presents ocular dominance columns—collections of neurons driven more strongly by the input from one eye than from the other. The development of these structures is a good place to examine the roles that intrinsic (e.g., genetic) factors and extrinsic factors (e.g., specific sensorial inputs) play in defining the final form of cortical and subcortical visual areas. Consider the fact that the retina of infant ferrets is swept every minute by spatiotemporal waves of intrinsically generated neural activity (Katz and Crowley, 2002). Importantly, these waves occur independently in the two eyes (in contrast to evoked activity). In other words, neighboring cells in one retina will fire at nearly the same time, but the firing of corresponding ganglion cells in the two retinas will be uncorrelated. It has been suggested that this withineye correlated retinogeniculate activity and the uncorrelated activity between the two eyes will lead to the laminar segregation of the LGN (Huberman, Stellwagen, and Chapman,

2002; Huberman et al., 2003) following the Hebbian postulate that coactive inputs are preferentially stabilized relative to temporally uncorrelated inputs (“cells that fire together wire together”). This is a good example of neural structure being formed by spontaneous activity rather than evoked activity. In the case of the ocular dominance (OD) columns, the initial view was that visual stimulation was needed to form these columns in the primary visual cortex. However, subsequent results have shown that retinal activity is very unlikely to be the cause of OD band formation. Experimental data suggest that the initial formation of the OD columns is not influenced at all by the balance between the inputs from the two eyes. The work of Crowley and Katz (1999) showed the presence of OD columns in ferrets that were enucleated at birth. The OD structure in these animals closely resembled, in spatial cortical periodicity, that of normal animals. The same investigators (2000) also showed that changing the balance between retinal inputs through unilateral enucleation had no significant effect on the sizes of the columns mapping the deprived and the nondeprived eyes. Based on these data, it looks as though the segregation of the OD column in layer 4 is driven mostly by molecular cues, although the role of spontaneous activity from the LGN cannot be completely excluded. Although the instructing influence of spontaneous electrical activity on the formation of cortical columns is questionable, convincing data exist showing that electrical activity is essential in maintaining these columns (Chapman, 2000). These findings call to mind the important distinction between induction and maintenance in development as noted by Gottlieb (1976). Although thinking about the role of neural activity in forming OD columns has undergone recent serious revision, the role of this activity in constructing some receptive field properties has yet to be challenged. The work of Weliky and Katz (1999) in particular makes a strong case for a causal relation between spontaneous correlated activity from the LGN and the early appearance of orientation selectivity in the visual cortex. Electrophysiological data in neonatal kitten cortex suggest that some neurons have a weak orientation preference, with these preferences mostly concentrated around the cardinal axes (Fregnac and Imbert, 1978). Weliky and Katz (1999) showed that synchronous bursts of spontaneous activity occur in the LGN of ferrets before eye opening with a frequency similar to that of spontaneous retinal waves. Significant binocular correlations were present only when corticothalamic feedback was intact. Weliky and Katz (1999) also showed that disruption of these natural input patterns results in the degradation of early cortical orientation selectivity. Chronic stimulation with synchronous electrical activity (through a nerve cuff implanted postnatally on one of the optical nerves, the other eye being enucleated) disrupted the spontaneous retinogeniculate drive. The result was that,

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Figure 9.3 (A) Time line illustrating many of the main events during the development of the visual cortex and its connections with the thalamus in ferrets. (Reprinted from Sur and Leamey, 2001, by permission from Macmillan Publishers Ltd.) (B, C) Developing neurons have a higher concentration of intracellular chloride than adult neurons. (B) The electrochemical equilibrium potential for Cl− decreases with age. (Reprinted from Ben-Ari, 2002, by permission from Macmillan Publishers Ltd.) (C) The Nerst equation relates the transmembrane chloride concentration gradient to the reversal potential. The curve is very steep at physiological concentrations; that is, a small change in the ion concentration is

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sufficient to cause GABAA reversal potential to be either below or above the resting membrane potential or the threshold for action potential generation, that is, acting as an inhibitory or excitatory messenger. (Reprinted from Staley and Smith, 2001, by permission from Macmillan Publishers Ltd.) (D) Cortical synaptic density (left y-axis) and the percentage of infants displaying stereopsis (right yaxis) plotted against postnatal age (x-axis). Synaptic density is plotted with open symbols, and stereopsis is plotted with closed symbols. There is a close, time-lagged correlation between this anatomical measure and this functional measure. (From Wilson, 1993, by permission.)

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although the macroscopic aspects of the orientation preference maps in the visual cortex apparently were not very different than in the normal case, orientation selectivity was lower at both the population and single-cell levels. Additional evidence shows that visual experience plays an instructive role in the formation of orientation selectivity within the neuronal population of V1 (Sengspiel, Stawinsky, and Bonhoeffer, 1999). Here, then is an example of a visual property that appears to depend both on intrinsic and extrinsic neural activity to achieve its mature form. These uniform waves of spontaneous activity, present throughout the brain as a mark of developing networks, are but one example of the complexity confronting our understanding of the relations between brain and behavior during development. Another example can be found in the role played by the balance between excitation and inhibition that one finds at very early stages in development. The signature electrical activity of developing circuits is the presence of giant depolarizing potentials (GDPs) (for a comprehensive review please refer to Ben-Ari, 2001). These potentials generate large oscillations of intracellular calcium and lead to an activity-dependent modulation of neural growth and the formation of synapses. Gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter in the adult brain, plays an important role in the generation of the excitatory GDPs (Ben-Ari, 2002) early in development. Although, as noted, it is the main inhibitory neurotransmitter in the adult brain, responsible for optimal information processing (any imbalances can lead to pathologic conditions, e.g., epilepsy or Tourette’s syndrome), GABA exerts an excitatory effect and generates significant trophic effects early in development (Owens et al., 1996; Hensh, 2005). Additional evidence shows that the marked difference in GABA activity in infancy and adulthood is due to yet another difference between immature and adult neurons: The concentration of the Cl− ion is higher in immature neurons with about 25 mM, a concentration sufficient to change the action of GABA from being inhibitory to excitatory (in adult neurons the intracellular chloride concentration is around 7 mM; see figure 9.3B). Furthermore, there are data that suggest that the same thing is true for the astrocytes of the developing but not adult optic nerve (Sakatani, Black, and Kocsis, 1992), possibly making this higher Cl− concentration a universal developmental signal throughout nervous system structures. Comparative research shows that GABA is a very well conserved developmental signal phylogenetically. GABA or its phylogenetic homologues have been described throughout the animal and vegetable kingdoms (from tomatoes, Gallego et al., 1995; to beetles, Wegerhoff, 1999; to Drosophila, Lee and O’Dowd, 1999). In the mammalian brain it has been shown in a variety of systems (spinal cord, Reichling et al., 1994; hypothalamus, Chen, Trombley, and Van Den Pol, 1996; hippocampus, Leinekugel et al., 1995;

cortex, Owens et al., 1996; and other areas) that GABA produces depolarization and an increase in intracellular calcium levels in the immature but not in the adult brain. Given this important developmental change in the action of GABA, it is also interesting to note that it is probably GABA itself that promotes the developmental switch of neuronal GABAergic responses from excitatory to inhibitory (Ganguly et al., 2001). Intimately related to the intracellular Cl− concentration (the main ion conducted by the GABA receptor channel) this shift changes the GABA-related stimulation from being a depolarizing signal to being a hyperpolarizing signal (i.e., inhibitory), with inhibitory GABA probably playing a very important role in the mature brain’s ability to adapt dynamically to evoked activity (see figure 9.3C). Considering the focus of this chapter, it is reasonable to ask whether these animal models of visual cortical development are also valid for the development of the human brain. Recent evidence from postmortem samples of postnatal V1 (Murphy et al., 2005) showed that the mature expression of the gene responsible for the formation of the GABAproducing enzyme and the conversion of GABA receptors to a state compatible with driving plastic changes within a network occurs slowly, over several years—a fact that is consistent with the extended length of the critical period for amblyopia in humans (Berardi, Pizzorusso, and Maffei, 2000). Additionally, particular injuries like brain lesions or retinal scotomas in adulthood produce, among other effects, a rearrangement of the excitatory-inhibitory balance to a more immature state (Arckens et al., 2000). The development of the brain function is a very dynamic process that involves both intrinsic and extrinsic factors. We have reviewed several of the intrinsic mechanisms that are important in the development of the visual system. An additional line of evidence offers striking support for the complementary idea that sensory experience itself (evoked neuronal activity) plays a significant role in defining the shape and the organization of the future mature brain. These studies look at the development of cerebral circuitry and resultant function in animals that have the afferents that carry information about one sensory modality redirected to cerebral targets that normally process different modalities. Some of the most direct evidence comes from the work of Sharma, von Melchner, and Sur (Sharma, Angelucci, and Sur, 2000; von Melchner, Pallas, and Sur, 2000), who showed in ferrets that rerouting retinal projections into the auditory pathway makes the neurons in what should be the primary auditory cortex respond to visual stimuli. The cortex becomes organized into orientation modules (a mark of visual cortex), and neurons show orientation tuning comparable to that of V1 neurons even though the orientation map is less orderly. Furthermore, when light stimuli were presented in the portion of the visual field seen by the “auditory” cortex,

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“rewired” animals responded as though they perceived the stimuli as being visual rather than auditory. While it is undoubtedly true that intrinsic factors play a large role in setting up the initial circuitry in cortex, this work provides a clear if somewhat artificial example of how the final organization and function of a section of cortex depends critically on the incoming, stimulus-evoked afferent activity.

Cortically motivated developmental brain–behavior models We turn next to the enterprise of trying to construct models of behavioral development during infancy from what is known about the anatomical and physiological development of the visual pathways beyond the retina. One good example of such a model was proposed by Wilson (1993). In this model, Wilson attempted to relate cortical synaptic density as reported by Huttenlocher and associates (1983; see also Huttenlocher and de Courten, 1987) to the development of various functions most likely to be mediated cortically (e.g., orientation selectivity, binocular rivalry, and stereopsis). Figure 9.3D shows that there is a time-lagged but close correlation between stereopsis development and the increase in synaptic density over the first year of life. Of course, this correlation cannot be interpreted causally, mainly because it is difficult if not impossible at this point in our understanding of cortical circuitry to relate quantitatively a measure like synaptic density to a measure like stereoacuity. Figure 9.4 shows Conel’s (1939, 1951) renderings of sections of visual cortex from a newborn human infant (left) and a six-monthold human infant (right). Although the postnatal increase in dendritic complexity is evident even to the naked eye, it is another matter to try to relate this increase quantitatively to some aspect of visual performance. All that one can do at this point is to agree with Wilson (1993) that until adequate models of functions like stereopsis are available, it is best to be content with the apparent correlation between the brain and behavioral measures. Several other investigators have proposed models of early visual development that involve cortical brain-behavior relationships (Atkinson, 1984, 1992, 1998; Banton and Bertenthal, 1997; Bronson, 1974; Johnson, 1990). One theme common to these models is that early visual development may be characterized by differential rates of maturation both within subpathways and between subpathways in the visual system (see figure 9.2). The most common example of this type of model involves earlier maturation of subcortical function than of cortical function (e.g., Bronson, 1974; Atkinson, 1984). This theme is also evident in Atkinson’s (1992, 1998) proposals that the parvocellular pathway may lead the magnocellular pathway in maturation during early postnatal development, thereby leading to the earlier emergence of certain visual functions typically attributed to the parvocellular pathway. Finally, Johnson

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(1990) and Banton and Bertenthal (1997) have used anatomical data showing differential maturation of the laminae in primary visual cortex to argue for the differential emergence of certain aspects of visual attention and motion processing. Bronson (1974) first proposed that early in postnatal life much of the visual capacity of the neonate could be explained by supposing that the subcortical pathway from retina to superior colliculus matured or was functional earlier than the cortical pathway from retina to visual cortex. Atkinson (1984) echoed this proposal that much of the observed visual behavior in the first month is controlled subcortically and added the additional postulate that the descending pathways from visual cortex to superior colliculus mature later, leading to changes around two months in certain visual capacities (e.g., attentional switching, binocular convergence). Using resting positron emission tomography (PET), Chugani and Phelps (1986) concluded that prior to three months of age, various subcortical areas (e.g., thalamus, midbrain–brain stem) were probably more functionally mature than various cortical areas (e.g., occipital, parietal, and temporal). Different cortical areas might also mature earlier than others (e.g., temporal prior to frontal, Erickson et al., 1998). Aspects of visual behavior such as orienting probably depend heavily on the superior colliculus, so it is natural to suppose that the visual orienting in newborns, however sluggish, reflects the function of this structure. Additionally, early postnatal asymmetries in optokinetic nystagmus (OKN), a reflexive visual tracking of large moving fields, and the disappearance of these asymmetries later in the first half year of life imply that the superior colliculus alone may mediate much of the visual behavior observed in the neonatal period. However, it is important when examining these models to determine just what vision is like with and without the operation of visual cortex. What visual capacities might we expect to be present when only subcortical structures are mediating vision or when vision is mediated by pathways other than the ones through primary visual cortex? Several reports exist that are relevant to this question (see Stoerig and Cowey, 1997, for a review). For example, color vision is typically thought to involve cortical processing within the parvocellular stream, although the substrate for this is surely set up initially within the color opponent ganglion cells of the retina. Despite this cortical involvement, Stoerig and Cowey (1992) and Barbur and associates (1998) reported color discrimination in individuals with significant primary cortical lesions or without primary visual cortices. Additionally, Braddick and associates (1992) have reported on several visual capacities present in unilaterally decorticate human infants. This report is important, because the absence of striate visual cortex does not necessarily imply that all

fundamentals of developmental neurobiology

Figure 9.4 Striate cortex from a newborn (left) and a 6-month-old (right) human infant. (From Conel, 1939 [left] and Conel, 1951 [right] by permission.)

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visual function is then mediated subcortically; it is possible that projections to extrastriate visual cortex could also be involved. In the two cases reported in Braddick and associates (1992), however, both striate and extrastriate cortex were missing, so subcortical pathways must have mediated the remaining visual capacities. These infants could visually orient to a conspicuous target presented to the contralesional visual field as would be expected if such a function were subserved by the superior colliculus. Somewhat unexpectedly, however, binocularly symmetric OKN, which had been thought previously to be mediated by crossed subcortical pathways, was found to be asymmetric in both these infants. This observation implies that contrary to earlier ideas that cortical involvement need not be invoked to explain symmetric binocular OKN, such cortical involvement may indeed be necessary. This possibility is significant, because even in the newborn infant OKN is binocularly symmetric. One other recent report is relevant to the question of what vision might be like in the absence of striate visual cortex. Shewmon, Holmes, and Byrne (1999) reported clinical observations on multiple functions in four congenitally decorticate children. Of particular relevance are their reports that some of these children could actively track, albeit not very smoothly, moving objects such as faces, visually orient to objects moved into the peripheral visual field, and in one case fixate steadily. Additionally, several of these children were said to be able to recognize familiar adults, although such recognition probably occurred through other sensory modalities. All these children were assumed to be cortically blind. Shewmon, Holmes, and Byrne (1999) proposed the interesting idea that some of these visual capacities might represent “vertical” plasticity in subcortical structures. It is generally accepted that “horizontal” plasticity between different cortical areas may take place when tissue in one area is disrupted and its function is taken over or usurped by remaining cortical tissue (Baseler, Morland, and Wandell, 1999; Chugani, Muller, and Chugani, 1996; Cohen et al., 1997; Sadato et al., 1996). What Shewmon, Holmes, and Byrne (1999) propose is that subcortical structures may be “vertically” plastic and organize prenatally or reorganize to take over supposedly cortical functions in the absence of target occipital cortex (see also Kalil and Behan, 1987). Until more systematic visual tests are done on such children, it is difficult to say precisely how good these remaining visual capacities are. Nonetheless, observations such as these are valuable in giving some insight into what vision is like without cortical pathways. The remaining models of visual development have all proposed differential maturation of specific subpathways in the visual system as explanations for the emergence of various visual capacities. Atkinson (1992, 1998) has proposed that the parvocellular pathway is more mature early

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in postnatal development than the magnocellular pathway. The behavioral evidence that is meant to be explained by this model is primarily pattern vision (e.g., orientation sensitivity). Johnson’s (1990) model is meant to explain primarily orienting and attentional behavior based on anatomical data from Conel (1939, 1951) that the primary visual cortex matures from deeper (levels 5 and 6) to more superficial layers (layers 1–3). Additionally, Johnson proposed that descending pathways from striate and extrastriate cortex to superior colliculus mature at different rates. Finally, Banton and Bertenthal (1997) also proposed that differential postnatal maturation of the laminar structure of striate cortex from deeper to more superficial layers may explain various aspects of the development of motion processing. We will use Atkinson’s (1992, 1998) model as an example of the theme of explaining early visual development by pointing to differential maturation of subpathways. This model is a good example of using behavioral evidence to infer the state of various cortical areas. As such it relies on modern visual neurophysiology with its major division of visual processing into parvocellular and magnocellular streams (see figure 9.2). This model affords a good opportunity to review the behavioral evidence on early visual development as well as the anatomical and neurophysiological evidence on the maturation of various areas in visual cortex. Atkinson’s model (1992, 1998) relies heavily on the idea that certain aspects of visual processing are the province of the parvocellular (ventral) stream while others are the province of the magnocellular (dorsal) stream. Additionally, development in this model consists of integration across these streams (the binding problem) so that the infant eventually comes to have a complete representation of objects in the world and of their spatial positions. This representation serves the purposes of object recognition and action. Atkinson argues that the ability of very young infants in the first few weeks of life to discriminate the orientations of grating patterns (Atkinson et al., 1988) and the temporal-frequency dependency shown by evoked responses to orientation changes are evidence of the early maturation of the parvocellular pathway (Braddick et al., 1986) because this function is subserved primarily by parvocellular neurons in adults (but see also Levitt, Lund, and Yoshioka, 1996). In contrast, motion processing that is selective for direction is supposed to be handled primarily by the magnocellular pathway. Excluding OKN, true evidence of direction-selective responding does not emerge until after 6–8 weeks of age, and it is evident at slow velocities before it is evident at faster ones (Wattam-Bell, 1991). Thus, unlike orientation discrimination, which is evident well before 6–8 postnatal weeks, this magnocellular characteristic—directional selectivity—is not evident until near

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the end of the second month. Atkinson (1998) concludes that magnocellular function lags parvocellular function early in development. Disparity sensitivity and binocular correlation detection do not emerge until approximately 3–4 months postnatally (Birch, Gwiazda, and Held, 1982; Fox et al., 1980). Depth processing is thought to be handled by the magnocellular pathway. In contrast, 8-week-olds (Hamer, Alexander, and Teller, 1982; Packer, Hartmann, and Teller, 1984) and possibly even neonates and 4-week-olds (Adams, Maurer, and Davis, 1986; Maurer and Adams, 1987) can detect pattern differences based on color alone, and color is thought to be processed primarily by the parvocellular pathway (although see Barbur et al., 1998). Once again, functions attributed mainly to the magnocellular stream (disparity, depth) appear to emerge slightly later than one attributed to the parvocellular stream (color). What do we know about the differential emergence of these two processing streams from anatomical and neurophysiological studies on humans or closely related species? The retinal ganglion cells that make up these two streams diverge early in embryonic development in the primate visual system, and they project to the appropriate laminae in the developing LGN well before any visual experience (Meissirel et al., 1997). The projections from relay neurons in LGN arrive at their targets in layer 4C in striate cortex prenatally, and the axons terminate in appropriate sublaminae within 4C, again before any visual experience is attained (Mates and Lund, 1983). Initially, it appears that LGN neurons may also project to layer 6 of striate cortex before birth (Rakic, 1976, 1977). As noted previously, intrinsic activity during prenatal development probably plays a major role in organizing the laminar characteristics, feature maps (e.g., orientation preferences, ocular dominance columns) and appropriate projections in the retinogeniculocortical pathway prior to extrinsic, visual experience (Hubener, 1998; Godecke and Bonhoeffer, 1996; Katz and Shatz, 1996; Shatz, 1996). Indeed, the laminar structure of visual cortex and its reciprocal connections with LGN even develop when slices of future visual cortex and LGN are cocultured in vitro (Toyama et al., 1991).2 Anatomical data on the development of visual cortical areas from humans and closely related primates present a mixed picture on whether or not one of the two processing streams is ahead of the other in terms of its state at birth and its postnatal maturation. One of the problems in using the anatomical data to make inferences about the presence of a particular function is that it is not clear exactly which characteristics should be used to infer effective function. One could examine myelination as a marker for effective function, or one could examine synaptic connectivity and morphology for clues to when a particular region appears to be functional. As others have noted (e.g., Banton and Ber-

tenthal, 1997), Conel (1939) pointed out that axons in the deeper laminae (5 and 6) of striate cortex appear to myelinate earlier than those in more superficial laminae. Thus extrastriate areas receiving projections from neurons in striate laminae 5 and 6 (e.g., area MT) might be expected to support effective function before those receiving projections for more superficial laminae. This reasoning is the basis for Banton and Bertenthal’s (1997) proposal that newborns may be expected to respond to translatory motion. If one examines other markers of anatomical development in striate cortex, differential gradients of maturation are either not as clear or are conflicting. For example, Lund and Holbach (1991) showed that the development of Type I dendritic spines that are associated with synaptic contacts reaches a peak in macaque monkeys approximately five weeks postnatally for neurons in lamina 4Cα (magnocellular), whereas a similar peak is not reached for neurons in lamina 4Cβ until eight weeks postnatally (see also LeVay, Wiesel, and Hubel, 1980; Lund, Holbach, and Chung, 1991). The same was also true of Type II inhibitory synapses in layer 4C with those in the magnocellular recipient layer developing slightly ahead of those in the parvocellular layer (Lund and Harper, 1991). It is also interesting to note that these presumed inhibitory synapses in layer 4C lag the development of excitatory synapses in the same laminae (Lund and Harper, 1991). Becker and associates (1984) showed that in humans, dendritic branching was more advanced in layer 5 than in layer 3 in visual cortex prenatally and during early postnatal development—a trend consistent with the myelination from deeper to more superficial laminae. Neuronal densities in human striate visual cortex also appear to follow the gradient noted earlier with deeper layers preceding more superficial layers until densities stabilize several months after birth (Leuba and Garey, 1987). Finally, Lund, Boothe, and Lund (1977) concluded that in macaque monkeys, maturation of the morphology of visual cortical neurons occurs at the same rate in all laminae with perhaps some advantage based on the size of the neuron; large pyramidal neurons in layer 5 tended to mature earlier than smaller neurons in other laminae. It is evident from this brief review that whether or not there is a noticeable advance in the maturation of parvocellular versus magnocellular neurons or by laminar layer depends on what aspect of cortical or neuronal morphology is being considered. However, we agree with Banton and Bertenthal (1997) that on many of these characteristics, magnocellular neurons appear to be slightly ahead of parvocellular neurons.

Subtleties of the differential maturation hypothesis Most of the models based on cortical development that we have discussed argue that the order of emergence of certain visual functions in early postnatal development reflects the

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differential maturation of subpathways in the visual system. As noted earlier, it is clear that intrinsic factors and extrinsic, evoked activity combine to determine the precise development of the visual pathways. A great deal of attention has been paid over the years to the effects of postnatal experience on visual development. Perhaps as important, but somewhat neglected, is the emergence of cortical structure determined by intrinsic factors with very precise timing relations (e.g., triggering a sensitive period by the precise balance between excitatory and inhibitory neurons in a given cortical area). We will conclude with several subtleties and implicit assumptions that can arise when models are formulated regarding explicitly developmental brain–behavior relations in vision. 1. The degree of independence of the visual subpathways involved in these models may not be as great as postulated. For example, Dobkins and Albright (1994) showed that neurons in area MT of the macaque, thought to be the paradigm case for magnocellular processing, are capable of using color differences to signal the directions of motion. Maunsell, Nealey, and DePriest (1990) had reported similar results earlier showing that both magnocellular and parvocellular influences can be detected in the responses of area-MT neurons. Color processing by a strict division of labor would be expected to fall to the parvocellular system, so this result indicates less independence of these subpathways than may be indicated in some of the models discussed earlier. Gegenfurtner and Kiper (1996) showed that as early as cortical area V2, neurons were multiply selective for form, color, and motion, and argued that there is no functional segregation of these stimulus features at this level of the visual pathway. These neurophysiological studies are supported by anatomical studies showing interneurons as early as cortical areas V1 and V2 that merge the parvocellular and magnocellular streams (Levitt, Lund, and Yoshioka, 1996; Levitt, Yoshioka, and Lund, 1994; Lund, 1987; Lund and Yoshioka, 1991; Yoshioka, Levitt, and Lund, 1994). 2. A particular visual function that is ascribed to one subsystem in the mature adult may not necessarily be mediated by that subsystem when it first emerges in development. For example, Dobkins, Lia, and Teller (1997) and Dobkins, Anderson, and Lia (1999) have argued from psychophysical tests of chromatic and luminance temporal-contrast sensitivity that the magnocellular pathway may mediate detection of both luminance and chromatic spatiotemporal contrast. If this assumption were true, then it would differ substantially from the case later in development in which chromatic contrast sensitivity is signaled primarily by a temporally low-pass parvocellular channel and luminance contrast is signaled by a temporally band-pass magnocellular channel. Dobkins and Teller (1996) concluded that for 3-month-old infants, patterns defined by color contrast alone or patterns defined by luminance

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contrast are both detected by mechanisms sensitive to the direction of motion. This situation contrasts strongly with the case in adults in which mechanisms sensitive to luminance contrast are selective for direction of motion, but those mediating detection of color contrast are not. Morton and Johnson (1991) have proposed that face processing, a function typically attributed to the ventral/ temporal cortical pathway, may first emerge during the newborn period mediated by subcortical structures. In all these cases, a visual function typically attributed to one level or subpathway within the mature visual system may emerge first in development being mediated by a different subsystem. If the hypothesis of differential rates of maturation of subpathways is taken seriously, then it should not be surprising that the most sensitive pathway for mediating detection of some stimulus feature early in development may not necessarily be the same pathway that mediates detection of that feature later in childhood or in adulthood (see also Banton and Bertenthal, 1997, for a similar suggestion). A corollary to this point is that infants and adults almost certainly differ in the effort required to execute visual tasks. Differences in effort or automaticity have been shown in adults to be related to differential activation of cortical versus subcortical areas (Alexander et al., 1999; Schneider, PimmSmith, and Worden, 1994). Thus the extent of cortical versus subcortical mediation for a given task early in development may differ substantially from that seen in mature adults. 3. Even if subpathways are quasi-independent in mature adults, when they first emerge there may be considerable overlap in how they process visual information. For example, Hawken, Blakemore, and Morley (1997) showed that in the monkey LGN, spatial contrast sensitivity functions of magnocellular and parvocellular neurons overlap quite substantially at birth. Only later in the first year do the contrast sensitivities of the magnocellular neurons improve substantially enough to separate them from the parvocellular neurons that showed little improvement in peak luminance-contrast sensitivity over 8 months. It may prove very difficult to attribute processing early in development to one subsystem or another simply because the subsystems may not show the degree of stimulus separation early in life that is characteristic of the mature visual system. This argument is similar to Johnson’s and Vecera’s (1996) “cortical parcellation” hypothesis in which the segregation of visual information processing into subsystems only occurs postnatally with many of these subsystems operating initially after birth unsegregated. A corollary to this point is that early in postnatal development there may be transient connections between cortical regions that disappear later in development (Huntley et al., 1988; Rodman and Consuelos, 1994). There is even evidence from the other end of the life

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span that the two major processing streams may become less distinct with age (Grady et al., 1992). 4. There may be significant temporal lags between the emergence of a given function at the single-unit level and behavioral evidence for that function. This possibility makes it very difficult to try to link the anatomical and neurophysiological data and the behavioral data. For example, Chino and associates (1997) demonstrated that binocular, disparity-sensitive neurons exist at or near birth in macaque area V1, although it is not for several weeks that stereopsis can be demonstrated behaviorally. 5. “Bottleneck” developmental theories (e.g., Banton and Bertenthal, 1997; Johnson, 1990) which propose that gradients of maturation within striate cortical laminae are responsible for the order of emergence of various behavioral functions may overlook the extensive alternative pathways to extrastriate cortex that do not go through striate cortex. For example, Benson, Guo, and Blakemore (1998) showed that many of the motion perception functions typically associated with cortical area MT (direction discrimination, perception of moving plaid direction, and coherent motion in random dot displays) remain after loss of primary (V1) visual cortex and its subsequent input to area MT. Benson, Guo, and Blakemore (1998) suggested that a subcortical pathway from superior colliculus through the inferior pulvinar to area MT may mediate such capacities in the absence of input/output from primary visual cortex. A similar suggestion was made by Baseler, Morland, and Wandell (1999). Differential maturation of laminae within primary visual cortex early in postnatal development would only be expected to impose an order on the emergence of visual capacities if these alternative routes from subcortex to extrastriate cortex were immature or if these extrastriate areas themselves were also immature.

Conclusions An immature and developing visual system is not necessarily an adult visual system that has been scaled down in terms of spatial and temporal processing, nor is it necessarily like an adult visual system in which some subpathways have simply been deleted. It is entirely possible that prior to the point at which these subpathways are segregated and subsequently integrated in a mature fashion they may interact and process information in ways that are not characteristic of the adult visual system. Thus the magnocellular pathway early in postnatal development may signal chromatic contrast to a much greater extent than in the adult, or scanning eye movements may produce enough spatiotemporal contrast to allow the magnocellular stream to process chromatic boundaries to the point at which discrimination is possible (Teller, 1998). Epelbaum and Teller (1995) showed that asymmetries in OKN in 2-month-olds are reversed, not eliminated, when isoluminant chromatic stimuli are used to drive the

eye movements—a result that is not found in adults. Unlike models based on photoreceptor morphology and geometry in which the brain–behavior relationships can be modeled based on well-understood physical and optical principles, our lack of understanding of complex cortical circuitry and of how various visual functions arise from such circuits makes it inherently more difficult to link brain and behavior when the brain side of the link is visual cortex. Nonetheless, much of the interesting development that occurs postnatally in the visual system undoubtedly involves striate and extrastriate cortical areas and inhibitory interactions within and between these areas. Our modeling should respect the additional, specifically developmental subtleties likely to arise when multiple subsystems develop “on-line” during the postnatal period. It should also be informed by the considerable cortical structure and connections present prior to the point at which visual experience begins to exert its effects on shaping cortical function. acknowledgment This research was supported by grant NICHD R01 HD32927 to JLD.

NOTES 1. We will not discuss the phenomenon of aliasing that occurs in undersampled systems. The interested reader can consult Thibos, Walsh, and Cheney (1987) for an example and a discussion of spatial aliasing in vision. 2. One note of caution is in order. Horton and Hocking (1996) reported the presence of clearly defined ocular dominance columns in newborn macaque monkeys, although Horton and Hedley-Whyte (1984) did not observe these columns in newborns, but only in a 6-month-old human. Possible species differences should always be kept in mind when generalizing from monkeys to humans.

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10

Motor Systems Development ROSA M. ANGULO-BARROSO AND CHAD W. TIERNAN

Typically, motor behavior has received little attention in developmental psychology. Most textbooks include a small section or a chapter outlining the stereotyped sequence of expected gross and fine motor accomplishments of a child. For example, gross motor milestones include rolling over, crawling, standing up, and walking independently. The development of these motor milestones tends to be discussed in isolation with little apparent relevance to the psychological development of the child. Within this chapter, we wish to accomplish five major objectives. First, we stress the importance of the motor systems in the wholeness of child development. Second, we will present a theoretical approach that facilitates the understanding of motor development as a complex and dynamic process where exploration (flexibility) and selection (stability) of motor actions coexist to allow the acquisition of skilled motor behavior. Third, we will expose and delineate the differences between postural and movement control. Fourth, we will summarize the neural substrates related to the development and control of posture and the different aspects of movement such as initiation, speed, delays, coordination, and sequencing. Fifth, we will outline the existing relationships between the motorcognitive and motor-emotional domains. To end, we will draw conclusions in an effort to guide therapeutic interventions in the motor domain.

Relevance of movement in the developing human being When considering the most common activities performed by a human being, one finds the involvement of some form of motor action almost invariably in every activity. The production of speech involves the movement of the tongue, lips, and vocal cords; typing on a computer requires the action of the fingers and the maintenance of good postural control; walking, eating, writing, dancing, and even sleeping entail movement. When the motor system of an individual fails, life becomes difficult and limited in many ways. Would it not make sense then to think that movement is an essential part of development? In fact, motor behavior is at the core of development, having implications for attention, motivation, perception, memory, and planning. It is imperative to realize that every motor action not only generates the physical movement, but also generates perceptual information by way of the proprioceptive systems, and it also constitutes a means to learn about anticipation, memory, planning, and

consequences of action. As stated by von Hofsten (2004), “Motor development is not just a question of gaining control over the muscles; equally important are questions such as why a particular movement is made, how the movements are planned, and how children (or even infants) anticipate what is going to happen next” (266). In fact, von Hofsten proposes an action approach to motor development, according to which planning of movement and prediction of its consequences are critical to understanding the emergence of skilled motor behavior. He proposes that actions are fundamentally different from reflexes and that even newborns’ movements are never just reflexes. In fact, abundant evidence supports the view that most newborn motor behaviors are flexible goal-directed actions. From rooting to sucking, gaze, and hand orienting, these actions have been shown to be flexibly controlled and adaptive depending on the context (Craig and Lee, 1999; Farroni et al., 2002; Haith, 1980; Rochat and Hespos, 1997; von Hofsten, 1982). An example of such flexibility can be found in studies by van de Meer where newborns and infants modified their spontaneous arm movements, so (1) the arm functionally controlled a weight in order to keep the arm in view (van de Meer et al., 1995) and (2) the hand was kept within the range of a light beam so the infant could see it (van de Meer, 1997). If moving the weight had no functional consequences, the newborn stopped compensating for it. Similarly, when the location of the light beam was moved, infants also moved their preferred arm locations so they could see their hands. Such newborn and infant actions are, therefore, the early practice that an individual needs to learn about control of movement, motivation, planning, and prospective control (consequences of action). Furthermore, learning about motor planning, motor memory, and motor consequences has implicit effects in the development of cognitive skills. Hence, these early actions may constitute the core of higher order cognitive development. Only a few of the well-accepted psychological theories of human development stress the importance of movement as a core foundation to early development: Piagetian theory (Piaget, 1952) and dynamic systems approaches (DSA) (Thelen and Smith, 1994; Thelen and Ulrich, 1991). Moreover, recent dynamic systems proposals claim that cognition is embodied, that is, emergent from bodily actions in the real world, not only during early development but also throughout the entire life span (Thelen, 2000; Thelen and Bates, 2003).

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Piaget (1952) proposed that infants initially discover interesting consequences of their actions by means of repetition (circular reactions). For instance, infants bang their hands or feet on a surface to make sounds. Using repetition, infants not only control their actions and become more skilled, but they also form action-perception relationships (sensorimotor schemata) that will be later used as a foundation for higher cognitive functions. Piaget called this period the sensorimotor phase, which lasts from birth to about one year of age. From this perspective, human cognition has its origins in the sensorimotor activities of the infant. With time, however, Piaget’s assumption is that the child can only form the abstract concept of, for example, an object once he/she has been able to disconnect (overcome) the bodily component. Similarly, ignoring the information of the senses and his/her initial egocentric references is critically necessary for the emergence of formal operations (Piaget, 1954). This cognitive developmental view of becoming progressively more disconnected from action and perception as the individual gains further cognitive skills has been recently challenged by DSA. Abundant research evidence points toward a relevant interaction between motion and cognition not only in early development but also throughout the life span, as we will see in more detail in a later section (“Interconnectedness . . .”).

Theoretical approaches to motor development Dynamic systems approaches (DSA) have been commonly used to explain the development and acquisition of motor

responses (Goldfield, Kay, and Warren, 1993; Heriza, 1991; Kelso, 1995; Thelen and Smith, 1994; Turvey and Fitzpatrick, 1993; Ulrich, 1997). One of the most important tenets of this approach is that new forms of behavior emerge in a nonprescribed fashion from the cooperative interaction of multiple subsystems. Therefore, human movement is defined as the emergent behavior of a complex and adaptive system (see figure 10.1). This definition means that human beings are open systems constantly exchanging energy and changing their levels of stability. Intrinsic factors (organismic, physiological, and psychological) as well as extrinsic factors (informational cues, surface of support, and context) cooperate to accomplish a unifying goal: the task. A complex and adaptive system creates variability, or diverse and coexisting alternatives, to initiate a process of exploration and selection. Through practice and action-perception coupling, the individual ultimately finds the most adaptive forms of motor patterns (i.e., the system retains relative flexibility to adapt to task demands). Change in movement activity can be seen in dynamic terms as a series of states and phase shifts, reflecting the probability that a pattern of movement will emerge under particular task constraints. From dynamic principles, one can predict that change is facilitated by the loss of stability. In other words, highly variable movement patterns might represent an exploration stage. Some subcomponents of the system must disrupt the currently stable movement pattern so that the system is free to change its state. The components of the system disrupting the current stability and thus

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Figure 10.1 Schematic diagram of the process of motor development from a dynamic systems approach. Infants and children are complex adaptive systems with particular intrinsic and extrinsic

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dynamics. Task-oriented activities drive development by means of the processes of exploration and selection so motor patterns emerge that are adaptive (flexible) to task demands.

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engendering change can only be known through careful empirical study. As Thelen (1995) stated, such study is difficult because these agents may be nonobvious and changing through time. For instance, growth or physiological factors, such as leg-mass-to-strength ratios, are important in early infancy for the development of functional leg movements. However, in situations later in development, experience or socioemotional conditions may have a greater influence. The task in question dictates the relevance of the contributing factors. There are critical implications of a dynamic system’s conceptualization of motor development: (1) A child is viewed as continually shifting his or her movement pattern stability: there will be times when some motor behaviors are very stable and other times when new patterns will emerge. (2) The stability of these motor patterns depends on the degree of coherence among the important parts. (3) Change in motor behavior cannot be explained on the basis of change in one subsystem or part, but rather by the interaction between the embedded components (i.e., nervous system, body) and environment. No one part of the child is privileged in this regard. (4) Finally, the current dynamic state of the child is a function of previous states and also serves as the basis for future states. From this perspective, one can say that children become skillful motor performers because they have the capacity to maintain flexibility in the degree of coupling among the nervous system, body, and environment at the same time that they are dynamically responsive to the task at hand.

Posture, movement, and overall level of motor activity Movement scientists may have different views about the definitions of posture, motor action/movement, and motor activity today. However, it is useful to agree about functional definitions of posture and movement so we can clarify their distinct contribution within motor development. We define posture as the maintenance of a specific body configuration that minimizes movement in some parts of the body (or all of it) while facilitating motor action efficiency in other parts of the body. In this case, a motor action could be maintaining posture itself, as we do when standing on an icy sidewalk while waiting for a bus on a winter day. However, the motor action could be the performance of any goal-directed movement such as reaching for an object. In general, movement could be defined as changes in the body configuration so a goal-directed action is accomplished. Finally, as scientists it is useful to estimate overall motor activity because it gives us a gross measure of all types of movements independent of goals. A large body of literature exists describing the orderly progression of postural control and movement acquisition observed in infants and children. Many of these studies

have also examined the underlying processes of developmental changes in the aforementioned areas (for reviews of motor development, see Angulo-Kinzler, 2001a; Bertenthal and Clifton, 1998; Fentress and McLeod, 1986; Jouen and Lepecq, 1990; Schmidt and Fitzpatrick, 1996; Thelen, 1995; Zelazo, 1998). Still other motor developmental studies have focused on how these motor actions become more efficient and finely tuned to environmental demands (Adolph, 1997; Angulo-Kinzler, 2001b; von Hofsten, 1979; Thelen et al., 1993). In this chapter we chose to present the development of reaching and walking as examples of how motor skills emerge and change through time. The Development of Reaching From 0 to about 4 months of age, infants demonstrate a level of poor arm and hand motor control. They are able to move their arms, but they are not successful at grasping an object. These initial movements, however, allow infants to practice and learn about their movements during this period when perceptionaction relationships are formed and selection of more effective action takes place. Infants learn to first move their arms to the vicinity of the object. Initial improvements in reaching skill come from advancements in postural trunk control (Spencer et al., 2000), eye-hand coordination (von Hofsten, 1982), and adjustments in arm velocity and muscle forces (Spencer and Thelen, 2000; Thelen et al., 1993). During this phase of spontaneous pre-reaching movements, infants explore a range of patterns and select those that take their arms close to the desired toy. Nevertheless, the range of patterns that infants explore is not infinite but rather is constrained by their initial preferences and other neurobiomechanical factors such as formation of linear synergies (i.e., coupling of two or more joints to reduce movement options) (Zaal et al., 1999). As infants transition to their first successful reaches, the muscle patterns change from a more predominant use of biceps and triceps to a preferred use of the deltoid muscle (Spencer and Thelen, 2000). Still, these reaches lack the smoothness in the hand path and the bell-shaped velocity profile observed in adult reaching (Morasso, 1981). Subsequent changes in the reaching abilities of infants continue during the second half of the first year. In fact, infants show increased hand skill in several dimensions. The 6-month-old generally grasps an object with the entire hand, showing only primitive aspects of coordination between the palm and fingers. By 9–10 months, the child begins to use the forefinger in grasping, and reaching and grasping are coordinated into one continuous movement. Although infants as young as 6–8 months are capable of precision grasping, the developmental trend is an increase in use of the pincer grasp over time along with progressive elimination of ulnar and palmar grasps. Younger infants do not

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exhibit a consistent grasp pattern with the smallest objects, while older infants use the inferior forefinger grasp or the pincer grasp. In addition, 6-month-old infants do not change grasp configurations as a function of object size, while 9month-olds adjust their hand opening (amplitude) accordingly. Also at 9 months, infants can correct hand trajectory to a moving target even after they started their reaching action. Initiating this adaptation, however, takes twice as much time as it does in adults (Berthier and Robin, 1998). In other words, the last half of the first year is characterized by the acquisition of a more stable form of reaching, which in turn allows the development of other forms that are more adaptive. Concomitant changes at the neuromotor systems level also occur during the first year of life. It has been proposed that direct connections between neurons in the cortex and motor neurons in the spinal cord by way of the corticospinal tracts are critical for the control of fine hand movements (Kuypers, 1982). These neurons and connections have a special role in precision grasping, as they are active in a pincer grasp but not in a palmar grasp (Muir and Lemon, 1983). Increasing hand skill parallels the decline in latencies of motor evoked potentials (motor responses resulting from stimuli over the motor cortex), increase in conduction velocity, and increase in myelination and axon diameter of the relevant corticospinal tracts (Eyre et al., 1991). However, other neuromotor areas are also important in the development of skillful reaching, since reaching involves motivation, attention, control of movement, planning, anticipation, and prospective control. The Development of Walking Infants undergo substantial changes during their transition to adultlike independent, upright locomotion over the first two years of life. Such transitions became the focal point of developmental research starting in the 1930s and 1940s with the work of Gessell and McGraw. McGraw (1945) suggested that infants progress sequentially through seven phases to achieve adultlike erect locomotion and ascribed maturation within the nervous system as a cause for these development acquisitions. The first phase was known as “reflexive stepping,” which occurs during the first two months of life. She described these movements as primitive and subcortically driven. The next period was classified as the “static phase.” During this time, there is an apparent decline in stepping behavior, which she attributed to cortical inhibitory processes. Third, the “transition phase” is marked by heightened variability in leg movements and a difficulty discerning whether or not the stepping movements are reflexive or deliberate. In the fourth phase, stepping is described as “deliberate”: infants take steps in an intentional manner resulting from more direct cortical participation. Next, rudimentary elements of “independent stepping” can be observed. These movements, although not yet refined, are

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characterized by the integration of posture and forward propulsion of the legs. The sixth phase was referred to as “heel-toe progression,” where coordination improves and infants more consistently initiate stance with a definite heel strike and end with pushing their toes off the ground. The final stage, “integrated walking,” shows refined coordination and smooth and automatic independent upright locomotion during the second year of life. While the general development sequence described by McGraw is largely agreed upon, both the extent of the role of the nervous system and the deterministic nature of these characterizations of walking has been largely debated over the years. According to Thelen and Smith (1994), McGraw herself later acknowledged that histological changes in the brain and the assumption of localization of function were a much too simplistic view of development. Similarly, Gessell (1945) stated that it was more appropriate to describe such developmental processes as dynamic and nonlinear, rather than stagelike and maturationally driven. Regardless, neural accounts of walking have still been popular throughout the years. In particular, many researchers believe that “central pattern generators” (CPGs—a set of neurons in the spinal cord capable of a patterned neural activation which matches that needed for gait) drive the development of adultlike walking because these neural networks within the spinal cord can generate muscle-specific activations (i.e., alternating activity of flexors and extensors) (Grillner, 1975, 1981; Forssberg, 1985). Forssberg (1985) argued for a hierarchical explanation of walking, whereby CPGs were the basis for locomotor development. He contended that adultlike locomotion resulted from the refinement of these neural networks as higher brain centers develop. Much of the support for CPGs stems from animal research, including spinalized cats that have been shown to produce hind-limb movements similar to those of their nonspinalized counterparts (Forssberg, 1980a, 1980b). Neural network accounts have been challenged, however, by proponents of DSA who question the functional capabilities of neural networks and argue that the nervous system should not be seen as a privileged component within the system. For example, Thelen and Fisher (1983) examined muscle activation patterns of infants during early stepping movements and concluded that the observed activation patterns were far too complex to be explained by CPGs. In addition, the decline in stepping after two months of age has been shown to be influenced by other than purely neural factors. Research has shown that step frequency in early infancy is related to infant arousal and rate of weight gain (Thelen et al., 1982). Similarly, Thelen, Fisher, and Ridley-Johnson (1984) manipulated leg mass by placing young infants upright in warm water to decrease the load of their legs. Results showed that infants increased their step rate when compared to the out-of-water condition. Finally,

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What about Overall Level of Motor Activity: Does It Have Any Role in Development? In the past, quantifying the frequency of motor activity was beset with a number of problems, including questionable reliability of self-report measures, time-consuming and intrusive direct observations, and complicated physiological measures such as heart rate telemetry and energy expenditures (Sirard and Pate, 2001). Recent advances in the memory capacity and sensitivity of activity monitors have improved the reliability and ecological validity of activity-based research (Puyau et al., 2002). The small lightweight devices simply attach like a wristwatch to the limb or to the trunk of the participant and allow researchers the opportunity to collect less intrusive, ecologically valid, and objective motor activity data for extended periods of time in children (Trost et al., 2005). Traditionally, activity monitors were used in sleep research (for review see Sadeh and Acebo, 2002) but more recently these devices have been shown to be sensitive enough to detect differences between spontaneous motor activity levels in awake and alert infants with and without iron-deficiency anemia (Angulo-Kinzler et al., 2002a, 2002b). Additional work in our laboratory has examined the spontaneous leg motor activity of infants with and without Down syndrome (DS) from 3 to 6 months of age. We used activity monitors to objectively quantify the frequency of spontaneous motor activity for 48 continuous hours. Data analyses, controlling for movement artifacts, indicated that infants with DS spent more time in low-intensity activity during the day and the night (see figure 10.2). Furthermore, the level of low motor activity showed a significant relationship with the onset of locomotor activities such as crawling and walking in both groups. That is, infants who spent more time in low activity also showed later onset of locomotion (McKay and AnguloBarroso, 2006). These results are important for illustrating the relationship between the amount of motor activity and motor development as measured by the onset of important milestones.

Time in Low Intensity Activity (min)

treadmill training has been found to increase functional stepping patterns in infants who initially showed little or no stepping (Thelen, 1986; Vereijken and Thelen, 1997). Advocates of DSA acknowledge the critical role of nervoussystem maturation in locomotor development but argue that such findings illustrate the importance of other, nonneural factors (i.e., body composition, strength, motivation) that cooperate with the nervous system during locomotorrelated tasks. In addition, the aforementioned findings suggest that locomotor development is not fixed and can be influenced by early intervention, a possibility that has implications for individuals with locomotor delays. Recently, Ulrich and associates (2001) found that treadmill training facilitated the onset of independent walking in infants with Down syndrome.

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Figure 10.2 Group means and SD values of time spent in lowintensity activity in infants with Down syndrome (DS) and typical development (TD) at 3 through 6 months of age. p < .05. (Data published by McKay and Angulo-Barroso in Infant Behavior and Development 29:153–168; reprinted with permission.)

Neural substrates of motor behavior As reviewed in the section on our theoretical approach to motor development, it is important to note that although the nervous system is an important contributor to development, it is not the only one. Previous research has shown that many other physical and contextual factors, such as body dimensions, proportions, postures, and inertial properties, contribute equally to motor behavior (Thelen and Fisher, 1982, 1983; Thelen, 1986; Ulrich et al., 2001). Although dynamic approaches to development make no explicit proposal of a neurobehavioral theory, they are perfectly compatible with neurodevelopmental theories that view the brain as a dynamic and complex system whose development complies with the similar dynamic principles proposed for motor development (Sporns and Edelman, 1993; Stiles et al., 2005). Currently, little is known about the specific changes occurring at the brain level that accompany the acquisition of new motor behaviors. The neural mechanisms that could explain the changes that have been described in the development of reaching or walking, for instance, are rather complex and involve many neuronal structures. A seemingly simple task such as reaching for an object requires motivation, goal identification, visual processing, postural control, planning the response, setting the appropriate amount of force and correct direction of movement, timing the different muscles involved, and so on . . . Since many of these processes occur in parallel, it is implied that many interconnected areas of the brain are activated. For instance, activity related to the visual cue of the object to be reached activates not only visual association areas but also areas related to motor planning and preexecution such as the supplementary motor/ premotor areas, cerebellum, and basal ganglia, all of which

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are involved in motor planning, motor memory, and learning ( Jeannerod, 1997; Schwartz, 1994). Similarly, the neurons that code for reaching direction involve not only those in the primary motor cortex (Georgopoulos, Kettner, and Schwartz, 1988) but also those in the parietal cortex. It seems that the distinction between what defines a visual cue, planning, motor memory, and execution of the action is becoming blurred as we learn more about the activation patterns of the entire brain (Anderson et al., 1997). Although accepting a wide distribution and multiple representations of motor skills in the young brain may seem problematic, it also brings the advantage of permitting exploration (flexibility) and selection (stability) in a child’s motor repertoire. In addition, it implies that brain areas are multiply and densely connected and that the child’s experiences with motor skills have a structural and functional impact on the development of the brain itself (experience-dependent plasticity, or more precisely in our case, activity-dependent plasticity; Jones, Kleim, and Greenough, 1996). From this perspective, brain development is a dynamic process. As stated by Stiles (2001): “the developing brain is a dynamic, responsive, and to some extent self-organizing system” (p. 266). One important aspect of a dynamic and complex system, such as the brain, is its capacity to self-organize and therefore generate stable patterns of action (Kelso, 1995). Throughout development, changes in the volume and activity of the different areas of the brain are not uniform, especially when comparing subcortical and cortical regions, or among the different cortical areas. For instance, peak density of synaptic connection occurs earlier in the visual cortex (4–12 months) compared to prefrontal cortex (after 12 months) (Huttenlocher, 1990; Huttenlocher et al., 1982; Huttenlocher and Dabholkar, 1997). Additionally, areas of higher activity (represented by glucose uptake) in the basal state in newborns are the sensorimotor cortex, thalamus, brain stem, and cerebellar vermis. In contrast, parietal, temporal, and occipital cortices, along with the basal ganglia and cerebellar cortex are most active at 3 months of age (Chugani, Phelps, and Mazziotta, 1987). Interestingly, the most active areas in the newborn phase are those that underlie motor execution, whereas those most active in the infancy phase are already capable of motor planning. Whether activitydependent experience is the guide for an already highly interconnected brain or the interconnections are formed as the child learns is still under debate ( Johnson, 2001). Further changes in brain connectivity and metabolism continue through development, including a temporary phase where these levels surpass those of adults. However, once the brain has reached a certain level of maturity, neuroscientists are more ready to ascribe specific function to particular areas of the brain. Table 10.1 summarizes the most relevant areas of the neuromotor system and their corresponding functions. The interconnectedness among these areas and

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pathways is summarized in figure 10.3. Please note that these graphics only begin to depict the complexity of the neuromotor system. As children get older the efficiency of each of these pathways improves, but they do not necessarily do so at the same time. An example of these developmental improvements is discussed in the work of Garvey and associates (2003) where they examined the cortical correlates of neuromotor development in healthy children using focal transcranial magnetic stimulation (TMS). They demonstrated that the corticospinal pathway improved its efficiency with time as evidenced by decreases in the threshold of motor evoked potentials (MEP) and decreases in the ipsilateral silent period (iSP), with the latter defined as a transient interruption of ipsilateral voluntary muscle activation. These measures were recorded while children of different ages performed fingertapping movements. It is thought that MEP threshold denotes the developmental myelination stage. However, it is important to note that myelination does more than improve conduction velocity—it also helps provide nutritional and structural support for neurons, in addition to aiding neuronal and neurotransmitter activity modulation (Fields, 2004). White matter within the nervous system has gained more and more functional relevance in recent years. For instance, reductions in frontal white matter have been found in otherwise healthy, male children who exhibit complex stereotypies (Kates, Lanham, and Singer, 2005). However, iSP is thought to depend on transcallosal connectivity and is mediated by inhibitory circuits in the motor cortex. Interestingly, the iSP is absent in children under 6, but it is present in children older than 10 years of age (Heinen et al., 1998). Therefore, transcallosal pathways seem to be involved in the production of finger movements, but other motor behaviors such as bilateral hand movements and mirror movements are affected by the corpus callosum function as well (Rademaker et al., 2004). To this point we have focused on brain areas involved predominately in motor functioning. However, other brain areas often ascribed to motor behavior, namely, the basal ganglia and the cerebellum, are also relevant to cognitive and emotional development (Allin et al., 2001; Diamond, 2000; Middleton and Strick, 1994, 2000). For example, the basal ganglia have two major interconnected loops: one for motor activity (cortico-putamen-thalamo-motor cortical circuitry) and another for cognitive–emotional processing (cortico-caudate-thalamo-prefrontal cortical circuitry). Similarly, the neocerebellum is more dedicated to cognitive tasks, while the rest of the cerebellum is devoted to motor control. Interestingly, both the cerebellum and basal ganglia are somatotopically organized, similar to the organization seen in the motor and sensory cortices (i.e., adjacent or connected fields of these areas correspond to adjacent parts of the body). Both structures develop relatively late in neuronal

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Table 10.1 Motor systems: components, afferent and efferent pathways, and functions (only most relevant are highlighted) Motor Systems Function Muscles Executors of action Developmentally: from Functionally: better motor control Motor unit = motor neuron and (as internal forces, more to less muscle implies fewer muscle fibers per (MN) and muscle fibers Motor but external forces fibers per MN (i.e., MN that innervate Units also impact initially less capacity motion) for fine motor control) Spinal Efferent: Corticospinal and many other Reflexes, inhibitory Execution of motor action Cord Motor from cortex spinal efferents (see BS) interneurons, CPGs Assist in coordination, locomotion (SC) Afferent: Dorsal column–medial Proprioception, fine Sensory info to lemniscal pathway touch Convey sensory info from action to BS-cortex and Anterolateral pathway Coarse touch, pain cortex and CB cerebellum Spinocerebellar (see CB) Somatosensory Brain Stem Efferent To SC and cortex Vestibulospinal Flexor and extensor tone, balance, (BS) Reticulospinal skilled arm/hand movement (cortico) Rubrospinal Afferent To cortex Spinocerebellar Convey somatosensory info from To cerebellum (see CB) action to cortex and CB Efferent Cerebellum Afferent (CB) Vestibulocerebellar Flocculonodular To axial MN Axial motor control and balance (lateral vestibular nucleus) Spinocerebellar Vermis To reticular form., and Axial and proximal motor control, (medial) (fastigial nucleus) MI ongoing execution Spinocerebellar Intermediate part. hemisphere To red nucleus and MI Distal motor control, ongoing (lateral) (interposed nucleus) execution Cerebrocerebellar Lateral part. hemisphere To red nucleus and Initiation, planning, timing of (dentate nucleus) premotor cortex movement Cognitive components of motor action Basal Motor loops Putamen Direct: Facilitation of Selection of movement, bimanual Ganglia movement (excited by coordination, sequential (BG) dopamine) movements Indirect: Inhibition of movement (inhibited by dopamine) Cognitive-emotional components of Emotion-cognitive Caudate movement loops Somatotopic organization, Motor Primary motor (MI) Output to SC Execution, force, and direction of input from PM, SMA, SI, Cortices (corticospinal) movement CB (reticulospinal) Premotor (PM) Input from PPC, CB, and Output to MI, BS Planning, preparation for next SMA movement, postural orientation

Other Cortex Areas

Supplementary motor area (SMA)

Input from PPC and BG

Output to PM and MI

Primary sensory (SI)

Somatotopic organization, input via thalamus Input SI, PM, VC, and limbic areas of cortex

Homotopic connections Output to MI, PPC Output PM, SMA, lateral CB

Posteriorparietal (PPC)

Planning, bimanual coord., sequential movements, motor imagery, coordination, balance, and movement Perception of somatosensory information Motivation to move, visual-motor transformation, spatial-visual attention

Note: Additional functions CB: motor coordination, sensorimotor integration, fine adjustment of muscle tone, sensory discrimination, posture, motor learning, motor timing (lateral hemispheres; Keele and Ivry, 1990), comparison of intended versus actual movement (Ghez, 1991), anticipation (feed-forward control), cognitive aspects of motor behavior. Optimizing movement by monitoring outcome of movement (sensory information processing; i.e., afferent component of action). BG: bimanual coordination and sequential movements (planning series of actions), higher order aspects of visual processing, cognitive and emotional aspects of motor behavior. Activated when selection of movement (efferent component of action). Both, CB and BG: improving motor performance, motor learning. Primary and premotor cortical areas: movement force and direction, execution of movement, involved when decision about next movement. Prefrontal cortical areas and BG (striatum): when attention demands increase in the movement.

Temporal

Parietal

Prefrontal

PPC Sensory Cortices via

TL

Sensory recept.

VC

vi

PM vi

Eye Muscles

L aT

SI

(Eyes)

Motor Cortices

via

TL

SMA via TL

L aT

BG

MI

CB via TL

TL

L via T

Corticospinal

via

BS

SC Sensory receptors

Muscles

(muscle, skin, joint)

Figure 10.3 Motor systems: A simplified version is presented with only the most relevant connections of both afferent and efferent pathways. Pathways of the visual system are clearly incomplete. The vestibular influences in movement have been ignored in this diagram. Cerebral cortex is represented in the top layer of the diagram with the exception of five important sensorimotor areas that have been placed lower and separated to facilitate representation of connectivity. These areas are PM (premotor), SMA (supplementary motor area), MI (primary motor area), SI (primary sensory areas), and VC (visual cortices). In addition, the posteriorparietal cortex has been highlighted because of its important role

in motor motivation and visual-motor transformations. BG (basal ganglia), CB (cerebellum), TL (thalamus), BS (brain stem), SC (spinal cord). Note that BG receives input from most cortical areas, while CB receives input mainly from sensorimotor areas. Also note the rapid efferent path between MI and neurons in SC activating muscles (corticospinal) and the fast afferent path from SC to CB allowing consequences of action to be processed quickly. These two pathways have been represented with thicker arrow lines. Finally, note the important role of the thalamus as a hub to relay information.

ontogeny. As the infant and child mature, the development of the cerebellar cortical circuitry closely parallels motor coordination and motor learning (Swinny, van der Want, and Gramsbergen, 2005). Extrapolation from animal models indicated that cerebellar cortex circuitry starts developing in the last trimester of pregnancy and lasts until beyond the first year of life (Gramsbergen, 2003). Indirect estimates of synaptic formation suggest a significant increase during infancy and childhood in the gray matter of the cortex, thalamus, and cerebellum. In contrast, a constant high level of synaptic formation was found in the entire basal ganglia (Pouwels et al., 1999). The basal ganglia (striatum [caudate/ putamen], globus pallidus, subthalamic nucleus, and substantia nigra) are differentiated at birth, start myelinating prenatally (Chugani and Phelps, 1986; Chugani, Phelps, and Mazziotta, 1987), and mature rapidly over early postnatal years. Because the basal ganglia send output to the supplementary motor area, tasks that involve bimanual coordination and sequential movements are thought to be affected by BG dysfunction (Ronald et al., 1980).

Relative increases in the activation of all motor cortices and association areas seem to occur during rapid learning periods or when the task demands increase drastically. As the motor action becomes skilled and well learned, the activations of the primary/premotor cortex seem to take over. In such cases, the cognitive and even emotional load is reduced, so deactivation of nonmotor cortical areas is expected (Muller et al., 1998). When the motor task has a large learning component, evidence shows a shift from more anterior to more posterior cortex activation as the learning consolidates (Shadmehr and Holcomb, 1997). As we have seen, the brain dedicates multiple, largely interconnected, and in some ways redundant areas to the control of movement (Schwartz, 1994; Georgopoulos, 1995). Redundancy and interconnectedness in the neuromotor system at the cortical and subcortical level mean that there are multiple ways to achieve a given motor behavior (Passingham, 1993). As motor actions are explored and selected, concomitant changes in neural substrates also occur. For instance, Martin and associates (2004) showed

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that corticospinal development depends on motor experience. The relationship between neural substrates and motor behavior is bidirectional (Kleim, Jones, and Schallert, 2003). In the same way that ongoing activity may change a neural state, the reorganization of the neural structures may also have an impact on behavior. According to Stiles (2001), the capacity to reorganize and change (i.e., plasticity) is crucial to neural development. However, neural plasticity is not unique to the postnatal period. It is preserved during adulthood, although to a lesser extent. In fact, plasticity is a basic process that underlies not only neural but also motor and cognitive functioning (Stiles, 2001).

Interconnectedness between motor, cognitive, and emotional domains Motor activity is interrelated with several domains, including perception, cognition, and emotion. We use perceptual information to execute both gross motor activities, such as walking down the street, and fine motor skills like taking a cap off of a pen. The perceptual information generated allows us to adapt our step length to clear a puddle or adjust our finger force to uncap a pen efficiently. However, the motor activity we generate also influences our perceptual skills. This cyclical process is known as perception–action coupling, where correlations between the information generated from perceiving and acting exist (Edelman, 1987; Gibson, 1988). While this phenomenon is important for understanding development, it is also quite intuitive and well documented. For the purpose of this chapter, we will concentrate on the relation between the motor domain and two domains more relevant to psychology—cognition and emotion. Motor–Cognitive Relationships Based on observations of his children, Piaget (1954) proposed that self-produced locomotion was related to the development of spatial cognition. Since then, more direct evidence has strengthened this postulation by means of spatial-orientation and spatialsearch-performance tasks. Findings from Acredolo (1978) renewed interest in the possibility that locomotor experience may influence the development of spatial orientation. In her study, infants were first trained to locate an object in a window to either their right or left side. Following the training, infants were repositioned so they were facing the opposite direction and then asked to search for the object in the original window. Results showed that young infants almost always looked to the incorrect window (egocentric coding), whereas infants in their second year of life were much more successful in looking to the correct window (allocentric coding). Furthermore, a shift from egocentric to allocentric coding seemed to coincide with established locomotor trajectories. The idea was that crawling

experience might facilitate the development of allocentric coding because once infants begin locomoting, an egocentric coding strategy would have to be continuously updated, a process that is very inefficient. Eventually, infants would begin to adopt allocentric strategies that are independent of the infants’ orientations in space (see Bremner, 1978; Bremner and Bryant, 1977). Researchers have subsequently manipulated locomotor experience while controlling for age on the aforementioned spatial orientation task and found that, in fact, infants with more locomotor experience were more likely to use allocentric coding (Enderby, 1984; Bertenthal, Campos, and Barrett, 1984). Therefore, the literature in this area suggests that the development of spatial orientation is impacted by infants’ experience with locomotion. Similar conclusions regarding locomotion and spatial search have also been drawn. Briefly, the traditional A-not-B task requires infants to sit at a table and search for an object hidden in one of two locations in front of them (A or B). The object is first repeatedly hidden at A and then eventually hidden at B. When the object is hidden at B, young infants often fail to search at B, making the “A-not-B error.” An abundance of research suggests that experience with selfproduced locomotion correlates with success in the A-not-B task (Acredolo, 1985; Bell and Fox, 1992; Bertenthal and Campos, 1990; Horobin and Acredolo, 1986; Kermonian and Campos, 1988). Further, it appears that providing infants with experience in locomotion in otherwise prelocomotor infants using artificial walkers increases success on B trials (Bertenthal and Campos, 1990). It has been argued that locomotor experience may enhance spatial search capabilities in infants because it both demands and sets up contingencies that teach spatial discriminations (Smith et al., 1999). It should be noted that the role of locomotor experience in development of spatial cognition is deemed to be one of facilitation, rather than one of necessitation. In addition to the link between locomotion and spatial cognition, motor–cognitive relationships have been identified in other areas. One example involves studies regarding motor imagery. In a comprehensive review of this topic (see Decety, 1996), functional correlates of motor imagery have been determined. It was concluded that the timing of mentally simulated actions closely mimics actual movement times. Similarly, autonomic responses during actual exercise appear to rival those during motor imagery. Finally, cerebral blood flow increases were observed in the motor cortices involved in planning movements. According to Decety (1996), these results suggest that imagined and actual movements share the same neural substrates, at least to some extent. Further support for the interrelatedness between cognitive and motor behaviors and their neural bases has been suggested by others as well. For example, Diamond (2000) has suggested that both the prefrontal cortex and cerebellum

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are important for both cognitive and motor tasks based on the close activation of these two areas in functional neuroimaging, in addition to the fact that children with “cognitive” disorders, such as ADHD, demonstrate motor deficits in conjunction with the well-documented cognitive deficiencies. Further, children with ADHD have been shown to have abnormal development and activity in both the cerebellum and prefrontal cortex in numerous studies (Berquin et al., 1998; Castellanos et al., 1996; Amen, Paldi, and Thisted, 1993). Collectively, research suggests that there is a connectedness between motor activity and cognition to a higher degree than previously thought. However, the extent to which these two areas influence each other and in what manner warrants further investigation. Motor–Emotional Relationships The word emotion comes from an old French word, esmovoir, which means “to set in motion.” In fact, emotions can be understood as a drive for the generation of action and thought (Thelen and Smith, 1994, 314). Motor activity is an important component of behavioral expression that shares with emotion many of its neural pathways in addition to some of its primary physiological correlates—heart rate and cortisol responses. Since the time of Darwin, a number of links between motion and emotion have been proposed. In the adult literature on emotion, several links have been made between emotion and gait (Montepare, Goldstein, and Clausen, 1987) as well as between motion quality and rated personality characteristics (Grammer, Honda, and Juette, 1999). In infants or children, co-occurring links between motion and emotion have rarely been examined. However, we know from iron deficiency and other nutritional insults (Lozoff and Black, 2004) that children who have such nutritional deficiencies have both motor and emotional characteristics that differ from normally developing children. In our research together with Lozoff and her colleagues, iron-deficient children were less engaged emotionally with flatter affect to a wide range of situations. The same children were also delayed in their general motor development and showed decreased motor activity (Angulo-Kinzler et al., 2002a; Lozoff, 1991). Developmentally speaking, it is possible that newly acquired patterns of activity may have implications for arousal and a developing sense of self-control (Robertson, Bacher, and Huntington, 2001; Watson, 1966, 1972). In addition, behavioral (arm, facial) and heart rate responses of 4-month-old infants appear to be correlated such that infant learning led to increases in operant arm responses and expressions of positive emotions (Lewis, Hitchcock, and Sullivan, 2004). In this study, there was an inverse relationship between facial expression and heart rate—when frustrated, infants’ negative expressions and heart rate increased. During the first few months of life, infants also show an increasing degree of organized periodicity of movement and

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better establishment of the activity/sleep circadian rhythm, and the establishment of this rhythmicity coincides with day-night periodicity of cortisol release (Gossel-Symank, Grimmer, and Siegmund, 2004; Kiess et al., 1995; Mantagos, Moustogiannis, and Vagenakis, 1998). Thus there are important links between motor activity, motor development, emotional tone, and physiological activation. Additional infancy research provides support for a motoremotional relationship. Fear in infancy has often been studied by examining infants’ wariness of heights during their performance on the visual cliff. After a series of studies, Campos and colleagues concluded that experiences generated by locomotion (both self-generated and artificial) make possible the development of wariness of heights (Campos, Bertenthal, and Kermonian, 1992). Similarly, locomotion is believed to be important for the development of attachment because it provides infants with physical proximity to the caregiver and allows them to move independently toward novel and possibly away from frightening situations. This locomotor experience also causes infants to be more in tune with their caregivers’ locations, show more distress during separation from them, and look toward them more often in ambiguous situations (Ainsworth et al., 1978; Bowlby, 1973; Campos, Bertenthal, and Kermonian, 1992). Finally, motor activity has also been linked to behavioral and mental health conditions. For example, early problems of overactivity, along with impulsivity and aggression, appear to be risk markers for a broad range of externalizing problems later in life, particularly ADHD (Barkley, 1998; Berger and Posner, 2000; Campbell, Pierce, and March, 1994). Among lower risk groups, infants who had high levels of motor activity and negative affect in response to novel auditory and visual stimuli at 4 months of age were likely to be behaviorally inhibited toddlers and described as “shy” with high cortisol levels at 4 years of age (Kagan and Snidman, 1991; Schmidt et al., 1997). These types of relations between motion and emotion have been argued to stem from overlapping pathways in the brain—specifically, in the basal ganglia and the hypothalamus, which rely on intact involvement of the dopaminergic pathways. The dopaminergic system and prefrontal cortex involvement in ADHD-related motor problems discussed earlier have long been suspected (Hoover and Strick, 1993; Swanson et al., 2000) and are known to be involved in motor and emotional development problems.

Guides for therapeutic interventions in the motor domain Early intervention programs designed to ameliorate motor developmental delays in infants and children have been applied for many years. However, the evidence collected thus far from such studies is largely inconclusive. The latest

fundamentals of developmental neurobiology

systematic review of the effects of early intervention on motor development (Blauw-Hospers and Hadders-Algra, 2005) concluded that a potential benefit for motor development exists from those early interventions using the Newborn Individualized Developmental Care and Assessment Program (NIDCAP) during the newborn intensive care unit (NICU) period. The NIDCAP is a holistic and naturalistic observation approach to best fit assessment and health care to the individual needs of each newborn. More robust effects were also found when utilizing interventions based on specific motor training or from general developmental programs, which yielded positive effects on motor development during the postnatal period. Nevertheless, the authors claim that further research is necessary to address issues such as what type of intervention is most beneficial or when to start the intervention. Interestingly, animal models of early intervention have demonstrated an interaction between the maturational process of the nervous system and timing of the training program. In these studies, rat pups were trained to recover good postural control before and/or after a cerebellectomy. If this surgery was conducted around 10 days of age, training either pre- or postoperative was ineffective. However, preoperative training was shown to be effective when surgery was done at 15 days of age, while postoperative training showed the greatest positive effects at 24 days of age (Zion et al., 1990). Additionally, these animal models have also suggested that the best combination for the timing of preand postoperative training is when there is training preinjury, then a rest period of a few days after the injury, followed by postinjury training (Caston, Jones, and Stelz, 1995). Additional research is needed to further delineate the link between early intervention and neuromotor development, particularly in humans. Such studies could have a tremendous impact for children with motor difficulties, rehab professionals, and scientists in this area. Based on the current knowledge of motor development and neuroscience, we propose the following practical points to be taken into consideration when designing an early intervention: 1. Active participation in therapy should be promoted to facilitate self-generated movements. 2. Diverse and challenging opportunities for exploration should be provided to increase repetitions of self-initiated movement. 3. Ability to change posture and movement patterns is more important than ability to sustain them. 4. Focus on periods/phases when motor patterns are less stable (more flexibly organized). 5. Concentrate on quality and quantity in relation to function. 6. Measure movement patterns in a natural environment whenever possible.

7. Focus on perceptions generated by movement and/or environment. 8. Identify perceptual deficits related to impaired movement patterns. 9. Determine variables (action, perceptual, cognitive, emotional) that constrain or facilitate change in movement patterns (both intrinsic and extrinsic).

Conclusions Based on the theoretical approach to motor development outlined in this chapter, as well as the evidence collected on early intervention programs designed to enhance motor development, the following theoretical guidelines to better understand motor development are proposed: First, changes in motor behavior are multicausal, context dependent, and selforganizing. Therefore, factors not directly or “obviously” related to the behavior may surprisingly affect change in the pattern. For instance, weight gain was critical in the appearance/disappearance of stepping. Both intrinsic and extrinsic factors may play important roles to drive change. Second, exploration and selection are two necessary processes that underlie the emergence of new behaviors. Without the creation of variability (exploration) and the establishment of a certain degree of stability (selection), advances in motor development are impossible. These two processes are not mutually exclusive, but rather they coexist throughout development. Third, stability/instability is a fundamental concept when assessing the functional quality of performance, the potential for change, and the effectiveness of a therapeutic intervention. Thus stable movement patterns must be identified in normal populations for comparative purposes. In addition, one ought to recognize that behaviors with high stability are very resistant to change. In such cases, strategies to induce some degree of instability may be necessary at the early stage of the intervention.

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Neurodevelopment of Social Cognition MELISSA D. BAUMAN AND DAVID G. AMARAL

PART 1: A BRIEF OVERVIEW OF THE NEUROBIOLOGY OF SOCIAL BEHAVIOR AND SOCIAL COGNITION Humans are so embedded in, and dependent on, their social milieu that it seems only natural that large portions of their brains would be dedicated to mediating social behavior and social cognition. It is true that large portions of the frontal, temporal, and parietal cortices, as well as subcortical structures such as the amygdala, have been associated with social function. However, in attempting to carefully deconstruct the component processes of social behavior and social cognition, as well as incorporating potentially relevant research such as on the “mirror neuron” system, the definition and independence of the “social brain” become less apparent. Even the terms “social behavior” and “social cognition” have many layers of complexity. At one level, social behavior is akin to togetherness. Bargmann and colleagues (de Bono and Bargmann, 1998; de Bono et al., 2002; de Bono and Maricq, 2005) have demonstrated that certain wild-type Caenorhabditis elegans nematode colonies demonstrate either social or solitary eating of bacteria. The choice of whether to eat in groups or to eat alone is dependent on the activity of a few nociceptive neurons that are responsive to stressful or adverse environmental conditions. The activity of these neurons and thus of feeding behavior is determined by a single polymorphism in the neuropeptide receptor npr-1. The less active allele is found in animals that feed in clusters, whereas the more active allele promotes solitary feeding. Thus, in an animal model with a nervous system consisting of only 302 neurons, substantial genetically determined differences in social behavior can be observed. In species such as the vole, with many more neurons, equally dramatic and neurobiologically based species differences in social behavior have been observed (Insel, 1997; Insel et al., 1997; Young et al., 2001). In these animals, the neuropeptides oxytocin and vasopressin play important roles in social behaviors such as pair bonding, affiliation, and paternal care of offspring. The monogamous prairie voles, for example, have a higher density of oxytocin receptors in the nucleus accumbens than the nonmonogamous montane

vole. Evidence that blockade of receptors in this important reward-related portion of the brain interrupts partner preference formation in the prairie vole indicates that this neuropeptide system plays an important role in orchestrating this social behavior. As impressive as these examples of neural control of social behaviors are, the complexity of social behavior and social cognition increases dramatically when one considers the topic in humans and higher order nonhuman primate species. Here the interest is not so much in the area of basic biological functions such as eating, sexual behavior, parenting, and affiliation but on the perception and interpretation of social signals for the accurate performance of subtle social interactions. A seminal paper in the evolution of research on the neurobiology of the social brain was published by Brothers in 1990. Brothers stated, “Primate social cognition is the processing of information which culminates in the accurate perception of the dispositions and intentions of other individuals” (Brothers, 1990, 28). In her view, dispositions are related to evaluations of the identity of other individuals, their posture, and direction of their movements, as well as their facial expression. Even more fundamentally, social cognition relies on the ability of an individual to build an internal representation of the psychological states of others in order to predict their intentions and actions. Premack and Woodruff (1978) in the course of studying cognition in chimpanzees referred to this ability of modeling the mind of another as Theory of Mind (ToM). Thus, developing a neurobiology of human social cognition would require a number of levels of analysis. In addition to the direct approach of using imaging and lesion techniques to study neurobiological correlates of human social cognition, an early step would be to decide what, if any, animal model is appropriate for the analysis of several components of human social cognition. While one would probably receive little argument that C. elegans will not contribute much to the understanding of human social cognition, there is still ample debate about the contribution of nonhuman primate studies (Gottlieb and Lickliter, 2004). Beyond identifying the appropriate species for study, one would need to determine whether certain brain regions are selectively responsible for the perception of social signals

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such as facial expressions, body postures, and such, whether other brain regions are involved in the evaluation of these social signals to determine intentionality, and finally how appropriate social responses are generated. Since the publication of Brothers, a number of authors have addressed these issues, and candidate brain regions have been identified (Adolphs, 1999, 2003). However, it is important to note that there remains enormous ambiguity about the neural circuits that underlie social cognition. Thus several sections of this chapter deal with fundamental issues such as how one would identify brain regions particularly associated with social cognition. It is also important to point out that while there is certainly a literature related to candidate brain regions for social processing in the mature brain, there is almost no published information on the development of these brain regions and of the correlation between anatomical or physiological development and the emergence of social cognition.

demonstrate some forms of social cognition. Through a series of experiments in which a dominant and subordinate chimpanzee compete for food, Tomasello and colleagues have demonstrated that chimpanzees have knowledge of what others see and that they know something about intention in action (Tomasello et al., 2003). In spite of these abilities, it is clear that chimpanzees do not have the same ToM capabilities that are seen in young children that require an understanding of attention, perspective, and communicative intentions. Our ability to study social cognition is clearly limited by our inability to directly ask nonhuman primates (and even young children) what they understand about the psychological states of others. However, it is clear that they understand much about the behavior of conspecifics and can respond in an appropriate social fashion. Consequently, the expression of overt social behavior is the level of analysis that we can most readily relate among adult, young child, and nonhuman primate models of social processing.

What is social behavior and social cognition? Model of social processing Before we attempt to outline the neural components of the social brain, it is important to attempt to define what is meant by the terms “social behavior” and “social cognition.” Broadly defined, social behavior is the use of species-typical social signals, including body postures, vocalizations, and facial expressions, for the purpose of interaction with conspecifics (members of the same species). Social behavior relies on the ability of the individual to recognize speciestypical social signals and to have at least a rudimentary understanding of their relationships within the social network. However, there is considerable variability in the complexity and awareness of social interactions across species, and here lies the basis for the differentiation of social behavior from social cognition. Brothers states, “While many non-primates (for example, ants) can interact in highly specific ways with others of their kind, it appears that primates, especially those closely related to ourselves, have developed a unique capacity to perceive psychological facts (dispositions and intentions) about other individuals” (Brothers, 1990, 28). It is this capacity to link first- and third-person social experiences that differentiates social behavior from social cognition (Gallese et al., 2004). Elements of social cognition have been described in nonhuman primates ranging from macaque monkeys to chimpanzees. Macaque monkeys, for example, can use information about where an experimenter is looking (at, versus away from, a food item) in order to successfully “steal” the food (Flombaum and Santos, 2005). This task capitalizes on a macaque monkey’s typical evaluation of the location and attention of conspecifics to determine its chances of gaining food in a normal social situation. Chimpanzees, perhaps more than any other nonhuman primate species,

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Given the complexity of primate social behavior, we have found it useful to generate a schematic of social information processing in order to clearly define the component processes of social interactions. Our schematic breaks down the interpretation and production of social behavior into component processes in order to more clearly define the essential and modulatory brain functions underlying social interactions (figure 11.1A and plate 18). In order to process the information conveyed in a social stimulus, such as a facial expression, the expression must first be perceived as an important source of information. The meaning of that particular expression must be evaluated, and finally an appropriate social response must be generated. There is, of course, the overarching precondition that the individual must be motivated to engage in social interactions or to interpret the gestures of others as social communication. Beyond this obligatory social impetus, the fundamental components of social processing include (1) perception of the social stimulus, (2) evaluation of its social significance, and (3) production of a species-appropriate response. The species-appropriate response could be either the production of a social response gesture or the interpretation of the disposition or intention of the other individual. The nature of that response (or whether a response will even be generated) will depend on several modulatory factors including (1) whether emotions such as fear are generated that might modulate the response, (2) the motivation to respond, and (3) whether the context is conducive to a social response. Given adequate perception, evaluation, motivation, emotion, and context, the brain must then execute an appropriate response that may take the form of thought or deed.

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Figure 11.1 Model of social processing. (A) Simplified diagram of the component processes essential for social behavior. An organism must first have an overarching motivation to interact with conspecifics. Given this proviso, the brain must be able to perceive a social stimulus through sensory processing. Once the social stimulus has been perceived, there must be an evaluative process in order to determine its intent. The intent of the social stimulus will have different consequences depending on the context in which it takes place. Context includes situational variables as well as the characteristics of the particular conspecific. Whether a social behavior is executed will depend not only on the development of a motor plan but also on whether modulatory influences are consistent with the

implementation of the behavior. (B) Amygdalocentric network mediating danger detection. A variety of sensory stimuli indicative of dangers are perceived by the ventral visual processing system. This information is directed to the lateral nucleus of the amygdala, where an evaluation of the potential danger of the stimulus is carried out. Internal connections within the amygdala convey information from the lateral to the basal nucleus, which, in turn, receives a prominent input from the orbitofrontal cortex. This input may provide context information that is utilized to determine whether an escape behavior is executed. It is not surprising that the amygdala, as a multipurpose danger detector, is attuned to facial stimuli such as fear expressions. (See plate 18.)

Is there a social brain?

cortices as key structures mediating declarative memory (Suzuki and Amaral, 2004). A central question for the field of social neuroscience is to determine whether the social brain is organized in a similar fashion. If it is, one would expect that the elimination of candidate social brain regions would dramatically alter normal social behavior. More than 15 years ago, Brothers proposed that the social brain was composed of several brain regions, including the amygdala, anterior cingulate cortex, orbitofrontal cortex, and temporal cortex (Brothers, 1990). However, the exact role that these structures play in social processing, or even if these structures are essential for social behavior, remains unclear to this day. Moreover, recent evidence indicates that regions of the brain not previously implicated in social

Our discussion of the neurobiology of social development is based on the assumption that regions of the brain are indeed specialized for processing social information and can therefore be identified and studied. This approach has been useful in identifying regions of the brain that are involved in other complex cognitive processes such as learning and memory. For example, it is well accepted that structures in the medial temporal lobe are essential for forming declarative memories (conscious memories for facts and events) (Squire et al., 2004). A combination of both neuroanatomical studies and lesion research has specifically identified the hippocampal formation along with the perirhinal and parahippocampal

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processing may indeed play an important role (e.g., the mirror neuron system of the ventral premotor and inferior parietal cortices). Before delving into the roles that these specific structures may play in social processing, it is first important to reconsider the logic that led to the identification of these regions as portions of the “social brain.” Brother’s seminal review included a summary of arguments supporting the existence of neural networks specialized for social processing. These included evidence (1) for a common evolution of social behavior, (2) that social knowledge is distinct from other knowledge, (3) that there is a well-defined developmental progression for the emergence of social behavior, and (4) that social behavior can be selectively disrupted in disorders such as autism or following damage to specific brain regions. In the following section, we will briefly summarize these main lines of evidence and incorporate new findings that lend support to the existence of a social brain. From an evolutionary perspective, the ability to interpret accurately and produce appropriate social behavior is of paramount importance for humans and other group-living primates. Sophisticated social interactions form the basis for primate societies and are necessary for forming and maintaining long-lasting relationships with other group members, acquiring resources, maintaining protection from predators (and competitors), and ultimately ensuring propagation of one’s genetic material (Cheney et al., 1986). In her review, Brothers highlights the phylogeny of facial expressions in primates as evidence of a common evolution of social processing, beginning with the following quote from Charles Darwin: “The community of certain expressions in distinct though allied species, as in the movements of the same facial muscles during laughter by man and by various monkeys, is rendered somewhat more intelligible, if we believe in their descent from a common progenitor” (Darwin, 1872; Brothers, 1990, 29). Despite the accuracy of his observations, Darwin’s work on emotional expressions, and in particular his emphasis on biological determinants of socioemotional behavior, was overlooked for many years. Nearly a century later, Ekman and colleagues provided evidence that a set of emotional expressions are universal to the human species (Ekman and Friesen, 1971; Ekman, 1993). The commonality of emotional expression across cultures suggests that these behaviors are innate components of our behavioral repertoire. Moreover, it appears that closely related species of nonhuman primates display similarities in facial muscular structure and produce facial expressions believed to be homologous to several human expressions (Parr et al., 2005). Brothers suggests that as this capacity to produce social signals evolved, it is likely that neural networks evolved to facilitate the accurate processing of this information. What remains unknown is the precise relationship between pressures from the social domain and

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corresponding changes in neural circuitry (Dunbar, 1998; Reader and Laland, 2002; Barrett et al., 2003). Another line of evidence proposed by Brothers is that social knowledge is operationally distinct from other domains of knowledge. She suggests that certain cognitive functions, such as making transitive inferences, operate most strongly in the context of social processing. In transitive inference, overlapping pairs of items are trained (e.g., A+B−, B+C−, C+D−, and D+E−, where + and − indicate correct and incorrect choices). During training, terms B and D are correct and incorrect equally often. Later, participants who choose B over D when presented with novel pair BD are said to demonstrate transitive inference. Although transitive inference has been demonstrated in several primate species in the laboratory using inanimate objects as the “terms,” it requires an enormous amount of training to reach criteria (McGonigle and Chalmers, 1977). In contrast, many primate species live in well-defined dominance hierarchies and are able to quickly and accurately determine the status of other group members in relation to one another (Cheney and Seyfarth, 1990). In a very real sense, they are using conspecifics as the “terms” of a transitive inference paradigm and are clearly much more adept at performing these complex logical operations within a naturalistic social context. This difference suggests that at least some aspects of primate intelligence evolved specifically to solve the challenges of interacting with conspecifics (Cheney et al., 1986). The increasing complexity of social groups may have forced the evolution of ever more sophisticated social cognitive processing. It is reasonable to presume that producing and interpreting social gestures was a first step to subtly applying this expertise for manipulation and deception of conspecifics. This reasoning raises the question of whether the same or different brain region(s) subserve the affiliative lip smack in macaque monkeys and the poker face in humans. Brothers also argued that the distinctive and characteristic trajectory that characterizes primate social development is also evidence for the existence of a social brain. Newborn human infants, for example, display visual preferences for facelike stimuli (Goren et al., 1975; Johnson et al., 1991; Morton and Johnson, 1991) and are capable of imitating adult facial gestures immediately after birth (Meltzoff and Moore, 1977, 1983). Interestingly, social smiling is present even in blind infants, adding further support to the notion that there is an innate mechanism for mediating early social predispositions (Freedman, 1964). It has recently been demonstrated that infant monkeys display a transient ability to imitate facial gestures in the first week after birth that is similar to the transient abilities reported in human infants (Ferrari et al., 2006). These innate social predispositions suggest that components of the human and nonhuman primate genetic endowment generate brain regions that are specialized to mediate complex social interactions.

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Maestripieri and colleagues have provided compelling evidence to support this view. For example, infant monkeys removed from their biological mothers and reared by foster mothers develop a pattern of social behavior (e.g., rates of social contact and aggression) that resembles their biological mothers rather than their foster mothers (Maestripieri, 2003). This study highlights a mode of direct genetic inheritance of complex behavioral traits, lending further support to the notion of biological predispositions for social behavior. Brothers further argues that selective changes in social behavior as a result of a developmental disorder, such as autism, support the existence of a social brain. Autism is a neurodevelopmental disorder characterized by deficits in social interactions, impaired communication, and restricted patterns of behaviors, interests, and activities. Of these three domains, the alteration in social behavior is often considered the “hallmark” feature. Indeed, DSM-IV criteria for autism diagnosis must include a qualitative impairment in social interaction, including two of the following characteristics: (1) marked impairment in the use of nonverbal behaviors that regulate social interactions, (2) failure to develop peer relationships, (3) a lack of spontaneous seeking to share enjoyments, interests or achievements, and (4) lack of social or emotional reciprocity (APA, 1994). Brothers (1990) suggests that the “inborn selective absence of social cognition” that characterizes autism provides evidence that social processing can selectively be disrupted and therefore supports the existence of a specialized neural substrate of social behavior. Unfortunately, the neuropathology of autism is still quite uncertain. But, information concerning which brain regions are impaired in autism may ultimately provide suggestive evidence concerning what regions subserve normal social cognition. Recent research on other neurodevelopmental disorders that alter social behavior may provide further insight into the social brain. For example, Williams syndrome is a rare genetic disorder caused by a hemizygous deletion in chromosome band 7q11.23. In contrast to the diminished social interest that is characteristic of patients with autism, patients with Williams syndrome display hypersociability (Bellugi et al., 1999). These changes in social behavior resulting from a genetic alteration provide additional evidence of a genetic/ biological basis of social development (Doyle et al., 2004) and support of the notion that there is indeed a social brain. After establishing the plausibility of a social brain, Brothers concludes her paper with a more direct line of evidence—the existence of socially responsive cells in the macaque monkey cortex. Brothers proposes that neurons responding to social stimuli have evolved to enable the interpretation of information about other individuals. Indeed, cells that respond to faces, body movements, gaze, and the like are found throughout the adult macaque temporal lobe,

concentrated in both the inferior temporal gyrus and on the banks of the superior temporal sulcus (Gross et al., 1972; Desimone et al., 1984; Baylis et al., 1987; Hasselmo et al., 1989; Perrett et al., 1992) and in the amygdala (Rolls, 1984; Leonard et al., 1985; Brothers et al., 1990; Brothers and Ring, 1993). In the past two decades, additional regions of the brain that may contribute to social processing have been identified, including the mirror neuron system and a region of the human fusiform gyrus known as the fusiform face area (FFA). In the following section we will briefly summarize neurobiological and functional evidence implicating certain brain regions in social function.

What are the putative structures of the social brain? Amygdala The amygdala has long been implicated in the mediation of emotional and social behavior. Even early lesion studies in primates (Rosvold et al., 1954) suggested that the amygdala might be essential for normal social behavior in macaque monkeys. However, our own work has questioned the notion that the amygdala is an essential component of the social brain. We view it as a modulatory influence on social and other behaviors. Here is the background evidence: Neuroanatomy. The amygdala is a cytoarchitectonically complex structure located in the anterior temporal lobe (see figure 11.2). The neuroanatomical connections of the amygdala give it a unique position to combine highly processed sensory information from all modalities with contextual information needed to evaluate stimuli and to elicit appropriate behavioral responses (Emery and Amaral, 2000). The amygdala receives high-level information from all sensory modalities, which is further processed through the complex intrinsic connections of the 13 amygdaloid nuclei (Amaral et al., 1992). The amygdala receives projections from a variety of cortical areas, including medial and orbitofrontal regions of the frontal lobe, anterior portions of inferotemporal cortex, superior temporal cortex, perirhinal cortex, and anterior cingulate cortex. In turn, the amygdala projects to numerous brain areas capable of influencing behavioral output. The amygdala can influence relatively early stages in cortical sensory processing (Freese and Amaral, 2005), and extensive projections from the central nucleus of the amygdala innervate many autonomic and visceral control regions of the brain stem. There are additional projections to the striatum and the hippocampal formation. Taken together, the amygdala is capable of interacting with widespread brain regions to orchestrate an appropriate behavioral response to a provocative stimulus. But, what types of stimuli does the amygdala respond to? Functional studies. The amygdala has been implicated in a wide range of behavioral and psychological processes,

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Figure 11.2 Schematic overview of neural regions implicated in social processing. (A) Lateral view: Anterior and posterior components of the human mirror neuron system (MNS) are shown. An anterior area with mirror neuron properties is located in the inferior frontal gyrus and adjacent ventral premotor cortex. A posterior area with mirror neuron properties is located in the rostral portion of the inferior parietal lobule. Cortex surrounding the superior temporal sulcus (STS) is also highlighted. (B) Ventral view: The fusiform gyrus forms the posterior portion of the occipitotemporal

gyrus, bounded by the collateral sulcus medially and the lateral occipitotemporal sulcus laterally. A region of the fusiform gyrus, selective for face stimuli (the fusiform face area, FFA) is generally located in the middle lateral fusiform gyrus. The orbitofrontal cortex (OFC) lies on the ventral surface of the frontal lobe just above the eye orbits. (C) Medial view: Anterior cingulate cortex (ACC) is the frontal part of the cingulate cortex and includes Brodmann’s areas 24 (ventral ACC) and 32 (dorsal ACC). The amygdala is located in the anterior portion of the temporal lobe.

including fear processing (Davis, 1992; LeDoux, 1998, 2000; Davis and Whalen, 2001), reward association (Malkova et al., 1997; Baxter and Murray, 2002; Gottfried et al., 2003), memory modulation (Cahill and McGaugh, 1998; Canli et al., 2000), and social behavior (Brothers, 1990; Kling, 1992; Adolphs, 1999; Bachevalier, 2000; Emery and Amaral, 2000). The specific contributions of the amygdala to some of these processes (e.g., fear processing) have been well characterized. However, the precise role of the amygdala in other behaviors, such as reward evaluation and social behavior, remains controversial.

Electrophysiological recordings from the nonhuman primate amygdala have indicated that it is responsive to many socially relevant stimuli, including faces, specific facial expressions and direct eye contact (Rolls, 1984; Leonard et al., 1985; Brothers et al., 1990; Brothers and Ring, 1993). In nonhuman primates, neural responses to appeasing faces are often marked by significant decreases of firing rates, whereas responses to threatening faces are associated with increased firing rate, suggesting that global activation in the amygdala might be larger to threatening faces (Gothard et al., 2007).

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Noninvasive functional neuroimaging experiments have indicated that the human amygdala is preferentially activated by a variety of meaningful social signals. Facial expressions are one of the most replicable elicitors of human amygdala activation, particularly facial expressions depicting fear (Morris et al., 1996; Morris, Friston, et al., 1998). Other facial expressions depicting negative emotions, such as disgust, anger, and sadness (Morris, Ohman, et al., 1998; Blair et al., 1999), as well as several positive emotions (Breiter et al., 1996; Canli et al., 2002), have also resulted in amygdala activation, but not as consistently as fearful expressions. The amygdala is also activated when subjects make complex social judgments, such as determining whether an individual is trustworthy (Winston et al., 2002), discerning what someone may be thinking (Baron-Cohen et al., 1999), or evaluating social content during a social attribution task that involves perception of humanlike interactions among simple geometric shapes (R. Schultz et al., 2003). Amygdala activation has also been reported during nonsocial testing paradigms, including fear conditioning (Buchel et al., 1998; LaBar et al., 1998; Cheng et al., 2003), viewing pictures of phobia-related stimuli (Dilger et al., 2003), anticipation of aversive stimuli (e.g., shock) (Phelps et al., 2001), and viewing threatening and fearful nonsocial stimuli (Hariri et al., 2003). One unifying hypothesis is that the amygdala, at least in part, functions as a danger detector (Amaral, 2003). It uses incoming sensory information to evaluate the environment for potential threats (figure 11.1B). In this role as a danger detector, the amygdala would evaluate a vast array of potentially fear-inducing stimuli, ranging from threats that are common to most vertebrates (e.g., snakes or fire) to speciesspecific displays of social aggression (e.g., open-mouthed threat facial expressions of macaque monkeys). Since indications of danger are conveyed socially through facial expressions and other biological gestures, it would not be surprising that the amygdala is particularly attuned to these “social” danger signals. One would not expect, however, that the evaluations carried out by the amygdala deal with the general category of others’ intentions or dispositions but only those that might present a danger. Thus ready activation of the amygdala by fearful faces (which convey the fact that there is a proximal danger) or when evaluating whether another individual is trustworthy (i.e., not likely to cause harm) would be consistent with its acting as a danger detector. It is clear from lesion studies in humans that bilateral lesions of the amygdala do not remove social function, nor do they gravely impair an individual’s ability to judge the intentions or dispositions of others. Lesion studies. Human patients with bilateral amygdala damage are very rare, yet they provide invaluable information regarding the function of the amygdala. One of the

most extensively studied patients with bilateral amygdala damage, patient S.M., developed her lesion during adolescence from Urbach-Wiethe disease—a rare syndrome associated with selective bilateral amygdala calcification and atrophy. Although patient S.M.’s social behavior remains remarkably intact (i.e., she has a high school education, lives independently, is married, holds a job, and is raising a family), she does have areas of impaired function. In general, patient S.M. shows the most consistent deficits related to fear processing. For example, patient S.M. was not able to recognize fearful expressions (Adolphs et al., 1994) and was impaired in judging how much to trust another person after viewing the person’s face (Adolphs et al., 1998). Patient S.M.’s deficits in identifying fearful expressions may be explained by an inability to make normal use of information from the eye region of the face, which is critical for identifying fear (Adolphs et al., 2005). Though her deficits are primarily related to fear processing, it is important to note that patient S.M. has shown abnormalities in her ability to assess social stimuli not related to fear, such as the social attribution task described earlier (Heberlein and Adolphs, 2004). In this case, patient S.M. fails to normally attribute social intentions to videos of inanimate objects. While the reason for this failure is unknown, it does seem to be clear that patient S.M. does not demonstrate autistic symptomatology, can interpret and produce the majority of social signals, and is at least fairly astute at engaging in social cognition. Patient H.M. is another famous example of an individual who has a complete bilateral loss of his amygdala and hippocampal formation (Corkin et al., 1997). Yet, despite his dramatic and near complete loss of the ability to form new episodic memories, his daily social interactions have been generally quite normal. The collective results from previous nonhuman primate studies indicated that bilateral amygdala lesions disrupt species-typical social behavior, generally resulting in decreased affiliative behavior and subsequent social isolation when tested in socially complex environments (Kling, 1992). However, recent studies utilizing more selective lesioning techniques combined with quantitative behavioral assessments have found that amygdala damage in adult monkeys does not preclude social interactions (Emery et al., 2001). In these animals, behavioral changes appeared more closely related to deficits in fear processing rather than specific impairments in social behavior, suggesting that the amygdala plays a modulatory rather than an essential role in social behavior. One caveat to this interpretation is that these experiments were conducted in mature animals, thus leaving open the possibility that the amygdala may play a role in acquiring social behavior at earlier ages. We discuss the results of neonatal lesions of the amygdala in part 2 of this chapter.

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Summary. The amygdala has been proposed to play an essential role in the production of normal social behavior. While there are intriguing neuroimaging and behavioral data indicating that the human amygdala is responsive to a variety of social signals, many of the evaluations carried out by the amygdala are driven by signals that may convey potential danger. Moreover, amygdala lesion studies both in rhesus monkeys and human subjects indicate that the amygdala is not essential for the expression of species-typical social interaction. These observations compel us to conclude that if the amygdala is a component of the social brain, it must be a nonessential component. Orbitofrontal Cortex Whereas the role of the amygdala in social function remains equivocal, there is a substantial and converging body of evidence that the orbitofrontal cortex is a central component of the social brain. Neuroanatomy. The frontal cortex of humans and apes occupies a larger percentage of total cortical volume as compared to smaller primates (gibbons and monkeys) (Semendeferi et al., 2002). The orbitofrontal cortex (OFC) lies on the ventral surface of the frontal lobe just above the eye orbits. This brain area has strong connections to the amygdala, cingulate cortex, and somatosensory areas (Ongur and Price, 2000). It is generally agreed that the cytoarchitecture of the orbital cortex is similar in humans and nonhuman primates (Petrides and Pandya, 1994). Functional studies. Traditionally the OFC has been implicated in processing a variety of sensory information relating to reward and punishment (Rolls, 1996). It is thought that the OFC plays an important role in processing socially relevant information by regulating responses to positive and negative reinforcers, thereby influencing a wide range of socioemotional behaviors (Kringelbach and Rolls, 2004; Rolls, 2004). Indeed, data from functional imaging studies indicate that the OFC is activated in response to complex social decisions, such as rating the attractiveness of a face (O’Doherty et al., 2003), using facial expressions to guide behavior in a reversal learning paradigm (Kringelbach and Rolls, 2003), choosing to cooperate with another person (Rilling et al., 2002), making social judgments of emotionally evocative or morally conflicted statements (Moll et al., 2002), and determining whether social norms have been violated (Berthoz et al., 2002). Lesion studies. Data from human lesion studies further support the role of the OFC in social processing. Human patients with bilateral lesions of the OFC show deficits in the identification of facial and vocal emotional expression (Hornak et al., 1996, 2003) and have abnormalities in regulating social behavior (Rolls et al., 1994; Angrilli et al., 1999; Blair and Cipolotti, 2000) and impulsivity (Berlin et al.,

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2004, 2005), despite performing well on conventional intelligence tests. A case study of a patient sustaining damage to the OFC reported profound changes in personality that resulted in the patient engaging in risky behavior, alienating friends and family, and acting out in many socially inappropriate ways (Eslinger and Damasio, 1985). More recently, Mah and Grafman (Mah et al., 2004) demonstrated that subjects with lesions of the orbitofrontal cortex were unable to make accurate judgments of videotaped interpersonal interactions on the basis of nonverbal information. These data indicate that damage to the OFC produces deficits in self-regulation and an inability to use affective and social information to guide social behavior and social decisions. Converging evidence from nonhuman primates with experimentally induced damage to the OFC support the role of the OFC in complex social interactions. Lesions of the OFC in macaque monkeys are associated with decreased aggressive behaviors and increased avoidance behaviors (Butter et al., 1970), as well as decreased social abilities in vervet monkeys (Raleigh et al., 1979). Social dominance, which serves as an important indicator of social competency in macaque monkeys, is also affected by OFC damage. Previously high ranking macaque monkeys that received damage to the OFC were unable to maintain their high social rank following the surgery (Butter and Snyder, 1972). A more recent study utilizing selective OFC lesions found that damage to the OFC was associated with decreased affiliative personality traits, increased avoidant personality traits, and atypical responses to specific social signals (Machado and Bachevalier, 2006). Summary. The orbitofrontal cortex appears to use socially relevant information combined with indicators of positive and negative consequences to define a social course of action that supports both self-interest and social compatibility. Social judgment and planning appear to be impaired following damage to the OFC. Yet it is not clear whether this impairment leads to deficits in judging the dispositions and intentions of others. Deficits in these types of “theory of mind” functions have often been attributed to damage in other brain regions discussed later in this chapter. Anterior Cingulate Cortex Neuroanatomy. The cingulate cortex is recognized as a complex collection of cortical subregions that subserve a variety of cognitive, emotional, and motor functions (Vogt et al., 1992; Bush et al., 2002). The anterior cingulate cortex (ACC) forms a large region surrounding the rostrum of the corpus callosum and can be distinguished from the posterior cingulate cortex based on cytoarchitecture (the ACC lacks

fundamentals of developmental neurobiology

layer IV) and connectivity patterns (Vogt et al., 2005). The human ACC has been divided into “affective” and “cognitive” domains, with the more ventral affective division sharing extensive connections with other regions capable of influencing social behavior, including the amygdala and autonomic brain stem nuclei (Devinsky et al., 1995; Bush et al., 2000). Interestingly, a morphologically unique class of large, bipolar cells has been found in layer V of anterior cingulate cortex in humans and apes (Vogt et al., 1995; Nimchinsky et al., 1999; Hof et al., 2001; Allman et al., 2005). The concentration of these spindle-shaped neurons (also known as Von Economo neurons) is greatest in humans, followed by chimpanzees, gorillas, and then orangutans (Nimchinsky et al., 1999). Though spindle cells have not been found in any other primate species, recent evidence indicates that several species of cetaceans do have spindle cells (Hof and Van Der Gucht, 2007). The possible parallel evolution of spindle cells in highly social species of hominids and cetaceans characterized by a very large brain and a large body size raises intriguing questions regarding the function of these neurons (Allman et al., 2005; Watson et al., 2006). Functional studies. The ACC has been linked to a number of cognitive processes, including affective and social behavior. For example, in nonhuman primates the ACC plays a critical role in the voluntary initiation and suppression of species-typical vocalizations ( Jurgens, 2002). In humans, activity within the ventral ACC is closely linked with aspects of socioemotional processing (Bush et al., 2000) such as empathy ( Jackson et al., 2005), deception (Spence et al., 2004), and receiving social feedback (Somerville et al., 2006). Lesion studies. Though there are a number of reports in the literature of patients with damage to the ACC that demonstrate deficits in social behavior, these cases have been complicated by the fact that the patients also sustained damage to the orbitofrontal cortex (Bechara et al., 1998). Lesion studies of the anterior cingulate cortex in nonhuman primates have produced inconsistent findings. Historical studies have reported that lesions of the ACC produce profound changes in monkey social behavior (Ward, 1948; Glees et al., 1950), while other studies claim that social behavior was essentially unchanged following damage to the ACC (Pribram and Fulton, 1954; Mirsky et al., 1957). A more recent study reported that pairs of cynomolgus macaques with cingulate lesions interacted less with one another and produced fewer vocalizations compared to control pairs (Hadland et al., 2003). Rudebeck and colleagues have compared lesions of the orbitofrontal cortex with lesions of the anterior cingulate cortex (Rudebeck et al., 2006). They found that animals with the anterior cingulate damage demonstrated alterations in social inter-

est, whereas those with orbitofrontal lesions had fear-related impairments not unlike those seen in animals with amygdala damage. Summary. While much additional work is necessary to evaluate the precise role of the anterior cingulate cortex in social behavior, existing literature certainly supports the consideration of this region as a component of the social brain. Temporal Cortex Single-unit electrophysiological studies in the primate as well as fMRI studies in the human have indicated that regions of temporal lobe are involved in social perception (Allison et al., 2000; Puce and Perrett, 2003). We will discuss these areas more generally in this section but then deal with the fusiform face area, in particular, in the following section. These regions are challenging as one attempts to determine whether they should be included as part of high-level sensory processing apparatuses or as part of the “social brain.” Neuroanatomy. Inferior temporal (IT) cortex is a region of visual association cortex that, in the monkey, occupies the inferior temporal gyrus and adjacent portions of the superior temporal sulcus (STS). Converging evidence from humans and nonhuman primates suggests that these regions play a role in social perception, though direct comparisons are difficult because homologies between human and nonhuman primate temporal cortex are not completely characterized. Functional studies. Cells responsive to social stimuli are found throughout the macaque temporal lobe, concentrated in both the inferior temporal gyrus and on the banks of the STS. Cells in the macaque STS respond to specific body movements (Perrett et al., 1985a), and some responses appear related to the object or goal of the movements (e.g., reaching for or walking toward a specific place) (Perrett et al., 1989). Much attention has been devoted to cells in the temporal cortex that respond preferentially to faces (Perrett et al., 1982; Desimone et al., 1984; Rolls, 1984) and are sensitive to biologically important characteristics such as face identity or expression (Perrett et al., 1984; Hasselmo et al., 1989), as well as direction of gaze (Perrett, Smith et al., 1985b). A highly face-selective region along the upper bank of the STS was recently identified using fMRI (Tsao et al., 2003) and subsequently confirmed to be almost entirely face selective by using fMRI-guided single-unit recordings of neurons (Tsao et al., 2006). This selective face region in the macaque may be homologous to the human FFA (to be discussed later), though additional research is needed to clarify this relationship. The causal relationship between the activity of face-selective neurons in temporal cortex and face perception has recently been demonstrated by artificially activating small clusters of IT neurons while

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monkeys judged whether visual images belonged to “face” or “nonface” categories (Afraz et al., 2006). Microstimulation of face-selective sites within IT cortex, but not other sites, strongly biased the monkeys’ decisions toward the face category. Neuroimaging studies in humans further support the role of the temporal cortex in processing socially relevant information. An array of biologically/socially relevant stimuli activate the STS, including body movement/biological motion (Bonda et al., 1996; Grossman et al., 2000), mouth movements in faces (Puce et al., 1998), and gaze direction (Hoffman and Haxby, 2000). Interestingly, it appears that the STS is involved not only in the low-level processing of gaze, but also in the processing of more sophisticated social information. Stronger STS activity is elicited by gaze shifts that violate the viewer’s expectations (Pelphrey et al., 2003) or by gaze shifts that simulate eye contact, as opposed to gaze aversion, with a stranger (Pelphrey et al., 2004). Indeed, other studies have suggested that the STS is concerned with more than basic perception of biologically important stimuli. For example, the posterior STS shows a greater response to animations of moving geometric shapes that demonstrate social interactions or complex goal-directed movements as opposed to animations depicting random motion (Castelli et al., 2000; J. Schultz et al., 2004). Moreover, a region in the temporoparietal junction (in the region of the posterior STS) is activated when subjects are required to perceive intentions and/or attribute mental states to others (Gallagher et al., 2000; Saxe and Kanwisher, 2003; Saxe et al., 2004). These studies are consistent with the proposal that the STS plays a role in the perception of biological motion as well as a possible role in making inferences about the mental states of others (Allison et al., 2000). Lesion studies. A recent case study indicates that a patient with a circumscribed lesion to the right superior temporal gyrus shows a diminished capacity to maintain eye contact during conversations (Akiyama et al., 2006a). Interestingly, this same patient was impaired in her ability to utilize biological directional information such as gaze, but showed no impairments for using nonbiological counterparts (arrows) (Akiyama et al., 2006b). Though nonhuman primates with bilateral lesions of the STS are also impaired in their ability to perceive eye gaze and to differentiate the angle of faces (Campbell et al., 1990; Heywood and Cowey, 1992), it is not clear if these deficits are the result of a more general impairment in visual discrimination learning (Eacott et al., 1993). Summary. Collectively, the studies from humans and nonhuman primates indicate that cells in the temporal cortex play a clear role in the perception of socially relevant stimuli. These initial perceptual stages are essential components of

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the cascade of information processing that culminates in the accurate analysis of the dispositions and intentions of other individuals. However, it is not entirely clear at what point basic perceptual processing transitions to more sophisticated representations of socially relevant concepts (i.e., at what point does the sensory brain end and the social brain begin?). Converging evidence from humans and nonhuman primates indicates that cells in the temporal cortex may be involved with more sophisticated aspects of social processing, including evaluations of goal directedness, predictability, and intention of action. These findings lend support to the proposal that some cells in the STS are essential building blocks in the capacity to understand goals and intentions of an individual (Jellema and Perrett, 2005). Fusiform Gyrus Research related to the fusiform gyrus has forced investigators to question whether a brain region dedicated to responding to a possible social stimulus, such as the face of a conspecific, provides evidence for a differentiated component of the “social brain” or an example of a highly refined cortex specialized for perception of classes of complex objects. Neuroanatomy. The fusiform gyrus forms the posterior portion of the occipitotemporal gyrus, bounded by the collateral sulcus medially and the lateral occipitotemporal sulcus laterally. A region of the fusiform gyrus selective for face stimuli (the fusiform face area; FFA), is generally located in the middle lateral fusiform gyrus (Kanwisher et al., 1997). Functional studies. In recent years, the FFA has been at the heart of the debate over specialized modules of processing versus a more distributed processing approach. The “domain-specific” side of the debate argues that the FFA is specialized for processing faces (Kanwisher, 2000). This interpretation is based on the findings that FFA activation for faces is twice as strong as for nonface stimuli, such as letter strings, objects, and animals (Puce et al., 1996; Kanwisher et al., 1997, 1999) and that FFA activation is highly correlated with detection and identification of faces, but not of objects (Grill-Spector et al., 2004). In contrast, the “domain-general” view of FFA function suggests that the FFA is part of a distributed object recognition system (Bukach et al., 2006). This view is supported by studies indicating that the FFA shows a statistically significant response to nonface objects driven by expertise (Gauthier et al., 1999, 2000; Haxby et al., 2001). According to this model, faces activate the fusiform area because humans are “experts” at identifying faces, not because faces constitute a special class of information. Though the debate over FFA function is ongoing (Gauthier and Bukach, 2007; McKone and Robbins, 2007), new research methods are providing insight into the finescale functional organization of the FFA and may prove

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useful in addressing the larger issue of neural specialization. For example, results from a recent high-resolution fMRI study indicate that the FFA is actually composed of faceselective cortical patches that are intermixed with cortical patches selective for processing other objects (Grill-Spector et al., 2006). These findings challenge the hypothesis that the FFA is a uniform brain region composed of face-selective neurons. Lesion studies. Lesions of the fusiform gyrus result in deficits in face processing (Farah, 1996; Farah et al., 1998). Moreover, bilateral lesions of the ventral occipitotemporal cortex, involving the fusiform gyri, are associated with prosopagnosia, a deficit characterized by the inability to recognize faces of known individuals (Sergent and Signoret, 1992; Wada and Yamamoto, 2001). Summary. Given the importance of faces in conveying social information, it is likely that the FFA contributes to the early stages of social perception. Indeed, Haxby and colleagues have proposed a model in which the FFA is part of a core system for the visual perception of faces, particularly invariant aspects of face perception such as identity (Haxby et al., 2000, 2002). Since the neuroanatomical connectivity of the FFA is not known, it is currently unclear which other brain regions might build on this face processing in order to create models of the thoughts and intentions of others. Mirror Neuron System Neuroanatomy. Mirror neurons are a recently discovered class of visuomotor neurons identified using single-unit recordings in macaque monkeys (for reviews see Rizzolatti et al., 2001; Rizzolatti and Craighero, 2004; Iacoboni and Dapretto, 2006). Mirror neurons in the ventral premotor cortex and inferior parietal cortex of nonhuman primates fire with both the execution and observation of an action (Gallese et al., 1996; Rizzolatti et al., 1996) and have received substantial interest because of their proposed roles in understanding the actions of others (Rizzolatti and Craighero, 2004) and imitation (Rizzolatti et al., 2001; Iacoboni, 2005). Indeed, functional imaging studies in human subjects provide indirect evidence that humans also possess a mirror neuron system located in the pars opercularis of the inferior frontal gyrus and another region in the rostral posterior parietal cortex (Iacoboni et al., 1999). Functional studies. A defining feature of mirror neurons is the ability to fire both when a monkey does a particular action and when it passively observes an individual performing a similar action (Gallese et al., 1996; Rizzolatti et al., 1996). The majority of nonhuman primate mirror neurons require an interaction between a biological effector (e.g., a hand or

mouth) and an object, as the presentation of either stimulus alone is not sufficient to evoke activity, though a small subset of mirror neurons appear to respond to communicative gestures (Ferrari et al., 2003). It has been speculated that the motor-neuron-mediated type of action understanding in the macaque mirror system may have evolved into a neural system that encodes cognitive understanding of others (Iacoboni and Dapretto, 2006). In support of this hypothesis, the putative mirror neuron system in humans is activated in response to a wide range of cognitive functions, including imitation, empathy, and theory of mind (Carr et al., 2003; Gallese et al., 2004; Iacoboni et al., 2004, 2005). Moreover, new evidence suggests that the mirror neuron system may be dysfunctional in individuals with autism (Oberman et al., 2005; Dapretto et al., 2006). Lesion studies. The mirror neuron system in nonhuman primates includes large portions of the parietal and premotor cortices. The extensive lesions of these cortices that would be needed to disrupt mirror neuron function would most likely lead to other cognitive deficits. Although the lesion technique has not been used to evaluate the function of the mirror neuron system in nonhuman primates, reports of deficits in emotion recognition tasks in a patient with damage to the left frontal operculum lends support to the possibility that the human mirror neuron system contributes to some aspects of social processing (Adolphs et al., 2002). Summary. The discovery of the mirror neuron system provides a plausible neurophysiological mechanism for the development of imitation and other important social behaviors. Though much additional work is necessary to evaluate how the mirror neuron system may interact with other regions of the brain involved with social cognition (Keysers and Perrett, 2004), the current literature certainly supports the consideration of this region as a component of the social brain. Other Brain Regions It has been suggested that several other regions of the brain play a role in particular aspects of social processing, though the evidence implicating these regions as part of the social brain is less substantial than for the regions that we have described in detail. For example, Adolphs and colleagues have demonstrated that recognizing emotions from visually presented facial expressions requires right somatosensory-related cortices (Adolphs et al., 2000). These findings support the suggestions that we may recognize another individual’s emotional state by internally generating somatosensory representations that simulate how the other individual would feel when displaying a certain facial expression. Likewise, recent evidence suggests that the insula is activated both during the experience of disgust and the observation of the facial expression of disgust in others

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(Wicker et al., 2003). These data lend support to the idea that there is a common mechanism for understanding an emotion in others and feeling the same emotion in ourselves. Finally, given our interest in the neural basis of social cognition, it is important to include preliminary neuroimaging studies that have begun to identify neural networks involved with high-order cognitive processes, such as ToM. Though lesion studies have not yet provided compelling evidence of any one region that is essential for ToM abilities (Apperly et al., 2005; Griffin et al., 2006), several regions have been activated in ToM tasks, including the amygdala, OFC and posterior cingulate cortex, medial prefrontal regions (anterior paracingulate cortex), the temporoparietal junction found within the superior temporal sulcus, and the temporal poles. Of these regions, three areas appear most consistently activated across a wide range of ToM paradigms: (1) medial prefrontal regions, (2) the temporoparietal junction, and (3) the temporal poles (Gallagher and Frith, 2003). Several different ToM paradigms have consistently reported activity in these regions, including viewing animated shapes moving with “intentions” (Castelli et al., 2000), reading stories that require mental-state attribution (Fletcher et al., 1995; Vogeley et al., 2001; Saxe and Kanwisher, 2003), and performing tasks requiring competition or cooperation with a human partner as opposed to a computer (McCabe et al., 2001; Gallagher et al., 2002). Recent studies indicate that specific regions may contribute to different components of ToM, though additional research is needed to further explore these relationships (Saxe, 2006; Singer, 2006).

PART 2: DEVELOPMENT OF THE SOCIAL BRAIN Given the continuing ambiguities in the definition of the social brain, it is all the more challenging for social neuroscience to evaluate the neurodevelopment of social cognition. Critical questions for this field of research include the following: How and when do neural networks become specialized for processing social information? Is the specialization innate or determined by experience? How can early social experiences alter the development of the social brain? These questions remain open-ended, as we are at the very early stages of understanding how the brain develops the capacity to process social information. In part 1 of this chapter, we outlined regions of the brain most commonly thought to play a role in adult social behavior, including the amygdala, anterior cingulate cortex, orbital frontal cortex, regions of the temporal cortex, and the mirror neuron system. Much of the evidence linking these particular brain regions with social processing has been obtained from functional-imaging research or lesion studies on adult

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human subjects or from animal models using mature subjects (Adolphs, 1999, 2003). In many cases, there is little if any evidence evaluating the contributions of these structures to the development of social behavior early in life. It is problematic to rely on information obtained solely from mature subjects because it is becoming increasingly evident that the developing brain is not simply an immature version of the adult brain. Indeed, converging evidence from humans and animal models has clearly shown that the brain undergoes profound changes in structural and functional maturation that extend into early adulthood. Using the amygdala as one example, research has demonstrated that young children show unique patterns of amygdala growth (Giedd et al., 1996; Schumann et al., 2004) and that their amygdala activation in response to social stimuli is different from the activation seen in adults (Thomas et al., 2001). Moreover, animal models have indicated that the amygdala may play a different functional role in the early postnatal period compared to adulthood (Amaral et al., 2003; Moriceau and Sullivan, 2005). Although the behavioral significance of these findings is not completely understood, these data emphasize the fact that the developing brain is structurally and functionally different from the adult brain. Thus it is quite possible that regions of the social brain may play very different roles during the acquisition of social behavior early in life as opposed to the production of social behavior later in development. Though the field of developmental psychology has provided a rich literature on the social development of infants and young children, these studies tell us little about the underlying neural substrates of these behaviors. Approaches that combine behavior and indices of neural function, such as recording event-related potentials (ERPs) as infants view social stimuli, provide important insight into the changes in neural response properties during development (de Haan and Nelson, 1997, 1999). However, the relatively low spatial resolution associated with these techniques does not facilitate the identification of specific brain regions in which the neural changes occur. Complementary techniques, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which are commonly used in adult subjects, are logistically challenging and pose ethical dilemmas for use in infants and children. Thus there have been very few functional imaging studies evaluating the development of the social brain in infants and children (Tzourio-Mazoyer et al., 2002). Given the challenges of conducting neuroscience research on human infants, alternative approaches, including the use of animal models, may be important in evaluating the specific neural underpinnings of social development. Unfortunately, there has been a relatively limited use of developmental animal models specifically designed to evaluate the emergence of species-typical social behavior. Moreover,

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there are methodological issues specific to developmental studies that must be taken into consideration. In order to study social behavior using animal models, it is essential to provide conditions that facilitate the development of speciestypical social behaviors. Providing such conditions is especially important for studies with immature subjects because the developing brain is particularly susceptible to influences from the environment (Meaney, 2001). Aspects of social development appear to rely on “experience-expectant” processes (Nelson, 2001), where the development of skills and abilities that are characteristic of a species are dependent on exposure to specific experiences at certain time points in development (Greenough et al., 1987). In human populations, the damaging effects of early institutional rearing on the social development of young children have been well documented (Gunnar, 2001; Parker and Nelson, 2005). Likewise, it is known that restricting social access of infant nonhuman primates has a profound effect on behavioral development (Capitanio, 1986). Though historical research focused on the profound deficits in social development that result from rearing nonhuman primates in total social isolation (Harlow et al., 1965), we now know that much less severe social restrictions (e.g., nursery or peer rearing of nonhuman primates) can alter both behavioral and neurobiological development (Winslow et al., 2003; Capitanio et al., 2005; Ichise et al., 2006). As we learn more about the interactions between the brain and the environment, it will become increasingly important to evaluate the social rearing conditions of the animals in order to determine whether the behavioral and neural development of these models accurately reflects typical developmental processes.

Macaque monkeys: An animal model of social development In order to understand the underlying neurobiology of human social development, it will be important to examine similar social processes in animal models that are more amenable to traditional neuroscience methodologies. The rich repertoire of social behavior shared by many nonhuman primates makes them a particularly good model in which to study the neural basis of social behavior. Macaque monkeys display remarkable similarities to humans in both social behavior complexity and neuroanatomical organization (Machado and Bachevalier, 2003). These animals are therefore often considered the model of choice for studying the neural bases of complex cognitive processes, such as social behavior. Despite these similarities, it is important to note that the last common ancestor of humans and macaques dates back more than 25 million years (Kay et al., 1997); thus homologous relationships among brain regions are not always clear (Sereno and Tootell, 2005). Moreover, not all aspects of human social cognition can be modeled and

studied in nonhuman primates. However, we are able to study fundamental aspects of social development common to many group-living primates, including the use of speciestypical social signals, the motivation to interact with group members, and the ability to form and maintain lifelong relationships with group members. In this section we will briefly outline the social development of macaque monkeys as a potential model for understanding the component processes of social behavior. Though there are clear behavioral differences among different species of macaques (Thierry, 1985a, 1985b; Maestripieri, 2005), most macaques share common social organization and utilize similar speciestypical social signals. Although it is an oversimplification, we will refer to all species of macaques simply as “macaques” in the following section. Macaque monkeys live in large and cohesive social groups where they form long-lasting relationships with other group members. In general, females will remain in their natal group for the duration of their lives, while males emigrate into a new group when they are 3–5 years of age (Altmann, 1967). As a result, macaques have a strong matrilineal structure in which females from several generations live together and form long-lasting social networks, or matrilines (Wrangham, 1980). Most species of macaques demonstrate well-defined dominance hierarchies in both free-ranging (Drickamer, 1975) and captive social groups (Bernstein and Mason, 1963). Prediction of social rank is closely linked to the dominance status of kin, with high-ranking mothers producing high-ranking offspring (Sade, 1967; Missakian, 1972). In general adult daughters will acquire rank just below their mothers and above their older sisters (de Waal, 1977; Datta, 1984). Macaques utilize a variety of social signals, including vocalizations, facial expressions, and body postures, to communicate with other members of their group. Like humans, infant macaques must rapidly learn to interpret and produce these social signals in order to interact successfully with members of their social group. This wellcharacterized sequence of social development (figure 11.3) in many ways parallels that of human infants, though at a maturational rate approximately four times faster (Suomi, 1999) (i.e., a one-month-old monkey is roughly comparable developmentally to a four-month-old human). As is the case for most primate species, infant macaque monkeys are born with largely functional sensory systems and display an array of reflexive motor responses (Mowbray and Cadell, 1962; Mendelson, 1982a). The mother is their primary source of social stimuli at these early time points, as infant macaques spend almost all of their time in ventral contact or nursing during the first two postnatal weeks (Hansen, 1966; Berman, 1980). This early period of development is characterized by frequent social interactions with their mother including face-to-face communication (e.g., mutual exchanges of lip smacks—an affiliative social signal)

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Figure 11.3 Social developmental milestones for infant macaque monkeys. The social development of macaque monkeys parallels that of human infants, though at a rate approximately four times faster.

(Hinde and Spencer-Booth, 1967; Ferrari et al., 2006). Indeed, infant macaques show a clear preference for facelike stimuli very early in development (Lutz et al., 1998; Kuwahata et al., 2004) and will produce lip smacks in response to pictures of unfamiliar monkey faces (Mendelson, 1982b; Mendelson et al., 1982). Beginning around 2 weeks of age, infant macaques start to explore the surrounding environment with brief trips away from the mother. Like human infants, the macaque infant appears to use the mother as a secure base and will return to her immediately if alarmed or distressed (Hinde et al., 1964; Berman, 1980). As infants spend more time exploring away from their mothers, they will begin to

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interact with other members of the social group, primarily their mother’s close female kin and their offspring (Berman, 1982). This period marks a critical stage in development when the infant must acquire the ability to correctly evaluate social signals, particularly signals that may convey potential aggression. At one week of age, infant monkeys do not respond differently to faces of conspecifics staring directly ahead as opposed to faces looking away. However, by 3 weeks of age, infants make fewer fixations on faces looking straight at them (Mendelson et al., 1982). Given that prolonged direct eye contact is often a threatening gesture for macaques (Altmann, 1967), the ability to correctly evaluate the meaning of direct eye contact and respond appropriately

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represents a critical developmental milestone. Between 2 and 4 months of age infant monkeys begin to respond to fearful stimuli with a species-typical expression of fear/subordination, the fear grimace (Sackett, 1966; Suomi, 1999) and develop the ability to regulate fear reactions to threatening stimuli (e.g., freezing to remain undetected in the presence of danger) (Kalin and Shelton, 1989). At approximately the same time as the emergence of the fear grimace, infant macaques develop a fear of unfamiliar conspecifics, a behavior that is possibly akin to the “stranger anxiety” observed in human infants between 8 and 12 months of age (Suomi, 1999). By three months of age infants regularly explore away from their mothers, spending approximately 50 percent of their time out of physical contact with her (Hinde and Spencer-Booth, 1967). As infants become more adept at social interactions, they become more fully integrated into the larger social group. At 4–8 months of age social play becomes the dominant activity of macaque infants, taking several forms including physical bouts of “rough and tumble” play, “approach withdrawal” play, and nonsocial play with objects (Ruppenthal et al., 1974). Social play is essential for practicing and refining social behaviors that will be required later in adulthood. Likewise, social grooming is another critical skill that must be acquired. Many primate species participate in social grooming, which plays an important role in establishing and maintaining social relationships (Matheson and Bernstein, 2000). During the second half of the first year, infants begin to consistently initiate grooming of their mothers and other members of their immediate social group (Hinde and Spencer-Booth, 1967). The mother-infant relationship begins to transition into weaning at 5–7 months (Hansen, 1966), though there remains considerable overlap between the social networks of mothers and their infants (Berman, 1982). In order to obtain their appropriate social rank, infant macaques must learn which individuals they outrank and which individuals outrank them. Some time in the second half of the first year of life, infants will begin to direct aggression to adult females (and their offspring) who are lower in rank than their mothers, while deferring to adults (and the offspring) of individuals who are higher ranking than their mothers (Datta, 1984). Thus the social context in which a behavior occurs begins to play an increasingly important role in producing an appropriate response (i.e., was the threat received from a dominant or subordinate group member?). Following the birth of a sibling, macaques continue to develop more independence from their mothers and progress from infant to juvenile social behavior (Devinney et al., 2001). This well-defined sequence of macaque monkey social development provides a rich context in which to study the neurobiology of social behavior. It will be necessary to apply tools of basic neuroscience (e.g., neuroanatomy,

neurophysiology, lesion research, etc.) in order to identify regions of the brain that underlie the acquisition of speciestypical social behavior. In the final section of the chapter we will examine how tools of basic neuroscience can be combined with behavioral studies to evaluate the development of the social brain. It should be apparent that establishing a causal role between neural structures and specific aspects of social behavior will require multiple methodological approaches.

Techniques for studying the neural bases of developmental processes One approach to understanding the developmental neurobiology of social behavior is to relate the functional maturation of the brain with well-defined social milestones, such as those outlined in figure 11.3. Unfortunately, we observe only a rudimentary correlation between overall neuroanatomical changes and the corresponding development of primate social behavior (Levitt, 2003; Machado and Bachevalier, 2003). Moreover, the idea that new social skills are expressed as components of the social brain come “online” may be too simplistic. (That is, how do we define that a region has become fully functional?) It will therefore be necessary to utilize a variety of neuroscience methods in order to identify and study regions of the brain that are essential for acquiring social behavior early in life. In the following subsections we will briefly examine several basic neuroscience techniques and highlight studies that have focused on the development of the social brain using macaque monkeys as a model. Neuroanatomical Studies Neuroanatomy provides a powerful technique to study the developmental progression and maturation of specific regions of interest. Several regions of the macaque brain implicated in social processing in adult animals appear to mature at relatively early developmental time points. For example, we know that neurogenesis of the macaque monkey amygdala begins around embryonic day 33 and is complete by embryonic day 56 of the 165-day gestation period (Kordower et al., 1992). Injections of neuroanatomical tracers during the early postnatal period indicate that macaque amygdalocortical connections already closely resemble connections in the mature subjects by two weeks of postnatal age (Amaral and Bennett, 2000). Likewise, adultlike projections from inferior temporal areas TE and TEO to both amygdala and orbitofrontal areas have been observed in one-week-old macaques (Webster et al., 1991, 1994). Though little is known regarding development of specific neurochemical systems within these regions, it appears that the distribution of opiate receptors within the amygdala and cingulate cortex is comparable to adult patterns as early as one week of age (Bachevalier et al., 1986) and that the pattern of serotonergic innervation of the

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amygdala resembles the adult pattern within the first postnatal month (Prather and Amaral, 2000). Clearly much more work is needed to provide a comprehensive assessment of neuroanatomical development. Nonetheless, these studies indicate that several regions implicated in social processing appear to mature very early in postnatal development and may therefore play a critical role in the emergence of speciestypical social behavior. Despite these adultlike properties, some aspects of neural development within these circuits may undergo postnatal maturation. As described in part 1 of this chapter, inferior temporal (IT) cortex is critical for visual pattern recognition in adult primates and contains cells highly selective for specific stimuli such as faces (Bruce et al., 1981; Fujita et al., 1992). Detailed studies on both neuroanatomical and physiological development of IT cortex have revealed that it undergoes an extended period of postnatal development and may not be functionally mature until the end of the first year of life (Rodman, 1994). Indeed, cortical inputs and outputs of the IT cortices undergo considerable refinement during the first postnatal months. Interestingly, infant IT cortex receives transient inputs and gives rise to transient outputs, forming connections that are not found in adult monkeys (Webster et al., 1991; Rodman, 1994). Although the functional significance of this unique maturational process is not known, the extended period of postnatal neuroanatomical development provides complementary information for developmental neurophysiological data (see next subsection). In addition to refinement of connections, another aspect of brain development that is protracted is the process of myelination. Although axons can propagate impulses prior to myelination, the process of myelination enhances functional efficiency and specificity. By 3 to 6 months, subcortical regions of the macaque brain are well myelinated, and most cortical regions contain some myelin. However, cortical layers continue to acquire myelin until at least 3.5 years of age in macaque monkeys (Gibson, 1991). Myelination of the cortex follows a similar pattern in humans and monkeys, beginning first and proceeding most rapidly in primary sensory and motor areas, followed by the more protracted myelination of association cortices. Although the general pattern of myelination has been examined, less is known about the progression of myelination of axons within and across brain regions implicated in social processing and how this patterning may relate to functional changes. We do know that myelination of axons within IT cortex has not reached adultlike levels in 7-month-old macaque monkeys (Rodman, 1994), and that myelination within the macaque orbitofrontal cortex may take 1–2 years to reach adultlike levels (Gibson, 1991). Additional studies on the pattern of myelination may provide insight into the functional significance of this prolonged aspect of neural development. Taken together, these neuroanatomical studies

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indicate that several key structures implicated in social processing are well developed at the time of birth, yet continue to undergo refinement during the first postnatal year of macaque development. Neurophysiology and Functional Imaging Neuroanatomical studies provide critical information regarding the putative processing capabilities and developmental trajectory of specific brain regions. However, this information alone does not imply a functional contribution to social processing. While human research has relied heavily on EEG/ERP and fMRI as indices of brain function, these techniques have not been commonly used in macaque-monkey models of social processing. In recent years PET neuroimaging has been used to identify regions of the adult macaque brain that are activated in response to social challenge (Rilling et al., 2004) or species-specific vocalizations (Gil-da-Costa et al., 2006). Though PET neuroimaging has not been extensively used in developing animals, the potential use of this technique has been demonstrated in a PET study that identified regions of the brain underlying maternal separation behaviors in juvenile macaque monkeys (Rilling et al., 2001). Nonhuman primate research has benefited from the ability to directly record from neurons in awake/behaving animals, providing more direct evidence that a region of interest is involved in social processing. As discussed in the first section of this chapter, cells that respond to faces, body movements, gaze, and so on are found throughout the adult macaque temporal lobe, concentrated in both the inferior temporal gyrus and along the banks of the STS (Gross et al., 1972; Desimone et al., 1984; Baylis et al., 1987; Hasselmo et al., 1989; Perrett et al., 1992) and in the amygdala (Rolls, 1984; Leonard et al., 1985; Brothers et al., 1990; Brothers and Ring, 1993). It would be of interest to developmental social neuroscience to evaluate whether these response properties are established at birth or whether social experience plays a role in developing these highly selective response properties. Unfortunately, little is known regarding how and when these neurons become specialized for processing social information because of the many challenges of conducting this type of research in infant monkeys. We are aware of only one series of studies that has extensively examined response properties of neurons in infant monkeys (Rodman, 1994). These labor-intensive studies revealed that within the second month of life, individual IT neurons show response selectivity for faces, though cells in the infant monkeys show lower response magnitudes and longer response latencies compared to adults (Rodman et al., 1991, 1993). These data suggest that adultlike face selectivity is present at early developmental time points, but undergoes considerable postnatal refinement. These studies highlight the potential use of neurophysiology in understanding how and when the brain becomes specialized for processing social information.

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Lesion Research Lesion research provides a means of evaluating whether a neural structure is essential for specific aspects of social behavior. A general approach that we have taken in our nonhuman primate studies is to explore the dependency of component processes of social behavior on putative brain regions. This purpose is achieved experimentally by making selective lesions of the regions of interest and then carrying out detailed behavioral observations in order to define how the behavioral repertoire of the subjects has been altered. As reviewed in part 1 of this chapter, the lesion technique has been extensively used in mature subjects in order to evaluate the contributions of specific brain regions to social processing (Kling, 1992). However, relatively few studies have evaluated the effects of producing similar brain lesions early in development. In this section we will briefly summarize lesion research on regions of the brain implicated in social processing, highlighting studies that have focused on the developmental contributions of these structures. Our discussion will focus primarily on regions of the temporal lobe implicated in social processing, as the contributions of other regions of the social brain (e.g., OFC, ACC, etc.) have not been extensively examined using neonatal lesions (Bowden et al., 1971). In order to evaluate previous lesion studies in neonatal nonhuman primates, it is first important to consider methodological issues that often complicate interpretation of these data. For example, previous lesion research has often utilized aspiration lesions, which may cause unintended collateral damage to surrounding structures and pathways (Meunier et al., 1999). Thus it is possible that behavioral changes are due to unintended collateral damage, rather than to damage to the target structure. Another methodological issue is the quality of behavioral observations. As described earlier, macaque monkeys display a sophisticated repertoire of social behavior. In order to associate changes in behavior to the experimental procedure, it is necessary to utilize a comprehensive behavioral ethogram and to evaluate social development over time and under a variety of testing conditions. Finally, an important consideration for developmental studies is the rearing conditions of the experimental subjects. The majority of previous developmental studies utilized peer- or isolate-reared monkeys, a practice that is standard animal husbandry procedure at many primate facilities. There is, however, reason to believe that this manipulation is sufficient to produce animals with atypical social behavior and altered neurobiological development (Suomi, 1997; Winslow et al., 2003). In order to assess the effects of early lesion damage on social development, it is necessary to provide the experimental subjects with a rearing environment that will ensure species-typical development. Thus any changes in behavior can be more confidently attributed to the experimental lesion and not to a combination of brain damage and restricted rearing conditions.

These methodological issues are important to bear in mind when interpreting the results of lesion research. Lesions of the temporal lobe have historically been implicated in behavioral changes related to socioemotional processing (Brown and Shafer, 1888; Kluver and Bucy, 1939). Indeed, this is one of the few regions extensively studied in developing animals. For example, peer-reared infant monkeys that receive large aspiration lesions of medial temporal lobe structures (including the amygdala, hippocampus, and surrounding cortices) demonstrate fewer social contacts and more withdrawals from attempted social advances compared to unoperated controls (Bachevalier et al., 2001). Although much of the lesion research has focused on larger lesions of the medial temporal lobe, it is beneficial to have information on more discrete lesions of temporal lobe structures in order to associate behavioral deficits with specific brain regions. For example, infant monkeys that sustained damage to the inferior temporal visual area TE within the first postnatal month display less social contact compared to controls at 6 months of age, but do not show deficits in other aspects of social behavior such as eye contact and approach/withdrawal (Bachevalier et al., 2001). Early damage to area TE is also associated with abnormal vocal responses to separation from mothers (Newman and Bachevalier, 1997). The amygdala has long been implicated as a key structure in social processing (Kling, 1992), though recent lesion studies in adult monkeys suggest that the amygdala is not needed to produce species-typical social behavior (Emery et al., 2001). The results from developmental amygdala lesion studies have produced conflicting results, most likely as a result of methodological differences. While early studies on neonatal amygdala damage had reported few changes in behavior (Kling and Green, 1967), subsequent studies reported pronounced changes in fear behaviors (Thompson, 1968; Thompson et al., 1969, 1977; Thompson, 1981) or deficits in social development (Bachevalier, 1994). Given that impaired social communication and a lack of social interest is the hallmark of autism, it was proposed that lesions of the medial temporal lobe, specifically the amygdala, might provide an animal model of autism (Bachevalier, 1994, 1996). However, macaque monkeys that are reared in a social environment and receive selective amygdala lesions at two weeks of age do not demonstrate profound impairments in social development within the first year of life. These subjects were able to produce and respond to a variety of species-typical social signals and did not differ from controls in the amount of their social interactions (Bauman, 2004a, 2004b). These monkeys did, however, show abnormal behavior in fear regulation (e.g., heightened fear of nonthreatening conspecifics and absence of fear to normally fear-inducing objects) (Prather et al., 2001; Bauman et al., 2004b). Our interpretation of these data is that the amygdala

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does not play an essential role in the development of social behavior but does play a modulatory role by means of regulating emotions (e.g., fear responses) within a social context. Indeed, over time the amygdala-lesioned infants have displayed changes in social behavior, such as decreased social dominance, that may be due to an inability to regulate fear responses (Bauman et al., 2006). Although lesion studies provide a means of assessing whether a given structure is essential for particular aspects of behavior, there are potential limitations to behavioral lesion research that warrant further discussion. We presume that behaviors that are significantly altered following the surgical procedure are normally dependent on structures that have been removed. In contrast, we presume that behaviors that are unchanged following the lesion are not dependent upon those structures. The research presented here is based on permanent, destructive lesions of brain regions implicated in social processing. However, these regions do not function in isolation from other neural structures. Consequently, neonatal damage will most likely affect other brain regions that share connections with these structures. For example, neonatal temporal lobe lesions have previously been associated with delayed maturation of the prefrontal cortex (Bertolino et al., 1997). These changes in brain development are characterized by dysregulation of prefrontal-striatal dopamine transmission that is not observed following similar lesions in adult animals (Saunders et al., 1998; Heinz et al., 1999). These findings of brain reorganization following early brain damage have implications for results obtained through lesion research. Moreover, it is possible that early damage triggers compensatory changes in the brain, recruiting structures that are not normally involved in social behavior to carry out these functions. Thus any sparing of social behavior may be due to compensatory mechanisms of other brain structures not normally involved in social behavior.

Final comments We started the preparation of this chapter with the goal of discussing the development of neural systems involved in social behavior and social cognition. The neurobiology of social cognition is a relatively new area of inquiry spurred on by the seminal paper of Brothers in 1990. However, we have found that the definition of social cognition is still in need of refinement. Moreover, the stages in social behavior from perception to evaluation to behavioral output have not been clearly associated with particular brain regions. In addition, the highest level of social cognition, the building of models of another’s intentions and dispositions—that is, theory of mind—has only recently begun to be evaluated with cognitive neuroscience tools. Thus this chapter does not provide a definitive statement concerning the development

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of social cognition but represents much more a first step in developing a framework for thinking about and studying developmental social cognition. This will remain an exciting and productive area of research for decades to come. This research will undoubtedly have important implications not only for understanding and nurturing normal social development but also for understanding the impairments of social function that are the hallmark of ever more common neurodevelopmental disorders such as autism spectrum disorders. acknowledgments

Original research described in this chapter was supported by a grant from the National Institute of Mental Health (R37MH57502) and through the Early Experience and Brain Development Network of the MacArthur Foundation. Our research is conducted, in part, at the California National Primate Research Center (RR0069).

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12

Pre- and Postnatal Morphological Development of the Human Hippocampal Formation LÁSZLÓ SERESS AND HAJNALKA ÁBRAHÁM

The hippocampal formation plays an important role in the process of memory formation both in nonprimate mammals and in primates, including the human. This chapter concentrates on human studies; therefore, experimentation is excluded, and the developmental processes are presented in a descriptive manner by monitoring the formation of mature structures from simpler, undifferentiated stages. Among the fundamental issues regarding development, we discuss neuronal cell formation, cell death, cell migration, and the morphological and neurochemical development of neuronal types. Since axonal pathfinding and synapse formation are very difficult to verify in preand postnatal human tissue, information about these developmentally important issues will not be presented. The possible influence of premature birth on cell formation, cell migration, and neurochemical maturation will also be discussed.

Germinal matrices and neuronal cell formation in the hippocampal formation The developmental pattern of cell formation is similar in most regions of the central nervous system of various species. However, in a few areas, such as the hippocampal formation, substantial differences are seen between rodents and primates, in addition to a certain level of resemblance. In all mammalian species, the germinal matrices (the sites of cell proliferation) are comparable. Pyramidal cells of Ammon’s horn are formed in the ventricular zone immediately under the ventricular wall. Significant difference can be seen between the archi- and neocortical ventricular germinal matrices. In the archicortex only the ventricular zone exists, whereas in all areas of the neocortex a wide subventricular zone participates in cell proliferation. Principal cells of the dentate gyrus, the granule cells, also start to proliferate in the ventricular zone. However, laterborn granule cells are generated in a secondary germinal matrix that is located in the hilus of the dentate gyrus. The formation of this secondary matrix will be discussed later.

In rodents, principal cells of Ammon’s horn, the pyramidal cells, are generated in the second half of the embryonic period, and granule cells of the dentate gyrus start to be formed only a few days before birth. As a consequence, all pyramidal cells are formed prenatally, whereas 85 percent of the dentate granule cells are formed postnatally (Angevine, 1975; Bayer, 1980). In contrast, neurogenesis in the primate hippocampal formation takes place relatively early in prenatal development. In rhesus monkeys, the first neurons appear almost simultaneously in the different subregions of the hippocampal formation, from the entorhinal cortex to the dentate gyrus, between embryonic days 36 and 38 (Rakic and Nowakowski, 1981). Except for the dentate gyrus, cell formation ceases during the first half of pregnancy, between embryonic days 62 and 65. Granule cell formation lasts until the end of the first postnatal month, with only 15 percent of the granule cells being formed postnatally (Rakic and Nowakowski, 1981). Granule cell formation has also been found in small numbers in the dentate gyrus of adult monkeys (Kornack and Rakic, 1999). Classic descriptions of the developing hippocampal fissure are centered on the morphological changes of the cytoarchitectonics of the human hippocampal formation from the time it is recognizable using the conventional histological stains such as toluidin blue, erythrosine, and hematoxylineosin (Hines, 1922; Humphrey, 1967). Previously, appropriate cell proliferation markers were not available; therefore, only recent studies addressed the question of local cell formation in the germinal layers of the human cerebral cortex as well as in different areas of the hippocampal formation (Zaidel, 1999; Seress et al., 2001; Tiu, Chan, and Yew, 2004; Curtis et al., 2005). Subfields of the hippocampal formation, such as the entorhinal cortex, subiculum, and Ammon’s horn, are discernable at the 10th gestational week (GW), whereas the dentate granule cell layer appears around the 11.5th GW (Humphrey, 1967). At the 25th GW the cytoarchitectonic characteristics of all divisions of the hippocampal formation are similar to what is observed in adults (Arnold and Trojanowski, 1996a; Humphrey, 1967).

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The human brains used in our study (table 12.1) were obtained from autopsy (24 GW old or older) and from legal abortions (14–22 GW). The gestational age is based on postovulation time and on somatic measurements (crown-rump length). For the description of the hippocampal formation the suggested terminology of Amaral (1990) is followed. The term “hippocampal formation” includes the dentate gyrus, Ammon’s horn, subicular subregions (subiculum,

presubiculum, parasubiculum), and the entorhinal cortex. In our study of cell formation, we used the MIB-1 clone of the Ki-67 cell proliferation marker that is a commercially available monoclonal antibody (DAKO A/S, Glostrup, Denmark) and is widely used in conventional histopathology (Verheijen, Kuijpers, Schlingemann et al., 1989; Verheijen, Kuijpers, van Driel et al., 1989; Gerdes et al., 1992; Rose et al., 1994).

Table 12.1 Gender and clinical diagnosis verified by autopsy of the cases used in this study Case Number Gender Age Diagnosis 1 Female 14 gestational weeks Legal abortion (maternal disorder) 2 Male 15 weeks Spontaneous abortion 3 Male 16 weeks Legal abortion (maternal disorder) 4 Female 16 weeks Legal abortion (maternal disorder) 5 Female 17 weeks Spontaneous abortion 6 Female 18 weeks Spontaneous abortion 7 Female 20 weeks Legal abortion (maternal Hodgkin disease) 8 Female 22 weeks Spontaneous abortion 9 Female 24 weeks IRDS 10 Female 28 weeks IRDS, CHD 11 Female 30 weeks IRDS 12 Male 32 weeks Sepsis, pneumonia, CHD 13 Female 34 weeks Pneumoniua, IRDS 14 Female 36 weeks IRDS, CHD 15 Female 38 weeks CHD, IRDS 16 Male 38 weeks Esophageal atresia, pneumonia 17 Male 39 weeks Respiratory distress, asphyxia 18 Male 39 weeks Pneumonia, asphyxia 19 Female 40 weeks Respiratory distress 20 Male 40 weeks CHD 21 Female 1 postnatal week SIDS 22 Male 1 week BPD 23 Female 1 week CHD 24 Male 1 month Pneumothorax, pneumonia 25 Male 2 months Sepsis 26 Male 3 months Pneumonias, muscular distrophy 27 Male 3 months Leukemia 28 Female 3 months CHD 29 Male 5 months Respiratory distress, asphyxia 30 Female 5 months Agenesis of pulmonary arteries 1 31 Male 8/2 months Pneumonia 32 Female 11 months Ileus 33 Female 2 years Pneumonia 34 Female 8 years Leukemia 35 Female 10 years Leukemia 36 Male 47 years Heart attack Abbreviations: BPD, bronchopulmonary dysplasia; CHD, congenital heart disease; IRDS, infant respiratory distress syndrome; SIDS, sudden infant death syndrome

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In the early descriptions of the hippocampal fissure, it was demonstrated that hippocampal formation and the future dentate gyrus form a straight line. The dentate gyrus is located at the peak that starts to bend away from the ventricular wall at the 12th GW (see figure 51 in Hines, 1922, and figure 8 in Humphrey, 1967). At this age, the ventricular germinal zone along the hippocampal formation is a continuous layer where immature cells are formed and move to the pyramidal layer of Ammon’s horn and to the granule cell layer of the dentate gyrus (see the cell-dense future stratum oriens and hilar region in contrast to the practically cell-free future stratum radiatum in figure 8, Humphrey, 1967). With the aid of the mitotic marker MIB-1 it has been shown that both germinative matrices, the ventricular zone and hilus, contain a large number of proliferating cells as

early as the 14th GW (the earliest age examined in our study). At the 14th GW the hippocampal ventricular zone is relatively thin and does not include a subventricular germinal layer. In contrast, the temporal neocortical germinal layer includes a thick ventricular zone and wide subventricular zone displaying high mitotic activity. At the age of 14 GW, the dentate gyrus is already bent toward the CA1 area of Ammon’s horn and is localized far from the ventricular germinal layer (figure 12.1A). Therefore, at this age, similarly to in the 15- and 16-week-old fetuses (figure 12.2A), the continuous germinative zone from the ventricular wall to the dentate gyrus is not outlined as a straight line, but a stream of MIB-1-positive cells along the pyramidal cell layer of the CA3 area and below the hilar region indicates the presence of a germinal layer. In the 16-week-old fetus, the ventricular

Figure 12.1 Changes of the cytoarchitectonics of the fetal hippocampal formation. Camera lucida drawings of the hippocampal formation in (A) 14-week-old, (B) 18-week-old, (C) 20-week-old,

and (D) 22-week-old fetuses. CA1–3, subfields of Ammon’s horn; h, hilus of the dentate gyrus; p, pyramidal cell layer. Arrows point to the hippocampal fissure. Calibration bar, 500 μm.

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Figure 12.2 Cell formation in the hippocampal formation of a 16-week-old fetus. Photomicrographs of MIB-1-labeled cells in cresyl-violet–counterstained coronal sections of the hippocampal formation of a 16-week-old fetus. (A) The ventricular zone (vz) along the CA1 area of Ammon’s horn and the region below the dentate gyrus (DG) and CA3 area is fully packed with labeled cells. There are far fewer labeled cells in the hilus (h) below the granule

cell layer (g). The area marked with a single asterisk is shown with higher magnification in B, whereas the region marked with double asterisks is shown in C. Arrows in A and C point to the section of the ventricular wall, where the proliferative germinal layer terminates. Calibration bars, 250 μm for A and 75 μm for B and C. (See plate 19.)

germinal layer terminates at that zone of the ventricular wall where the CA3 pyramidal layer (CA3/CA2 border) of Ammon’s horn bends away from the ventricular surface (figure 12.2B,C). Groups of dispersed MIB-1-positive cells occur close to the ventricular wall, but the major stream of the proliferating cells locates alongside the CA3 pyramidal cells (figure 12.2A). In addition, large groups of proliferating cells occur in the hilus, below the suprapyramidal blade of the dentate gyrus (figure 12.3A). Six weeks later, in a 22week-old fetus, the proliferating cells disappear from the future stratum oriens of the CA3 area of Ammon’s horn, and groups of MIB-1 positive cells are only visible in the hilar region (figure 12.3B). (See also plates 19 and 20.) These developmental changes of the cytoarchitectonics (figure 12.1B–D) and the location of the groups of MIB-1positive proliferating cells indicate that the formation of the hilar secondary germinal matrix can be explained with the following developmental events. The hilar region and the dentate gyrus originally form the peak of the growing hippocampal formation that starts to bend at the 12th GW, probably because of an increasing number of fibers in the fimbria-fornix that pass between Ammon’s horn and the ventricular surface. The growing axonal bundle of afferent and efferent fibers pushes the dentate gyrus away from the ventricular wall and increases the distance between the ventricular germinal layer and the dentate gyrus. The passing fibers separate the stream of migrating cells from the ventricular wall, and the originally continuous ventricular germinal layer is disrupted at the zone where the fimbria-fornix starts from the CA3 area (figure 12.2A). The ventricular germinal zone is becoming continuously thinner after the 16th week and progressively disappears along the CA1–3 areas. In the case of the earlier-maturing CA3 area, only remnants of the ventricular germinal zone remain by the 20th gestational week (figure 12.1B). In the CA1 area, the ventricular germinal zone persists longer (figure 12.1C,D). In the 22-week-old fetus the ventricular germinal layer along the CA1 area still contains proliferating cells, although fewer in number than in the adjacent neocortical germinal layer (figure 12.4A–D and plate 21). In the 28-week-old fetus the ventricular germinal layer of the hippocampal formation is thin and only occasionally contains dividing cells. A similar case can be found in the full-term newborn, where the ventricular germinal layer contains 5–6 layers of loosely packed cells and rarely displays MIB-1 immunoreactivity (figure 12.4D). The adjacent subventricular zone of the temporal neocortex is thicker and still contains a few proliferating cells in the ventricular zone—and even more in the subventricular zone (figure 12.4D). At 3 months of age, the ventricular germinal layer along the hippocampal formation completely disappears, whereas in the temporal neocortex the subventricular germinal layer still contains scattered groups of MIB-1-labeled cells. The temporal cortical subventricular

zone is still recognizable in the 5-month-old child and disappears by 1 year of age (Seress, 2001). In the hilus of the dentate gyrus a high rate of cell proliferation is observed between the 16th and 22nd postnatal weeks (figure 12.3A,B). The number of dividing cells decreases rapidly after the 24th GW (Seress, 2001), but in a low percent (>0.1%) MIB-1 positive cells can be observed during the first six postnatal months (figure 12.5C). Only a portion of the labeled cells might be neuronal precursors, whereas the others are glial and endothelial cells (Seress et al., 2001; Ábrahám et al., 2004). The MIB-1 antibody against the Ki-67 nuclear protein labels cells in G1-S-G2-M phases of cell cycle independently of their nature (neurons, glial cells, endothelial cells). (See also plate 22.) Since the proteins that help to identify cells of the central nervous system appear postmitotically, the exact determination of the fate of MIB-1-labeled cells is not possible. Therefore, it has to be considered that not all proliferating cells are neurons, and a proportion of them are probably glial or endothelial cells. We suggest that before and around midgestation, most of the MIB-1-positive cells in the germinative zones become neurons, and parallel with the decreasing proliferative rate the proportion of the dividing neuronal precursors is decreased. It also has to be noted that neuronal precursors are exclusively formed in the germinal zones, and, therefore, proliferating cells in layers other than the germinal zones, according to our present knowledge, are all glial and endothelial cells. Recent observations emphasize long-lasting granule cell proliferation in the dentate gyrus of the human hippocampus (Eriksson et al., 1998; Roy et al., 2000). However, in surgically removed adult hippocampi we failed to detect more than a few dividing cells (>0.01%), independently of the cause of operation—for example, epilepsy or a benign tumor that did not invade the hippocampus itself. We only occasionally found MIB-1-labeled granule cells in the dentate gyrus of children who were older than 1 year (figure 12.5D), suggesting that a few neurons may preserve their capability of proliferation, but the frequency of such cells is extremely low in humans. This statement may appear negativistic in light of previously published data. However, if one carefully compares the data across studies it will be evident that experimental results in mice and rats support the idea of adult neuro-genesis (Kuhn, Dickinson-Anson, and Gage, 1996; Gould and Gross, 2002), but statements about results in monkeys are very carefully formulated (Rakic, 1998; Kornack and Rakic, 1999). In addition, only one publication indicates granule cell formation in the adult human dentate gyrus (Eriksson et al., 1998), and the other published reports are based on in vitro studies (Murell et al., 1996; Roy et al., 2000). Similarly, only one study suggested neurogenesis for the adult primate neocortex (Gould, Reeves, Graziano, et al., 1999), but it is clear now that neurogenesis in the primate neocortex is an

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Figure 12.3 Cell formation in the dentate gyrus of 16- and 22week-old fetuses. Photomicrographs of MIB-1-labeled and cresylviolet–counterstained coronal sections of the dentate gyrus of (A) 16-week-old and (B) 22-week-old fetuses. Equally large numbers of

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MIB-1-labeled cells appear in the hilus (h), below the granule cell layer (g), while only a few labeled cells are in the molecular layer (m) and in Ammon’s horn (CA3). Calibration bars, 75 μm. (See plate 20.)

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Figure 12.4 Cell formation in the ventricular germinal layers. Photomicrographs of cresyl-violet–counterstained sections (A,B,C) from 22-week-old fetus and (D) from newborn child. In the hippocampal ventricular zone (v) there are only a few MIB-1-labeled cells, whereas both the ventrical zone (v) and the subventricular zone (SVZ) of the temporal cortex is fully packed with MIB-1positive cells. The area marked with an asterisk is shown with

higher magnification from an adjacent section in B, whereas a similar area marked with double asterisks is shown in C. In the newborn child MIB-1-positive proliferating cells are not visible in the hippocampal ventricular germinal zone (v) and are sparse in the zone of the temporal neocortex (D). Calibration bars, 100 μm for A and 50 μm for B, C, and D. (See plate 21.)

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Figure 12.5 Postnatal cell proliferation and migration. Photomicrographs of coronal sections showing the border between the granule cell layer (g) and the hilus (h) of the dentate gyrus. The subgranular zone contains large numbers of migrating cells (a few pointed out by arrows) in a newborn (A), whereas such cells are absent in a one-year-old child (B). In a one-year-old child the

neurons in the granule cell layer (g) are easily distinguishable from astroglial (arrows) and oligodendroglial (open arrow) cells (B). In the newborn child a few MIB-1-positive cells are in the hilus (arrows) (C). Labeled cells (arrow) are rare in the hilus of the oneyear-old child (D). Calibration bars, 20 μm. (See plate 22.)

early embryonic phenomenon (Kornack and Rakic, 2001; Rakic, 2002; Bhardwaj et al., 2006). Moreover, data concerning neurogenesis in the epileptic dentate gyrus are also controversial in rodents and primates, because both enhanced and reduced neurogenesis was found in epileptic rodent models (Parent et al., 1997; McCabe et al., 2001), but neurogenesis was not changed in the resected hippocampi of epileptic patients (Fahrner et al., 2007; Heinrich et al., 2006). Similar results were found in our laboratory, where no indication of enhanced neurogenesis was found in the hippocampi of epileptic patients (Seress et al., 2001). Very limited possibilities for neuronal repair were found in humans after stroke and irradiation (Price, 2001; Snyder and Park, 2002; Arvidsson et al., 2002), although lesion-induced neurogenesis was reported in rodents (Gould and Tanapat, 1997), and a recent report indicates that neurogenesis may occur in the human brain after stroke (Jin et al., 2006). In mental disorders, reports about neurogenesis are controversial, because stem-cell proliferation was found to be decreased in schizophrenia but not in depression, and in Alzheimer’s disease glial and endothelial cell proliferation but not neurogenesis was found (Boekhoorn, Joels, and Lucassen, 2006; Reif et al., 2006). In conclusion, very few data indicate that neurogenesis would occur in the adult human hippocampal formation or in the adult human brain in general.

Evidence of cell death Since cell formation is accompanied by cell death, we examined the frequency of occurrence of pyknotic cell nuclei, which indicate that cell death has occurred in the hippocampal formation. Pyknotic cell nuclei are inevitable morphological signs, and pyknosis was a reliable marker of acute cell death in experimental models (Seress, 1977), although recent apoptotic markers provide a more modern approach. However, in the postmortem human brain one may expect a variable number of apoptotic cells, especially because the sensitivity of that method is great. In those cases where other morphological signs (large perineuronal and pericapillary spaces, etc.) also indicated strong hypoxia, pyknotic nuclei were frequent. However, in the brains of fetuses and young infants, where postmortem delay was short and fixation was good, only a few pyknotic cells were visible inside the granule cell layer, and almost no pyramidal cells were pyknotic in Ammon’s horn. These results are in harmony with our previous results from newborn monkeys where pyknotic nuclei were rarely observed (approx. 0.3%) in the granule cell layer, although granule cells in monkeys are still formed postnatally (Rakic and Nowakowski, 1981). In conclusion, neuronal cell death is not a necessary requirement for the formation of cytoarchitectonic layers of Ammon’s horn and that of the dentate gyrus in humans, and probably the overwhelming majority of the newly formed neurons survive. This finding

is in obvious contradiction with findings in experimental animals that indicate that programmed death of neurons and glia is always found in the postnatal rat brain (Siman et al., 1999). In addition, continuous apoptotic cell death may explain why an increased number of granule cells of the dentate gyrus cannot be found in old rodents when compared with young adults, although it would be expected following a constant granule cell generation in adulthood (Biebl et al., 2000; Cameron and McKay, 2001; Dayer et al., 2003).

Cell migration in the hippocampal formation Postmitotic pyramidal cells and GABAergic interneurons find their final position in the developing cortex through two different migrational processes. Excitatory cells that originate from the ventricular/subventricular zones of the telencephalon migrate through two forms of radial migration: somal translocation and radial glia-guided migration (Rakic, 1971; Nadarajah and Parnavelas, 2002). Pyramidal cells build the cerebral cortex according to an inside-out migrational grandient that results in the positioning of earlygenerated neurons in the deeper layers (VI, V) and, younger, later-born neurons migrating through the deeper, alreadyformed layers and forming superficial layers (II, III). In rodents, virtually all cortical inhibitory local circuit neurons, including hippocampal interneurons, are generated in the ganglionic eminences and migrate to their destination by means of tangential migration and ventricle-guided migration (Anderson et al., 1997; Nadarajah and Parnavelas, 2002; Nadarajah et al., 2002). However, in humans, only 35 percent of the neocortical interneurons have been shown to originate from the ganglionic eminences, and 65 percent of GABAergic cells are formed in the neocortical ventricular and subventricular zones and migrate radially guided by glial processes (Letinic, Zoncu, and Rakic, 2002). Experimental studies in rodents and primates have shown that the ventricular zone underlying the hippocampal formation is the source of neurons (Bayer, 1980; Nowakowski and Rakic, 1981). Migrating principal cells in Ammon’s horn, subiculum, and entorhinal cortex bypass previously generated neurons on their way to the superficial limits of the developing cortical plate (Nowakowski and Rakic, 1981). The inside-out migration pattern is similar to that in neocortex. The exception is cell migration of the dentate gyrus, where the granule cell layer is formed according to an outside-in pattern (Bayer, 1980; Nowakowski and Rakic, 1981). The dentate gyrus receives neurons both from the ventricular zone and from the hilus, which is a separate proliferative zone after the 16–18th GW as indicated by a dynamics of cell formation that is very similar in the hilus and in the subventricular cortical layer (Bayer, 1980; Seress, 1977). In nonhuman primates, groups of small cells with thin cytoplasm and dark cell nuclei persist in the subgranular

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zone of the dentate gyrus throughout the first postnatal year (Eckenhoff and Rakic, 1988). Since no neurons were labeled with 3H-thymidine-autoradiography after the first postnatal month, these cells had to be formed in the prenatal or early postnatal period. Not all of the undifferentiated, immaturelooking cells are positive for glial fibrillary acidic protein in the 6-month- and 1.5-year-old monkeys, indicating that these immature cells may be neurons and may differentiate into granule cells later in development (Eckenhoff and Rakic, 1988). Indeed, recent observations suggest that neurogenesis and cell migration occur in the dentate gyrus of adult monkeys, although the initiator factors of cell proliferation from these “dormant” progenitor cells are not clear (Gould, Reeves, Fallah, et al., 1999; Kornack and Rakic, 1999). Prenatally, in 14–22-week-old fetuses and in 24–30-weekold infants, a large number of migrating cells are visible below the granule cell layer of the dentate gyrus (figure 12.3A) as well as in the intermediate zone (future stratum oriens) between the ventricular wall and pyramidal cell layer. In 32-week-old and older infants, CA1–3 areas of Ammon’s horn lack migrating cells (Arnold and Trojanowski, 1996a). In contrast, large numbers of immature cells still persist in the subgranular zone of the dentate gyrus in older (32–36week-old) infants and even in neonates (Seress, 1992; Seress et al., 2001). In cresyl-violet–stained preparations, relatively large numbers of immature cells display a dark, elongatedovoid nucleus and a thin cytoplasmic rim in neonates (figure 12.5A). In a 5-month-old child, the proportion of immature cells is much lower, although clusters of dark, immature cells occur in the subgranular zone of the 8- or 11-month-old children. After the first postnatal year, the hilar border of the granule cell layer and the deep hilus are free of immature cells (figure 12.5B), and the cytoarchitectonic features of the dentate gyrus appear to be adultlike. This finding correlates well with the observation of an extremely low number of proliferating granule cells (>0.01%) of the dentate gyrus in children and in adults, suggesting that granule cell proliferation in adults may occur from localized pools of progenitor cells that may locate in islands along the longitudinal axis of the dentate gyrus. So far we were unable to detect such a pool in the human dentate gyrus, either in surgically removed hippocampi of adults or in autopsy material of children.

Cajal-Retzius cells in the developing hippocampal formation Precise regulatory mechanisms of cell migration are required for the development of a normally functioning cerebral cortex. One of the most studied regulatory pathways of neuronal migration is controlled by reelin. Reelin is an extracellular matrix glycoprotein expressed by several classes of different cells, particularly by large neurons of the developing marginal zone (MZ, future layer I) of the cerebral

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cortex (D’Arcangelo et al., 1995). These neurons are called, after the first descriptions provided by Cajal and Retzius, Cajal-Retzius cells (Cajal, 1891; Retzius, 1893). Reelin secreted by these cells is responsible for the correct lamination of the cerebral cortex, which follows the inside-out migrational pattern. The importance of reelin is highlighted by the phenotype of a naturally occurring mouse-mutant reeler caused by a mutation of the reelin gene (D’Arcangelo et al., 1995). This mutation results in cytoarchitectonic abnormalities that are pronounced in the cerebral cortex and hippocampal formation, in addition to the cerebellum. In the absence of reelin, young neurons are unable to migrate through the layers of their predecessors, resulting in an altered neocortical structure. In the archicortical hippocampal formation the abnormalities are mainly restricted to the dentate gyrus, although the arrangement of pyramidal cells of the CA1 region is also affected (Stanfield and Cowan, 1979; Drakew et al., 2002). Instead of one single layer, pyramidal cells of CA1 form two distinct layers (Deller et al., 1999), while granule cells of the dentate gyrus are not arranged in one layer but are dispersed in the hilus with their dendrites oriented in all directions (Stanfield and Cowan, 1979; Drakew et al., 2002). Investigations of the reelin signaling pathway show that the protein plays a role in the radial glia-guided migration in the cerebral cortex, a finding that explains the cytoarchitectionical abnormalities found in the reeler mutant (for review see Lambert de Rouvroit and Goffinet, 2001). In the dentate gyrus, reelin has been shown to promote the differentiation and orientation of radial glia and, therefore, to direct the migration of granule cells (for review see Forster et al., 2006). In addition, in the cerebral cortex it functions as a stop signal for the migrating neurons as they reach the border of the MZ and most superficial layer of the cortical plate. Mutation of the reelin gene in humans results in a low or undetectable level of this extracellular protein and causes autosomal recessive lyssencephaly (Hong et al., 2000). In addition to the robust neocortical and cerebellar malformations found in this disease, the hippocampal formation appears flattened, lacking its normal folded shape and definable upper and lower blades, indicating the importance of the reelin in the development of the human archicortex. Moreover, abnormal reelin signaling in the hippocampus was reported to be associated with neurological and mental illnesses such as epilepsy, schizophrenia, bipolar disorders, depression, and autism (Bartlett et al., 2005; Fatemi, Earle, and McMenomy, 2000; Haas et al., 2002). Reelin-secreting Cajal-Retzius cells can be visualized using immunhistochemical methods (Ogawa et al., 1995; Meyer and Goffinet, 1998). Independently of the reelin secretion, most Cajal-Retzius cells express a calcium-binding protein, calretinin, and a few of them calbindin (Soriano et al., 1994; Weisenhorn, Prieto, and Celio, 1994; Ábrahám

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and Meyer, 2003). However, both reelin and calretinin are also produced by interneuronal populations that, in the hippocampal formation, make the clear identification and quantification difficult (Schiffmann, Bernier, and Goffinet, 1997; Hof et al., 1999). A nuclear transcription factor p73 that belongs to the family of the tumor-suppressor protein p53 was found to be expressed exclusively by Cajal-Retzius cells in the archi- and neocortex of both rodents and humans (Yang et al., 2000; Meyer et al., 2002; Ábrahám and Meyer, 2003). For the exact morphology of Cajal-Retzius cells we have to consult the drawings of Cajal (1891) and Retzius (1893), who used Golgi impregnation for the visualization of these cells in the developing cerebral neocortex. Since the method developed by Golgi allows the clear visualization of the whole cell with its processes, Ramón y Cajal, in his original work (1891), described and drew bipolar and fusiform large neurons with dendrites and filopodia-like appendages in the MZ of the fetal human cortex. In addition, Retzius (1893) drew large cells in the MZ, which display profuse dendritic and axonal arborization with dendritic side branches ramifying perpendicular to the main dendrites and running toward the pial surface. These perpendicular dendritic side branches endow these cells with a bizarre appearance that can never be observed in other neuronal cell types. Regarding the differences between the morphology of reelin-labeled and calretinin-labeled Cajal-Retzius cells, reelin immunohistochemistry reveals only somata and only the proximal parts of the main dendrites of these cells, whereas calretinin labels longer segments of dendrites as well as the axons of CajalRetzius cells. In the cerebral cortex, because of these unique morphological characteristics, Cajal-Retzius cells can be clearly identified even after reelin or calretinin immunohistochemistry that allows a description of morphological changes of Cajal-Retzius cells in different stages of cortical development (Meyer, Goffinet, and Fairen, 1999). Unfortunately, Cajal (1891) and Retzius (1893) described these cells only in the neocortex and not in the hippocampal formation. Being a selective marker of the Cajal-Retzius cells in the neocortex, p73 immunoreactivity indicates that reelincontaining early-generated cells of the hippocampal marginal zone can be considered as the archicortical equivalents of the neocortical Cajal-Retzius cells (Ábrahám, PerezGarcia, and Meyer, 2004). Compared to the neocortical Cajal-Retzius cells, they form a rather homogeneous population displaying bipolar or fusiform shapes. In most cases, reelin and calretinin immunohistochemistry visualizes neither the perpendicularly running dendritic side branches nor filopodia. However, Golgi impregnation reveals a large number of small dendritic appendages running mostly perpendicular to the main dendrites of hippocampal Cajal-Retzius cells (figure 12.6D). Although these processes are smaller and less pronounced than in the neocortex

(figure 12.7E,F), their arrangement and the localization of the cells exclude any morphological similarity with any other neuronal cell type. (See also plates 23 and 24.) Cajal-Retzius cells form an early neuronal population in the marginal zone of the developing fetal cerebral cortex (Meyer and Goffinet, 1998). They can already be found at the 7th–10th GW expressing p73 and reelin in the cerebral cortex including the hippocampal primordium, and their number and morphological diversities increase afterward (Ábrahám, Perez-Garcia, and Meyer, 2004). The proposed sites of origin of Cajal-Retzius cells, where they invade the marginal zone through tangential migration, are the retrobulbar basal forebrain and the cortex-choroid plexus boundary, the so-called cortical hem (Meyer and Wahle, 1999; Zecevic and Rakic, 2001; Meyer et al., 2002). Based on morphological characteristics and on spatial vicinity, it was suggested that Cajal-Retzius cells of the hippocampal formation are derived from the ventricular epithelium of the cortical hem (Ábrahám, Perez-Garcia, and Meyer, 2004), which is a putative signaling center of cortical patterning (Grove et al., 1998; Grove and Tole, 1999). Parallel with the folding and maturation of the hippocampus, the fibers of the fimbria fornicis grow between the choroid plexus and the ventricular zone of the hippocampus, shifting the birthplace of Cajal-Retzius cells near the dentate-fimbrial boundary. From the 15th–16th GW and onward, Cajal-Retzius cells proliferate at the dentate-fimbrial boundary often marked by an indentation of the lateral ventricle. Immunocytochemical investigation of dividing cells both in the cortical hem and dentate-fimbrial boundary using the Ki-67 cell proliferation marker showed that Cajal-Retzius cells start to express p73 after exiting from the cell cycle. Reelin immunostaining appears later, when the p73-immunoreactive cells approach the marginal zone. Their migrational route is located medially from the proposed migrational path of the granule cell precursors. Therefore, Cajal-Retzius cells appear in the outer region of marginal zone of the dentate gyrus (future stratum moleculare). Parallel with the decrease in number of proliferating precursors in the hippocampal ventricular zone between the 21st and 25th GW, the number of p73-positive cells is also decreasing at the dentate-fimbrial boundary. While in the early fetal stages, Cajal-Retzius cells are numerous in the marginal zone of Ammon’s horn and of the dentate anlage that corresponds to the suprapyramidal or dorsal blade of dentate gyrus described in rodents. Later-born Cajal-Retzius cells populate mostly the infrapyramidal (ventral) blade of the dentate gyrus, following in time the developmental gradient of Ammon’s horn and supra- and infrapyramidal blades of the dentate gyrus. Parallel with this regional shift, reelin-immunoreactive p73-negative cells that are morphologically identical to the reelin-positive hippocampal interneurons (Alcantara et al., 1998; Ábrahám and Meyer, 2003)

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Figure 12.6 Cajal-Retzius cells in the developing hippocampal formation. (A) Calretinin-immunoreactive Cajal-Retzius cell, displaying characteristic filopodia (open arrows) at the hippocampal fissure of a newborn child. (B) Reelin-immunostained Cajal-Retzius cells (arrows) at the hippocampal fissure of a newborn child. (C) A large, bipolar calretinin-immunoreactive Cajal-Retzius cell with

long dendrites running along the hippocampal fissure in a 2-yearold child. (D) Photomontage of a Golgi-impregnated large, bipolar Cajal-Retzius type cell at the hippocampal fissure of a newborn child. Filopodium-like processes (open arrows) are on both the soma and the dendrites. Calibration bars, 25 μm. (See plate 23.)

Figure 12.7 Cajal-Retzius cells in layer I of the temporal neocortex. (A) Photomicrographs of calretinin-positive Cajal-Retzius cells (arrows) in the temporal neocortex of a newborn child. Arrowheads point to calretinin-immunoreactive axonal plexus deeper in layer I, which may correspond to the axonal plexus of the Cajal-Retzius cells as shown on (F). (B, C) Calretinin-positive Cajal-Retzius cell (arrow) of different morphology in 5-month-old infant. (D) Reelinimmunoreactive Cajal-Retzius cells (arrows) and reelin-positive interneuron (curved arrow) in a 3-month-old infant. (E, F) Golgiimpregnated Cajal-Retzius-type cells (arrows) in the temporal

cortex of a newborn child corresponding to the cells first described by Retzius. (E) One of the cells has a long dendrite that runs parallel with the pial surface displaying the characteristic side branches (open arrows) that can be observed on many drawings of Retzius. (F) The other cell shows a fuzzy cell body (arrow) with several dendritic branches that run in different directions. The thin side branches leave the main dendrites perpendicularly to them (open arrows). Arrowheads show axonal plexus of Cajal-Retzius cells in the deep layer I. Calibration bars, 25 μm for A–C and E, 20 μm for D, 50 μm for F. (See plate 24.)

start to appear in the inner marginal zone overlying the ammonic plate, while p73 reelin-immunoreactive cells predominate in the outer marginal zone. With further development, the number of reelin-immunoreactive interneurons increases in the strata lacunosum-moleculare and radiatum of Ammon’s horn. Cajal-Retzius cells form a predominant cell population in the developing archi- and neocortex approximately until midgestation. After that, their relative number continuously decreases, and they are reported to disappear from the cerebral cortex after cortical neurons have found their final position (Meyer et al., 1999). However, in newborns large numbers of Cajal-Retzius cells can be identified in the hippocampal formation (figure 12.6A,B,D). Their number gradually decreases with age, although many of them are visible in older infants (figure 12.6C) or adolescents. Moreover, a few Cajal-Retzius cells persist in the adult hippocampal formation and temporal neocortex (Meyer et al., 2002; Ábrahám and Meyer, 2003). In the postnatal archicortex a higher number of CajalRetzius cells can be observed than in the neocortex. Based on reelin immunostaining, it was demonstrated that only small, bipolar cells can be found in layer I of the neocortex after the migration period that terminates around the 30th gestational week (Meyer, Goffinet, and Fairen, 1999). However, in addition to Golgi impregnation (figure 12.7E,F), calretinin-immunohistochemistry also reveals large cells with bizarre morphology in the temporal neocortex of newborns (figure 12.7A), as well as later in the first few postnatal months (figure 12.7B,C). Similar large cells are reelin immunoreactive, indicating that they may also belong to the Cajal-Retzius cells (figure 12.7D). In most cases they are found in the vicinity of small reelin-containing interneurons (figure 12.7D). The prolonged formation of the granule cell layer of the dentate gyrus may explain why Cajal-Retzius cells persist postnatally (figure 12.6C,D) in the hippocampal formation. It might be hypothesized that their presence in the adult hippocampus would verify the proposed postnatal neurogenesis of the dentate granule cells. However, Cajal-Retzius type cells can also be found in the perinatal neocortex, and rarely in the mature neocortex, where neurogenesis and migration are not assumed. Therefore, the functional importance of Cajal-Retzius cells in the postnatal human cerebral cortex may be complex, and these cells may support possible plastic changes both in the neocortex and in the hippocampal formation. This theory is supported by the study of Del Rio and associates (2002) using in vitro slice culture of the mouse hippocampal formation to show that Cajal-Retzius cells promote the regeneration of entorhino-hippocampal fibers after transection of the perforant pathway. Although the identity of the axonal growth-promoting signals is still unknown, they are effective in young adult nervous tissue

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with an inhibitory nonpermissive milieu for the regrowth of injured axons. In addition, Cajal-Retzius cells are found in large number in the human hippocampal formation of preschool children and adolescents, and their number significantly decreases after that age (Ábrahám and Meyer, 2003). Developmentally important events (as will be discussed later in this chapter), such as axonal growth, synapse formation, and reorganization, occur in the hippocampal formation in children that may require the contribution of Cajal-Retzius cells.

Development of excitatory and inhibitory neurons of the human hippocampus Granule cells, hilar mossy cells, and CA3 pyramidal cells of monkeys are in an advanced stage of development at birth (Seress and Ribak, 1995a, 1995b). In contrast, in humans, a large proportion of the principal cells are in an early stadium of dendritic and spine development at birth (Purpura, 1975; Seress, 1992; Seress and Mrzljak, 1992). However, fully matured granule cells displaying densely spiny dendrites and an axon that gives rise to several collaterals in the hilus can be found in neonates (Seress, 1992). In contrast, other granule cells display varicose, stubby, short, and spineless dendrites that terminate in growth cones (Seress, 1992). These latter granule cells are still growing and are supposed to be those that are formed in the perinatal period. Therefore, the diversity in maturation of granule cell development seen by Purpura (1975) in the 33-week-old fetus is still observable at birth. A few immature-looking granule cells are still seen in the 15-month-old child, suggesting that granule cells exhibit a prolonged period of cell proliferation and maturation (Seress, 1992). The long-lasting development of principal cells of the dentate gyrus can also be illustrated with the neurochemical maturation of granule cells. Calbindin, a calcium-binding protein, is a marker of granule cells and can be visualized with immunohistochemistry (Seress et al., 1993). During fetal development, granule cells start to express calbindin relatively early. At the 22nd GW a few granule cells of the dentate gyrus are already calbindin-positive (figure 12.8B). This first calbindin-positive cell group is found in the area of granule cell layer that locates between the CA3 and CA1 regions, and corresponds to the dorsal blade of the rodent dentate gyrus. Granule cells form the granule cell layer in an outside-in migrational pattern because older granule cells locate more superficially than the younger ones. Correspondingly, the granule cells locating closer to the molecular layer are calbindin positive, whereas the deeper locating cells at the hilar border are calbindin negative (figure 12.8B,C,D). At birth a large number of granule cells are calbindin positive, but the majority of granule cells of the ventral blade are calbindin negative (figure 12.8C). In addition, the granule

fundamentals of developmental neurobiology

Figure 12.8 Calbindin immunoreactivity in granule cells of the dentate gyrus and in the pyramidal cells of Ammon’s horn. (A) Pyramidal cells that are located closer to the ventricular germinal layer express calbindin at the 16th GW in the pyramidal cell layer of CA1 area. (B) At the 21st GW, virtually all pyramidal cells of the CA1 area are calbindin immunoreactive. In contrast, only the oldest granule cells are calbindin positive at the border of the granule cell (g) and molecular layers (m). (C) At term, in the dorsal blade (arrow) of the granule cell layer (g) most cells display strong immunoreactivity for calbindin, while in the ventral blade (open curved arrows) only cells in the outer part of the granule cell layer

(g) close to the molecular layer (m) are immunoreactive. In the hilus (h) of the dentate gyrus a few scattered calbindin-positive cells are probably interneurons, whereas in the pyramidal layer of the CA3 area all cells are immunonegative. (D) Calbindin-immunoreactive cells in granule cell layer (g) of the dentate gyrus in a 5-month-old child. Many of the cells display immunoreactive dendrites that run toward the molecular layer (m). In contrast, large numbers of cells with elongated cell nuclei (one pointed out by an arrow) are immunonegative at the hilar border (h). These are probably the newly generated, still-migrating granule cells. Calibration bars, 50 μm for A, 100 μm in B and C, 20 μm for D. (See plate 25.)

cells at the hilar border of the granule cell layer are calbindin negative both in the dorsal and the ventral blades (figure 12.8C). As late as the 5th postnatal month, the granule cells at the hilar border are still calbindin negative, indicating arrival of new granule cells from the hilus (figure 12.8D). (See also plate 25.) Formation of complex spines of hilar mossy cells occurs exclusively postnatally, because no mossy cells with thorny excrescences could be observed in humans at birth (Seress and Mrzljak, 1992). The first small thornlike excrescences appeared on human mossy cells by the third postnatal month. At the age of seven months thorny excrescences are frequent on mossy cells, but their number and size continue to increase up to the third year (Seress and Mrzljak, 1992). The thorny excrescences of the adult human mossy cells are much larger than the complex spines of mossy cells in monkeys (Frotscher et al., 1991). There is a similarly extended postnatal period of development of thorny excrescenses of CA3 pyramidal cells. A period of maximum dendritic growth for hippocampal pyramidal cells was observed between the 20th and 28th weeks of gestation (Purpura, 1975). Pyramidal cells of the CA3 area display only a few small spines (spicules) and filopodia on their dendrites in a 22-week-old fetus. In the 33-week-old fetus, the first thornlike excrescences appear on the dendrites of the CA3 pyramidal cells (Purpura, 1975). Therefore, the first complex spines appear 3–4 months earlier on the dendrites of CA3 pyramidal cells, which project to the CA1 region, than on the dendrites of mossy cells, which innervate the granule cells. CA1 pyramidal cells also show considerable postnatal morphological changes, although their neurochemical maturation starts early in fetal development. Pyramidal cells of the CA1 area express calbindin, similarly to the granule cells (Seress, Gulyás, and Freund, 1992). The first calbindin-immunoreactive cells in the CA1 pyramidal layer can be observed at the 16th GW (figure 12.8A). First, cells that locate closer to the ventricular zone are immunoreactive, indicating the inside-out migrational pattern of pyramidal cells. Although virtually all pyramidal cells are calbindin positive at birth, they have few basal dendritic branches and poorly developed side branches of the apical dendrites. A similar pattern has been found in neocortex, where the pyramidal cells of the newborn child display a few varicose, short basal dendrites and a few poorly developed side branches of the apical dendrite (Purpura, 1975; Seress, 2001). There are only a few spines on the apical and basal dendrites of pyramidal cells of the newborn infant, whereas the equivalent portions of the dendrites in the adult neocortex and hippocampus are fully covered with spines. In rodents, similar to the neocortical local circuit neurons, virtually all hippocampal interneurons are generated in the ganglionic eminences (Anderson et al., 1997). In humans, however, the possibility cannot be excluded that inhibitory

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neurons originate from the hippocampal ventricular zone (Letinic, Zoncu, and Rakic, 2002). Interneurons are formed early, parallel with the proliferation of pyramidal cells of Ammon’s horn. However, at the site of proliferation they cannot be differentiated from pyramidal cells of Ammon’s horn because of the late expression of their neurochemical markers. Large numbers of GABAergic cells of the human hippocampal formation express calcium-binding proteins— calretinin, calbindin, or parvalbumin (Freund and Buzsáki, 1996). In addition, reelin-immunoreactive interneurons form a distinct interneuronal population, although reelin is partly colocalized with calretinin and calbindin (Schiffmann, Bernier, and Goffinet, 1997; Ábrahám and Meyer, 2003). In adults, calretinin-positive interneurons occur in high numbers at the border between the strata radiatum and lacunosummoleculare of the CA1 region. The dendritic orientation of these small bipolar cells is perpendicular to the hippocampal fissure. In addition, large multipolar calretinin-containing interneurons are also found in all layers of the hippocampal formation. As early as the 14th GW, reelin-positive non-Cajal-Retzius cells appear in the inner marginal zone of the hippocampal formation. These cells might form the first interneuronal population. Their number increases afterward, and they gradually display a mature phenotype. Calretininimmunoreactive interneurons are recognizable in all layers of Ammon’s horn at the 16th GW. At around midgestation (22GW) their characteristic location is prominent at the border of the strata lacusonum-moleculare and radiatum (figure 12.9B). In addition, their morphology corresponds to that found in adults, although at this age cells are smaller and display fewer and shorter dendritic branches. Parallel with the appearance of calretinin in the layers of Ammon’s horn, small immature cells express calretinin in the ventricular zone, below the ventricular epithelium. Their size, shape, and location indicate that they are young, postmitotic cells and that they might migrate from this position to CA1–3 areas (figure 12.9C). In fact, a population of calretininimmunoreactive cells with immature cell body and with one or two small dendritic processes can be seen in the future strata oriens, pyramidale, and radiatum of Ammon’s horn at the age of 16th–18th GW, indicating a probable migration of these cells to their final destination. It has to be mentioned, however, that a transient expression of calretinin in ventricular germinal cells cannot be excluded. Another calcium-binding protein, calbindin, expressed by granule cells of the dentate gyrus and pyramidal cells of Ammon’s horn, is also a neurochemical marker for a characteristic group of interneurons in the hilus and molecular layer of the dentate gyrus and in the strata radiatum and oriens of Ammon’s horn (Seress et al., 1993). Cabindinpositive interneurons initially appear in the fetal period, although slightly later than the calretinin-positive interneu-

fundamentals of developmental neurobiology

rons. At 16th GW only a few very immature immunoreactive cells can be observed. The first calbindin-immunoreactive cells with morphology and location identical to matured hippocampal interneurons are seen only at the 20th–22nd GW, both in Ammon’s horn and in the dentate gyrus (figure 12.9A). However, at birth, calbindin-positive interneurons still have an immature appearance with short and rarely branched dendritic trees. (See also plate 26.) The third calcium-binding protein, parvalbumin, is contained by interneurons providing axo-somatic or axo-axonic inhibition to glutamatergic principal cells (Freund and Buzsáki, 1996). Parvalbumin-positive cells are usually large and multipolar and are located in close proximity to the population of their target cells—for example, inside or at the borders of the pyramidal or granule cell layers. In contrast to the previous two populations of interneurons, parvalbumin expression starts late in development. In the late fetal period, parvalbumin-positive cells could not be found in the human hippocampal formation. At birth, in Ammon’s horn only a few, immature small parvalbumin-positive somata are seen displaying short, rarely branching dendrites. At birth, parvalbumin-positive cells are not visible in the dentate gyrus. In a 1-month-old infant parvalbumin-immunoreactive cells still display poorly developed dendritic branches (figure 12.10A), but already a few parvalbuminpositive axons and a few axon-terminal-like boutons can be observed (figure 12.10A, 12.11A). In the next few months (before the second year of age) both dendritic and axonal arborization expand, although the developmental delay between Ammon’s horn and the dentate gyrus is still visible (figures 12.10B, 12.11B). Even in a 2-year-old child, the morphology of the dendrites and axonal branching as well as the number of terminal boutons of the parvalbuminimmunoreactive cells are less developed than in 8- or 10year-old children. In our material, the adultlike morphology of parvalbumin-containing cells and the adultlike pattern of parvalbumin-containing axonal network in the principal cell layers of Ammon’s horn and the dentate gyrus appeared in an 8-year-old child (figures 12.10C, 12.10D, 12.11C, 12.11D). However, we still have no specimens between the 2nd and 8th years; therefore, we suggest that maturation of these cells is completed during this period, similarly to the development of hilar mossy cells (Seress and Mrzljak, 1992). It has to be emphasized that lack of parvalbumin in inhibitory neurons does not mean that perisomatic inhibitory terminals would be completely missing in the hippocampal formation at birth. Our unpublished electron microscopic observations revealed that perisomatic inhibitory synapses exist on somata of both granule and pyramidal cells in neonates, although those axon terminals are not parvalbumin immunoreactive. (See also plates 27 and 28.) Interneurons play a crucial role in the processes of memory formation through their role in the γ-oscillation

of the hippocampal neuronal network induced by the synchronized activity of GABAergic local-circuit neurons (Wang and Buzsaki, 1996; Wallenstein and Hasselmo, 1997). Recent investigations showed that parvalbumincontaining local-circuit neurons are critical in this process. Hippocampal parvalbumin-positive neurons form a syntitium through their dendro-dendritic gap junctions (Fukuda and Kosaka, 2003) and supposedly mediate inhibition-based coherent γ–rhythms (Tamás et al., 2000). In the hippocampal formation of adult parvalbumin-deficient mice, in which perisomatic inhibitory neurons exist but do not express parvalbumin, inhibition-based γ-oscillation increases, resulting in a lower threshold for the development of epileptiform activity (Schwaller et al., 2004). In addition, the lack of parvalbumin in interneurons may affect the higher cognitive functions associated with γ–oscillation (Vreugdenhil et al., 2003). We propose that the long-lasting postnatal maturation of parvalbumin-containing axo-somatic inhibitory cells in humans might increase the susceptibility of the hippocampal formation in newborns or in infants for stimuli that later cannot induce a similar effect. This hypothesis might clarify why a high fever may cause febrile seizures in young infants and may also explain why such early generated seizures disrupt the normal development of the inhibitory circuitry, resulting in manifestations of epileptic activity and epileptic morphological changes in the hippocampal formation later in life. However, the delayed expression of parvalbumin in the perisomatic inhibitory cells coincides with the prolonged maturation of the principal cells, such as the granule and mossy cells of the dentate gyrus (Seress, 1992; Seress and Mrzljak, 1992). The long postnatal development of both principal and inhibitory cells may offer an explanation for the long-lasting cognitive development of children.

Cell proliferation, migration, and neurochemical maturation in preterm infants The better chance of survival of preterm infants is coupled with the growing concern about the neurodevelopmental outcomes of the infants (Hack and Fanaroff, 1999; Vohr et al., 2000). The lower IQ scores and educational difficulties of preterm children can be correlated with the reduced size of several brain areas, including the hippocampus. The neurobehavioral outcome of preterm infants has been reported to worsen with younger gestational age at birth and with lower birth weight (Hack, Friedman, and Fanaroff, 1996; McCormick, Workman-Daniels, and Brooks-Gunn, 1996). A plausible reason for reduction, in case of extreme preterms, would be the reduced cell formation following premature birth or some defect in the migratory pathway that would guide the neurons to a wrong place. Although in

seress and ábrahám: pre- and postnatal human hippocampal formation

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Figure 12.9 Development of the calbindin- or calretinincontaining interneurons. (A) Calbindin-immunoreactive local circuit neurons (arrows) with immature morpholgy in the hilus (h), the stratum moleculare (m) of the dentate gyrus, and the stratum radiatum (r) of the CA3 area of the hippocampal formation of a 22-week-old fetus. (B) Large numbers of calretinin-positive interneurons at the border of the strata radiatum (r) and lacunosum-

moleculare (l-m) of the CA1 region of a 22-week-old fetus. The location is similar to that found in the adult, although the cellular morphology of these cells is still immature. (C) Small calretininimmunoreactive cells in the ventricular germinative zone (vz) along the CA1 area of the hippocampus. These calretinin-positive cells may migrate later to the strata oriens (o) and pyramidale (p) of Ammon’s horn. Calibration bars, 50 μm. (See plate 26.)

Figure 12.10 Parvalbumin immunoreactive interneurons in the CA1 region. (A) Large parvalbumin-positive cell with immature morphology and sparse dendritic branches in a 1-month-old infant. Arrowheads point to parvalbumin-immunoreactive axons with terminal-like axonal swellings. (B) A multipolar, large parvalbuminimmunoreactive interneuron with lightly stained branching dendrites in a 3-month-old infant. Arrowheads point to rare terminal-like boutons. (C) Large, multipolar parvalbumin-positive cell in the pyramidal layer (p) of Ammon’s horn with dendrites running

both toward stratum oriens and through stratum radiatum (r) in an 10-year-old child. The morphology of this cell is comparable to parvalbumin-positive cells found in adults. The parvalbuminpositive axonal network is confined to the pyramidal cell layer (p). (D) High-magnification photomicrograph of the parvalbumin-positive axonal network in the pyramidal layer (p). Arrowheads point to axonal swellings (terminal-like boutons) that appear to surround individual neurons (n). Calibration bars, 20 μm for A and D, 25 μm for B, and 50 μm for C. (See plate 27.)

Figure 12.11 Parvalbumin-immunoreactive interneurons in the dentate gyrus. (A) Parvalbumin-immunoreactive axonal branches displaying large swellings (arrowheads) in the granule cell layer (g) and in the hilus (h) of the dentate gyrus in a 1-month-old infant. (B) Soma and main dendrites of a parvalbumin-immunoreactive cell in the granule cell layer of the dentate gyrus in a 3-month-old infant. Axonal branching (arrowheads) is similarly sparse as in 1month-old child. (C) Large-magnification photomicrograph of parvalbumin-immunoreactive axon terminals in the granule cell layer of an 10-year-old child. The axons (arrowheads) display large

numbers of boutons that appear to surround somata of granule cells (n). (D) A large parvalbumin-immunoreactive hilar (h), neuron with long dendrites that cross the granule cell layer (g). Inside the granule cell layer (g) the dense parvalbumin-immunoreactive axonal network appears to be denser at the hilar border (h), suggesting uneven perisomatic innervation of granule cells in the width of the granule cell layer. Density of axonal branches is low in the hilus (h), corresponding with a lower cellular density in the hilus than in the granule cell layer. Calibration bars, 20 μm for A, 25 μm for B, 10 μm for C, 50 μm for D. (See plate 28.)

preterm infants the hippocampus is reduced in volume, reduced cell formation was never demonstrated. To study a possible change in cell proliferation, migration, or neurochemical development we compared the hippocampi of preterm infants with age-matched controls (table 12.2). Different groups were created to compare the effects of preterm delivery in extremely preterm infants (gestational age at birth adults Sac ( children for all frequencies See text for more information Gain ↑ for both 0.25 and 0.5 Hz Gain reached adult level by midadolescence Phase: ns

Resultsc Power ratio: no effect and no interaction between age and frequency among the three child groups Phase: no effect of age within child groups, but interaction; eye movements lagged behind target in 3- to 4-year-olds, ahead of target in 5- and 6-year-olds

karatekin: eye tracking studies of normative and atypical development

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11.4 (7–15)

Ross, Radant, and Hommer, 1993 53

62 39 36

6°/s, 12°/s (constant)

0.4 Hz (sinusoidal)

Dyslexics

D Intervention Dyslexics> Non-Intervention Dyslexics

Figure 45.2 Functional anatomy of phonological manipulation in (A) typical adult readers, (B) dyslexic adult readers, and (C) typical readers greater than dyslexic readers. Activation maps were generated by contrasting simple repetition of an aurally presented word with repeating words after performing sound deletion. Signal increases attributed to phonological manipulation were observed in left occipitotemporal, inferior parietal, and inferior frontal cortex in typical readers (A) and in bilateral inferior parietal, inferior frontal, middle temporal cortex, precuneus, and cerebellum in the dyslexic group (B). A between-group statistical comparison of the control and dyslexic groups revealed less activity in the dyslexic group in left inferior parietal regions (supramarginal and angular

gyri), superior parietal lobule, precuneus, and medial frontal gyrus and right hemisphere occipitotemporal junction (C). (D) Functional anatomy of phonological manipulation following reading remediation (group X session interaction) revealed increases during phonological manipulation in left parietal cortex and fusiform gyrus. Right hemisphere increases included posterior superior temporal sulcus/gyrus and parietal cortex. (Reprinted from Eden, G. F., K. M. Jones, K. Cappell, L. Gareau, F. B. Wood, T. A. Zeffiro, N. A. E. Dietz, J. A. Agnew, and D. L. Flowers, 2004. Neural changes following remediation in adult developmental dyslexia, Neuron, 44(3). Copyright 2004, with permission from Elsevier.)

to and following a phonologically based intervention (see figure 45.2) that resulted in improved performance in reading accuracy. Behavioral gains were associated with increased activity in bilateral parietal and right perisylvian cortex. These results suggest that in adult developmental dyslexia, behavioral plasticity involves two neural mechanisms, the same increases in the left hemisphere as reported for children (Aylward et al., 2003; Simos et al., 2002) and a right-hemisphere increase not unlike that observed in acquired reading disorders (Adair et al., 2000). Future studies will need to directly examine intervention-induced changes in children of different ages, to better understand the role of brain maturation in the context of brain plasticity.

Conclusions and future goals Sensorimotor and cognitive skills follow developmental trajectories that adhere to different time scales and are shaped by the interplay of biological factors (e.g., genetic predisposition, brain architecture) and environmental factors (e.g., instructions). A comprehensive understanding of the relationship that exists between learning, development, and experiential factors is urgently needed for the field of reading. The processing of written language is one of the most complex cognitive skills that we master, as it relies on the integration of numerous sensorimotor and cognitive processes. Their successful coordination, in turn, relies on explicit, staged instructions addressing numerous domains of oral and written language. Reading itself does not develop naturally, and the process of learning to read further promotes a number of skills that alter the brain’s function and anatomy (Castro-Caldas et al., 1998, 1999). How these can best be used to optimize reading skills of dyslexic readers is still a matter of investigation. This chapter serves to summarize the most relevant and prevalent findings in our current understanding of the reading disability developmental dyslexia. From these, a variety of theoretical frameworks have emerged to best explain the multiplicity of difficulties experienced by individuals with dyslexia, and consequently these have led to the advocacy of different types of interventions. A defining deficit in dyslexia is in phonological coding, the inability to understand how words are broken up into their constituent sounds, how these can be manipulated, and how they represent print. This core phonological coding deficit has become the primary target for intervention strategies in dyslexia, and future research will continue to quantify the efficacy of these approaches. Compounding the difficulty in establishing an allencompassing account by which to explain all the symptoms of dyslexia across the genetic, behavioral, and neurological levels is the fact that dyslexia is comorbid with other condi-

tions that also interfere with learning and language. At the same time, the study of the hereditary mechanisms of these co-occurring conditions may help to provide insight into dyslexia. Independent of these complexities, there is general agreement on the cognitive processes that are involved in reading. It is known that if these fail to become established, inaccurate and dysfluent reading follows. One of the more challenging questions revolves around the degree to which interventions are successful in bringing about improved ability across a range of reading skills, that is, not just reading accuracy, but also reading fluency and comprehension, the latter representing the raison d’être for reading. Not only is transfer of learning crucial for specific words (gains should not only be observed on trained exemplar items, but also transfer to untrained items), but dyslexic readers should be able to generalize their newly acquired skills to all domains of reading. Coupled with this realization, investigators who design brain-imaging studies to probe the neural correlates of reading gain will need to increase their repertoire of paradigms to evaluate the brain-based changes that occur beyond single-word reading. acknowledgments

The authors are supported by grants from the National Institute of Child Health and Human Development (HD40095, HD37890, HD21887). We thank Ashley Wall and Eileen Napoliello for preparation of this manuscript. REFERENCES

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The Development and Cognitive Neuroscience of Anxiety DANIEL S. PINE AND CHRISTOPHER S. MONK

This chapter provides an integrative perspective on pediatric anxiety disorders. The chapter is divided into four sections. The first defines key terms, differentiates normal from pathological anxiety states, and describes three specific anxiety disorders. The second section delineates three major advances that profoundly influence plans for future work on anxiety. Third, an integrative framework is described that draws insights from basic and clinical science. Finally, this framework is applied specifically to research on pediatric anxiety disorders.

Definitions The terms “fear” and “anxiety” define diverse phenomena across distinct research perspectives, including cognitive and affective neuroscience, biological psychiatry, and experimental and clinical psychology. This chapter integrates these approaches; accordingly, a uniform set of definitions is proffered. Specifically, the term “fear” defines a neural, cognitive, and behavioral state elicited in an organism by a dangerous stimulus or circumstance (Pine, 2007). Cues associated with danger are referred to as “threats” (Grillon, 2002). In these instances, threats, or dangerous stimuli and circumstances themselves, represent stimuli that an organism classifies as capable of producing harm and worthy of being avoided. Threats engage diverse processes across many neurocognitive levels (Pine, in press). For example, very briefly presented threats can engage an isolated set of neural, cognitive, and behavioral responses, whereas threats presented for longer time periods can engage a more elaborated set of responses (Davis, 1998). The term “anxiety” refers to a neural, cognitive, and behavioral state that resembles fear in terms of the organism’s response but differs from fear in terms of the associated stimuli. Whereas fear occurs in the immediate presence of threat, anxiety occurs in the absence of threat. Specifically, anxiety can be elicited by cues that suggest a threat may be forthcoming, or anxiety can represent a state that persists in the organism following elicitation of a fear response. Normal versus Pathological Anxiety Fear and anxiety represent normal aspects of childhood. Children and adolescents exhibit a characteristic pattern of fears and

anxieties that show striking similarities across cultures (Ollendick et al., 1996). Thus children consistently exhibit fears of strange situations and people within their first years of life. As these fears abate prior to school entry, they typically are replaced by concerns about separation and fears of animals or other danger, such as darkness or natural threats. Finally, as children approach adolescence, social concerns emerge as a dominant focus of anxiety. In all these areas, anxiety and fear represent aspects of normal development. Indeed, parents might be equally concerned by the absence of prototypical fears as by the presence of extreme fears. The ubiquity of anxiety during normal development raises major questions about delineating normal and pathological anxiety states. Fear and anxiety represent aspects of various psychopathological states, including personality disorders, psychoses, and mood disorders. The term “anxiety disorder” refers to the class of mental illnesses where anxiety represents the predominant focus of symptoms. Fear or anxiety is considered pathological when it causes marked distress or interferes with the child’s ability to function, typically leading to avoidance of situations perceived as dangerous. The fourth, revised edition of the Diagnostic and Statistical Manual (DSM-IV) recognizes more than 10 anxiety disorders as unique conditions, characterized by disorder-specific sets of criteria (American Psychiatric Association, 1994). Some of these disorders, such as panic disorder, occur primarily in adults: panic disorder virtually never manifests before puberty and rarely presents in adolescence (Pine et al., 1998). For the other anxiety disorders, which present more commonly in children and adolescents, considerable debate persists concerning the validity of the current narrow definitions in DSM-IV. As a result, the current chapter reviews prior research on pediatric anxiety disorders both broadly considered as a group of syndromes and considered individually, from the more narrow DSM-IV perspective. When data on individual disorders are discussed, the emphasis is on three specific conditions that are prevalent in childhood and diagnostically related. These comprise social anxiety disorder and generalized anxiety disorder, both of which are classified similarly in children, adolescents, and adults; and separation anxiety disorder, which is only classified in children and adolescents. These three conditions are

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discussed as a group in the clinical and research literature because of high comorbidity and physiological similarities. A fourth condition, specific phobia, is often also discussed with these other three conditions. The current chapter provides limited discussion of specific phobia, since it shows weak associations with impairment and a distinct course (Shaffer et al., 1996; Pine et al., 1998). Finally, the chapter also briefly discusses data on pediatric posttraumatic stress disorder (PTSD). While most research on pediatric anxiety disorders excludes children with PTSD, interest in this condition has grown in light of data in rodents demonstrating robust effects of early life stress on threat response behavior throughout adulthood. Specific Pediatric Anxiety Disorders Social anxiety disorder involves extreme fear in situations where an individual is scrutinized. The condition typically emerges in late childhood or early adolescence, though subclinical signs typically manifest earlier. Similarly, behavioral inhibition, a temperamental classification in toddlers, also shows an association with social anxiety disorder (Perez-Edgar and Fox, 2005). Generalized anxiety disorder (GAD) involves worries about various situations and circumstances. Classically, these worries reflect concerns about competence or potential harm. Worries about social situations also typically occur in GAD. Technically, GAD is only diagnosed when associated worries extend beyond social situations. However, there are high rates of comorbidity between GAD and social phobia. Furthermore, GAD is highly comorbid with other conditions, such as depression (Angold, Costello, and Erkanli, 1999). Such high rates of comorbidity for GAD with other conditions raise questions about the degree to which GAD represents a valid, distinct pathological condition. Separation anxiety disorder involves concerns about harm to an attachment figure. Such worries lead affected children to show anxiety in anticipation of separation and to avoid situations, such as sleepovers, where separation occurs. Separation anxiety disorder shows a robust inverse relationship with age, representing the most common anxiety disorder before puberty and one of the rarer ones in late adolescence. Some evidence from both family-based and psychophysiological research suggests that separation anxiety disorder during childhood may represent a specific precursor of panic disorder, which emerges during adulthood (Pine et al., 1998; Biederman et al., 2001). Finally, PTSD involves anxiety associated with exposure to a traumatic event that produces marked and immediate fear in a child. Following exposure to trauma, children with PTSD demonstrate a pattern of reexperiencing this reaction as either flashbacks or recurring nightmares, in tandem with avoidance of situations related to the trauma and signs of increased arousal, such as exaggerated startle.

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Major research advances and clinical implications Current classifications of psychopathological states rely entirely on descriptions of symptom patterns. Unlike other medical conditions, information on physiological indicators of perturbed organ system function is not currently useful in making distinctions between normal and pathological behaviors or emotions. Unfortunately, mental health professionals must wait some time before they will have tools analogous to the electrocardiogram. The practice of basing diagnoses on symptom reports represents a necessary compromise, given the limited understanding of physiologic influences on human behavior. While clearly limited, the current classification system revolutionized clinical approaches to mental disorders in the final decades of the 20th century. Such advances capitalized on the availability of reliable terminology that facilitated communication. Nevertheless, as scientific approaches mature, pathophysiology data will play an increasingly prominent role in classification. Arriving at a framework that allows integration of physiology data with symptom-based approaches represents a major challenge for mental health science (Pine et al., 2002). Integrating developmental approaches creates further complications. Three Major Advances Over the past quarter century, advances in three distinct research areas have made this a particularly opportune time for integrating clinical and basic research to better understand the development of anxiety. These three areas are research in developmental psychopathology, the neural correlates of fear, and cross-species parallels in the threat response. For developmental psychopathology, refinements in psychiatric nosology in the early 1980s produced a classification scheme for mental disorders that provided reliable definitions of various mental syndromes. This classification scheme was originally codified in the third version of the Diagnostic and Statistical Manual (DSM-III). The criteria for individual disorders in this manual have undergone two revisions, and various other classification schemes have been published (American Psychiatric Association, 1994). However, similarities among these schemes are far greater than the differences between any of these schemes and classification schemes used in the earlier parts of the 20th century. Two developments became possible following this change in classification. First, a series of large-scale epidemiological investigations could be initiated that defined the range of normal and abnormal behaviors in representative populations (Costello et al., 2002). This work revealed the tremendous morbidity associated with psychiatric states, including anxiety disorders. Second, studies used these innovations to track large cohorts of children. As these children aged throughout the end of the 20th century, the developmental unfolding of psychiatric states was revealed (Rutter,

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Kim-Cohen, and Maughan, 2006). Of note, these longitudinal studies revealed anxiety disorders to represent more clinically significant conditions than previously recognized, given the strong associations between pediatric anxiety disorders and various forms of adult psychopathology. Moreover, these data also served to stimulate debate concerning the validity of narrow DSM definitions of the anxiety disorders, given that an individual, narrowly defined anxiety disorder present in childhood typically showed equally strong associations with the same and with other anxiety disorders in late adolescence or adulthood. At the same time, the theoretical perspective of developmental psychopathology was becoming more broadly accepted. This school of thought focused on both normal and abnormal fluctuations in behavior across development. By understanding the range of behaviors that typically manifest in normal children, clarification of what is abnormal could be grasped. Moreover, by focusing on trajectories, the school placed behaviors in a developmental context. From this perspective, one symptom or another might raise concerns both for its immediate effect and its association with perturbed development (Gross and Hen, 2004). The importance of the developmental psychopathology perspective appears particularly prescient for anxiety disorders. Pediatric anxiety disorders consistently emerge as the most prevalent form of psychopathology in children and adolescents, with the rates of the conditions covered in this chapter appearing particularly high (Costello, Egger, and Angold, 2004). Moreover, pediatric anxiety disorders represent an early-life manifestation of various pathological states that unfold across the life span (Pine et al., 1998). This association appears particularly strong for anxiety disorders, such that pediatric social anxiety disorder, GAD, and separation anxiety disorder predict high risk for a range of adult anxiety disorders (Pine et al., 2007). Moreover, these three pediatric anxiety disorders also predict risk for other adverse adult outcomes, including mood disorders, substance use, and suicide (Pine et al., 2007). In terms of the second major advance, work in rodents and nonhuman primates demonstrates robust developmental influences on the threat response, involving genetic effects, environmental effects, and interactions between the two. For this chapter, the focus will be on measuring the threat response in immature organisms. This focus reflects the fact that variations on how immature organisms respond to threat may provide insight into why humans with anxiety disorders respond differently to situations and events that are perceived as threatening. The animal work suggests that immature rodent and primate brains show unique levels of plasticity in the way they process threat. The effects of rodent maternal behavior provide the most compelling evidence of the role of the environment on an organism’s response to threats. Stimulated by Levine’s

studies on maternal behavior (Levine, 1957, 1967), Meaney and colleagues defined the manner in which variations in maternal care during the initial weeks of life produce robust alterations in stress responses (Meaney, 2001; Gross and Hen, 2004). Rat pups exposed to intensive stimulation, associated with licking and grooming from their mothers, exhibit relatively low levels of threat response behavior throughout life. Cross-fostering work demonstrates that this effect is mediated by environmental influences. Moreover, a developmental window exists: rats exposed to environmental influences during adulthood do not show these permanent alterations in stress responses. While less extensive work examines environmental influences in nonhuman primates, analogous long-term influences have been demonstrated (Amaral, 2002). Thus this work shows that environmental effects have the capacity to interact with genes to shape an organism’s lifelong pattern of threat responses. Moreover, the environmental effects may have greater impact when the organism is young than when mature. Research in rodents has been vital in delineating developmental plasticity in genetic effects. Here, the most compelling examples emerge in gene-manipulation studies. Mice with a deletion of the serotonin 1A receptor (5-HT1a) exhibit extremely high levels of anxiety-like behaviors. This genetic effect emerges through developmental influences on anxietylike behaviors, as the effect only occurs if a functional gene is absent prior to day 21 of development: mice that are allowed to mature past this point with a fully functional 5HT1a receptor show no changes in anxiety-like behaviors when the gene is deleted later in life (Gross and Hen, 2004). Moreover, work with a related gene, which codes for the 5HT transporter (5HTT) protein, suggests that these genetic effects interact with environmental influences to shape anxiety-related phenotypes (Ansorge et al., 2004). Such work is consistent with studies in both nonhuman primates and humans (Suomi, 2003). Thus both environmental and genetic studies demonstrate unique plasticity in the immature rodent and nonhuman primate. Influences on fear behavior appear more robust and lasting in immature than in mature organisms. Finally, the third major advance involves research in cognitive neuroscience on cross-species parallels in the neural circuitry of fear responses. As reviewed in this chapter, work in this area has delineated a suite of information-processing functions that become engaged when an organism processes threats. These functions can be reliably engaged in rodents, nonhuman primates, and humans, as indexed by comparable measures of behavior (LeDoux, 2000). Moreover, with the advent of modern brain-imaging techniques, parallel measures of brain activity can be derived across these species. Furthermore, the inclusion of participants with brain lesions in conjunction with neuroimaging facilitates further understanding of cross-species similarities and differences (Amaral,

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2002; Adolphs et al., 2005). As a whole, this work demonstrates strong cross-species parallels not only in informationprocessing functions engaged by threats but also in the neural architecture that mediates the engagement of these processes. While such work has focused predominantly on mature organisms, recent studies in nonhuman primates suggest the neural correlates of these information-processing functions develop gradually as organisms mature (Prather et al., 2001; Amaral, 2002). Moreover, methods used to demonstrate neural correlates of these functions in adult humans have recently been successfully applied in research with children (Monk et al., 2006; McClure et al., 2007). These advances set the stage for truly novel research approaches to pediatric anxiety disorders. Clinical Questions on Pediatric Anxiety Realization of the potential afforded by these three advances will require studies to integrate emerging insights from neuroscience to address major questions emerging in clinical domains. Three specific questions have emerged in available research on pediatric anxiety. For advances in cognitive and affective neuroscience to have traction, methods might be brought to bear directly on these questions, thus integrating research on pathophysiology and clinical symptomatology. One major question concerns the validity of current nosology. On the one hand, preliminary data document specific longitudinal trajectories and cross-generational associations for some anxiety disorders (Pine et al., 1998; Merikangas et al., 1999; Biederman et al., 2001). For example, pediatric social phobia appears to show the strongest association with adult social phobia in longitudinal and familybased studies, whereas pediatric separation anxiety disorders appear to show the strongest association with adult panic disorder in such work. Generalized anxiety disorder (GAD), in contrast, shows associations with virtually all of the anxiety disorders, as well as with major depression. This finding has led to greater questions on the validity of GAD than either social phobia or separation anxiety disorder. On the other hand, none of the findings documenting specificity for social phobia and separation anxiety disorder are well replicated, and some studies generate contradictory findings (Costello, Egger, and Angold, 2004). Moreover, because of the high rates of comorbidity among all anxiety disorders, most studies of therapeutics target the three disorders together, rather than as distinct conditions. Thus controlled trials of either anxiolytic medication or psychotherapy typically randomize children, as a group, who present with any combination of social phobia, separation anxiety disorder, and GAD, to experimental and control treatments. The data on treatment response emerging from these studies also document stronger similarities than differences among the three disorders. Thus the weight of the evidence provides limited support for the validity of current classifications.

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Data on therapeutics give rise to a second major question. Specifically, pediatric anxiety disorders exhibit nonspecificity in treatment outcomes; while both selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral psychotherapy (CBT) represent effective treatments, the treatments work equally well for all pediatric anxiety disorders (Kendall et al., 1997; RUPP, 2001). Moreover, heterogeneity clearly exists in terms of treatment response, and at present it is unclear what variables predict a favorable response (Birmaher et al., 2003; RUPP, 2003). Finally, the third major question concerns specificity in familial aggregation. Family studies document strong and consistent relationships between anxiety in parents and their offspring, with perhaps the most consistent association emerging between panic disorder in parents and separation anxiety disorder in offspring (Capps et al., 1996; Biederman et al., 2001). Nevertheless, family studies of depression document similarly strong associations: offspring of parents with major depression also show high rates of anxiety disorders in general and pediatric separation anxiety disorder specifically (Beidel and Turner, 1997; Weissman et al., 1997; Biederman et al., 2004). Moreover, genetic studies provide further evidence of nonspecificity in risk: identical genetic factors contribute to anxiety during childhood and major depression during adolescence or adulthood (Silberg, Rutter, and Eaves, 2001). These data raise basic questions concerning the degree to which children manifest narrow as opposed to broad vulnerabilities. Answers to questions on nosology, therapeutics, and risk are unlikely to emerge from current research approaches. The inconsistent pattern of associations in prior research suggests that current classification schemes do not accurately capture underlying variations in pathophysiology. A novel approach might integrate insights from neuroscience.

Explicating a model of clinical anxiety An Integrative Framework Progress in mental health science requires an integration of data on pathophysiology with data on nosology and developmental psychopathology. Ultimately such integration will lead to a nosology based on both symptomatic expression and pathophysiology. Figure 46.1 illustrates a framework for achieving such integration. As shown on the left of the figure, pathophysiological processes in virtually all forms of psychopathology ultimately involve interactions between genetic and environmental influences, with distinct disorders involving different mixtures of these effects and distinct genes or environmental influences. These effects do not directly influence expressions of or risks for psychopathology. Rather, genetic and environmental influences are thought to sculpt functional aspects of neural circuits, with specific circuits playing distinct roles in specific disorders.

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Genetic influences

Information Processing Functions Fear Circuit Medial Temporal Lobe (Amygdala & Hippocampus)

Prefrontal Cortex (PFC)

Clinical Features Anxiety Disorders

Fear Conditioning, Extinction & Other Aspects of Emotional Memory

Temperament

Emotion-Attention Interaction

Exposure to Risks

Response to Innate Threats Sub-Clinical Fears Other Functions (e.g., stimulus-reward association, stimulus classification, etc.)

Environmental influences Figure 46.1 This figure depicts a model that integrates neuroscience research on pathophysiology with clinical research on between-subject variation in anxiety-related constructs. The patho-

physiologic chain of events flows from left to right in explicating causes of between-subject variations in clinical anxiety states.

Current understanding of brain-behavior associations appears much stronger for between-subject variations in cognitive or affective processing than for between-subject variations in psychiatric symptoms. In research on brainbehavior associations, children can be classified based on profiles derived from measures of brain imaging or from information-processing paradigms. These neuroscienceoriented classifications then can be linked to clinically based classification schemes. The term “endophenotype” has been used to refer to neuroscience-based classifications that show consistent relationships with clinical classifications. Thus a crucial step in refining current nosology involves linking measures of brain function and information-processing function to clinical measures of psychiatric symptoms and risk. For research on anxiety disorders, this will require investigators to assess information-processing functions engaged when subjects respond to threats. These information-processing functions can then be mapped both onto clinical symptom patterns and onto underlying neural circuitry engaged by threats.

threat responses, as well as responding to innately threatening stimuli or situations. Perhaps the most extensive literature examines fear conditioning, the process whereby a neutral conditioned stimulus, such as a light or a tone (CS+), is repeatedly paired with an aversive unconditioned stimulus (UCS), such as an electric shock or a loud sound (LeDoux, 2000). As shown in figure 46.2, following repeated pairings, mammals rapidly learn that a CS+ predicts the UCS, leading the organism to treat the CS+ as a threat. Mammals also learn to represent the context in which the UCS–CS+ relationship is established. Such contextual representations code for the place in which the pairing occurred as well as the state of the organism during conditioning. The neural circuit engaged during fear conditioning is centered on the amygdala and distributed throughout various regions, including sensory-cortical pathways and the thalamus (LeDoux, 2000; Phelps and LeDoux, 2005). Though disagreements persist concerning the precise role of specific amygdala subnuclei in fear conditioning, consensus has emerged regarding the role of the amygdala in a broader series of processes. These processes include aspects of reward evaluation, each characterized by rapid evaluations of stimulus salience (Baxter and Murray, 2002). Representation of context involves a series of associated regions, which include

Neural Circuits Engaged by Threats Research in rodents and nonhuman primates precisely elucidates neural circuits engaged by distinct aspects of threat processing. These aspects include formation and extinction of learned

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TRAINING

Exposure to context (2 min)

Onset of sound (CS: 30 s)

Onset of shock (US: 2 s)

TESTING: Context Test at 1 hour and 24 hours

Same context (5 min)

TESTING: Cued Test at 1 hour and 24 hours

Onset of sound (CS: 3 min)

Figure 46.2 This figure depicts methods for a fear-conditioning experiment in rodents. Following exposure of the rodent to the context (i.e., the cage), a series of shock-unconditioned stimulus presentations (UCS) is paired with repeated presentation of a conditioned stimulus (the “sound CS”). At a later time, the rodent shows fear both of the context, when placed in the cage where conditioning occurred, and fear of the sound CS, when placed in

a novel cage and presented with the same sound CS. Following repeated presentation of this sound CS in the novel context, without the shock UCS, the rodent exhibits extinction of fear to the sound CS. (Figure reproduced with permission from Larry R. Squire and Eric R. Kandel, Memory: From Mind to Molecules, 1999, Scientific American Library, Henry Holt and Company, LLC.)

the hippocampus and prefrontal cortex (PFC) (LeDoux, 2000; Miller and Cohen, 2001). Advances in research on fear conditioning during the past decade have stimulated increasing interest in extinction, the processes whereby mammals learn that previously learned CS+ are no longer dangerous. Extinction requires new learning about the CS+–UCS relationship, as opposed to forgetting that a connection exists between CS+ and UCS. This new learning requires reclassification of a CS+ as currently not dangerous, which can be achieved by representing the context of CS+ presentation, instantiated in the PFC (Bouton, 2002; Quirk and Gehlert, 2003). Virtually no research examines development changes in either conditioning or extinction in the periadolescent period, a particularly important time for understanding of clinically significant anxiety. As a result, work in this area represents a particularly important focus for future research efforts. Fear conditioning and extinction are used to examine processes whereby affective value is assigned to initially neutral stimuli. Other stimuli are recognized as dangerous independent of any previous exposure, presumably because of the evolutionary advantage afforded to ancestors who

demonstrated the capacity to immediately recognize such threats in the absence of prior exposure. Such stimuli relate to species-specific dangers. For example, well-lit rooms represent innate threats for nocturnal rodents, whereas dark rooms represent innate threats for diurnal organisms, such as humans (Davis, 1998). The circuitry through which these innate threats are processed shows both parallels and discontinuities with the circuitry of learned fears. For example, whereas both types involve the amygdala, the basolateral nucleus may play a particularly strong role in processing innate threats (Davis, 1998). Moreover, components of the extended amygdala, such as the bed nucleus of the stria terminalis, are more strongly implicated in processing of innate fears. Finally, pharmacological studies demonstrate distinctions among these classes of fears. Thus rodent and nonhuman primate studies elucidate neural structures engaged by various classes of threats. Most of this research examines aspects of neural function that are presumed to operate in all individuals of a given species. Relatively few studies reveal factors that are associated with individual differences. Moreover, to the extent that neural contributions have been identified, available studies from

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neuroscience most strongly implicate developmental factors in individual differences (Amaral, 2002; Gross and Hen, 2004). For example, work on the effects of amygdala lesions in monkeys shows that the developmental timing of an injury influences the nature of effects on individual differences in responding to specific threats. When injury occurs in adulthood, amygdala lesions reduce responding to a range of threats. When injury occurs during childhood, in contrast, such lesions reduce responding to snakes but augment responding to social threats. Similarly, as noted previously, studies in rodents and nonhuman primates show that alterations in the rearing environment produce robust and enduring alterations in fear responses. For example, in rodents, handling manipulations produce long-term alterations in threat-response behavior and associated neural circuit functioning that persist through adulthood. Similar manipulations of the social environment in mature rodents produce no such lasting effects. These effects emerge through alterations specifically on the fear circuit. Information-Processing Functions of the Fear Circuit As depicted in figure 46.1, integration of data from neuroscience and clinical science may emerge through research on information processing. Such work benefits from a cross-species perspective that examines how functions manifest in rodents, nonhuman primates, and humans at various developmental stages. Given the precise anatomical understanding of fear conditioning, it should come as no surprise that this process has been examined in many studies. Brain-imaging studies reliably implicate neural circuits in various aspects of fear conditioning among humans (Phelps and LeDoux, 2005). Thus the amygdala is consistently engaged during the acquisition of fears to specific cues, whereas the PFC is engaged during extinction (Kalisch et al., 2006). This research extends to humans as depicted in figure 46.1. Nevertheless, the relationship that between-subject variation in fear conditioning shows with either risk for, or overt expression of, clinical anxiety remains unclear. Thus the tie between informationprocessing functions and clinical states in figure 46.1 appears more tenuous. The only meta-analysis addressing this issue, which included all studies in children as well as adults, found a weak relationship (Lissek et al., 2005). Moreover, the relationship was entirely accounted for by studies using simple fear conditioning, which involves only a CS+ but no CS− stimulus. Without this experimental control, simple fear conditioning experiments cannot differentiate associations with conditioning from sensitization in anxiety disorders. Recent interest focuses on processes beyond the acquisition of conditioned fears. Thus Grillon suggests that clinical anxiety states result more from perturbations in discrimination among various cues associated with a UCS, as opposed to a tendency to show strong conditioning to one or another

CS+ (Grillon, 2002). Alternatively, Bouton suggests that clinical anxiety states result from deficits in extinction as opposed to formation of conditioned fears (Bouton, 2002). Data supporting this position emerge from studies demonstrating the efficacy of cognitive behavioral therapy, which uses extinction to reduce anxiety (Ressler et al., 2004). This position received further support from recent studies demonstrating the clinical benefits of therapy involving extinction learning in combination with medication that enhances extinction (Ressler et al., 2004; Hofmann et al., 2006). Fear conditioning is conceptualized as a form of emotional learning or memory formation; distinct memoryrelated processes associated with fear conditioning require distinct functions that can be classified as either declarative or nondeclarative in nature. Recent interest in clinical states has focused on declarative forms of emotional learning. For example, emotionally arousing stimuli are thought to receive prioritization during cognitive processing, and this prioritization is thought to produce a memory advantage on certain declarative mnemonic tasks (Phelps, 2006). Such a memory advantage is conceptualized as a memory bias to selectively encode certain aspects of emotionally arousing stimuli. Subjects show a consistent memory advantage on declarative tasks for arousing words, pictures, or stories, relative to neutral items. Moreover, neuroimaging data implicate the amygdala in this memory advantage (Cahill, 1999). Nevertheless, clinical research reveals weak and inconsistent relationships between emotional memory and anxiety disorders (Pine et al., 2004). From the clinical perspective, perturbations in attention probably represent the most consistently implicated information-processing function in anxiety disorders. Thus tasks that probe interactions between emotion and attention reveal a strong tie in figure 46.1 between this specific information-processing function and clinical anxiety states (Williams, Mathews, and MacLeod, 1996; Bar-Haim et al., 2007). These associations have been found in both children and adults, both in children with specific anxiety disorders, such as GAD or social phobia, and in children with high ratings on anxiety symptom scales in the absence of a clinical anxiety disorder diagnosis. On these tasks, cues signaling threats have been shown to exert greater disruptions in attention among patients with anxiety disorders than in healthy individuals. Moreover, emerging evidence suggests that these attention biases play a role in the genesis of clinical anxiety disorders, such that between-subject variation in attention predicts response to stress (MacLeod et al., 2002). Such attention disruptions manifest as biased processing of neutral information that appears particularly proximal or distal to threatening information. This capacity to bias attention has been demonstrated using diverse tasks, each designed to tap distinct aspects of attention. For example, spatial orienting has been probed

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using either dot-probe tasks or tasks modeled after the spatial-orienting paradigm of Posner (Williams, Mathews, and MacLeod, 1996; Bar-Haim et al., 2007). These tasks explicitly ask subjects to attend to neutral target cues by performing a motor or oculomotor response to the target. Potentially distracting stimuli of both neutral and emotionally evocative valences are introduced using various methods, and the effects of these distractors are manifested in variations of motor or oculomotor responses. A differential bias in patients is reflected in the effects of emotion on responses to neutral targets. Alternatively, strategic attention has been monitored using variants of the Stroop task, whereby emotional distractors are introduced based on the meaning of color words. In this task, subjects are required to ignore the word meaning as they identify word colors. With this design, threat words produce greater perturbations in color naming among patients with anxiety disorders than among healthy subjects (Williams, Mathews, and MacLeod, 1996). A final set of tasks asks subjects to vary their attention focus as they attend to distinct aspects of stimuli varying in valence. For example, subjects have been asked to alternatively attend to either physical aspects of photographs or their own subjective response to these photographs (Lane et al., 1997; Pine et al., 2005a). Both the emotional content in photographs and the subject’s focus of attention influence task performance in these designs, providing yet another example of an emotion-attention interaction. Both neuroimaging and lesion studies among adult humans implicate the amygdala as a crucial modulator of the emotion-attention interface, as probed by these tasks. In other clinically focused research, anxiety disorders have been characterized based on their relationships with hypersensitivities to innate threats. Probably the most developed line of work examines associations with respiratory threats (Klein, 1996; Pine et al., 2005a, 2005b). Stimuli associated with suffocation represent innate threats for airbreathing organisms such as humans. Adults with panic disorder, which typically afflicts adults but not children, show hypersensitivity to suffocation cues. Moreover, consistent with data on cross-generational transmission, children with separation anxiety disorder also show signs of such hypersensitivity, suggesting that pediatric separation anxiety disorder represents a precursor of adult panic disorder. Data examining the response to other innate threats generate less consistent associations. Risk for anxiety in adolescents has been linked to extreme responses to aversive air puffs (Merikangas et al., 1999). However, adolescents at risk for depression also show this hypersensitivity, but adolescents with ongoing anxiety disorders do not (Grillon et al., 2005). Finally, other studies in patients implicate the amygdala and associated fear circuitry in an array of other information-processing functions. For example, studies in rodents, nonhuman primates, and humans clearly implicate the

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amygdala in formation of stimulus-reward associations (Baxter and Murray, 2002). During tasks that tap this information-processing function, the amygdala is thought to interact with the PFC to represent in a flexible manner the current valence of a stimulus. Similarly, the amygdala has been implicated in the classification of stimuli that vary in emotional details. For example, both lesion and neuroimaging studies in adults implicate the amygdala in the decoding of face-motion cues (Adolphs et al., 2005). However, sparse evidence implicates these information-processing functions in clinical anxiety states. Specifically, the few available studies in children with social phobia, GAD, or separation anxiety disorder find normal performance on tests of faceemotion classification. Modulators of Fear-Circuit Function Studies in humans demonstrate the robust effects of various factors on the fear circuit. These effects derive from both environmental and genetic influences. Studies that manipulate serotonin (5HT)–related genes in rodents using knockout strategies reliably produce changes in threat response behavior. These effects have been found both for manipulations of the serotonin transporter gene (5HTT) and of the 5HT1a gene (Gross and Hen, 2004). Interestingly, these effects emerge during development, in that manipulations of the 5HTT and 5HT1a gene produce stronger effects when they occur in immature rodents than when they occur in mature ones. Integrating insights from this work with insights from Meaney’s work on early-life environmental manipulations leads to the suggestion that genes interact with the environment as organisms mature to sculpt activity in the fear circuit. Studies in humans have not fully extended this model. Nevertheless, variation in the 5HTT has been reliably shown to predict response in the fear circuit in at least five studies among adults (Hariri et al., 2002; Canli et al., 2005; Hariri et al., 2005; Heinz et al., 2005; Pezawas et al., 2005). This response is reflected in the threshold for engaging both the amygdala and associated expanses of the PFC (Pezawas et al., 2005). Individuals who inherit the low-activity form of the 5HTT exhibit a lower threshold for engaging this circuit. As in studies among rodents and nonhuman primates, this heightened sensitivity is thought to interact with the environment to ultimately shape in tandem neural circuit function, information-processing capacities, and risk for psychopathology (Caspi et al., 2003; Gross and Hen, 2004). Environmental factors also have been implicated in between-subject variations in fear-circuit function. Probably the most extensive line of research examines the degree to which stress exposure relates to clinical compromise and associated fear-circuit dysfunction. A sizable minority of individuals exposed to traumatic events will develop chronic anxiety, manifested as posttraumatic stress disorder (PTSD),

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which involves both subject-specific and environmental factors: humans exhibit robust between-subject variability in response to comparable aversive stressors, but the risk for PTSD clearly increases significantly among all people as a function of stressor severity (Pine, 2003). Research using both information-processing and neuroimaging approaches implicates fear-circuit dysfunction in PTSD, although most work focusing on these issues examines adults. Thus adults with PTSD, relative to traumatized adults without PTSD, show signs of perturbed attention and memory, occurring in tandem with perturbations in amygdala, hippocampal, and PFC function. Nevertheless, consistent variability emerges across studies in terms of the precise nature of the associated abnormalities. Finally, other evidence of fear-circuit dysfunction emerges in a surprisingly wide range of adult clinical conditions, including various anxiety disorders (Rauch, Shin, and Wright, 2003). Fear-circuit dysfunction also occurs in a range of other clinical states. Most prominent among there is major depressive disorder (MDD), where abnormalities in both amygdala and PFC emerge with consistency (Drevets, 2000; Whalen et al., 2002). In summary, adult variations in threat response behavior and fear-circuit function reflect ontological influences of genetic and environmental factors. Clinical research demonstrates that adult anxiety disorders reflect comparable ontogeny, such that the majority of adult disorders have their roots in childhood. Therefore, a full application of the framework depicted in figure 46.1 is predicated on an extension to children and adolescents.

Affective neuroscience and pediatric anxiety Integrative studies on pediatric anxiety must consider two major factors, each related to key constructs illustrated in figure 46.1. First, as noted at the right-hand side, betweensubject clinical features can be conceptualized from various perspectives. Second, various information-processing functions, as appearing in the middle of figure 46.1, can be probed and linked to functional aspects of the fear circuit, as appearing at the left-hand side of figure 46.1. Clinical Classification Four distinct categories of children and adolescents have been studied from integrative research perspectives. These categories involve group classification based on either particular features of the child or the child’s level of risk. First, studies in children have examined individuals affected by specific pediatric anxiety disorders. Typically, this work focuses on children with social phobia, GAD, or separation anxiety disorder to provide vital data concerning the pathophysiology of clinical anxiety states. One would expect to detect perturbations in information-processing

and fear-circuit functions implicated in these conditions. Nevertheless, such work is complicated by the heterogeneity of most clinical conditions. For example, children may manifest syndromes that appear similar, in terms of their overt symptomatic expressions, but emerge from distinct forms of pathophysiology. This pattern is ubiquitous in many chronic medical illnesses, such that various forms of cancers or endocrine-based syndromes might present with similar clinical manifestations, each emerging from diverse pathophysiologies. Second, children and adolescents can be classified meaningfully based on their temperament, which can be observed early in development. Perhaps the strongest data linking temperament and pediatric anxiety come from work on behavioral inhibition: the tendency for toddlers to react with wariness, avoidance, and suppression of motor response when confronted with novel stimuli or situations (PerezEdgar and Fox, 2005). Questions remain concerning the optimal stimuli to elicit inhibitory reactions, because inhibition has been linked to biases in the processing of threats and rewards, as well as neutral novel or discrepant stimuli. Nevertheless a clear association with clinical anxiety states has been demonstrated. Children formerly classified as inhibited exhibit an increased risk for various anxiety states, with some evidence suggesting a particularly strong tie with social anxiety disorder (Perez-Edgar and Fox, 2005). Moreover, behavioral inhibition in children is associated with a high rate of panic disorder in parents (Rosenbaum et al., 2000). However, the degree to which specific associations emerge with risk for anxiety remains unclear. Behavioral inhibition is not associated with behavior disorders, such as attention-deficit/ hyperactivity disorder, but strong associations with risk for MDD emerge in both longitudinal and family-based data (Caspi et al., 1996; Rosenbaum et al., 2000). This pattern of associations is consistent with the data reviewed earlier linking fear-circuit dysfunction to various adult psychopathological states. Third, independent of any temperamental or clinical factors associated directly with the child, risk for anxiety in children is manifest based on aspects of the environment in which they are raised. These risks can be mediated by internal factors related to the child, they may be purely environmental, or they may be combinations of both (Rutter, 2000; Silberg, Rutter, and Eaves, 2001; Costello et al., 2002). Some categorizations involve classifications likely to be influenced by constellations of these factors. For example, as described previously, cross-generational transmission of pathological states has been noted, such that pediatric anxiety is strongly linked to both anxiety and MDD in parents (Beidel and Turner, 1997). Similarly, children exposed to trauma show high risk for mood and anxiety disorders (Pine, 2003). These associations are likely to reflect complex interactions among

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genetic and environmental factors. Other classifications can involve more narrowly conceptualized risk factors. For example, the same 5HTT polymorphism associated with adult fear-circuit dysfunction has been implicated in pediatric anxiety (Fox, 2005). Nevertheless, such factors are likely to interact in complex ways with other factors to increase risk. In the instance of the 5HTT, this polymorphism interacts with experiential factors to increase risk for mood and anxiety disorders (Caspi et al., 2003). Complex interactions involving the 5HTT gene have been demonstrated as predictors of overt clinical states in adults and children as well as temperamental variations in adults and children. Finally, as noted earlier, major questions persist concerning boundaries between normal and pathological manifestations of anxiety in children. Particularly important questions emerge concerning specific fears, which can be classified as normal or as clinically significant, in the form of specific phobias. Children with subclinical fears clearly represent a population at risk for various adult psychopathologies, particularly MDD (Weissman et al., 1997; Pine, Cohen, and Brook, 2001). Such associations emerge in both longitudinal and family-based studies. Information-Processing and Fear-Circuit Functions in Pediatric Anxiety Relatively few studies in children and adolescents use associations between clinical anxiety and information-processing functions and relate them to findings of fear circuitry dysfunction. Virtually no research in children and adolescents specifically examines the relationship between fear conditioning and anxiety disorders, one of the best-studied areas in adults. Clearly, ethical factors complicate attempts to implement such work. For instance, the use of electric shocks, the bestvalidated UCS in adult fear-conditioning studies, raises ethical questions for a vulnerable population. In general, attempts to use less aversive UCS in children generate suggestive but far from definitive associations between various indices of fear conditioning and clinical symptoms or risk (Merikangas et al., 1999; Grillon et al., 2005). Moreover, brain-imaging studies using such UCS in children and adolescents reveal amygdala engagement but in a manner less consistent than in adult studies with more aversive UCS (Monk et al., 2003). As described previously, research on attention provides the best evidence of perturbed information processing in anxiety disorders, and this statement holds true in pediatric anxiety as well. A relatively small but growing constellation of studies demonstrates relationships between biased attention and pediatric anxiety disorders (Bar-Haim et al., 2007). The most comprehensive literature examines biases in attention orienting, and this work raises questions concerning developmental influences on attention-emotion interactions. For example, studies using orienting paradigms,

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which reveal vigilance for threats in adult anxiety states, have revealed attention avoidance in pediatric anxiety disorders (Monk et al., 2006). Figure 46.3 (plate 62) depicts the task on which this finding emerged: GAD patients studied with this task tended to show avoidance of asterisk targets appearing proximal to angry-threat faces, whereas healthy adolescents tended to show attention toward such asterisk targets. Adults with and without GAD show the opposite patterns of responding. This finding may suggest that the threshold for disengaging attention to avoid threats may change during development. Another measure of attention is the emotional Stroop task. Using these procedures, both children and adults with anxiety disorders, relative to healthy age-mates, exhibit greater disruption in color naming of threat relative to neutral color words (Vasa and Pine, 2004). Finally, one study used an attention task that required subjects to shift attention while viewing evocative facial photographs (Pine, Mogg, et al., 2005). This study found that both ongoing pediatric anxiety disorders and a history of panic disorder in a parent predicted enhanced attention allocation when monitoring internal fear states. Neuroimaging studies extend these data by examining attention: of the three functional magnetic resonance imaging (fMRI) studies on pediatric anxiety disorders, two examine attention. The first study compared 12 pediatric patients with GAD or panic disorder to 12 healthy children and adolescents, acquiring data while subjects passively viewed blocks of face-emotion photos (Thomas et al., 2001). The study generated provocative findings by showing that patients exhibited greater amygdala activation than healthy peers when viewing fearful relative to neutral faces. However, the study failed to demonstrate amygdala engagement to fear faces in the healthy subjects. Since no behavioral data were acquired at imaging, the study left unanswered questions concerning attention influences that might account for the group difference in amygdala activation. Data from other fMRI studies implicate attention in the modulation of these group differences. Monk and colleagues found attention avoidance in 18 adolescents with GAD, relative to 15 healthy adolescents, using a dot-probe task that presented facial threat cues to manipulate orienting (Monk et al., 2006). This study demonstrated enhanced right ventrolateral PFC (VLPFC) engagement in GAD relative to healthy adolescents during threat processing. Based on a negative correlation between VLPFC activation and anxiety severity, PFC engagement in this study was seen as essentially regulatory in nature. Figure 46.3 presents data from this study. Specifically, the figure shows brain regions where GAD patients showed greater activation than healthy adolescents, for events where angry threat faces appear spatially incongruent with asterisk targets, contrasted with events where angry threat faces appear

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Direction of Attention Avoid Anger

Attend to Anger

vs. *

*

Figure 46.3 This figure shows a group contrast of an eventrelated analysis in 18 pediatric GAD patients and 15 age-matched healthy adolescents, drawing data from Monk and colleagues (2006). The specific contrast shown maps brain areas where GAD patients show enhanced activation for a contrast of events where

asterisk targets appear proximal to neutral faces, relative to when they appear proximal to angry faces. As shown in the upper left, a group difference emerges in the prefrontal cortex, at coordinates of x, y, z = 51, 30, −2, in Talairach space, with t = 3.3 (p = .001). (See plate 62.)

spatially congruent with asterisk targets. While the study found consistent between-group differences in VLPFC engagement, the study failed to detect differences in amygdala activation. These data suggest that anxiety-related differences emerge in a distributed circuit encompassing the PFC. The only other fMRI study in pediatric anxiety disorders supported this suggestion. In a study of 15 GAD patients and 20 age-gender-IQ-matched healthy adolescents, McClure and colleagues used a paradigm that required subjects to shift attention while viewing photographs of faces depicting evocative poses (McClure et al., 2007). This study found enhanced activation in a distributed network encompassing the fear circuit. This network included portions of the ventral and medial PFC, as well as the amygdala. Attention modulated group differences in circuitry engagement: patients only differed from healthy adolescents when monitoring their internal fear state but not when viewing faces while adopting different attention states. Taken together, these three studies implicate perturbations in fear-circuit function in pediatric anxiety disorders, particularly GAD. The degree to which these perturbations

reflect state as opposed to trait factors remains unclear. Studies in behavioral inhibition, a major risk factor for pediatric anxiety disorders, generate some insights on this question. The only published study in this area compared amygdala activation in 22 adults formerly classified as either inhibited or noninhibited as toddlers (Schwartz et al., 2003). Examining the response to novel relative to nonnovel faces, the formerly inhibited subjects showed greater amygdala activation, suggesting that hypersensitivity in the brain’s fear network may represent a risk factor for anxiety. These imaging data on the relationship between anxiety and fear-circuit function raise questions on associations among anxiety, brain function, and brain structure. Studies in adults suggest that individual variations in anxiety might reflect individual differences in anatomy of structures encompassing the fear circuit (Drevets, 2000; Whalen et al., 2002). Probably the most consistent findings document reduced volumes of medial PFC structures, which occur in a range of anxiety disorders, most prominently in PTSD, as well as in individuals inheriting genetic risk factors for anxiety disorders (Pine, 2003; Pezawas et al., 2005). Such reductions also occur in depression. Other studies in adults

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find reductions in medial temporal lobe structures, including both the hippocampus and amygdala. Reduced hippocampal volumes emerge most consistently in studies of traumatized individuals. Reduced amygdala volumes emerge in studies of various conditions, albeit with relative inconsistency, including studies of depression, anxiety, and risk for depressive or anxiety disorders. Interestingly, at least for data in depression, a model of pathophysiology implicating glutamate excitotoxicity emerges from this confluence of findings (Siegle et al., 2003). Specifically, this model is consistent with data in the amygdala documenting reductions in brain volume, in tandem with increases in neural activation, as well as negative correlations between volume and activation. As with fMRI data, fewer studies examine morphometry in pediatric anxiety disorders. Two studies specifically examined amygdala volume. Findings from these two studies were inconsistent: one reported increased volume, whereas the other found decreased volume (De Bellis et al., 2000; Millham et al., 2005). However, data documenting increased activation and reduced volumes in the same series of adolescents support excitotoxicity-based models (De Bellis et al., 2000; Millham, 2005). Studies of other brain structures have generated other findings that also need replication. Beyond studies of fear-circuit function, research implicating other aspects of perturbed information processing in pediatric anxiety appears weak or inconsistent. Thus some evidence of perturbed reward-system function is found, but results have not been well replicated. Similar findings emerge in studies of memory. In fact, the only fMRI study of emotional memory found enhanced amygdala activation in adolescents with major depression but not in adolescents with anxiety disorders, relative to healthy adolescents (Roberson-Nay et al., 2006).

Conclusions Research in pediatric anxiety disorders might provide an important guide for investigators attempting to integrate research on clinical conditions with research on underlying brain function. From the clinical perspective, advances in developmental psychopathology have delineated the range of anxiety states exhibited by children and adolescents. While the current chapter provides the most detailed discussions of work on three specific anxiety disorders, major questions remain concerning the validity of nosology for the broad family of anxiety states manifest during development. Research in neuroscience documents the manner in which early-life individual differences in fear-circuit function relate to long-term information-processing functions instantiated in the fear circuit. Studies applying cognitive neuroscience methods hold the hope of using these findings

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to ground clinical research in current understandings of pathophysiology. Of particular relevance for future advances, this chapter has reviewed recent data from fMRI studies that suggest the potential utility of this integrative, translational approach. While this research raises as many questions as it answers, the work does provide an important road map for possible future research endeavors. Namely, recent work on information processing and fMRI demonstrates the capacity to map in children brain systems and associated informationprocessing functions previously mapped in fine detail among rodents and nonhuman primates. As such, future work on pediatric anxiety disorders might map in both healthy and anxious children the nature of developmental relationships between information processing and brain function associated with threat processing. For example, such work might involve longitudinal analysis examining changes in PFC and amygdala activation on dot-probe threat paradigms in both healthy and anxious children, studied as they mature from adolescence to adulthood. Such work would be expected to show the manner in which changes in attention and fear circuit function relate to developmental changes in anxiety. REFERENCES Adolphs, R., F. Gosselin, T. W. Buchanan, D. Tranel, P. Schyns, and A. R. Damasio, 2005. A mechanism for impaired fear recognition after amygdala damage. Nature 433:68–72. Amaral, D. G., 2002. The primate amygdala and the neurobiology of social behavior: Implications for understanding social anxiety. Biol. Psychiatry 51:11–17. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, fourth edition. Washington, DC: American Psychiatric Association. Angold, A., E. J. Costello, and A. Erkanli, 1999. Comorbidity. J. Child Psychol. Psychiatry 40:57–87. Ansorge, M. S., M. Zhou, A. Lira, R. Hen, and J. A. Gingrich, 2004. Early-life blockade of the 5-HT transporter alters emotional behavior in adult mice. Science 306:879–881. Bar-Haim, Y., D. Lamy, L. Pergamin, M. J. BakermansKranenburg, van M. H. Izendoorn, 2007. Threat-related attentional bias in anxious and non-anxious individuals: A metaanalytic study. Psychol. Bull. 133(1):1–24. Baxter, M. G., and E. A. Murray, 2002. The amygdala and reward. Nature Rev. Neurosci. 3:563–573. Beidel, D. C., and S. M. Turner, 1997. At risk for anxiety. I. Psychopathology in the offspring of anxious parents. J. Am. Acad. Child Adolesc. Psychiatry 36:918–924. Biederman, J., S. V. Faraone, D. R. Hirshfeld-Becker, D. Friedman, J. A. Robin, and J. F. Rosenbaum, 2001. Patterns of psychopathology and dysfunction in high-risk children of parents with panic disorder and major depression. Am. J. Psychiatry 158:49–57. Biederman, J., M. C. Monuteaux, S. V. Faraone, D. R. Hirshfeld-Becker, A. Henin, J. Gilbert, and J. F. Rosenbaum, 2004. Does referral bias impact findings in highrisk offspring for anxiety disorders? A controlled study of

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Silberg, J. L., M. Rutter, and L. Eaves, 2001. Genetic and environmental influences on the temporal association between earlier anxiety and later depression in girls. Biol. Psychiatry 49:1040– 1049. Suomi, S. J., 2003. Gene-environment interactions and the neurobiology of social conflict. Ann. NY Acad. Sci. 1008:132– 139. Thomas, K. M., W. C. Drevets, R. E. Dahl, N. D. Ryan, B. Birmaher, C. H. Eccard, D. Axelson, P. J. Whalen, and B. J. Casey, 2001. Amygdala response to fearful faces in anxious and depressed children. Arch. Gen. Psychiatry 58:1057– 1063.

Vasa, R., and D. Pine, 2004. Neurobiology. In T. L. Morris and J. S. March, eds., Anxiety Disorders in Children and Adolescents, 2nd ed., 3–26. New York: Guilford Press. Weissman, M. M., V. Warner, P. Wickramaratne, D. Moreau, and M. Olfson, 1997. Offspring of depressed parents: 10 Years later. Arch. Gen. Psychiatry 54:932–940. Whalen, P. J., L. M. Shin, L. H. Somerville, A. A. McLean, and H. Kim, 2002. Functional neuroimaging studies of the amygdala in depression. Semin. Clin. Neuropsychiatry 7:234–242. Williams, J. M., A. Mathews, and C. MacLeod, 1996. The emotional Stroop task and psychopathology. Psychol. Bull. 120:3– 24.

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Developmental Neuropsychology of Unipolar Depressions IAN M. GOODYER AND ZOË KYTE

Over the past two decades research findings have emphasized marked individual differences in the characteristics, treatment response, and outcomes of depressive disorders in young people (Goodyer, 2001a). Longitudinal studies suggest that a depressive illness before 18 years of age may have adverse consequences for the developmental trajectory into adult life (Pine et al., 1999; Fombonne et al., 2001a, 2001b; Dunn and Goodyer, 2006). Though there has been considerable research on the effects of social adversities on onset, course, and outcomes of depressions, it is only in the last decade that psychological functions have begun to be intensively studied (Tavares, Drevets, and Sahakian, 2003; Kyte and Goodyer, 2005). The majority of the work has been in depressed adults using predominantly cross-sectional designs comparing this group with nondepressed control participants. These important proof-of-concept studies have noted that currently ill patients with unipolar depressions show a range of deficits in memory, attention, social cognition, decision making, and planning that are not explained by variations in general intelligence within the normal range or other premorbid factors. Most recently, functional neuroimaging studies have delineated some of the putative neural substrates that subserve these functions (Liotti and Mayberg, 2001; Jack, Sylvester, and Corbetta, 2006). The precise role of these mood-valent cognitive processes in the onset, treatment response, and outcome of unipolar depressions will unfold through prospective and longitudinal research in population- and clinical-based studies. The application of sensitive tests of emotion and cognition to children and adolescents brings the added complexity of maturation of the central nervous system and the need for developmentally sensitive methods and measures (Luciana and Nelson, 2002; Luciana, 2003a; Luciana et al., 2005). At the neural level, we lack a clear understanding of the correlates between brain structure and functions over the first two decades of life, although these are beginning to emerge (Durston et al., 2001; Paus et al., 2001; Paus, 2005; Blakemore and Choudhury, 2006a). As a result, we do not yet possess a clear understanding of the continuities and discontinuities between skills at different ages and stages of development and the role of deficits in these skills for the onset and outcomes of depressions in young people. For example, prospective psychosocial research has noted that

greater than 90 percent of episodes of depression in adolescents arise in populations who have been exposed to longstanding chronic adversities including marital disharmony, negative effects of parental psychiatric disorder, neglect, and abuse (Goodyer, 2001b). Within this exposed adolescent group, however, a significant proportion does not become mentally ill. Exposure to acute highly undesirable life events brings forward the onset of episodes in those exposed to chronic social difficulties, but such exposure alone has minimal effects on the risk for unipolar depression (Goodyer et al., 2000a, 2000b). These marked variations beg the question, what are the affective-cognitive processes that mediate the relationship between social difficulties and adverse behavioral response (Goodyer, 2002). The psychological findings to date represent a first pass at characterizing the neuropsychological components of the intermediate biology of depressions in young people.

Building a brain Primary neurulation is a critical phase of early development in which there is considerable shaping, by means of folding and rolling of neural components giving rise to the brain and spinal cord (Brown, Keynes, and Lumsden, 2001). Secondary neurulation refers to morphogenetic movements of the developing nervous system. The size, shape, and movement of cells are under genetic control, and these changes promote cell migration and the development of specialized portions of the nervous system, such as sensory organs. Whether defects in secondary neurulation can affect neural systems that are required for higher mental functions such as emotion recognition and behavioral inhibition is not yet known. Neurotransmitters and their receptors are expressed on a wide scale before synaptic connections are made, with regulatory mechanisms coordinating the patterning and connectivity of neurotransmitter phenotypes. Early disruptions of the normative trajectory of monoamine pathways may have profound effects on the development of neural systems subserving psychiatric syndromes (Whitaker-Azmitia, 2001). Neurochemicals may also have quite different actions at differing times in development. In addition to acting as switches in serotonin functions, steroids such as cortisol and testosterone exert organizing effects on brain structure during

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prenatal life but activating effects on functions during postnatal life (Buchanan, Eccles, and Becker, 1992). Similarly, GABA exerts excitatory and inhibitory functions in the prenatal and postnatal brain, respectively. Cross talk is frequent between chemical systems. For example, high central neurosteroid levels may diminish inhibitory GABA activity, and both may play a role in modulating emotional state (Barbaccia et al., 2001). For many aspects of their development and normal physiology, neurons are dependent on a small group of highly specific proteins known as neurotrophic factors (Huang and Reichardt, 2001). The principal roles of these factors are to regulate axonal and dendritic growth, synaptic plasticity, and neuronal survival. Reduced neuronal survival and deficits in morphological differentiation can lead to abnormal formations of brain regions. In the mature nervous system, neurotrophins can regulate short-term synaptic transmission and long-term potentiation, a process that is used as a model for learning and memory. Later in development, they may determine the phenotype of developing sensory and autonomic nerve cells during the critical period of rearrangement and stabilization of nerve cells. Throughout the first few years of postnatal life, there is simultaneous progressive and regressive restructuring of neural networks occurring in a region-specific manner (Durston et al., 2001). A striking feature of this neural development and growth is synaptogenesis (the formation of new synapses), with differential bursts of activity and variations in peak density occurring at different ages in different brain regions (Huttenlocher and Dabholkar, 1997). The prefrontal cortex is the slowest brain area to reach peak synaptogenesis—by about the end of the first year of life. Sensory areas tend to myelinate earlier than motor areas. Magnetic resonance studies of children and adolescents have demonstrated a gray-white matter contrast in a temporal sequence reflecting the time course of myelination (Durston et al., 2001; Paus et al., 2001). White matter increases in overall volume and becomes increasingly myelinated in a region-specific manner. Thus the brain myelinates from caudal to rostral and from inferior to superior, with the prefrontal cortex continuing this process (albeit more slowly than in the first two years) until young adult life (Casey, 1999; Giedd et al., 1999; Sowell et al., 2000). The largest white matter tract, the corpus callosum (CC), also continues to increase in size as a consequence of myelination (Giedd et al., 1999). Contrary to the typical pattern of caudal to rostral development in the brain, the corpus callosum and anterior regions of the cortex, which have been related to primary sensory and motor functions, appear to mature in childhood and the posterior areas in adolescence (Jernigan et al., 1991; Thompson et al., 2000). In contrast to white matter, gray matter generally exhibits a net decrease in volume across the school-age years (Jernigan et al., 1991; Thompson et al., 2000). This may be due

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to the ongoing processes of pruning and cell death (apoptosis) among both neurons and glial cells into young adult life. This apoptotic effect of programmed cell death is critical for normal brain development (Kuan et al., 2000). Subcortical gray matter typically shows the same pattern, with decreases in basal ganglia volume with increasing age. By contrast, the temporal lobe structures amygdala and hippocampus appear to increase in volume with age (Giedd et al., 1996). Some sex differences have been reported, with the male brain being 10 percent larger than the female and most structures displaying this volume difference (Giedd et al., 1997, 1996). The caudate, and possibly the globus pallidus and hippocampus, are disproportionately larger in female brains, whereas the amygdala is disproportionately smaller. The rate and pace of nonlinear decreases in gray matter and linear increases in white matter over time are correlated with individual differences in general intellectual ability, indicating a broad association between psychological functions and neural maturation (Barnea-Goraly et al., 2005; Paus, 2005; Shaw et al., 2006). Thus there is a developmental correlation between brain and behavioral maturation, the precise details of which remain underspecified. Defining the changing relationships between mental functions and the neural systems that subserve these will form the thrust of developmental neuroscience in the next few years. Such studies will be a key component in mapping typical development and will provide a platform for elucidating the mechanisms and processes that result in atypical development.

Building a mind The maturing brain over the child and adolescent period subserves the emergence of complex emotions and cognitions that will be lifelong functions (Blakemore and Choudhury, 2006a; Shaw et al., 2006). Although the origins of key features of cognitive functions, including executive processes, are embedded early in neurodevelopment (Luciana and Nelson, 1998, 2002; Hughes et al., 2000; Luciana et al., 2005; Blakemore and Choudhury, 2006b), their comprehensive emergence as mature functions unfolds over the first two decades. Indeed neural system reorganization during the adolescent period is itself associated with improving executive capacity to control and coordinate feelings, thoughts, and behaviors. Thus information-processing speed, working memory, and decision making continue to improve throughout the second decade of life (Luciana et al., 2005; Blakemore and Choudhury, 2006b). One striking observation of studies to date is an overall transient dip in performance during puberty (Blakemore and Choudhury, 2006a, 2006b). This has led to the suggestion that there is a perturbation of cognitive performance in early adolescence perhaps associated with synaptic pruning into specialized

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efficient networks. These behavioral findings are consistent with histological studies showing synaptic proliferation in the prefrontal cortex during childhood, with a subsequent elimination and reorganization of these connections after puberty (Huttenlocher, 1979; Huttenlocher and Dabholkar, 1997). Adolescence is also a period associated with the emergence of major mental illnesses including unipolar depressions. During adolescence, clinical depressions are estimated to occur in around 3–5 percent of the population, compared with around 1–2 percent in prepubertal children (Angold and Costello, 2001). Among these, some 50 percent will experience a second episode before their twenties, and around 20 percent will not recover at all (Lewinsohn et al., 1999; Fombonne et al., 2001b; Dunn and Goodyer, 2006). An episode of depression during a period of marked brain reorganization may have long-term effects on subsequent structure and function. Thus neuropsychological processes may be altered as a consequence of depressive disorders as well as being vulnerability factors that may be instrumental in their emergence.

Clinical characteristics of depressive disorders Depressive disorders constitute a serious group of mental disorders with considerable risk for recurrence and subsequent psychosocial impairment with, in some cases, onset in childhood and adolescence and continuity into adult life (Brotman et al., 2006; Dunn and Goodyer, 2006; Fombonne et al., 2001a, 2001b). Use of the American Psychiatric Association DSM-IV clinical criteria (American Psychiatric Association, 1994) successfully identifies the same clinical syndromes in children as young as 4, adolescents, and adults, although with differing clinical presentations, which we will describe in detail. Phenomenological psychopathology remains the core construct guiding the identification of criteria for diagnosis with no dependency on putative causation. This system has proven highly successful in generating a common language for the diagnosis of depressive disorders that makes research findings comparable between investigations despite marked differences in the nature of the populations studied. The categorical approach to classification of depression, however, is not comprehensive. For example, within diagnostic groups the relative importance of different types of symptoms that are present at different ages is underspecified (Cooper and Goodyer, 1993; Goodyer and Cooper, 1993). In addition, many individuals present with a range of symptoms that fall below the inclusion threshold for a diagnosis (too few or insufficient duration) but are of sufficient severity to cause personal impairment and a poor adjustment in the long term (Costello et al., 1996; Fergusson et al., 2005). Some advantage is gained by describing patients as suffering from mild, moderate, or severe disorder depending

Mood Dysphoria or irritability in children

Table 47.1 Symptoms of major depressive disorder Cognitive Physical Anhedonia Weight changes Feelings of (includes a failure worthlessness to make expected Inappropriate guilt weight gains) Diminished ability Fatigue or loss of to think or energy concentrate Psychomotor Recurrent thoughts agitation or of death or retardation suicidal ideation

on the number of symptoms (4–5, 6–7, 8 and above, respectively), thereby incorporating some of the dimensional qualities of depression. Unipolar Major Depression According to DSM-IV (American Psychiatric Association, 1994), a diagnosis of unipolar depression requires establishing first the mandatory presence of lowered mood (dysphoria, or irritability if in children) or loss of interest/pleasure (anhedonia) together with four of seven other possible nonmandatory symptoms from the two broad domains of disordered cognitions and physical changes. These are shown in table 47.1. It is essential that the five symptoms occur concurrently over a minimum two-week period. In children and adolescents, but not in adults, the entry criteria of lowered mood can be met by the presence of irritability or sadness. It is important to establish that these symptoms are not accounted for by the direct effects of substance misuse or a general medical condition, particularly one that involves known brain changes, as these reduce the likelihood of reliable and valid mental state assessments. In addition, the symptoms should not be accounted for by recent bereavement. It is worth noting, however, that these caveats do not imply that such subjects cannot be subsequently clinically depressed as a consequence of these experiences, rather that symptoms essential to the diagnosis may be acute or transient and therefore increase the liability of a false positive diagnosis at the time of presentation. There are no requirements for a particular pattern of cognitions and/or physical symptoms, although such patterns may be developmentally sensitive (Ryan, Puig-Antich, and Ambrosini, 1987; Goodyer and Cooper, 1993; Kovacs, 1996). Depressed children are more likely to complain of physical symptoms such as headache and abdominal pains with low levels of suicidal thinking, hopelessness, and helplessness present at clinical assessment (Kovacs, 1996). In contrast, by midadolescence negative cognitions about the self, the world, and the future become prominent together with higher levels of suicidal thoughts and acts (Ryan,

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Puig-Antich, and Ambrosini, 1987). Anhedonia, a complex state characterized by lack of interest and pleasure and often accompanied by low energy, also appears more common in depressed adolescents than in children. Symptom profiles may also change over the adolescent period. For example, girls over 15 years report higher rates of feeling and looking depressed and having self-deprecatory thoughts than younger depressed females (Goodyer and Cooper, 1993). There are no community studies that have examined age effects on clinical symptoms in adolescent males. In general, psychotic symptoms are less common than in depressed adults. Collectively, evidence suggests that there are significant developmental influences on the nature, characteristics, outcomes, and treatments of depressions (Kaufman et al., 2001). Because of these developmental influences, the precise identification of mood disorders in young people remains problematic. For example, severe mood dysregulation with high levels of irritability, disorganized behavior, and hyperarousal in childhood are frequently diagnosed as suffering from conduct disorder or attention-deficit/hyperactivity disorder (ADHD), yet many of these individuals develop frank clinical depression by early adult life (Brotman et al., 2006). This broad concept of severe mood dysregulation syndrome requires much further investigation than has been provided to date. Children as young as 3 or 4 years may, with careful assessment, show the full major depressive syndrome (Luby et al., 2003). The superficial presentation of externalizing symptoms in childhood (the most common referral characteristic to clinicians) may mask underlying mood and cognitive disturbance. Thus “hard to manage children” who show executive difficulties may do so because of mood as well as behavioral dysregulation. In contrast to symptom profiles, no developmental distinction is made regarding the duration of unipolar depressions which, providing symptoms have been present for at least two weeks, may vary in length for any period of time, up to a number of years. The disorder may also differ in its severity or degree of psychosocial impairment ranging from mild, indicating only a modest deviation from normal behavioral functioning, to being unable to care for oneself and requiring 24-hour intensive psychiatric care. A significant proportion of unipolar depressions across the life span are preceded by the existence of nondepressive clinical disorders that appear to convey an increased risk for a poor outcome of the depressive disorder (Kim-Cohen et al., 2003). There is now general agreement that unipolar depressions are heterogeneous in etiology and clinical presentation. Genetic and environmental (physiological, psychological, and social) processes combine in as yet undetermined ways to increase the liability for these complex and common mental disorders. For some, episodes are sporadic and relatively short-lived (weeks), whereas for others, a first

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episode heralds the emergence of a protracted and disabling condition with a high risk of recurrence and persistence for many months and years (Goodyer, Herbert, and Tamplin, 2003; Dunn and Goodyer, 2006). Mild disorders with fewer than six symptoms have a better prognosis overall, with some two-thirds likely to undergo spontaneous recovery with no increased risk of relapse in the ensuing 5-year period (Fombonne et al., 2001a, 2001b; Dunn and Goodyer, 2006). In contrast, child and adolescent patients with severe depression (more than seven symptoms at presentation) are at considerable risk of relapse and in some cases chronic illness into adult life (Pine et al., 1999; Fombonne et al., 2001a, 2001b; Dunn and Goodyer, 2006). The majority of clinical neuropsychological research into affective disorders in young people has been focused on moderate to severe unipolar major depressions where there are at least six symptoms and often more. These studies have, in general, been cross-sectional in nature involving participants with a recent or a current episode of unipolar depression. In this regard, studies to date represent the first step toward an understanding of the neurocognitive and neuroaffective systems that may act as vulnerability processes for the clinical phenotype of the unipolar depressions.

Social cognition, unipolar depression, and the brain Social cognition refers to the cognitive processes that subserve the diverse and flexible range of social behaviors. A key issue in investigating the psychology of depressions in recent years has been to compare and contrast cognitive processes in neutral mood (cold cognitions) and dysphoric mood (hot cognitions) (Kyte and Goodyer, 2005). This approach is taken because of the distinctions reported between mood-valent and mood-neutral cognitive processing (Kelvin et al., 1999; Teasdale and Barnard, 1993). Given that unipolar depression appears to be associated with abnormal functioning in both higher cognitive and limbic domains, consensus is now emerging to explain the phenomenology of depression on the basis of a malfunction in the regulation of an entire network of brain regions involved in both emotional behavior and social cognition (Drevets, 2000; Seminowicz et al., 2004). These neural structures are now considered within two critical circuits: a limbic-thalamic-cortical (LTC) circuit, involving the amygdala, medial thalamus, and orbital and medial prefrontal cortices; and a limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, involving components of the previous circuit with the addition of related parts of the striatum and pallidum. From the developmental perspective, we have yet to map neural systems that subserve depressive symptoms at different ages. Thus it is not known to what extent some of these brain regions may be developmentally impaired, structurally immature, or functionally inactive prior to the

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onset of a disorder at different ages. Equally, it is unclear to what extent depressive episodes may alter the pathway of neural development, resulting in a vulnerable brain liable to recurrent illness. Thus a number of features of the study population need to be taken into account when researching the underlying cognitive processes that mediate the liability for clinical symptoms. Etiological research needs to study individuals at risk but with no prior history of mood disorder; developmental influences on etiology require at-risk populations at different stages of maturity to determine the constancy and stability of vulnerable brain regions in the risk process; pathophysiological studies require patients with matched clinical characteristics including duration, severity, number of prior episodes, comorbidity, and levels of impairment. Such precision is likely to inform neuro-cognitive systems at differential levels of sensitivity to the disease process. Finally, there is a critical need to distinguish experimentally between “cold” and “hot” cognitive processing at both the psychological and neural systems levels.

Cognitive theories of depression Cognitive theories have been developed in relative ignorance of underlying neural systems. In addition, while all theories of depression postulate a role of adverse early experiences in current thinking style, none have determined precisely what these experiences are or how they result in the formation of negative cognitions. Developmental psychology has, however, extensively examined the effects of social influences on general cognitive development. One focus of this work is to add key information on how individual differences in early maternal experiences and the resultant relational psychology between mother and child could result in negative views of the self (Murray et al., 1999; Gibb and Alloy, 2006; Murray et al., 2006). These distal processes have yet to be fully delineated or incorporated into theoretical frameworks. The majority of investigations to date have focused on elucidating proximal psychosocial mechanisms that lead to clinical depression. Appraisal and Attribution Theories A key component of these theories is the presence of a latent negative cognitive set that is activated by a recent undesirable personal experience and results in clinical depression (Beck, 1967; Abramson, Seligman, and Teasdale, 1978; Seligman et al., 1984). Under these proximal conditions, working memory is fully occupied by negative dysphoric thoughts about the self and/or attributing negative effects of events to the self. There is considerable evidence that such cognitive vulnerabilities exist within adults with a previous episode of depression (Segal and Ingram, 1994; Ingram, Miranda, and Segal, 1998; Just, Abramson, and Alloy, 2001).

The Oregon Adolescent Depression Project (OADP) reported that dysfunctional attitudes acted as a vulnerability factor for both first episode and recurrent adolescent depressive disorder, if combined with conditions of stress (Lewinsohn et al., 1994; Lewinsohn, Rohde, and Seeley, 1998; Lewinsohn, Joiner, and Rohde, 2001). The Cognitive Vulnerability to Depression Project showed that depressogenic cognitive styles conferred specific risk for first-onset depression, regardless of negative life events (Alloy et al., 1999, 2000). Mood-Valent Negative Cognitions There is some evidence that perhaps up to 50 percent of adolescents with “cold” negative cognitions exposed to social adversities develop depression in the short term (Goodyer et al., 2000a, 2000b). Teasdale and Barnard (1993) suggested that vulnerability to intense and persistent depression may be determined by individual differences in the patterns of negative thinking that become activated as a consequence of an event-related dysphoric mood state. During such a state, vulnerable individuals are hypothesized to access qualitatively different and more depressogenic types of negative cognitions. Teasdale and colleagues initially demonstrated dysphoric activation of an attentional bias for negative thinking style within individuals with a prior history of depression (Teasdale and Dent, 1987; Teasdale et al., 1995; Teasdale, Lloyd, and Hutton, 1998). Subsequent experimental work demonstrated the presence of mood-valent negative cognitions in well adolescents with no lifetime history of affective disorders (Kelvin et al., 1999). Kelvin and colleagues further showed that this mood-related attentional bias was particularly salient in those at temperamental risk (defined as persistently high emotionality over 3 years but with no evidence of psychiatric disorder) for mood disorder and provided the first evidence for a potential causal role of cognitive vulnerabilities arising in the first two decades of life. Individual differences in basic attentional processing of social information may be a key variable in determining moodvalent attentional bias. Mood-Valent Cognitive Response Style Moderate to severely depressed patients are frequently severely preoccupied by their negative thoughts, invariably being unable to remove them from consciousness. Mood-valent theories of activating negative cognitions do not explain how or why these become persistent and in many cases immovable from consciousness. A ruminative response style to dysphoric activated negative cognitions has been proposed as another intermediate vulnerability process between underlying latent negative cognitions and the onset of clinical depressions (Just and Alloy, 1997; Nolen-Hoeksema, 1991). Ruminating on the putative causes (“Why am I like this?”) and the salience of depressive symptoms (“I won’t be able to do my work because I feel so bad”) has been proposed as the most

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proximal cognitive mechanism through which other cognitive vulnerability factors affect depression (Spasojevic and Alloy, 2001). Ruminative response to dysphoria and related negative thoughts is associated with prolonged low mood, increased depressive symptoms, onset of clinical depression in young adults, and persistence of an episode in adolescents (Lyubomirsky and Nolen-Hoeksma, 1993; Nolen-Hoeksema, 2000; Just, Abramson, and Alloy, 2001; Spasojevic and Alloy, 2001; Goodyer, Herbert, and Tamplin, 2003). Interestingly, little is known about either the developmental origins or the neuropsychological correlates of this highly dysfunctional process of perseverative negative thinking. Some early evidence argues that ruminating is more likely in those with a childhood history of abuse or neglect, or may be a familial cognitive style with a stronger association reported between mothers’ and daughters’ ruminative style than between that of fathers and sons (Nolen-Hoeksma, 2004).

Executive dysfunctions of unipolar depression Within the domain of human social interaction, performance of any task generally requires that a sequence of operations occurs that includes both mental processes and overt actions. Among others, these consist of developing an awareness of the desired goal or outcome of the event, developing a strategic plan of action in order to achieve this goal, and inhibiting or deferring inappropriate behavioral responses so that the most appropriate response option can be initiated. Each of these individual processes, or “context-specific action selections” (Pennington and Ozonoff, 1996) is thought to be dependent upon the integration of information from a range of abilities, including attention, planning, decision making, inhibition, and memory (Tamminga, 2000). These abilities are referred to as executive functions. Any social interaction or circumstance that demands optimal responding is therefore likely to recruit and be reliant upon, at least in part, the expression of these abilities. Depressed patients’ inabilities to change their negative mental set and their tendencies to remain highly dysphoric and become increasingly inactive and withdrawn suggest dysfunctions in one or more executive processes. Overall, however, studies have been unsuccessful in accumulating evidence to suggest a profile of deficits that is specific to unipolar depression compared to other psychopathologies. Instead, it seems there is an extensive range of neuropsychological deficits measurable during an episode or after recovery that are associated with a globaldiffuse impairment in executive functions that may be dependent on the age of the population and the severity, duration, and characteristics of the depressive episode (Lockwood et al., 2000; Schatzberg et al., 2000; Sweeney, Kmiec, and Kupfer, 2000a; Grant, Thase, and Sweeney, 2001; Weiland-Fiedler et al., 2004; Paelecke-Habermann, Pohl,

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and Leplow, 2005; Pardo et al., 2006). These and many other studies demonstrate difficulties in the areas of attention, behavioral inhibition, memory, decision making, and planning. Very little is known about healthy individuals who are at risk for depression. Studies are beginning to emerge that document executive skills in child and adolescent populations in general (Hughes et al., 2000; Luciana, 2003b). There are few on depressed young people (Kyte, Goodyer, and Sahakian, 2005; Perez-Edgar et al., 2006). The main areas that have been examined to date have focused on three domains: decision making, behavioral inhibition, and attentional processing. Decision Making in Unipolar Depression Efficient and effective decision making is required by individuals to ensure that correct choices are made between alternative courses of action in order to keep behavior coherent and adaptive. Making decisions involves considering and anticipating desired future outcomes, weighing the probability of an outcome being successful, and judging whether personal resources are available and necessary to achieve that outcome. Central components of the decision-making process involve attentional and working-memory resources, are context sensitive, and are influenced by mood. The development of a computerized decision-making task (Rogers, Everitt, et al., 1999) has improved the measurement precision of decision making. In this task, the individual is required to make a decision, in terms of color, as to where the computer has hidden a yellow token (under a red or under a blue box, with ratios of each color changing across trials from 6 : 4 through 9 : 1). Once participants have made their decision on a given trial, they must then place a bet depending on how confident they are that they have chosen the correct color. Possible bets begin low and increase (ascend condition), or begin high and decrease (descend condition), and in each case, if the correct choice is made, the individual wins the points they were willing to bet. If the wrong decision is made, the individual loses the points she or he was willing to bet. In an attempt to best model real-life decision making, three features of this task are of interest: decision-making behavior across a range of contingencies (manipulation of the ratio of red to blue boxes across trials); individual efficiency (allowing individuals to decide for themselves how many of their points they wish to bet); and impulsivity versus genuine risk-taking behavior (offering bets in both ascending and descending conditions). From this conceptual framework, three principal measures are derived: (1) speed of decision making—how long it takes to decide which color box is hiding the token; (2) quality of decision making—how much of the time the subject chooses the most likely outcome; and (3) risk adjustment—the rate at which a subject increases the percentage of available bets

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100 Controls (n = 49) Cases (n = 30)

90 80 Mean % bets

70 60 50 40 30 20 10 0 6:4

7:3

8:2

9:1

6:4

7:3

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Figure 47.1 Decision making characteristics of recently depressed adolescents and never depressed controls. The figure shows formerly depressed adolescents making significantly higher bets on the descend condition on all ratios except 9 : 1 and in the ascend condi-

tion on ratios 6 : 4 and 7 : 3 only. The presence of inappropriate allocation of resources in both conditions is consistent with impulsive rather than risk-taking decision making.

in response to more favorable ratios of red to blue (i.e., 9 : 1 versus 6 : 4). In adult patients with unipolar depression, administration of this task has resulted in reports of suboptimal decision making characterized by a protracted time to make decisions and the employment of less responsive betting strategies indicated by the allocation of an inappropriate number of points to a given decision (Murphy et al., 2001). The ability to make decisions that are likely to produce the desired outcome, however, appears to be less impaired (Murphy et al., 2001), indicating that adults with unipolar depression remain able to effectively encode information about the likelihood of a reward response and make decisions accordingly. Using the same task, adolescents who had recently experienced a first-episode unipolar depression also displayed a preserved ability to make decisions that were likely to produce the desired outcome (Kyte, Goodyer, and Sahakian, 2005) as well as displaying suboptimal decision making in the form of an inappropriate allocation of resources (i.e., betting a large proportion of their points on a decision with an unfavorable outcome), although to a greater extent than seen in previous adult populations (Elliot et al., 1996, 1997). Although this finding may be consistent with the notion that adolescents are more inclined to take risks than older populations, when combined with results that indicated higher bets made overall in descending conditions (where bets begin high and decrease) compared to ascending conditions (where bets begin low and increase), the pattern becomes more consistent with that of a tendency to be impulsive (i.e., a failure to consider, analyze, and reflect prior to engaging in a response, leading adolescents with recent first-episode

unipolar depression to choose bets early in both conditions). This pattern of decision making is shown in figure 47.1. Although increases in impulsivity may serve as some explanation for the patterns of suboptimal decision-making profiles reported in unipolar depression, a number of other neuropsychological processes have also been implicated. These include a hyposensitivity to reward (the prospect of a large immediate gain outweighing any prospect of future loss); insensitivity to punishment (prospect of a large loss not overriding any prospect of gain); or insensitivity to future consequences (behavior always guided by immediate prospects). Recent fMRI studies in children and adolescents have noted hypoactivation in currently depressed patients in reward-related brain areas that involve the prefrontal cortex (Forbes et al., 2006). There are suggestions that the neural systems that subserve reward may be impaired in development, blunting the ability to discern positive emotional states (Forbes and Dahl, 2005; Forbes et al., 2006), and there is some evidence for similar impairments in adult depressed patients (Tremblay et al., 2005). Impairments in reward systems seem very pertinent to the symptom of anhedonia in currently depressed individuals. Developmentally, this finding suggests that reward systems may be less impaired in younger depressed patients, since anhedonia is less common as a depressive symptom in childhood than it is in adolescence (Goodyer and Cooper, 1993). Not all impairments on the decision-making task reflect deficits in reward systems. Depressed individuals may display compromised decision making as a result of an inability to resolve effectively between two competing response options (Rogers, Owen et al., 1999), a loss of the ability to ponder different courses of action (Tranel, Bechara, and Damasio,

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2000), or a degree of cognitive (rather than behavioral) impulsivity (Bechara, Tranel, and Damasio, 2000). A task of further developmental research is to disaggregate the effects of maturation on decision-making processes and the neural systems that subserve these (Crone et al., 2005; Crone and van der Molen, 2004). This would include describing the expected level of impulsivity (for example, by age, pubertal stage, or some physiological index of developmental change; see chapter 54 by Crone and van der Molen, this volume) in order to examine the unique contribution of being at risk for or suffering from illness to task performance at different stages in the life cycle. For example recent neuropsychological findings on participants aged 9 to 17 years suggest that the ventromedial prefrontal cortex or its connections are functionally maturing during adolescence in a manner that can be distinguished from maturation of other prefrontal regions. Development of these functions may continue into young adulthood (Hooper et al., 2004). Determining decision-making skills in adolescents in the population at large would also help us to understand how individual differences in risk taking and impulsivity produce age- and sex-differentiated standards and examine the extent to which variability in decision making acts as a vulnerability factor for mood disorders. Behavioral Inhibition and Biases in Unipolar Depression Inhibitory control is another central cognitive process that is contained within the range of executive functions. Inhibitory processes exert effects on the interrelated components of reasoning, planning, and control of behavior. In addition to using executive control to choose, construct, execute, and maintain optimal strategies in a given situation, an individual must also be able to inhibit strategies that become inappropriate when goals alter or task errors occur. In order to keep behavior coherent, relevant actions need to continue while irrelevant actions are inhibited. Plaisted and Sahakian (1997) proposed the “inhibition hypothesis,” which was developed in response to reports that patients with frontal lobe damage display deficits in behavioral inhibition in relation to salient social scenarios but not when tested on abstract scenarios. This hypothesis was based on observations that damage to the prefrontal cortex (PFC) often results in a loss of inhibitory control over inappropriate responses to a current situation (Dias, Robbins, and Roberts, 1997). Loss of behavioral inhibition can be characterized by deficits of PFC inhibitory mechanisms to suppress inappropriate behaviors elicited by cues from the immediate environment. Such disinhibition would prevent the selection of alternative and more appropriate action plans that are governed by more long-term goals, leaving behavior to become dominated by the immediate emotional evaluation of the environment. The result is that reactions are based purely on the experience of associated emotions and that behavior is unconstrained

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because of a disregard for social norms, often, therefore, seeming inappropriate. Thus patients with deficits in social cognition respond to immediate but not future social situations. In a sense, such emotionally based, behaviorally disinhibited reactions may be seen as a form of impulsivity: a failure to fully consider, analyze, and reflect before engaging in a particular behavior. Since depressed patients have been shown to exhibit a bias in their processing for negative affective stimuli in both adult (Murphy et al., 1999) and adolescent (Kyte, Goodyer, and Sahakian, 2005) populations, impulsivity is likely to be emotion dependent during episodes of mood disorder. These findings raise the possibility of a primary attentional vulnerability in a subpopulation of adolescents that is activated under specific dysphoric-related circumstances that leads to impulsive behavior. Such vulnerability could underpin the selective encoding and/or recall of negative information. As already noted, mood-congruent processing biases are among the most robust findings in neuropsychological studies of depression and are central to several of the cognitive theories of the disorder discussed previously. The evidence for such a bias among adolescents with no lifetime history of unipolar depression who possess a temperamental vulnerability of high emotionality suggests that such a process may indeed be a true mood-valent cognitive vulnerability for subsequent affective disorders (Kelvin et al., 1999). Functional neuroimaging findings in adults using a moodvalent behavioral-inhibition task have shown that depressed patients show a marked adverse response to negative feedback, which may play a key role in mediating the interaction between mood and cognition in affective disorder (Elliott et al., 2002). It is unclear if this sensitivity to critical comment about performance is a state- or trait-related phenomenon. As yet there are no prospective studies of this process in individuals at risk for depressive disorders. For example, negative feedback sensitivity may be more common in adolescents with underlying latent self-devaluative traits or in those with higher mood-related ruminative response style. Heightened social sensitivity is found in adolescents exposed to postnatal depression and with a history of insecure attachment indicating a developmental ontogeny for an adverse reaction to negative feedback (Elliot et al., 1996; Murray et al., 2006). Furthermore this process when depressed suggests a psychological mechanism that may give rise to poor friendships that are present in depressed adolescents. They may be oversensitive to the ordinary rough and tumble of day-to-day interpersonal communications resulting in high levels of personally disappointing life events resulting from misinterpretation of perturbations in relationships. Interestingly, the relative strengths and weaknesses of the attentional system itself have not been well studied in depressive conditions. As noted previously, determining the relative strength of the attentional system in those at risk for

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depressive disorders is an important research task for the future. Whether individuals who show a mood-valent attentional bias also have subtle deficits in one or more aspects of the attentional processing system that are mood independent is not known. Depression and Impaired Attention A striking clinical symptom of unipolar depressions is difficulty with attention and concentration. For example, clinical interviews with adolescent girls have noted impaired concentration and/or attention in around 9 percent of well adolescents in the community but 70 percent of those with current major depression (Cooper and Goodyer, 1993). Findings from studies on mood-related ruminative styles and behavioralinhibition difficulties both suggest vulnerabilities in one or more aspects of the attentional system. Attention itself is not, however, considered a unitary concept and may consist of an interrelated set of components comprising searching, selection, sustaining, and switching strategies. The precise deficit, if present, within depressed individuals remains unclear. Perseverative problems with mood-valent ruminating on negative thoughts suggest problems in switching attention. Mood-valent attentional biases noted on the behavioralinhibition task suggest that if switching is not the problem, then there may be vulnerability in sustained attention that is activated in dysphoric circumstances. This vulnerability, in turn, evokes the bias. Studies using the Wisconsin Card Sort Task have shown that depressed adults have some difficulties in switching attention (Merriam et al., 1999; Grant, Thase, and Sweeney, 2001). Studies using a computerized analogue of this task, the Intra-Dimensional, Extra-Dimensional Set Shifting task (ID-ED) of the Cambridge Neuropsychological Test Automated Battery (CANTAB), have found somewhat equivocal findings, with attentional switching being deficient in some studies of depressed individuals (Beats, Sahakian, and Levy, 1996; Purcell et al., 1997) but not others (Elliot et al., 1996; Sweeney, Kmiec, and Kupfer, 2000b). Recent findings on recovered depressed adults noted a sustained attention deficit not explained by residual depressive symptoms (WeilandFiedler et al., 2004), suggesting that this may be either a vulnerability marker or a consequence of disorder. There is only one published study using the ID-ED in recently depressed adolescents, which showed no difference compared to healthy controls, suggesting no clear-cut vulnerability present in younger individuals (Kyte, Goodyer, and Sahakian, 2005). As noted earlier, recently depressed adolescents and adults have difficulty in switching attentional set away from a sad focus. The assumption could be made that attentional biases are only relevant in mood-valent contexts. To date, however, there has been little detailed investigation of attentional processing in depressed children and adolescents or in those

young people at risk for mood disorders. The Test of Everyday Attention for Children (TEA-Ch; Manly et al., 2001) was developed and validated to provide a comprehensive test of three components of attentional processing, namely, attentional control/switching (the ability to switch from one way of working to another), selective attention (the ability to filter information to detect relevant and ignore irrelevant stimuli), and sustained attention (the ability to maintain performance in a task that is inherently uninteresting and unrewarding). This noncomputerized task, presented as a board game, may provide a more comprehensive evaluation of attention than has been available to date. The tests are shorter and simpler than computerized tasks (thus minimizing the need to be able to sustain attention); therefore, the attentional switching tasks may be purer measures of shifting attentional set. Utilizing this measure in currently depressed adolescents confirms that depressed adolescents are significantly slower than controls at switching attentional resources from one task to another independent of mood (Wilkinson and Goodyer, 2006). The findings were consistent with depressed adolescents finding the task more difficult rather than simply being more careful on the task. The increased latency in attentional switching remained when processing speed was controlled for and, together with the absence of a deficit in educational ability, suggests that the findings are unlikely to be due to reduced motivation. Differences were greatest in those not taking antidepressants, suggesting this was not a treatment effect. Impairments in selective and sustained attention were also noted, although it is possible that the antidepressants, possibly because of the side effect of tiredness, led to the difficulties with sustained attention. There was no evidence for an association between the degree of rumination and impairments in attentional switching. Thus ruminating on dysphoric-related negative cognition does not appear to be arising from any of the measured components of attentional processing in this sample. Deficits in attentional switching and increases in mood-related ruminations in adolescent depression may, therefore, be independent processes. Since the TEA-Ch attentional switching tasks are emotionally neutral, they suggest that adolescents who develop mood disorders may have a basic deficit in attentional switching that renders them vulnerable to attentional biases during dysphoric-related circumstances. The neuropsychological components of mood-valent rumination remain underspecified. It is possible that such perseverative thinking reflects deficits in other aspects of executive functions such as decision making or planning. Alternatively, one can consider ruminative thinking as a normative process that is activated when depressed as initially an adaptive process. Reflecting and being preoccupied by problems may lead to solution-focused thinking. This appears not to be the case in some depressed individuals whose ruminating is little more

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than brooding and perseverating on depressogenic thoughts. It should be emphasized that this is not a deficit of attention. Rather, the recruitment of a normative and adaptive thinking style has become maladaptive for unknown reasons. This result may indicate a deficit or block in mnemonic retrieval processes, with the perseverative thinking indexing a failure in recall of alternative thoughts, either mood-valent, happy, distracting ones or enabling solution-focused ones. There is some recent experimental evidence in support of this notion. Dysphoric-related ruminative thinking diminishes the precision of autobiographical recall independently of level of depression (Park, Goodyer, and Teasdale, 2004). This recall interference may increase metabolic effort at both the physiological and psychological levels but inhibit emotional processing and block alternate forms of more hedonic or solution-focused thoughts.

Cognitive resilience There has been considerable interest for decades in the notion of resilience in the face of adversity (Connor and Zhang, 2006). Why is it that individuals exposed to the same or similar undesirable life events can show markedly different responses, with some showing no negative effects? Many different studies with varied designs, populations, and measures have demonstrated that key resilience processes are most likely to be found within the person (Yehuda et al., 2006). As yet, however, little is really known about the resilience mechanisms that result from activating neurocognitive processes during exposure to social adversities (Goodyer, 2002). For example, what adaptive cognitive process is required following dysphoric mood change to prevent negative cognitions and depressive outcomes? Perhaps this is the function of executive processes, whose role is to construct, execute, and maintain optimal response patterns. Thus resilience may be dependent on the processes of attention, behavioral inhibition, and decision making working in a cooperative manner to either effect a reduction in negative outcomes or to enhance positive and alternative strategies. Cognitive resilience can be considered as occurring cooperatively at three levels of processing: (1) Selective and sustained attention + Behavioral inhibition = Construction phase; (2) Choice + Action = Execution phase; and (3) Outcome = Maintenance phase (Kyte and Goodyer, 2005). The cooperative implication is that disruption in one or more of these individual phases may result in interference in optimal behavioral responses and diminish resilience and therefore adaptive outcomes. The absence of sufficient resilience therefore increases the likelihood of psychopathology. Impaired resilience may be characterized at any one or more of the following levels: behavioral rigidity, distractibility, inability to maintain goal-directed behavior, prevention of selecting the most appropriate or alternative action plans,

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impaired shifting of attention and response, suboptimal decision making, and a diminished opportunity for adaptive coping. Thus cognitive resilience is likely to arise from an aggregation of complex processing and will require multiple testing of each component to determine its efficiency and effectiveness. As yet this has not been undertaken in experimental studies of young people evaluating the neuropsychology of mood disorders. Specific psychological processes that are involved in the processing of and responding to adversity and in turn perhaps activating cognitive resilience processes have also been associated with discrete brain regions within a limbic-cortical circuitry. For example, recognizing the emotional tone of a social experience has been associated with activation primarily within the amygdala (Calder, Lawrence, and Young, 2001; Thomas et al., 2001). Processing affect-related meanings of life events appears to be mediated by the medial PFC functioning as the executive component for limbic-cortical activity (Teasdale et al., 1999). The neural basis for evaluating, organizing, and consolidating the meaning of environmental stimuli in declarative memory has been shown to be a function of the hippocampus (Eichenbaum, 1999). And finally, establishing the degree of difficulty of inductive inference from external stimuli appears preferentially associated with activation within the orbitofrontal cortex (Goel and Dolan, 2000).

Conclusions Over the next decade we can expect continuing refinement of the affective-cognitive tasks for use in functional neuroimaging studies to determine the neural correlates that underpin mental processes in well individuals, adults, and those at high risk for and those suffering from unipolar depression (Elliott et al., 2002). The findings to date indicate that there is no single cognitive processing component or related brain area that is the key to psychological vulnerability for unipolar depression. Rather, there is a coherence of neurocognitive systems involved in the recognition, processing, and response formation to emotionally meaningful stimuli from the environment that is required to prevent inefficient mental functions. Exactly how these neural systems operate to process affectively valent information is slowly emerging, but we remain relatively ignorant of the precise cooperative psychological processes that, if not functioning, will result in depressive disorders. It seems highly likely that fully integrated social and performance functions are needed for mental competence and resilience in the face of adversity. The breakdown in one or more components involved in processing environmental stimuli may result in unipolar depression. The characteristics of depression, the severity and duration of the episode in particular, may result in further or new cognitive deficits. Thus

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the neuropsychology of individuals with a single episode of illness may not be the same as that in individuals who experience recurrent episodes. Whether there are developmental differences that result in neurocognitive profiles being distinct in younger versus older individuals, or in those at high versus low risk for psychopathology, remains unclear. Unipolar depression is one of the most serious mental disorders to emerge in the second decade of life. Delineating the neurocognitive phenotypes of this heterogeneous condition is an important and achievable goal for the early part of the 21st century. acknowledgments

This chapter was completed within the Adolescents Mood Disorders Program funded by the Wellcome Trust and sponsored by the Cambridge and Peterborough Mental Health Partnership Trust.

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VI EMOTION/ COGNITION INTERACTIONS

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Toward a Neurobiology of Attachment MYRON A. HOFER AND REGINA M. SULLIVAN

The word attachment has come to refer to a broad range of behavioral processes and mental states unified by a single central concept. Attachment theory envisages a unique motivational system with evolutionary survival value on a par with hunger and sex that is organized to maintain physical proximity to the mother/caretaker soon after birth and is responsible for psychological proximity, or sense of closeness, later in development, the “bond” (Bowlby, 1969). Rupture of this “bond” by separation was the concept used to explain the traumatic effects of early loss on behavior and physiology of the child. The special qualities of the bond reflect the nature of the infant’s and the caretaker’s mental representations of the behavioral dynamics and patterns of their many previous interactions, referred to as an “internal waking model” of the relationship. Most of the research on attachment subsequent to Bowlby’s landmark volume defining the field (Bowlby, 1969) has focused on the different patterns of early attachment and their later developmental correlates, including the transmission of patterns of attachment to the next generation. This focus left the earlier observations far less studied: the developmental processes through which attachment is initially formed in altricial (slow-developing) mammals and the behavioral mechanisms underlying the intense behavioral and physiological responses of infants to separation. Meanwhile others interested in processes underlying the early development of motor and sensory systems, perception, attention, learning, memory, communication, motivation, and emotion, within the field of developmental psychobiology, developed new methods (Shair, Barr, and Hofer, 1991) and a knowledge base in each of these areas (Michel and Moore, 1995) without relation to the concept of attachment or its field of research. To these researchers, the behaviors included within the concept of attachment did not appear to be organized as a unitary system, and attachment concepts were too global to generate incisive research questions that could lead to a deeper understanding of the phenomena described. Now that a knowledge base has been established in the various research areas of developmental psychobiology, we can revisit the concept of basic attachment theory and begin to describe the underlying processes responsible for them in terms that can be related to neural structure and function.

When one does these things, as will be described in the body of this chapter, it becomes apparent that the unique features of early attachment phenomena can be shown to be the result of certain unique features of early sensory and motor integration—of early learning, communication, motivation, and the regulation of developing biological and behavioral systems by the mother-infant interaction. The apparently unified nature of attachment and its function as a system are the result of the fact that for the mammalian fetus and infant, its environment virtually begins and ends within the confines of a unified source of interaction, the mother. Thus “attachment,” like “hunger,” is made up of a number of underlying component processes and exists as a useful concept because it describes the output of these subprocesses as they work together within the larger-scale arena of social relationships. The observations that attachment theory was formulated to explain will be used as a framework for this chapter. In each case we will describe recent evidence for the basic behavioral and neurobiological processes that underlie the concepts of attachment theory. First, how does the infant come to know and prefer its own mother, maintain proximity with her, and continue to do so, even despite abuse and neglect at her hands? Second, why does separation from the mother produce such intense and widespread emotional responses in the infant? Third, how do individual differences arise in the characteristic patterns or qualities of the motherinfant interaction, and how do these early interactions become translated into long-term effects on development and ultimately into the transmission of similar maternal behavior to the next generation? In this chapter we will focus on those areas where we have the most detailed knowledge of the underlying behavioral, cognitive, and neurobiological processes involved, using laboratory animal models. It has been a surprise to find how many of the basic processes of attachment can be studied in relatively simple mammalian species, and the results interpreted in relation to similar observations in humans.

Initial formation of a specific attachment Infants of mammalian species that are born in an immature state, such as the human and the rat, face a daunting

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cognitive task. They must eventually learn to identify, remember, and prefer their own mother, and they must learn to use these new cognitive capacities to reorganize their simple motor repertoires, long adapted to the uterine environment, so as to be able to approach, remain close, and orient themselves to their mothers for the first nursing bout. It has been assumed until recently that these processes were well beyond the capacities of newborn mammals (except in precocial species such as the sheep) and that the relationship initially depended almost entirely upon maternal behavior until well into the nursing period (Bowlby, 1982; Kraemer, 1992). Attachment has thus been supposed to be built up slowly in the weeks or days after birth in human or rat. But the last decade has produced a number of studies revealing earlier and earlier evidence of learning, even extending into the prenatal period, as described in the next subsection. In addition, coordinated motor acts have been demonstrated in fetuses in response to specific stimuli that will not be encountered until after birth. Thus the solutions for the infant’s cognitive tasks appear to be found much earlier than previously thought and to take place through novel developmental processes that had not been imagined until recently. Prenatal Origins The first strong evidence for fetal learning came from studies on early voice recognition in humans, in which it was found that babies recognize and prefer their own mother’s voice, even when tested within hours after birth (DeCasper and Fifer, 1980). William Fifer continued these studies using a device through which newborns can choose between two tape-recorded voices by sucking at different rates on a pacifier rigged to control an audiotape player (reviewed in Fifer and Moon, 1995). He has found that newborn infants, in the first hours after birth, prefer human voices to silence, female voices to males, their native language to another language, and their own mother to another mother reading the same Dr. Seuss story. In order to obtain more direct evidence for the prenatal origins of these preferences (rather than very rapid postnatal learning), Fifer filtered the high-frequency components from the tapes to make the mother’s voice resemble recordings of maternal voice by hydrophone placed within the amniotic space of pregnant women. This altered recording, in which the words were virtually unrecognizable to adults, was preferred to the standard mothers’ voice by newborns in the first hours after birth, a preference that tended to wane in the second and third postnatal days. Furthermore, there is now evidence that newborns prefer familiar rhythmic phrase sequences to which they have been repeatedly exposed prenatally (DeCasper and Spence, 1986). In a striking interspecies similarity, rat pups were subsequently shown to discriminate and prefer their own dams’ amniotic fluid to that of another dam when offered a choice

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in a head-turning task (Hepper, 1987). Newborn pups were also shown to require amniotic fluid on a teat in order to find and attach to it for their first nursing attempt (Blass, 1990). Robinson and Smotherman (1995) have directly tested the hypothesis that pups begin to learn about their mothers’ scent in utero. They have been able to demonstrate one-trial taste-aversion learning and classical conditioning in late-term rat fetuses, using intraoral cannula infusions and perioral stimulation. Taste aversions learned in utero were expressed in the free feeding responses of weanling rats nearly 3 weeks later. The researchers went on to determine that aversive responses to vibrissa stimulation were attenuated or blocked by intraoral milk infusion, a prenatal “comfort” effect they found to be mediated by a central kappa-opioid receptor system. Interestingly, when artificial nipple stimulation was presented as a predictive cue (CS) for intraoral milk in a learning paradigm and a reduction in fetal responsiveness to the CS was acquired in this way, the conditioned response was blocked by a mu-opioid antagonist rather than a kappa antagonist. This finding suggests that the opioid system plays an important role in the organization of fetal behavior by redirecting and focusing responsiveness to a subset of available stimuli (Robinson and Smotherman, 1995). These forms of fetal learning, involving maternal voice in humans and amniotic fluid in rodents, appear to play an adaptive role in preparing the infant for its first extrauterine encounter with its mother. They are thus the earliest origins we have yet found for attachment to the mother. The Perinatal Transition In addition to the evidence for fetal learning that we have described, specific adaptive motor-response capabilities have also been found in laterterm rat fetuses, using an exteriorized in vivo preparation (Smotherman and Robinson, 1992). The reflexes of licking, mouthing, and sucking necessary for postnatal nipple grasp and nursing can be elicited in late-term fetuses, and even the characteristic stretch response to oral milk and the facial wiping (rejection) response to unfamiliar tastes that are usually associated with rat pups tested several days postnatally. The emergence of these reflex responses in anticipation of the postnatal environment in which they will be expressed provides the newborn with the components needed for the transition to postnatal life and for the rapid organization of an integrated response repertoire tuned to specific characteristics of the maternal body. The spontaneous motor acts needed for an attachment system also appear to be developing prior to birth. Rat fetuses engage in a number of spontaneous behaviors in utero, including curls, stretches, and trunk and limb movements. These acts were observed to increase markedly in frequency with progressive removal of intrauterine space constraints, as pups were observed first through the uterine

wall, then through the thin amniotic sac, and finally unrestrained in a warm saline bath (Smotherman and Robinson, 1986). When newborn pups are observed prior to their first nursing bout, they resemble exteriorized fetuses, until the mother lowers her ventrum over them. Their behavior then changes rapidly over the first few nursing bouts, into the complex repertoire described in the next subsection. An Attachment System in the Newborn When pups less than a day old are stimulated gently by soft surfaces from above, as when the mother hovers over them, they show a surprisingly vigorous repertoire of behaviors (Polan and Hofer, 1999). These include the curling and stretching seen prenatally, but now also include locomotor movement toward the suspended surface, directed wriggling, audible vocalizations, and, most strikingly, turning upside down toward the surface above them. Evidently these behaviors propel the pup into close contact with the ventrum, maintain it in proximity, and keep it oriented toward the surface. They thus appear to be very early attachment behaviors. In a series of experiments, we found that these are not stereotyped reflex acts, but organized responses that are graded according to the number of maternal modalities present on the surface (e.g., texture, warmth, odor) (Polan and Hofer, 1999). Furthermore, they are enhanced by periods of prior maternal deprivation, suggesting the rapid development of a motivational component. We found that pups discriminate their own mother’s odor in preference to equally familiar nest odors by two days of age (Polan and Hofer, 1998), and Hepper (1986) has shown that pups discriminate and prefer their own mother, father, and siblings to other lactating females, males, or age-mates by the first postnatal week. These results show that a highly specific “behavioral attachment system” (Bowlby, 1969), capable of approach and proximity maintenance to the mother and motivated by brief periods of separation from her, may occur much earlier in development than previously supposed. The remarkable specificity of the approach response of the infant rat to individual family members acquired within the first few postnatal days demonstrates that specificity of attachment does not require long experience or advanced cognitive and emotional capabilities. Olfaction in the rat and vision in the human provide the necessary basis for approach responses that are specific to a single individual. But this remarkable capability can develop independently of the specificity of the rat pup’s contact comfort response. For even by two weeks of age a rat pup will show an equal comfort response to contact with any female that is available. This nonspecificity is limited, however, and a form of “stranger anxiety” develops by the second week of life, well before weaning. Pups will avoid the odor of unfamiliar adult males (but not of familiar or unfamiliar prepubertal males), and they show

immobility and a brisk adrenocortical response when the stranger is too near (Takahashi, 1992). This early fear response, like approach responses to the mother, depends upon olfactory cues (Shair et al., 1997). Recent work in humans, inspired by these findings in lower animals, has shown that human newborns too are capable of slowly locomoting across the bare surface of the mother’s abdomen and locating the breast scented with amniotic fluid in preference to the untreated breast (Varendi, Porter, and Winberg, 1996). Although newborns are attracted to natural breast odors even before the first nursing bout (Makin and Porter, 1989), amniotic fluid can override this effect. Apparently, human newborns are not as helpless as previously thought and possess approach and orienting behaviors that anticipate the recognized onset of maternal attachment at 6–8 months. Postnatal Learning Although specific olfactory and/or auditory predispositions toward the infant’s own mother are acquired prenatally, after birth the newborn mammal enters a new world where contingent events, so important for more advanced forms of learning, are now occurring with great frequency. As noted earlier, the abrupt transition from prenatal to postnatal life appears to be eased for the newborn by the presence of prenatal stimuli continuing into the postnatal environment (i.e., the odor of amniotic fluid; Lecanuet and Schaal, 1996; Mennella, Johnson, and Beauchamp, 1995; Schaal, Marlier, and Soussignan, 1995; Varendi et al., 1998). However, neonatal capacities for stimulus discrimination and preferential approach, orienting, and proximity-maintenance behavior described in the previous subsection seemed likely to have been formed by some type of rapid postnatal learning process, resembling avian imprinting, as hypothesized by Bowlby. Yet until recently, no such process had been discovered. When developmental psychobiologists first began assessing infant rat development, any form of learning appeared beyond the capabilities of the neonatal rat. However, as our understanding of the newborn’s environment began to evolve and experimental conditions became more naturalistic, the surprising learning capabilities of rodent neonates have emerged. Since then, extensive work characterizing early learning has demonstrated that the basic laws of adult learning also apply to infant rats and that learning occurs naturally within the nest (Brunjes and Alberts, 1979; Campbell, 1984; Galef and Kaner, 1980; Leon, 1975; Miller, Jagielo, and Spear, 1989; Pedersen, Williams, and Blass, 1982; Rudy and Cheatle, 1979; Sullivan, Brake, et al., 1986; Sullivan, Hofer, and Brake, 1986; Sullivan, Wilson, et al., 1990; Terry and Johanson, 1996). These early studies revealed that newborn pups were capable of learning to discriminate, prefer, approach, and maintain proximity to an odor that had been associated with

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forms of stimulation that naturally occurred within the early mother-infant interaction. Random presentations of the two stimuli (odor and reward) had no such effect and provided the control procedure necessary to identify the change in behavior as due to associative conditioning. Since this learning required only two or three paired presentations and since the preference was retained into adulthood to enhance sexual behavior (Fillion and Blass, 1986; Moore, Jordan, and Wong, 1996), it seemed to qualify as an “imprinting-like process” that is likely to be central to attachment in slowdeveloping mammals. Indeed, a human analogue of this process was found by Sullivan, Tabarsky-Barba, and colleagues (1991), who showed that when human newborns were presented with a novel odor and then rubbed along their torsos to simulate maternal care, the next day they became activated and turned their head preferentially toward that odor. This result suggests that rapid learning of orientation to olfactory cues is an evolutionarily conserved process in mammalian newborns. Somatosensory information is also of importance in mother-infant interactions, and pup mortality rate (caused by disturbance of nipple orientation and grasp) increases markedly when a pup’s facial somatosensory system is disrupted (Hofer, 1981). Somatosensory stimulation evokes specific orientation behaviors in 2- to 3-day-old pups (Polan and Hofer, 1999), and specific contingent stimulation can be shown to alter pups’ responsiveness to somatosensory stimulation. Following a conditioning procedure where whisker stimulation was paired with a reward, pups showed more vigorous responding to whisker stimulation (Landers and Sullivan, 1999a, 1999b; Sullivan, Landers, et al., 2003), resulting in enhanced responding to tactile stimulation from the mother. From an evolutionary perspective, a reliance on learning for early attachment might appear rather risky considering the potential for inappropriate object choice. However, considering the physical constraints of the nest and pups’ immature motor system, the range of possible attachment figures is limited. Additionally, unique characteristics of infant learning appear to greatly enhance the likelihood of pups developing odor preferences necessary for forming maternal attachment. However, while pups can learn to avoid odors, this learning is greatly constrained. For example, while neonatal rats are capable of learning to avoid tastes/odors paired with malaise, this learning is blocked if pups are nursing during conditioning (Gemberling and Domjan, 1982; Haroutunian and Campbell, 1979; Martin and Alberts, 1979; Melcer, Alberts, and Gubernick, 1985; Rudy and Cheatle 1977, 1978; Shionoya et al., 2006). Other types of learning such as fear conditioning, inhibitory conditioning, and passive avoidance do not appear to emerge until after postnatal days 10–11 (Blozovski and Cudennec, 1980; Camp and Rudy, 1988; Collier et al., 1979; Goldman

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and Tobach, 1967; Haroutunian and Campbell, 1979; Stehouwer and Campbell, 1978; Sullivan, Stackenwalt, et al., 2000; Myslivecek, 1997), presumably because this learning could interfere with pups’ interaction with the mother. Clinical observations have taught us not only that attachment occurs to supportive caretakers, but also that children tolerate considerable abuse while remaining strongly attached to the abusive caretaker (Helfer, Kempe, and Krugman, 1997). Although it may initially appear to be counterproductive to form and maintain an attachment to an abusive caretaker, from an evolutionary perspective, it may have been better for an altricial infant to have a bad caretaker than no caretaker. We suggest that this aspect of human attachment may be modeled in the infant rat. During the first postnatal week, we and others have found that a surprisingly broad spectrum of stimuli can function as reinforcers to produce an odor preference in rat pups. These stimuli range from apparently rewarding ones such as milk and access to the mother (Alberts and May, 1984; Brake, 1981; Galef and Sherry, 1973; Johanson and Hall, 1979; Johanson and Teicher, 1980; Leon, 1975; McLean et al., 1993; Pedersen, Williams, and Blass, 1982; Sullivan, Brake, et al., 1986; Sullivan, Hofer, and Brake, 1986; Weldon, Travis, and Kennedy, 1991; Wilson and Sullivan, 1994) to apparently aversive stimuli such as moderate shock and tail pinch (Camp and Rudy, 1988; Moriceau and Sullivan, 2006; Roth and Sullivan, 2005; Sullivan, Hofer, and Brake, 1986; Sullivan, Stackenwalt, et al., 2000; Spear, 1978). These aversive stimuli supporting odor-preference learning elicit escape responses from pups during conditioning, and neonatal rat pups feel pain (Barr, 1995; Collier and Bolles, 1980; Emerich et al., 1985; Fitzgerald, 2005; Stehouwer and Campbell, 1978). As pups mature and reach an age when leaving the nest becomes more likely (Bolles and Woods, 1964; pups begin to walk between 9 and 11 days old), olfactory learning comes to resemble learning in adults more closely and effectively ends the enhanced-learning “sensitive period.” Specifically, odor aversions are easily learned by two-week-olds, and acquisition of odor preferences is limited to odors paired with stimuli of positive value (Camp and Rudy, 1988; Haroutunian and Campbell, 1979; Sullivan and Wilson, 1995; Spear, 1978). Thus the odor learning that underlies early attachment appears to take place in response to a very broad range of contingent events while pups are confined to the nest, but becomes more selective at a time in development when pups begin leaving the nest and encountering novel odors not associated with the mother. Neural Basis of Olfactory Preference Learning The development of a specific olfactory-based attachment system in the rat pup during the first week and a half of life is associated with the acquisition of olfactory bulb neural changes. We found that rat pups express this modified

olfactory bulb response to both natural maternal odors and artificial odors experienced in the nest (Sullivan, Wilson, et al., 1990), as well as to odors in controlled learning experiments (Sullivan and Leon, 1986; Johnson et al., 1995; Moriceau and Sullivan, 2004b; Roth and Sullivan, 2005; Yuan et al., 2002; Wilson and Sullivan, 1991; Wilson, Sullivan, and Leon, 1987). The modified olfactory bulb response is characterized by enhanced immediate-early gene activity (c-Fos), optical imaging, and enhanced 2deoxyglucose (2-DG) uptake in focal, odor-specific glomerular regions in response to the conditioned odor. Within the underlying neural substrate, modified single-unit response patterns of mitral/tufted cells near the enhanced glomerular foci were found (Wilson, Sullivan, and Leon, 1987; Wilson and Leon, 1988; Wilson and Sullivan, 1990), and olfactory bulb anatomical changes were reflected in enlarged glomeruli within these foci (Woo, Coopersmith, and Leon, 1987). As with the behavioral changes in attachment, these neural changes are retained into adulthood, but their acquisition is dependent upon experiences during infancy (Woo and Leon, 1988; Pager, 1974). In adults, learning is distributed throughout the brain, and a similar distribution is also likely occurring in pups. Thus far, in addition to the olfactory bulb learning-associated changes found in sensitive-period pups, piriform “olfactory” cortex and anterior olfactory nucleus also appear to encode learning (Roth and Sullivan, 2005; Kucharski and Hall, 1987; Moriceau et al., 2006; Sullivan and Leon, 1986; review, Roth, Wilson, and Sullivan, 2004). These brain areas, as well as other yet unidentified areas, may mediate the development of a close association of emotional states with events within the sphere of attachment processes (see figure 48.1). Many neurotransmitters have a role in early olfactory learning in neonatal rats (5-HT—McLean et al., 1993;

dopamine—Weldon, Travis, and Kennedy, 1991; Barr and Wang, 1992; glutamate—Mickley et al., 1998; Lincoln et al., 1988; opiates—Barr and Rossi, 1992; Kehoe and Blass, 1986; Roth and Sullivan, 2003, 2006; GABA—Okutani et al., 2003), although the action of norepinephrine (NE) appears particularly important in neural plasticity during early development and in the form of olfactory (Brennen and Keverne, 1997; Wilson and Sullivan, 1994) and somatosensory (Landers and Sullivan, 1999b) learning-induced plasticity used in early attachment. Norepinephrine input to the neonatal olfactory bulb is widespread in the granule cell layer (Woo and Leon, 1995; McLean and Shipley, 1991), which is composed of inhibitory interneurons modulating the bulb’s output through the adjacent mitral cells (Brunjes, Smith-Craft, and McCarty, 1985; Lauder and Bloom, 1974; Trombley and Shepherd, 1992; Wilson and Leon, 1988). Wilson has shown that activation of the NE input to the infant rat’s olfactory bulb during an odor presentation maintains mitral cell responsiveness to that odor, preventing the habituation that these cells normally exhibit to repeated odor presentations (Wilson and Sullivan, 1992). Olfactory bulb NE increases by activation of the locus coeruleus (LC) by the reward during infant odor learning (Rangel and Leon, 1995). Manipulation of NE within the bulb during odor learning further supports a critical role for NE in pup learning. Specifically, an odor paired with increasing olfactory bulb NE (intrabulbar infusions or stimulating LC which is the source of NE) supports odor-preference learning (Sullivan, Stackenwalt, et al., 2000). Conversely, odor learning is blocked if olfactory bulb NE is depleted or its receptors are blocked (Langdon, Harley, and McLean, 1997; Sullivan, Zyzak, et al., 1992; Sullivan, Wilson, et al., 1994). Together, all this suggests that the contingent events of increasing

Figure 48.1 The neural circuit for learning changes with development. In early life, during the sensitive period, the olfactory bulb and anterior piriform cortex code odor learning. With maturation (>10 days old) and termination of the sensitive period, the circuit

expands and includes the posterior piriform cortex and amygdala. Other yet-to-be-identified brain areas are likely involved in this circuit.

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olfactory bulb NE and odor stimulation support the neural plasticity responsible for the acquisition of olfactory-based attachment behavior. Developmental changes within the noradrenergic pontine nucleus LC (McLean et al., 1989; McLean and Shipley, 1991; Shipley, Halloran, and De la Torre, 1985) may contribute to the termination of the infant rat’s rapid odorpreference learning. The neonatal LC has unique response characteristics that enable it to respond to a broad range of sensory stimuli and release abundant olfactory bulb NE. Specifically, compared to the adult LC, the infant LC responds to a broader range of sensory stimuli and fails to habituate after repeated presentation of stimuli (Kimura and Nakamura, 1985; Nakamura and Sakaguchi, 1990; Foote, Aston-Jones, and Bloom, 1980; Harley and Sara, 1992; Sara, Dyon-Laurent, and Herve, 1995; Vankov, Herve-Minvielle, and Sara, 1995; Kimura and Nakamura, 1985; Nakamura and Sakaguchi, 1990). The most dramatic response difference between the adult and infant LC is the prolonged response time of the infant. Specifically, a 1-second tactile stimulation is likely to cause a response of a few milliseconds in the adult LC, but a 20- to 30-second response in the neonates. The neonatal LC’s prolonged response is probably due to extensive electronic coupling of neurons through gap junction but also through the unique functioning of the infant’s autoreceptors located on the somadendritic membranes of the LC neurons (Marshall et al., 1991; Nakamura and Sakaguchi, 1990; Winzer-Serhan et al., 1997). Specifically, the LC has recurrent axon collaterals that go back into the LC to release NE. When this NE is released in the adult LC, it activates noradrenergic α2 inhibitory autoreceptors that effectively terminate the LC response within milliseconds. The neonatal LC’s α2 autoreceptors, while present, do not appear functional. The neonate LC also has abundant excitatory α1 autoreceptors that maintain the infant response longer than the adult LC response. At about 10 days old, as the sensitive period ends, the infant LC begins to take on adult characteristics with functional emergence of the α2 inhibitory autoreceptors and diminished α2 excitatory autoreceptors. These results suggest that the neonate’s LC is designed to release copious amounts of NE to support the olfactory bulb neural changes required for neonatal pup odor-preference learning. With maturational changes in the LC during the second week of life, NE release into the bulb is reduced and effectively closes the sensitive period for the imprinting-like learning underlying attachment in this species. We have tested this hypothesis by pharmacologically altering the LC autoreceptors in older pups to reinstate the neonatal LC characteristics. Specifically, we implanted bilateral cannulas into the LC of older pups and infused an NE α2 receptor antagonist to block the newly emerging α2 inhibitory autoreceptors that have begun to terminate the LC response and an NE

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α1 receptor agonist to further excite the diminishing α1 excitatory autoreceptors, all while stimulating the LC. Indeed, we were able to reinstate the rapid NE-dependent preference learning in pups simply by pharmacologically reinstating the neonatal characteristics of the LC (Moriceau and Sullivan, 2004b). Neural Basis of Attenuated Aversion Learning Another characteristic of attachment identified by Bowlby (1969) was the child’s strong ties to an abusive attachment figure, which others have identified in other species (Harlow and Harlow, 1965; Fisher, 1955, cited in Rajecki, Lamb, and Obmascher, 1978; review, Helfer, Kempe, and Krugman, 1997; Salzen, 1970). We have been attempting to model this aspect of attachment in rats in an attempt to understand this paradoxical learning in infants. In adults, pairing a neutral stimulus with pain results in the neutral stimulus becoming aversive, eliciting fear as well as avoidance, fleeing, or freezing depending on the experimental context. This learning involves the amygdala, although many additional brain areas are involved in this complex neural circuit (Davis, Walker, and Lee, 1997; Fanselow and Gale, 2003; Fanselow and LeDoux, 1999; Herzog and Otto, 1997; Maren, 2003; McGaugh, Roozendaal, and Cahill, 1999; Pape and Stork, 2003; Pare, Quirk, and LeDoux, 2004; Rosenkranz and Grace, 2002; Sananes and Campbell, 1989; Schettino and Otto, 2001; Sevelinges et al., 2004). We have data suggesting that the inability of pups to learn this early-life version of fear conditioning may be due to the amygdala’s failure to participate in early-life learning. During the neonatal sensitive period, when pups readily learn to prefer odors paired with pain, the two major odor inputs to the amygdala (olfactory bulb and piriform “olfactory” cortex) (see figure 48.1) show activation during learning (Roth and Sullivan, 2005; Moriceau et al., 2006), although the amygdala did not appear to participate as indicated by 14C 2-DG or c-Fos (Roth and Sullivan, 2005; Moriceau and Sullivan, 2006; Moriceau et al., 2006). A similar analysis in older, 12-day-old pups that easily learn odor aversions with odor–0.5-mA shock conditioning shows activation of the basolateral, lateral, and cortical amygdala nuclei (Moriceau et al., 2006; Moriceau and Sullivan, 2006; Sullivan, Stackenwalt, et al., 2000) (see figure 48.2). These results suggest the amygdala or its connections mature around 10 days of age to permit its participation in learning and avoidance acquisition (Berdel and Morys, 2000; Berdel, Morys, and Maciejewska, 1997; Bouwmeester, Wolterink, and van Ree, 2002; Cunningham, Bhattacharyya, and Benes, 2002; Nair and Gonzalaz-Lima, 1999; Morys et al., 1999; Schwob, Haberly, and Price, 1984; Wilson, Best, and Sullivan, 2004). However, our ability to manipulate the age at which pups begin to learn the odor-shock avoidance fear conditioning and amygdala activity with

Figure 48.2 Learning an odor preference during early life from odor–0.5-mA shock does not activate any amygdala nuclei (basolateral complex, BLA/LA; and central, CeA; others not shown) but

corticosterone (CORT) suggests amygdala maturation is not the limiting factor in pup learning (see figure 48.3). There was existing literature that strongly suggested CORT might alter the age when pups begin to learn avoidance and fear to unlearned predator odor (Bialik, Pappas, and Roberts, 1984; Blozovski and Cudennec, 1980; Collier et al., 1979; Moriceau and Sullivan, 2004a; Myslivecek, 1997; Takahashi, 1992; Wiedenmayer and Barr, 2001). Additionally, the emergence of odor–0.5-mA shock-learned fear was related to the declining “stress hyporesponsive period” when pups’ low CORT level is not raised by most stressful stimuli (i.e., restraint, shock; Levine, 2001; Rosenfeld, Wetmore, and Levine, 1992). Indeed, as illustrated in figure 48.3A, we can cause the precocious emergence of fear conditioning and amygdala activation (odor–0.5-mA shock) in pups simply by increasing endogenous CORT (Moriceau and Sullivan, 2004a, 2006; Moriceau et al., 2006). Specifically, CORT injected (3mg/kg) 24 hours and 30 minutes before 8-day-olds received odor–0.05-mA shock conditioning causes pups to learn an odor aversion rather than the age-typical odor preference. This learning activated the amygdala. Moreover, as illustrated in figure 48.3B, decreasing CORT in older pups (conditioning at 12 days old with adrenalectomy at 8 days old) prolongs pups’ inability to learn fear and odor-shock conditioning and continues to produce an odor preference without amygdala activation. The amygdala appears to be the site of CORT action, since intra-amygdala infusions of CORT receptor agonists and antagonists during conditioning respectively terminate or prolong the odor-pain-induced odor-preference learning (Moriceau et al., 2006; Moriceau and Sullivan, 2004a; 2006). We suggest that natural fluctuations in pups’ CORT level can occur in nature and can prematurely end or

does alter its input (GCL, olfactory bulb granule cell layer; PIR, piriform; Roth and Sullivan, 2005).

Figure 48.3 Odor-shock conditioning produces an (A) odor preference in young sensitive period pups ( Positive FB 18–20 yrs (n = 20)

14–15 yrs (n = 20)

9–11 yrs (n = 15)

RCI contrast value

2 1.5 1

18–20 years 14–15 years 9–11 years

0.5 0 –0.5 –1 First warning Efficient negFB Error negFB First posFB Correct posFB

RCI contrast value

2 1.5 1 0.5 0 –0.5 –1 First warning Efficient negFB Error negFB First posFB Correct posFB

Plate 66 Neural activity associated with the processing of positive and negative performance feedback for children, adolescents, and adults. The pattern of activation in anterior cingulate cortex

shows an adult pattern in adolescence, whereas the pattern of activation in DLPFC does not reach adult levels until late adolescence. (See figure 54.4.)