Issues of Diversity in Clinical Neuropsychology
For further volumes: http://www.springer.com/series/7416
Elaine Fletcher-Janzen Editor
The Neuropsychology of Women
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Editor Elaine Fletcher-Janzen Private Practice Cleveland, Ohio USA
[email protected] ISSN: 1930-4633 ISBN: 978-0-387-76907-3 DOI 10.1007/978-0-387-76908-0
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Library of Congress Control Number: 2008937457 c Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com
Preface
This book, The Neuropsychology of Women, is about brain-behavior relationships across the female life span. Historically, sex differences in research were either ignored or prohibited due to concerns about negative impact on reproductive health. A movement at the Federal level to include women in research studies and outcomes has emerged over the past 20 years. It now appears that there is just enough research about sex and gender differences in the neurosciences and health sciences to suggest that not only should these differences be included in all analyses but also there are enough major differences to produce the need for a specific set of competencies for health care practitioners working with female clients. To that end, the chapters in this volume are designed to provide the reader with a synopsis of neuropsychological perspectives on brain development, brain imaging, brain injury, chronic illness, epilepsy, dyslexia, aging and eating disorders with the female client in mind. It intended to be a helpful resource for anyone who works in the field of neuropsychology and related fields who wishes to increase his or her knowledge and competencies in working with women. If we write about women we must write about the stages of their lives, usually tied to reproduction options and changes, and characterized by a dynamic brainbehavior burden. The word burden is used here not to depict a heavy and obstructive load - but more as an important variable that demands consideration because it affects all aspects of female functioning. The burden unfolds from birth without conscious intent on the part of the woman and it rests with the severance of reproductive ability and a completely different hormonal makeup that guards against some forms of dementia and nudges the woman towards other forms of dementia later in life. Hence, neuroscientific research literature now tends to be more specific in terms of relating genotype to phenotype and sex versus gender differences in development, etiology, course, and quality of life. There are 10 chapters in this small handbook that represent multiple aspects of research and practice with female clients. Phylis Anne Teeter-Ellison and Amy Nelson explore current research on brain development, including new findings on gender differences. They have found that males and females appear to have meaningful differences in brain maturation that create interesting applications for the way in which we educate and nurture our youth. In this chapter, they provide a brief overview of neurodevelopment and v
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highlighted major gender differences in brain development. Gender differences in gray matter development have been found, specifically in maximum cortical gray matter thickness in total brain volume, frontal lobe, parietal lobe, and caudate nucleus. Regional differences were not found to be as pronounced in temporal gray matter development. Peak growth curves were also found in the basal ganglia, where female brains reach peak volume almost three years earlier than males. The extent to which gender differences in brain development can be linked to cognitive and behavioral differences is still under study. The authors suggest that future studies will no doubt explore this linkage with better neuroimaging technologies and measures of neuropsychological functioning. They also state that this avenue of neuroscience holds promise for educational practices, early interventions and comprehensive treatment programs for children with biogenetic and acquired brain related disorders. In the chapter on neuroimaging, Drs. Margaret Semrud-Clikeman, Jodene Goldenring Fine, and Jesse Bledsoe present the various types of imaging techniques (MRI, fMRI, PET, SPECT and DTI) as well as a comprehensive review of literature on gender differences found with neuroimaging. Findings on gender differences in normal development, pediatric through geriatric are reviewed with attention to gray matter, white matter, hormonal influences, and specific brain regions in each of the developmental sections. Adult mental health, aging, and neurodegenerative issues are represented by a review of one condition for each: schizophrenia, Alzheimer’s and Multiple Sclerosis. Being a nascent methodology, the state of neuroimaging research is of variable quality and somewhat neglectful of women’s issues in general. The authors offer advice on how to recognize research of quality and suggest that more effort be focused on establishing research lines that include women in particular, especially for those issues that are salient to women’s health. Regarding brain injury, Drs. Elaine Clark and Janiece Pompa find that females are not as well represented as males in the scientific literature regarding brain injury. What scant research that does exist, is mixed. They conclude that whereas some studies have found a clear advantage for females compared to males in terms of functional outcomes, others have found that women do much worse, even when similar outcome variables are assessed (such as these studies investigating vocational outcomes). The authors relate that in the late 1990s, the National Institutes of Health (NIH) convened a panel to evaluate the existing traumatic brain injury (TBI) research. The panel concluded from their review that the research sum at that time was inadequate to address the issue of recovery, especially with regard to long-term outcomes. The panel recommended that more investigations be conducted with females who sustain brain injuries. Since 1999, there has been an increase in systematic research studying the effects of TBI’s on sex differences, including sex differences in brain anatomy and physiology that might account for differences in post-injury outcomes in males and females. Dr. Clark and Dr. Pompa introduce a new area of brain injury recovery has that opened up with the increase of women in the military returning from combat areas with blast related injuries. They propose that the need for information on sex and gender differences in brain injury treatment and recovery has never before been more pressing.
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In the chapter entitled, The Neuropsychology of Attention-Deficit/Hyperactivity Disorder in Females, Dr. Nancy Nussbaum presents research pertaining to gender differences in presentation of symptoms, neurobiology and biological factors, and on neuropsychological measures of ADHD. This chapter is an excellent and provocative exploration of current research on gender differences that promote improved quality of psychological care provided to girls and women and to highlight future areas of research on this disorder. In their second chapter, Amy Nelson and Phyllis Anne Teeter-Ellison review the evidence for gender differences in dyslexia. Their findings are mixed with both similarities and differences across genders. The genetic basis of the disorder and the same cortical substrates - temporo-parieto-occipital brain regions - are involved in the reading process for both males and females. On average, females have larger cortical areas in regions underlying language, and women have greater bi-hemispheric representation for some language skills compared to males. Females may have some advantages that protect against early insult or neurodevelopmental anomalies affecting these brain regions. The authors suggest that he extent to which genetic and anatomical differences are protective needs further investigation. Specifically, differences in early literacy environment and instruction may ultimately affect the development of neural networks involved with reading accounting for gender differences. There are a number of important environmental factors that explain individual differences in reading skills, including: socioeconomic status of the child’s family, school and community; teachers and their pedagogical approach; and, availability of materials to enhance early learning and literacy. Dr. Teeter-Ellison concludes that while these pre-literacy experiences predict reading abilities in grade 3, to date, there is nothing to suggest that males or females may be different in these early home experiences. LeAdelle Phelps has provided an insightful chapter to this volume entitled Critical Issues in Chronic Illnesses of Women. She reviews two disorders that affect far more females than males: fibromyalgia and multiple sclerosis. The biological bases for such differences are likely related to the sex hormones as fibromyalgia has a female-to-male incidence ratio of 9:1, which is likely related to the effect of estrogen on the hypothalamic-pituitary-adrenal axis (HPAA) and autonomic nervous system (ANS). By comparison, multiple sclerosis has a female-to-male ratio of 3:1. Although is has been estimated that genetics account for 20-50% of the disease probability, large prospective studies have documented a significant reduction in disease activity during pregnancy. Interestingly, Dr. Phelps suggests that it is important that we identify possible protective variables, even if temporary such as pregnancy in women with MS, that is affiliated with improved resistance and resilience. She also suggests that treatment programs could be developed that includes highly specific strategies with the prevailing intent to reduce risk factors while enhancing protective factors. Dr. Catherine Cook-Cottone provides an in-depth review of the neuropsychological aspects of eating disorders. Eating disorder prevalence rates have the highest female to male ratio of any psychiatric disorder and there have been fewer neuropsychological studies of eating disorders than of any of the other major psychiatric
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disorders. Dr. Cook-Cottone summarizes research results of neuropsychological assessments that have been used to explore possibilities that there may be dysfunction in the central nervous system contributing to the risk, etiology, maintenance, and/or intractability of symptoms of eating disordered behaviors. She states that it is important to note that there are researchers who argue that neuropsychological studies have yet to produce an explanatory neurofunctional model of eating disorders. However, neuropsychological tests are now being used to assist with diagnosis, to obtain quantifiable data on the eating disorder condition, and to develop effective treatment plans. Lastly, Dr. Jennifer Dunkin, reviews relevant findings from brain imaging, neuropsychology, and genetic research regarding the effects of aging and gender on cognition, brain structure and function in normal aging and dementia. She relates that the prevalence of age-related cognitive disorders is increasing dramatically, making it imperative to continue to focus research efforts on prevention and treatment. As has been the case in other research areas, only recently have investigators questioned whether the cognitive aging process affects men and women differently and the results cited in this chapter are surprising. While gender differences in brain structure and function during normal aging are remarkably small, gender exerts powerful differential effects on risk for different forms of dementia. The reader may note an unusually large Appendix included in this handbook. The entire document of ”Women’s Health Care Competencies for Medical Students: Taking Steps to include Sex and Gender Differences in the Curriculum” was reproduced verbatim as a resource for neuropsychology professionals desiring a guide or template to determine the exact scope of competencies needed for working with female clients. While the document is geared towards the training of physicians it can be easily seen that most of the competencies translate efficiently into neuropsychology practice. The intent of the document, and its inclusion in this volume, is to illuminate the breadth and depth of issues surrounding sex and gender differences in health care and, more important, are evidence-based practices that improve treatment outcomes. The Association of Professors of Gynecology and Obstetrics (APGO) graciously granted us permission to reproduce the Competencies for our readers and their generosity is very much appreciated. Readers may also find many other excellent resources at the APGO website: http://www.apgo.org. In terms of thanking those individuals associated with this project who were critical to its inception and fruition, this volume would not have been possible without the vision and patience of our Senior Editor at Springer, Janice Stern. Janice has a superior knowledge of the magical ways to support projects with busy authors. She is thanked for her expertise, advice, grace, style, and kindness: It did not go unnoticed or unappreciated! The authors of this volume also need to be thanked. When approached with the project everyone was enthusiastic and interested in the outcome. They all came in on deadline, were cheerful about edits, and responded promptly to queries! This was definitely the ”dream team” of authors for a handbook! The authors of the chapters in this volume have exemplary career records and they have all served the field, clients, and colleagues well for many years. This rare collective work is a statement
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of their outstanding wisdom about what it means to be a woman and a statement of the level of competency that is needed to adequately treat women and achieve maximal treatment outcomes: Their contributions to this work are outstanding and greatly appreciated. Cleveland, Ohio March, 2008
Elaine Fletcher-Janzen
Contents
1 Introduction to the Neuropsychology of Women . . . . . . . . . . . . . . . . . . . . Elaine Fletcher-Janzen, Margaret Semrud-Clikeman, Nancy L. Nussbaum, Phyllis Anne Teeter Ellison, Elaine Clark, LeAdelle Phelps, Jennifer J. Dunkin, and Catherine P. Cook-Cottone
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2 Brain Development: Evidence of Gender Differences . . . . . . . . . . . . . . . 11 Phyllis Anne Teeter Ellison and Amy Nelson 3 Neuroimaging in Women . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Margaret Semrud-Clikeman, Jodene Goldenring Fine, and Jesse Bledsoe 4 Women and Traumatic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Elaine Clark and Janiece L. Pompa 5 Attention-Deficit/Hyperactivity Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Nancy L. Nussbaum and Katherine N. Shepard 6 The Neuropsychology of Dyslexia: Differences by Gender . . . . . . . . . . . 131 Amy Nelson and Phyllis Anne Teeter Ellison 7 Sex and Gender Differences in the Assessment, Treatment, and Management of Epilepsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Elaine Fletcher-Janzen 8 Critical Issues in Chronic Illnesses of Women . . . . . . . . . . . . . . . . . . . . . . 165 LeAdelle Phelps 9 Neuropsychology of Eating Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Catherine P. Cook-Cottone 10 Aging and Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Jennifer J. Dunkin xi
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Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
Contributors
Jesse Bledsoe Department of Educational Psychology, Michigan State University, East Lansing, MI 48825, USA Elaine Clark Department of Educational Psychology, University of Utah, Salt Lake City, UT 84112-9255, USA Catherine P. Cook-Cottone Department of Counseling, Educational and School Psychology, University at Buffalo, SUNY, Buffalo, NY 14260, USA Jennifer J. Dunkin UCSD Department of Psychiatry, VA San Diego Healthcare System Psychology, San Diego, CA 92161, USA Elaine Fletcher-Janzen Private Practice, Cleveland, Ohio, USA Jodene Goldenring Fine Department of Educational Psychology, Michigan State University, East Lansing, MI 48825, USA Amy Nelson Department of Special Education, University of Wisconsin-Milwaukee, Milwaukee, WI, USA Nancy L. Nussbaum Austin Neuropsychology, University of Texas at Austin, Austin, TX 78705, USA LeAdelle Phelps Department of Special Education, University at Buffalo, SUNY, Buffalo, NY 14260-1000, USA Janiece L. Pompa Department of Educational Psychology, University of Utah, Salt Lake City, UT 84112-9255, USA xiii
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Margaret Semrud-Clikeman Departments of Psychology and Psychiatry, Michigan State University, E. Lansing, MI 48824, USA Katherine N. Shepard University of Texas at Austin, Austin, TX 78751, USA Phyllis Anne Teeter Ellison Professor Emeritus, University of Wisconsin-Milwaukee, Aiken, SC 29803, USA
Chapter 1
Introduction to the Neuropsychology of Women Elaine Fletcher-Janzen, Margaret Semrud-Clikeman, Nancy L. Nussbaum, Phyllis Anne Teeter Ellison, Elaine Clark, LeAdelle Phelps, Jennifer J. Dunkin, and Catherine P. Cook-Cottone
It is becoming increasingly understood in the fields of medicine and neuropsychology that health and illness are sex and gender specific in causation, response, and systems. The historical focus on sex- and gender-“neutral” research and care has contributed to current disparities among outcomes for women. Currently, contemporary models of professional training are increasingly inclusive of cultural competencies and also interdisciplinary in nature, with assessment of competency based upon observable outcomes (WHEO, 2008). The Association of Professors of Gynecology and Obstetrics Women’s Health Care Competencies Project (APGO, 2008) is an example of a new systemic understanding that “by identifying and addressing gender inequalities throughout medical school curricula today, we will substantively improve women’s health in the future” (WHEO, 2008, p. 1). As the field of medicine moves toward the understanding of gender and sex differences in the prevention, diagnosis, management, and treatment of health disorders in an organized fashion, so should the field of neuropsychology. This volume of work, The Neuropsychology of Women, serves as an introduction for professionals to the issues surrounding differential diagnosis, assessment, research, and treatment of women with conditions that require neuropsychological inquiry.
Research There are multiple reasons why sex and gender contributions have not historically been included in general neuroscientific analyses. Years ago, research studies on the brain many times did not include female subjects because of risk of reproductive harm or because it would make the studies “too complicated” (Society for Women’s Heath Research, 2008). In imaging studies, for example, most of the samples were men, and females were excluded because of important concerns about reproductive safety. For this reason many imaging studies were helpful for understanding
E. Fletcher-Janzen (B) Private Practice, Cleveland, Ohio e-mail:
[email protected] E. Fletcher-Janzen (ed.), The Neuropsychology of Women, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-76908-0 1,
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the male physiology and brain but were not helpful for understanding women. Initially it was believed that findings for men would map onto what was present in the female brain. There is a growing body of research in the fields of psychology, neuropsychology, and related fields where gender is now considered as an independent variable to study versus an extraneous variable for which to control. Early research with psychiatric and neurological patients emphasized the disease, and ecological validity regarding sex differences was assumed. Recently, studies about differences in metabolism, hormones, response to medication, and structure in both the female heart and brain have found this assumption to be faulty. Hence, national efforts have been made to incorporate sex and gender issues into research and treatment across the board. The Society for Women’s Health Research (2008) summarizes a list of milestones that have occurred in the efforts to have women adequately represented in medical health research: 1977 The Food and Drug Administration (FDA) bars women of childbearing potential from participating in most early-phase clinical research. 1985 A United States Public Health Service task force concludes that inclusion of women from clinical research was detrimental to women’s health. 1986 The National Institutes of Health (NIH) adopts guidelines urging the inclusion of women in NIH-sponsored clinical research. 1990 The Society for Women’s Health Research is founded and asks the General Accounting Office (GAO) to examine whether NIH is following its 1986 guideline. 1990 AGAO report reveals that the NIH guidelines are not being followed. The Physician’s Health or “aspirin” study, designed to examine the impact of taking aspirin on cardiovascular disease, is one of many large studies not including women highlighted by the report. 1993 The NIH Revitalization Act of 1993 mandates that the NIH must ensure that women and members of minorities and their subpopulations are included in all human subject research. 1993 The FDA rescinds earlier guidelines barring the participation of women with childbearing potential from most early-phase research 2000 The GAO concludes that although women are now included in clinical research proportionate to their representation in the population, analysis by sex of subjects is rare. 2001 The GAO concludes FDA was not effectively monitoring research data to determine how sex differences affect drug safety and effectiveness. (p.1) Efforts such as these reflect a growing awareness that sex and gender differences in disease and health directly relate to outcomes.
Competencies The American Psychological Association states general guidelines for professional practice in the Code of Ethics (APA, 2008). Practices regarding the analyses of sex
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differences in research, treatment, and outcomes expectations are addressed in the Boundaries of Competence section:
Competence Boundaries of Competence • (a) Psychologists provide services, teach, and conduct research with populations and in areas only within the boundaries of their competence, based on their education, training, supervised experience, consultation, study, or professional experience. • (b) Where scientific or professional knowledge in the discipline of psychology establishes that an understanding of factors associated with age, gender, gender identity, race, ethnicity, culture, national origin, religion, sexual orientation, disability, language, or socioeconomic status is essential for effective implementation of their services or research, psychologists have or obtain the training, experience, consultation, or supervision necessary to ensure the competence of their services, or they make appropriate referrals, except as provided in Standard 2.02, Providing Services in Emergencies. While the APA competencies are laudable and give directives to neuropsychologists and other psychology professionals, they do not really articulate competencies that can be measured, and on the surface, it would seem difficult to do so. However, in 2004, the Association of Professors of Gynecology and Obstetrics (APGO, 2008) published a document that created a specific mandate for health care professionals to be trained in all aspects of women’s health care. The document was focused on the education of physicians but also seems apt in many ways for neuropsychology professionals. The document Women’s Health Care Competencies for Medical Students: Taking Steps to Include Sex and Gender Differences in the Curriculum outlines, in detail, specific competencies for health care professionals that address all areas of physical and social functioning. Graduates of medical schools are expected to be able to I. Explain sex and gender differences in normal development and pathophysiology as they apply to prevention and management of diseases. II. Effectively communicate with patients, demonstrating awareness of gender and cultural differences. III. Perform a sex-, gender-, and age-appropriate examination. IV. Discuss the impact of gender-based societal and cultural roles, and context on health care and on women V. Identify and assist victims of physical, emotional, and sexual violence and abuse. VI. Assess and counsel women for sex- and gender-appropriate reduction of risk, including lifestyle changes and genetic testing.
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VII. Access and critically evaluate new information and adopt best practices that incorporate knowledge of sex and gender differences in health and disease. VIII. Discuss the impact of health care delivery systems on populations and individual receiving health care. The curriculum itself is an 89-page document that outlines specific learning objectives, levels of competencies in each objective, appropriate evaluation methods, and references to scientific articles that illuminate the objective. Such a document is a major step forward in actualizing health care professional competence in treating women. The competencies outlined in the curriculum offer guidance to neuropsychologists and can be adapted easily for neuropsychological research, assessment, intervention, and outcome evaluation. A copy of the curriculum can be found in Appendix 1 for those readers who are interested in learning about the extent of professional practice boundaries that surround sex and gender differences.
Concerns About Sex/Gender Differences in Neuropsychology Practice Brain Development For the most part, the extent to which gender differences in brain development can be linked to cognitive and behavioral differences is still very much under study. This avenue of neuroscience holds promise for educational practices, early interventions, and comprehensive treatment programs for children with biogenetic and acquired brain-related disorders. The current research literature examines the physiological differences between men and women with regard to functional brain organization; cerebral metabolism rates and differences in mean hemispheric blood flow; differences in metabolization of medications; and the implications of these differences for recovery in women compared to men. Whether estrogen and progesterone in women constitute a protective factor in injury and recovery is also an area of great interest. Future studies will no doubt explore this linkage with better neuroimaging technologies and measures of neuropsychological functioning.
Traumatic Brain Injury The effect of traumatic brain injury on women has assumed renewed importance, given the fact that many female soldiers have incurred blast injuries in the current wars in Iraq and Afghanistan. It is important to review the research regarding the etiology and incidence of traumatic brain injury in women of different ages; variables influencing the outcome of traumatic brain injury in women; and the extent of
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physical recovery from traumatic brain injury in women. Research on psychosocial and vocational outcomes and the development of psychological symptoms following traumatic brain injury, including depression, anxiety, and post-traumatic stress disorder, is essential.
Chronic Illness There are several determinants of sex and/or gender differences that occur in chronic illness: Sex chromosomes can malfunction, some disorders naturally occur far more commonly in one gender or the other, and there are diseases that are directly related to the sex organs. Two disorders that affect far more females than males are fibromyalgia and multiple sclerosis. The biological bases for such differences are likely related to the sex hormones. For example, fibromyalgia has a female-to-male incidence ratio of 9:1, which is likely related to the effect of estrogen on the hypothalamicpituitary-adrenal axis (HPAA) and autonomic nervous system (ANS). Adult men have higher HPAA and autonomic responses than women of childbearing age, but before puberty and after menopause, the sex differences are small. This is congruent with fibromyalgia occurring primarily in adult women of childbearing age. By comparison, multiple sclerosis has a female-to-male ratio of 3:1. Although it has been estimated that genetics account for 20–50% of the disease probability, large prospective studies have documented a significant reduction in disease activity during pregnancy. The physiological basis that provided such protection during pregnancy was related to the alteration of estrogen and progesterone levels. Numerous questions remain regarding genetic etiology, environmental risk factors, and treatments that affect the progression of chronic illnesses that affect predominately women.
Dyslexia The extent to which gender differences exist in individuals with dyslexia is still unresolved, as research shows similarities as well as differences among females and males. Current evidence of gender disparities in identification rates for special education shows the greatest differences in LD and emotional disturbance where boys represent 73% and 76% of students in each category, respectively. The reasons for these disparities, particularly reading disabilities (RD), should be explored, including explanations emphasizing teacher–referral biases, statistical artifacts, biogenetic and neurological differences. Several underlying factors may explain gender differences in RD including connections between biology and environment. Specifically, differences in early literacy environment and instruction may ultimately affect the development of neural networks involved with reading, accounting for gender differences.
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Attention-Deficit Hyperactivity Disorder Although attention-deficit hyperactivity disorder was once thought of as a predominantly male disorder, extant research suggests that the number of women with ADHD may be nearly equal to that of men with the disorder (Faraone et al., 2000). Due to the greater awareness that ADHD affects men and women at almost equivalent rates, researchers have begun to explore gender differences in the biology, presentation, and treatment of females with ADHD. The majority of research clearly indicates significant gender differences exist in the prevalence and clinical course of ADHD. Compared to males with ADHD, females with ADHD are more prone to have difficulties with inattentive symptoms than hyperactive and impulsive symptoms. Females often receive a diagnosis of ADHD significantly later than males (Gaub & Carlson, 1997; Gershon, 2002). While this may represent a true difference in the developmental course of ADHD in women, it seems more likely that females display significantly less “acting out” symptoms and are therefore not referred for services. In addition, emerging evidence suggests that significant gender differences exist in the neuropathology of ADHD (Ernst et al., 1994). Finally, Quinn and Nadeau (2002) have hypothesized that hormonal factors may play an important role in understanding ADHD in females. Although research unequivocally demonstrates that females with ADHD differ from males with ADHD in important ways, little research exists that evaluates gender differences in treatment response. Given the significant gender differences in presentation and developmental course of ADHD, it is essential that both clinical practice and research be informed by awareness of these differences.
Epilepsy The current research literature on epilepsy clearly points to consideration of gender and sex differences in all phases of management of seizures. There is a significant interaction between seizure control and the different reproductive cycles in women’s lives. Women with epilepsy have life-long concerns about the effectiveness of birth control, anticonvulsant effectiveness during pregnancy, birth defects associated with medications, and seizure frequency changes in menopause (to name just a few concerns) mainly due to the seizure agonist properties of estrogen and the antagonist properties of progesterone. Treatment protocols for physicians and neuropsychologists have to be adjusted not only for the biological issues associated with hormone stimulation/regulation and ictal activity, but also for the quality-oflife issues that surround important issues such as the decision to have children. An understanding, of both sex and gender differences in assessment, treatment, and ongoing management, is essential for good outcomes for women with epilepsy.
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Aging In terms of cognitive aging, gender effects on cognition and brain function depend on whether one is looking at normal aging, or aging in the absence of a disease process, versus pathological aging, or aging in the presence of disease. Gender differences in normal brain aging appear to be relatively small and inconsistent. Some studies have found that females have a relative advantage in the aging process in that they display less overall brain volume loss than males. This has led some researchers to suggest that aging is somehow “kinder” to women, at least in terms of volume loss. Certainly, women have longer life expectancies than males and less overall disease burden. However, newer studies have failed to replicate this finding, and studies looking for age by gender interactions on neuropsychological testing have failed to find consistent differences that could not be explained by males’ relatively greater degree of cerebrovascular disease. On the other hand, clear gender differences emerge when looking at rates of neurodegenerative disorders that affect primarily aging individuals. Women are at greater risk of Alzheimer’s disease, even after taking into account their greater proportions in elderly cohorts. Hormonal effects, genetic differences, and differences in life experiences such as exposure to education (a protective factor) all may play an important role. Males are at greater risk of other age-related cognitive disorders that may be related to their greater risk of cardiac disease and greater exposure to environmental toxins through occupation. Obviously, it has only been that when women were included in studies of cognitive aging, or when gender was actually investigated as a variable of interest, that these gender differences emerged and could be fully explored. Understanding these gender effects has led to a greater understanding of the mechanisms of these devastating illnesses and has been beneficial to outcomes for both genders.
Eating Disorders Eating disorders are difficult-to-treat, chronic, clinical disorders that place patients at considerable medical risk (e.g., van Hoeken, Seidell, & Hoek, 2003). Distinct from other disorders and medical complications in which much of the research has focused on primarily male populations, eating disorder researchers have studied predominantly female populations (Cook-Cottone & Phelps, 2006; Dobson & Dozois, 2004). In fact, eating disorder prevalence rates have the highest femaleto-male ratio of any psychiatric disorder (see prevalence rates, The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IVTR), American Psychiatric Association [APA], 2000). There have been only limited grant dollars allotted for research, and comparably few studies have been conducted. Tchanturia, Campbell, Morris, and Treasure (2005) reported that there have been
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fewer neuropsychological studies of eating disorders than of any of the other major psychiatric disorders. Definitive research is further complicated by heterogeneity in classification systems, inconsistent construct definition, and the lack of effective assessment tools. Most neuropsychological research has been done with patients with anorexia nervosa (AN) with few and less conclusive findings in the area of bulimia nervosa (BN). Anorexia nervosa has been associated with alterations in overall intellectual functioning, visuospatial processing, psychomotor speed and speed of processing, and somatosensory processing (e.g., Duchesne et al., 2004; Gillberg, Rastam, Wentz, & Gillberg, 2007). Patients with AN have also demonstrated deficits in attention (sustained attention/vigilance) as well as memory (short term, long term, and working memory; Duchesne et al., 2004). Symptomatology has also been associated with alterations alexithymia and in implicit and explicit memory for words related to shape, weight, and eating. Beyond memory, learning processes may be involved in AN manifestation including implicit learning capabilities as well as planning and problem-solving concerns (e.g., Steinglass & Walsh, 2006; Subic-Wrana, Bruder, Thomas, Lane, & Kohle, 2005). Bulimia nervosa is considered a culture-bound syndrome with symptoms presenting as more context-dependent. Symptoms are theorized to be associated with the aggregation of environmental and ontological influences (Cook-Cottone, 2006). There are few published studies and of those many are less conclusive when compared to AN findings. Neurobiological findings have implicated serotonin, peptide, and hormonal dysfunction (e.g., Steiger & Bruce, 2007). Neuropsychological studies suggest deficits in impulsivity, attention, visuospatial processing, concept formation, problems solving, and psychomotor speed (e.g., Dukarm, 2005; Steiger, Lehoux, & Gauvin, 1999). Researchers agree that more investigation is needed as there remain many questions still unanswered within the basic neuropsychological domains for both AN and BN.
Summary Health care professionals, and the institutions that fund and train them, are becoming more and more aware of the importance of including an examination of gender and sex differences in research, diagnosis, assessment, treatment, and outcomes measurement. The minimum standards of practice for physicians, for example, are outlined in Appendix 1 and represent a holistic and pervasive sense of competency in training and practice. Neuropsychological practice should be no less sensitive and, indeed, is generally directed to be so in many practice standards and codes of ethics. For the most part, technological and protective mandates influenced early neuroscientific and neuropsychological research efforts. It was simply not safe to include women of reproductive age into imaging studies, for example. However, technological advances have essentially eliminated or managed this concern. Advances in genetics and mapping of the human genome have also brought forward new
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understanding of sex differences in the incidence and course of disease and health. In aging research, for example, it is now regular practice to determine gender and sex differences in the context of pathology and wellness. Indeed, even the fact that we now have to differentiate between sex differences (genetic/biological determinants) and gender differences (behavioral, social, psychological, cultural) in manifestations of disease suggests that we understand that brain and behavior relationships can be reflections of those same differences. This volume is a seminal attempt to begin the codification of sex and gender perspectives of neuropsychology practice. This is probably the first time in the history of neuropsychology as a field that we have just enough perspective to be able to start synthesizing data on this subject. Therefore, there is much to be done, and more will get done if we keep competencies about sex/gender differences in our awareness, and either we try to adopt training and practice competency guidelines or the field of neuropsychology develops its own. The reason to study sex/gender differences, in any aspect of neuropsychological study, is to support the actualization of better treatment outcomes and quality of life for female and male patients. To that end, The Neuropsychology of Women affirms: Vive la Difference!
References APGO (2008). Women’s health care competencies for medical students: taking steps to include sex and gender differences in the curriculum. www.apgo.org. Accessed 18 Feb 2008. American Psychiatric Association (APA) (2000). The diagnostic and statistical manual. fourth edition-text revised (DSM-IV-TR). Washington, DC: American Psychiatric Association. American Psychological Association (2008). APA code of ethics. www.apa.org. Accessed 18 Feb 2008. Cook-Cottone, C.P. (2006). The attuned representation model for the primary prevention of eating disorders: An overview for school psychologists. Psychology in the Schools, 43, 223–230. Cook-Cottone, C.P., & Phelps, L. (2006). Adolescent eating disorders. In G.G. Bear & K.M. Minke (Eds.), Children’s needs III (pp. 977–988). Bethesda, MD: NASP Publications. Dobson, K.S., & Dozois, D.J.A. (2004). Attentional biases in eating disorders: a meta-analytic review of Stroop performance. Clinical Psychology Review, 23, 1001–1022. Duchesne, M., Mattos, P., Fontenelle, L., Veiga, H., Rizo, L., & Appolinario, J.C. (2004). Neuropsychology of eating disorders: a systematic review of the literature. Review of Brazilian Psychiatry, 26, 107–117. Dukarm, C.P. (2005). Bulimia nervosa and attention deficit hyperactivity disorder: a possible role for stimulant medication. Journal of Women’s Health, 14, 345–350. Ernst, M., Liebenauer, L.L., King, A.C., Fitzgerald, G.A., Cohen, R.M., & Zametkin, A.J. (1994). Reduced brain metabolism in hyperactive girls. Journal of the American Academy of Child and Adolescent Psychiatry, 33, 858–868. Faraone, S.V., Biederman, J., Mick, E., Williamson, S., Wilens, T.E., Soencers, T., Weber, W., Jetton, J., Kraus, E., Pert, J., & Zallen, B. (2000). Family studies on girls with attention deficit hyperactivity disorder. American Journal of Psychiatry, 157, 1077–1083. Gaub, M., & Carlson, C.L. (1997). Gender differences in ADHD: a meta-analysis and critical review. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 1036–1045. Gershon, J. (2002). An overview of research. In P.O. Quinn & K.G. Nadeau (Eds.), Gender issues and AD/HD: research diagnosis and treatment (pp. 23–38). Silver Spring, MD: Advantage Books.
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Gillberg, I.C., Rastam, M., Wentz, E., & Gillberg, C. (2007). Cognitive and executive functions in anorexia nervosa ten years after onset of eating disorder. Journal of Clinical and Experimental Neuropsychology, 29, 170–178. Quinn, P.O., & Nadeau, K.G. (2002). Gender issues and ADHD. Silver Spring, MD: Advantage Books. Steiger, H., & Bruce, K.R. (2007). Phenotypes, endophenotypes, and genotypes in bulimia spectrum eating disorders. La Revue canadienne de psyciatrie, 52, 220–227. Steiger, H., Lehoux, P.M., & Gauvin, L. (1999). Impulsivity, dietary control and the urge to bingeing bulimic syndromes. International Journal of Eating Disorders, 26, 261–274. Steinglass, J.E., & Walsh, B.T. (2006). Habit learning and anorexia nervosa: a cognitive neuroscience hypothesis. International Journal of Eating Disorders, 39, 267–275. Subic-Wrana, C., Bruder, S., Thomas, W., Lane, R.D., & Kohle, K. (2005). Emotional awareness in inpatients of a psychosomatic ward: a comparison of two different measures of alexithymia. Psychosomatic Medicine, 67, 483–489. Tchanturia, K., Campbell, I.C., Morris, R., & Treasure, J. (2005). Neuropsychological studies in anorexia nervosa. International Journal of Eating Disorders, 37, S72–S76. van Hoeken, D., Seidell, J.C., & Hoek, H.W. (2003). Epidemiology. In J.L. Treasure, U. Schmidt, & E.F. van Furth (Eds.), Handbook of eating disorders (pp. 11–34). Chichester: Wiley. WHEO (2008). Women’s Healthcare Education Office (WHEO): Women’s health care competencies for medical students. http://wheocomp.apgo.org/importance.cfm. Accessed 18 Feb 2008.
Chapter 2
Brain Development: Evidence of Gender Differences Phyllis Anne Teeter Ellison and Amy Nelson
Brain development may be one of the most exciting frontiers of the neurosciences, where neuroimaging, genomics, neurobehavioral, longitudinal, and animal research are converging for remarkable discoveries. New medical technologies allowing for structural and functional magnetic imaging (fMRI) and success in mapping the human genome have advanced our understanding of typical and atypical neurodevelopment, including gender differences from infancy into adolescence and adulthood. The impact of hormonal influences is being more thoroughly explored, particularly their influences on cognitive, executive functions, and emotional behavior. The past 15 years have yielded extraordinary findings on the brain–behavior relationship and provided avenues for further exploring the gene–behavior link across genders. In the 1990s, the “Decade of the Brain” marked one of the most ambitious undertakings of scientists from the United States. Research findings from this remarkable decade included new discoveries in brain plasticity, specifically that the brain creates new cells later into life than previously known, and that well over half of the genome is dedicated to brain-related cells (National Institutes of Mental Health Authors, 2007). While brain growth and maturation sets the foundation, ultimate human potential lies in the interaction between environmental experiences and genetic influences on the developing brain. This chapter explores the current research on brain development, including new findings on gender differences. Males and females appear to have meaningful differences in brain maturation that have interesting applications for the way in which we educate and nurture our youth. Here, a brief overview of brain development will be presented followed by a review of new research on gender differences.
P.A.T. Ellison (B) University of Wisconsin-Milwaukee, 152, Highland Reserve Court. Aiken, SC. 29803 e-mail:
[email protected] E. Fletcher-Janzen (ed.), The Neuropsychology of Women, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-76908-0 2,
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Early Brain Development: Prenatal Milestones While the masterful unfolding of brain development is scripted in the genetic code, numerous prenatal, peri-natal, and postnatal factors influence this process. At birth, the average human brain weighs approximately 400 g and grows to approximately 1350–1410 g (about 3 lbs) in adulthood (Kolb & Whishaw, 2003). It is estimated that there are “100,000 billion neurons, each receiving input from an average of 10,000 others, that’s 1015 connections – an information-processing network of staggering complexity, responsible for our perceptions, thoughts, emotions, intelligence, language, and creativity” (Blakemore, 2000, p. 1). How this process unfolds has been well documented, especially the development of the cerebral cortex (Carlson, 2005). Bartley, Jones, and Weinberger (1997) describe brain development as a dynamic process where biogenetic factors affect the overall brain size and the morphological (structural) characteristics of the brain; while non-genetic, environmental, and experiential factors affect the complex synaptic networks that develop in early childhood and well into young adulthood. Teeter Ellison and Semrud-Clikeman (2007) describe a transactional model for exploring brain development whereby genetic and environmental factors interact to influence the developing brain and subsequent cognitive, academic, and psychological functions. For successful brain development to occur, each major component or region must be well formed and becomes inter-related to other components. There is a structure– function relationship, whereby typical child development is predicated on healthy brain development. The genetic-environment interplay is integral to this relationship. Bartley et al. (1997) suggested that although altered brain structures may be accompanied by functional changes or impairment, this is not always the case. While genetics influence the range of “possible brains,” experience is the architect that shapes and influences the function. The brain undergoes predictable growth spurts during development where points of discontinuity or sharp changes in brain growth influence the function (Bartley et al., 1997). Thirteen growth spurts have been identified at 3–4 weeks, 7–8 weeks, 10–11 weeks, 15–18 weeks, 8 months, 12 months, 20 months, 4 years, 7 years, 11 years, 15 years, 19 years, and 20 years (see Chugani, Phelps, & Mazziotta, 1987; Fischer & Rose, 1996; Huttenlocher, 1994; Kolb & Fantie, 1989; Lenroot & Giedd, 2007). In addition to genetic controls, these particular cycles of brain growth are influenced by the environment and experience. We will explore the consequences of these influences later in this chapter.
Stages of Brain Development The stages of brain development occur in phases: neurulation, neurogenesis or neuronal birth, cell migration, differentiation, synaptogenesis, cell death and synaptic pruning, myelination, axonal and dendritic arborization, and competitive
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Fig. 1 Time course of critical events in the determination of human brain morphometry
neuronal elimination (Lenroot & Giedd, 2007). “Neurogenesis was originally thought to occur during embryonic life, and was therefore regarded as a part of embryology. We now realize that events related to neurogenesis and the laying down of neural circuits continue into early life: moreover, many of the properties underlying the development of neurons and their functional capacities continue to be expressed throughout adolescence and even after, and can be changed under normal conditions or in response to injury or aging” (Shepherd, 1994, p. 200). Lenroot and Giedd (2007) outline these stages and timelines. See Fig. 1 for developmental timelines.
Neurulation and Neurogenesis The central nervous system (CNS) develops early in gestation, where parts of the embryo thicken and form the neural plate within 18 days – this phase is referred to as neurulation (Carlson, 2005). The neural plate becomes the neural tube at about 22 days gestation, and forms the structural basis of the brain and spinal cord (Bear, Connors, & Paradiso, 2001). “The entire central nervous system develops from the walls of the neural tube” (Bear et al., 2001, p. 176). Neurons (brain cells) and glial cells are produced within the cells in the ventricular zone (inside the neural tube) and migrate out forming basic cells of the CNS. The neural tube closes by the 28th day of gestation and forms three chambers that eventually develop into ventricles where the three major brain areas evolve – the forebrain, the midbrain, and the hindbrain (Carlson, 2005). At about 10 weeks’ gestation, the brain is approximately 1.25 cm (0.5 in), and is comprised primarily of ventricles or hollow spaces (Carlson, 2005).
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The embryonic growth of the brain is quite spectacular where the shape of a matured brain can be seen as early as 20 weeks gestation, when it is only 5 cm in length (2 in).
Cell Proliferation and Migration Cells in the ventricular zone form neurons, “founder cells,” which divide and produce two new identical cells within 7 weeks of inception. The founder cells divide asymmetrically and migrate away from the ventricular zone (Carlson, 2005). This division lasts about 3 months, and there may be as many as one billion neurons migrating daily (Carlson, 2005). During the third and fourth months of gestation, massive neuronal proliferation occurs, with glial cells (i.e., support or guidance cells to the neurons) developing throughout the first year of postnatal life. Simultaneously, neurons begin to migrate from their site of origin, near the ventricles, to cortical regions farther away. Radial glial cells leave a chemical pathway for these migrating neurons (Rakic, 1990). The producing cells remain in place, while earlier migrating cells take about a day to reach their location. Later developing neurons, with the longest distances to travel, may take about 2 weeks to reach their final destination (Carlson, 2005). At the end of cortical development, the founder cells send chemical signals that turn on killer genes in the neurons, which cause them to die – this process is called apoptosis. Only some of the cells respond to the signal and die off.
Axonal and Dendritic Arborization, Synaptogenesis, and Myelination Once the cells reach their final destination, they continue to grow and differentiate (Teeter Ellison & Semrud-Clikeman, 2007). Axons – long extensions that develop off the soma or cell body – follow the chemical pathways that set the course or direction of growth (Brodal, 1992). Axons grow at a rapid rate even while neurons are migrating. Proteins stimulate the axonal growth patterns into specific regions. These proteins – nerve growth factor (NGF) – are present only during certain phases of development so that axons make contact with target neurons. Estimates suggest that up to 50% of axons do not find appropriate post-synaptic cells and die off (Carlson, 2005). Pre-synaptic chemical signals influence these connections allowing some axons to survive while others die off. At about 3 months’ gestation, large bundles of axons cross and form commissures, which connect different regions of the brain. Temporal lobe connections are made through the anterior commissure while the hippocampal commissure joins the two hippocampi (Reitan & Wolfson, 1985). The corpus callosum connects the right and the left hemispheres, which continue to develop after birth. These association regions are later developing structures.
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Dendrites or outward extensions of the cell body receive signals from other neurons via thousands of tiny synapses (Bear et al., 2001). Dendritic connections appear at about 5 months, become quite elaborate, and continue to develop well into childhood and adolescence (Kolb & Fantie, 1989). Dendrites are covered with thousands of tiny synapses that make connections with other dendrites, axons, and/or cell bodies. Neurotransmitters are released at the pre-synapse and enervate specialized receptor sites at the post-synaptic membrane (Bear et al., 2001). Synaptic density is inversely related to cognitive development whereby brain efficiency and functional refinement occurs as synaptic capacity declines. Selective elimination has been verified in PET studies (Caesar, 1993). A young brain has greater synaptic capacity than an adult brain, where early overproduction of synapses decreases during development with significant reductions occurring in adolescence. The development of dendritic networks is highly dependent upon experience so that neural activity shapes the connections that are made. In some cases, cognitive impairments occur in children living in impoverished environments where stimulation is inadequate, and appear to be related to a failure of normal circuits in the brain (Bear et al., 2001). While individuals with cognitive impairments have been found to have fewer dendritic spines (Purpura, 1975), there is some evidence that “deprivationinduced” abnormalities in brain development can be altered with early, intensive interventions. Many axons are covered by a myelin sheath, which serves as a protective layer and is white in appearance (Bear et al., 2001). Signal conduction occurs along the axon, and areas of constriction (Nodes of Ranvier) in the myelin allow the nerve impulses to “jump” for quicker and more efficient firing (Reitan & Wolfson, 1985). Myelination accounts for the dramatic changes in brain weight from birth to adulthood, and corresponds to increased functional abilities during early childhood and adolescent development. Brain regions myelinate at different times, and roughly correspond to changes in cognitive, motor, and language development. Primary sensory and motor regions myelinate prior to birth while secondary regions myelinate postnatally – into the 4th month after birth (Kolb & Fantie, 1989). Association and frontal lobe regions myelinate later in development and continue into adolescence and adulthood (Lenroot & Giedd, 2007).
Differentiation of Major Brain Regions Within 3 months gestation, key forebrain, midbrain, and hindbrain regions develop from three primary vesicles of the neural tube (Kolb & Whishaw, 2003). See Table 1 for an initial timeline for the development of major CNS regions and their ultimate functions. The forebrain is composed of three major regions: (1) the telencephalon – endbrain, (2) the diencephalons – between brain, and (3) the optic vesicles (Bear et al., 2001). The telencephalon divides and differentiates into the right and left cerebral hemispheres. The olfactory bulbs grow off the surface of the hemispheres and
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P.A.T. Ellison and A. Nelson Table 1 Major divisions of the central nervous system: initial developmental timeframe
Timeframe
28 days
36 days
48 days
Major divisions Forebrain
Subdivisions Telencephalon
Structures Olfactory bulb Cerebral hemispheres
Ultimate functions Sense of smell Frontal, parietal, occipital, temporal lobes Control of movement Connects right/left hemispheres Learning, memory, emotions Major sensory relay center Controls autonomic nervous system
Basal ganglia Corpus callosum Limbic system
Midbrain
Hindbrain
Diencephalon
Thalamus Hypothalamus
Optic vesicles Mesencephalon
Optic nerve Tectum Inferior/superior colliculi Tegmentum Reticular activating system Periaqueductal gray matter Red nucleus Substantia nigra Pons Cerebellum Medulla oblongata
Metencephalon Myelencephalon
Auditory/visual systems Sleep, arousal, attention, muscle tone Neural circuits for movement Motor axons Motor axons Sleep and arousal Complex movement Regulates cardiovascular, respiration, muscle tone
eventually regulate the sense of smell. Two types of gray matter are formed in the telencephalon – neurons in the cerebral cortex and the basal telencephalon, while the axons form the white matter for communication with other brain regions. The axon bundles form three systems in the forebrain (comprised of the telencephalon, diencephalon, and optic vesicles): the cortical white matter, the corpus callosum, and internal capsule. The white matter is comprised of the axons connecting neurons in various cortical regions; the corpus callosum connects the right and left hemispheres; and the internal capsule connects the cortex to the brainstem. The cerebral cortex contains four major lobes: the frontal lobe is the primary motor cortex; the parietal cortex is the primary somatosensory cortex; the occipital lobe is the primary visual cortex; and the temporal lobe is the primary auditory cortex. Major association cortices allow for intricate communication among the four lobes. The forebrain also contains the basal ganglia and the limbic system. The basal ganglia contain the globus pallidus, the putamen, and the caudate nucleus, all of which are regions in the motor system regulating the movement. The limbic system contains the hippocampus, the amygdala, and other forebrain structures for the regulation of emotions, motivations, and memory (Carlson, 2005).
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The diencephalon differentiates into the thalamus and hypothalamus, while the center of the diencephalon becomes fluid-filled spaces – the lateral and third ventricles. The thalamus is a major relay center for sensory information, including the visual and auditory systems, while the hypothalamus regulates the autonomic system, the endocrine system, and survival behaviors (feeding, fighting, fleeing, mating; Carlson, 2005). The midbrain, also called the mesencephalon, is comprised of the tectum (roof) and the tegmentum. The primary structures of the tectum include portions of the visual system (superior colliculi) and portions of the auditory system (inferior colliculi). The hindbrain is composed of the metencephalon and myelencephalon. The main structures of the metencephalon include the cerebellum (integrates auditory, visual, vestibular, and somatosensory information for the regulation of the movement) and the pons (contains portions of the reticular activating system for the regulation of sleep and attention; Carlson, 2005).
Cell Death/Synaptic Pruning More neurons are produced than are needed in the ventricular zone, which means that neurons must compete for survival (Carlson, 2005). If there are no axons to form connections, these cells die off rapidly. Once the connections are made, preand post- synaptic signals permit the cell to survive. In short, the brain is genetically coded to produce more neurons than are necessary, and these compete for synaptic connections for survival. Later arriving cells often have no such available synaptic sites and die off. From 6 months gestation to about 1 month after birth, rapid cell death occurs and up to 50% of neurons die at this stage of development. Synaptic elimination has been observed between the ages of 7 and 16 years (Huttenlocher, 1979). Synaptic pruning appears to be dramatic during adolescence, which “reflects a fine-tuning of neural connectivity, perhaps to set the stage for a more mature patterning of brain effort and efficiency” (Spear, 2007, p. 374). Spear further suggests that “the reduction in excitatory synaptic input in the cortex, the decline in synaptic connections supporting reverberating cortical circuitry, and the acceleration of information flow provided by myelination of selected axons likely contribute to the refinements in brain effort and efficiency seen in adolescence” (2007, p. 375). There are a number of prenatal and perinatal factors that impact the developmental processes described above. These are briefly discussed in the next sections.
Prenatal and Perinatal Influences on Brain Maturation There are a number of factors that significantly impact prenatal brain development including maternal health (i.e., HIV/AIDS, hypotension) and non-biological maternal factors (i.e., stress, nutrition, alcohol, and drug use/addiction). Maternal
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stress can adversely affect the developing fetus and place the infant at risk for major neurological complications, including low birth weight, while maternal hypotension can affect circulation in the developing brain (Teeter Ellison & SemrudClikeman, 2007). Other maternal health factors also have been shown to have adverse effects on the developing fetus and are associated with various childhood disorders, including rubella (German measles) with autism, depression, and mental retardation (Carlson, 2005); influenza with increased vulnerability to schizophrenia (Brown et al. 2004); and genital reproductive infections with increased risk for schizophrenia (Babulas, Factor-Litvak, Goetz, Schaefer, & Brown, 2006) [see Penner and Brown (2007) for a review]. Other conditions including prenatal thalidomide exposure, encephalitis caused by the herpes virus, and tuberous sclerosis (a genetic disorder that causes benign brain tumors) have also been found in 20% of children with autism (De Long, 1999; Rapin, 1999). Mothers with HIV (human immunodeficiency virus) and/or AIDS (autoimmune deficiency syndrome) are likely to have babies with embryonic and fetal malformations, facial deformities, and microcephaly (Berk, 1989). Babies with AIDS often die within 5–8 months (Minkoff, Deepak, Menez, & Firkig, 1987), while young children with HIV are at risk for developing sensory-motor, visual-perceptual, language, and cognitive delays (Belman et al., 1986). See Teeter Ellison and SemrudClikeman (2007) for a more in-depth discussion. Maternal drug and/or alcohol abuse has serious consequences for the developing fetal and infant brain. Cocaine crosses the placenta and affects the regions of the brain responsible for cognition, learning, memory, and attention (Aylward, 2003). Other narcotic drugs (i.e., heroin, methadone, codeine) taken during pregnancy have adverse effects on infants, including prematurity and microcephaly. Infants exposed to these drugs often show withdrawal symptoms at birth and have numerous difficulties (i.e., state dysregulation, irritability; Aylward, 2003). Alcohol exposure produces a range of deficits from fetal alcohol effects (FAE) to fetal alcohol syndrome (FAS). Cognitive, academic, and psychosocial problems have been found in children with prenatal exposure to alcohol. Children with FAE and/or FAS have also been reported to be at high risk for ADHD (Riikonen et al., 2005). Effective postnatal care provided to mothers with drug-dependence may assist the development of strong mother–child relationships and parenting skills, thereby reducing the ongoing problems associated with adverse environmental conditions (e.g., behaviors related to obtaining drugs, financial strain of paying for drugs, poor parental supervision due to active, ongoing drug use). Recently, maternal cigarette smoking has been found to be a risk factor for ADHD in the offspring (Linnet et al., 2005), even after controlling for socioeconomic status and co-morbid disorders (Dean & Davis, 2007). Milberger, Biederman, Faraone, Guite, and Tsuang (1997) found a relationship between ADHD and perinatal complications (maternal bleeding), maternal health behaviors (smoking and drug use), and family adversity. It is important to note that not all individuals with ADHD or other childhood disorders listed above have the same maternal health factors; however, there seems to be some environmental risk factors that make children vulnerable to these disorders.
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In addition to prenatal influences, perinatal risk factors have been found to impact infant brain development, particularly hypoxic-ischemic encephalopathy (HIE; Aylward, 2003). Hypoxemia refers to reduced oxygen in the blood; brain hypoxia is reduced oxygen to brain tissue; ischemia refers to a reduction in the blood flow to the brain; asphyxia is a disturbance in the oxygen–carbon dioxide exchange due to some disruption in respiration, while anoxia is total lack of oxygen (see Alyward, 2003). Asphyxia can be measured by Apgar scores, which reflect the child’s heart rate, respiration, and muscle tone, but care should be taken not to over-interpret these scores. Aylward (2003) cautions “Apgar scores have been misused and are not predictive of subsequent outcome. Apgar scores should simply be considered indicative of the infant’s condition during and immediately after birth” (p. 258). Asphyxia, a precursor to HIE, can cause cell death in various brain regions (i.e., cerebral cortex, diencephalons, brainstem, and cerebellum), and has been found to be a major cause of neuro-developmental disorders, including microcephaly, epilepsy, cognitive and motor deficits, and cerebral palsy (Aylward, 2003). Severe HIE is associated with negative cognitive and academic outcomes resulting from multiple complications (i.e., coma, seizures, flaccid muscle tone, and suppressed brainstem functions). Pre-term babies are susceptible to cell death in the white matter of deep brain structures involved with sensory-motor functions. The consequences of HIE in pre-term infants vary depending on the level or site of the insult. Aylward (2003) provides an in-depth discussion of these outcomes. Perinatal complications are associated with infantile autism, including intrauterine stress, gestational age at birth, maternal morphology, abnormal delivery, low birth weight, maternal prescription use during pregnancy, and maternal viral infections (Wilkerson, Volpe, Dean, & Titus, 2002; Dombrowski et al., 2007). See Dean and David (2007) for a more in-depth discussion. Other maternal health factors (e.g., hypertension) during pregnancy increase the risk for anxiety disorders in the offspring (Hirshfield-Becker et al., 2004). Maternal depression and anxiety also increase the risk for these disorders in children. Obviously, genetic factors contribute to this risk, but psychological problems can interfere with optimal caretaking by mothers suffering from mental illness. Effective treatments for maternal depression have been found to reduce the risk in the offspring (see Dawson, et al., 1992).
Postnatal Influences on Brain Development Shortly after birth, the brain creates trillions of connections between neurons. The brain begins to eliminate these connections if they are seldom used or stimulated. Dramatic synaptic pruning occurs in a highly competitive manner where atrophy and loss of connections is influenced by the “use it or lose it” phenomenon (Bear et al., 2001). Further pre-synapatic axons activated at the same time as post-synaptic sites form strong connections – “neurons that fire together wire together” (Bear
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et al., 2001, p. 731). Conversely, strong pre-synaptic patterns with weak postsynaptic activation result in weak connections, which are eventually eliminated. “Synaptic rearrangement” refers to a change in the pattern of connections between neurons. “. . .Synaptic rearrangement occurs as a consequence of neural activity and synaptic transmission. In the visual system, some of the activitydependent shaping of connections occurs prior to birth in response to spontaneous neuronal discharges. Significant activity-dependent development occurs after birth, however, this process is profoundly influenced by sensory experience during childhood. Thus, we will find that ultimate performance of the adult visual system is determined to a significant extend to the quality of the visual environment during the early postnatal period. In a very real sense, we learn to see during a critical period of postnatal development” (Bear et al., 2001, p. 723). Aylward (2003) describes critical periods as times when specific influences must be present for child development to occur normally. “A sensitive period is the time during which the CNS is highly susceptible to the effects of harmful or deleterious internal or external conditions” (Aylward, 2003, p. 256). Critical periods appear to be more time-specific while sensitive periods are more variable. Postnatal experiences influence the developing brain, including birth complications, environmental toxins, and stressful environments. Birth complications during labor and delivery have been shown to result in early brain damage. Specifically, long labor, oxygen deprivation, breech delivery, abruption placenta, low Apgar scores, vacuum extraction, meconium staining, very low or high birth weight, and placenta infarcts may result in subtle or more severe abnormalities across the lifespan (see Alyward, 2003; Teeter Ellison & Semrud-Clikeman, 2007). Environmental toxins also have an adverse effect on early brain development, particularly exposure to high levels of lead. Poor nutrition and vitamin deficiencies (vitamin B1, B12 , folic acid) directly affect brain development, including myelination. Social experiences, particularly negative emotions and emotional deprivation, affect the developing stress-response systems, including the sympathetic adrenal medullary (SAM, fight/flight system) and the hypothalamic-pituitary-adrenal axis (HPA), which counteract or suppress acute stress reactions (Adam, KlimesDougan, & Gunnar, 2007). Cortisol – a hormone in the HPA system – has been found to both sustain brain development and/or be detrimental to the neurons. “More recently, the link between early experience, brain development, and both normal and disordered functioning has become increasingly evident and better understood, due largely to evidence that early experience (especially deprivation experiences) reduces neural plasticity to stress experienced later in life (e.g., Mirescu, Peters, & Gould, 2004) and even permanently silences genes critical to the regulation of the stress response (e.g., Weaver et al., 2004)” (Adam et al., 2007, p. 266). Changes in cortisol levels occur in three stages – anticipating the stressor, reacting to the stressor, and recovering to pre-stress levels. Secure early attachment to the primary caregiver buffers cortical responses in stressful situations; thus, the HPA system comes under social regulation. Sensitive and responsive care buffers cortisol levels and ultimately reduces distress in infants, while high-conflict family environments have negative effects on stress responses. “Although parents can clearly serve as
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buffers on the effects of social environments on young children’s HPA-axis, they can also serve as a profound source of social strain if their behavior is threatening or fails to provide appropriate comfort” (Adam et al., 2007, p. 274). Children exposed to severe deprivation (e.g., institutions) and those who experience physical and sexual abuse are at increased risk for long-term alterations of the HPA system. Peer interactions in childhood and adolescence also appear to affect cortisol levels in both fearful, anxious children and under-controlled children. Peer rejection, stressful social interactions, social isolation, and chronic social strain affect cortisol levels in children and adolescents. Individual differences in HPA-axis activity have been found in youths with internalizing and externalizing disorders, memory and cognitive deficits, and educational performance. Positive early childhood and later school environments (i.e., quality child care, safe schools) can buffer the adverse effects of high cortisol levels (see Adam et al., 2007). Biological and environmental risk factors interact in meaningful ways to affect the overall development. “Medical and biological factors were found to determine whether a developmental problem occurred, but environmental factors have a tempering or exacerbating effect on the degree of the problem. . . The biomedical and environmental risk interaction becomes highly complex when one considers school and functional outcomes. In general, biomedical variables are related to neurological, neuromotor, neuropsychological, and perceptual–performance functions. Environmental factors are more strongly associated with verbal, academic, and IQ measures” (Aylward, 2003, p. 262). Later outcome appears to be related to child resiliency, which can be improved with supportive, caring parent–child interactions, social support for the family, and quality early childhood interventions.
Plasticity and Neuroplasticity in the Developing Brain The brain has remarkable properties allowing for reorganization or plasticity following an injury, particularly prior to the age of 10 years for language and between 8 and 10 years for the visual system (Chugnani, 1996). Researchers have investigated cerebral glucose metabolism to measure developmental patterns of maturation and brain plasticity. Local cerebral metabolic rates for glucose (LCMRglc) in normal children show patterns similar to that in adults at about 1 year of age, but changes in brain metabolism occur in a non-linear pattern. Neonatal brains show about 30% lower glucose flow as compared to adult brains, but it increases dramatically by 2 years of age. LCMRglc increases in early childhood, hits a plateau at the age of 4 years, which extends into middle childhood (9–10 years of age), and reaches the adult levels at about 16–18 years (Chugnani, 1996). The relative decline in glucose metabolism corresponds to the phases of elimination of synapses and the pruning of dendrites described by Huttenlocher. “Clinically, there appears to be a relationship between diminishing brain plasticity in children and the gradual decline of LCMRglc.” (Chugnani, 1996, p. 192)
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Prior research suggested that plasticity occurred only in early brain development – new evidence suggests that this may not be the case (Bear et al., 2001). Recent studies have found that neurogenesis – the birth of new neurons – continues into adulthood. Jacobs, van Praag, and Gage (2000) found that adult brains continue to grow new neurons particularly in the dendate gyrus of the hippocampus. However, stress adversely affects neurogenesis. Apparently high levels of glucocorticoid interfere with the birth of new brain cells. This relationship can be reversed when serotonin levels are increased. Jacobs et al. (2000) further suggest that depression may result from stress-related decreases in the birth of neurons in the hippocampus, and that recovery from depression can be enhanced with serotonin. This brief overview of brain development provides a context for investigating gender differences. Current research provides insight into the brain development of males and females in early childhood and adulthood.
Gender Differences in Brain Development Longitudinal research using magnetic resonance imaging (MRI) scans allows for unprecedented opportunity to study brain growth and changes in the structural morphology of healthy children. Scientists from the NIH and the Child Psychiatry Branch of the National Institute of Mental Health (CPB/NIMH) have scanned the brains of 145 healthy children at 2-year intervals (Giedd et al., 1999; Lenroot & Giedd, 2007). The first phase of the study, initiated in 1989, investigated participants between 4 and 25 years of age. The cross-sectional design was expanded in later study phases so that a longitudinal investigation of brain development was possible, and gender differences could be explored. Currently, 3600 scans have been completed on 1800 subjects.
Structural Variations in Brain Development: Differences Between Females and Males Total cerebral volume increases over time and reaches 95% of maximum brain size by the age of 6 years (Lenroot & Giedd, 2007). Absolute brain size does differ between males and females, with males having approximately 9% greater brain volume than females. Lenroot and Giedd caution that variations in absolute brain size do not imply functional differences in healthy brains, where children of the same age may show up to 50% variation in volume. In general, the NIMH longitudinal study showed linear increases in white matter between 4 and 20 years of age, and non-linear changes in cortical gray matter (Giedd et al., 1999; Lenroot & Giedd, 2007). White matter showed linear increases with age, with males showing greater increases than females. The developmental curves did not differ across various brain structures or regions, showing about a 12.4% increase from the age of 4 to 22 years.
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Of particular interest were regional and non-linear changes in gray matter over time (Giedd et al., 1999; Lenroot & Giedd, 2007). Increases in gray matter were significant in pre-adolescence followed by a rapid decrease in post-adolescence. Regional differences were found in the frontal and parietal regions, which peaked at about the age of 12 years, and in the temporal regions, which peaked at about the age of 16 years, with occipital gray matter continuing to increase through the age of 20 years. Frontal gray matter increased during pre-adolescence followed by a decrease in post-adolescence, with peak size at 12.1 years for males and 11.0 years for females (see Table 2). There was a decrease in total volume in frontal gray matter in post-adolescence. Parietal regions showed a similar growth curve, with females reaching peak growth at 10.2 years and males at 11.8 years. Pre- and post-adolescent changes were also found in total parietal lobe volume, with steady increase followed by a net decrease. The growth curves in the parietal regions were steeper than those measured in the frontal regions across the same age-span. Temporal lobes were later developing, with peak gray matter growth at 16.5 years for males and 16.7 years for females, followed by a slight decline in post-adolescence (Giedd et al., 1999; Lenroot & Giedd, 2007). Occipital regions showed linear gray matter increase over time with no post-adolescent decline. These variations in growth curves were significantly different across regions, with frontal and parietal regions showing the most similar growth curves. While total gray matter volume was about 10% greater in males, the growth curves were similar between genders. A summary of the major findings of this study suggest gender differences in brain development. Lenroot and Giedd (2007) found that cortical gray matter loss appears to occur first in regions controlling basic motor and sensory functions (primary sensorimotor regions), and later in association regions (dorsolateral pre-frontal cortex, DLPFC) and the superior temporal gyrus (STG). The temporal regions are involved with integrating memory, audiovisual, and object recognition, while the DLPFC controls impulses, judgment, and decision-making. Other developmental changes were found in regions controlling emotions, language, and memory functions – temporal lobes, amygdala, and hippocampus, respectively (Lenroot & Giedd, 2007). Males showed significant volume increases in the amygdala between 4 and 18 years of age, while females showed significant increases in the hippocampus. Emotional and affective regulation is highly dependent on the frontal networks connecting with the subcortical regions, including the amygdala. Table 2 Summary of gender differences in brain development using NIH longitudinal data Structures
Female age
Male age
Total volume peaks Frontal lobe gray matter Temporal lobe gray matter Parietal lobe gray matter Caudate nucleus (muscle tone and movement)
11.5 years 11.00 years 16.7 years 10.2 years 7.5 years
14.5 years 12.1 years 16.2 years 11.8 years 10.0 years
In general, males have 9% larger brains than females. See Giedd (2004) and Lenroot and Giedd (2007).
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Interesting gender differences were also found in the subcortical regions where females show early peak growth in the basal ganglia (specifically the caudate nucleus), which plays an important role in the control of movement and has some influence over higher cognitive functions like attention and affective states (Graybiel & Saka, 2004). Growth curves in the caudate peak at 7.5 years for females, while for males they peak at 10.0 years (Lenroot & Giedd, 2007). It is unclear if these gray matter increases are a result of neuronal, axonal, or dendritic changes. “If the increase is related to a second wave overproduction of synapses, it may herald a critical stage of development when the environment or activities of the teenager may guide selective synapse elimination during adolescence.” (Giedd et al., 1999, p. 863) Furthermore, early developmental peaks for females in the frontal and parietal regions suggest hormonal influences that correspond to early onset of puberty. According to Giedd et al., a study of the gene by hormone–environment interaction may prove to be helpful in understanding gender variations in psychiatric disorders. Further, Spear (2007, p. 373) suggests that “among the brain regions undergoing dramatic transformations during adolescence are mesocortical and mesolimbic regions of the forebrain that form part of the neural circuitry modulating executive functions and affect regulation and that influence the value attributed to motivationally relevant stimuli, including novelty seeking, alcohol, and other drugs.” Experiences during adolescence may have both positive and/or negative effects on the developing brain that last well into adulthood. “Adolescence may be a vulnerable period not only because of the high prevalence of risk taking, but also because of the potential lasting consequences of perturbations to the brain as it is sculpted during this time. For instance, brain regions undergoing particularly marked remodeling during adolescence (e.g., PFC, amygdala, nucleus accumbens) are among those that are most sensitive to alcohol and/or other drugs of abuse. This fact raises the possibility that exposure to alcohol or other drugs during adolescence may alter ongoing processes of adolescent brain development, with a long-term impact on neurobehavioral function in adulthood” (Spear, 2007, p. 383). On the other hand, teens exposed to beneficial, stimulating, enriched environments may have the opposite effect. The extent to which we can provide healthy environments for youths may be critically important for long-term adult outcome because of the protective impact on the brain. There is strong evidence that there are male/female differences in the developmental growth patterns, but to what extent are gender differences found in the brain structures and functions? This dynamic issue is considered next.
Structural Dimorphism: Do Male and Female Brains Differ? Male and female differences in brain structures, or sexual dimorphism, have been inferred due to the distinct behavioral differences across genders (Bear et al., 2001). Is there evidence that there are two distinct male and female brains? While data on
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gender differences are still controversial, recent research reports interesting trends that are noteworthy (see Goldstein et al., 2001). The most consistent findings across studies investigating sexual dimorphism are that males have significantly larger brains than females (Filipek, Richelme, Kennedy, & Caviness, 1994; Witelson, Glezer, & Kigar, 1995). When measuring the total volume of the cerebrum, females have greater gray matter volume compared to males (Gur et al., 1999) and larger white matter tracts connecting the right and the left hemispheres (Witelson, 1989). Differences in the size of corpus callosum have also been found (larger in males than in females; Witelson, 1990), but overall brain size may account for this variation (Bear et al., 2001). The posterior regions of corpus callosum (splenium) were larger in females as compared to males, but these findings are not consistent across studies. Gender differences in the isthmus (posterior portion of corpus callosum) have also been reported. The isthmus, a part in the network system relevant to language and visuospatial skills, was found to be larger in females. However, isthmus size predicted handedness in males, while it did not in females (Witelson, 1989). Allen and Gorski (1991) also found that females had larger fiber tracts in the anterior commissure (fibers connecting temporal lobes) and the massa intermedia (structure crossing the third ventricle between the right and the left thalamus). Other studies measuring proportional differences report that females have larger brain regions in the following sites: Broca’s area in the superior temporal region, including the planum temporale (Harasty, Double, Halliday, Kril, & McRitchie, 1997); caudate regions (Filipek et al., 1994) and hippocampal regions (Giedd et al., 1996); dorsolateral pre-frontal regions (Schlaeper et al., 1995); and right parietal regions (Nopoulos, Flaum, O’Leary, & Andreason, 2000). The cells in planum temporale (that regulates language functions) are packed more densely in the female brain than in the male brain (Witelson et al., 1995). Other gender differences in morphology have been reported. For example, Sowell et al. (2007) measured cortical thickness using MRI scans of 176 healthy individuals of age 7–87 years. In this extensive investigation, women showed thicker cortical regions in the right parietal and posterior temporal regions even after controlling for differences in brain size. Male brains have larger overall volume (Filipek et al., 1994) and significantly larger cerebellar regions. Significant differences were also reported in the limbic system, including the amygdala (Giedd et al., 1996), and the anterior portions of corpus callosum (genu; Witelson, 1989). Witelson (1976) found that functional asymmetry is less marked in females, where males showed greater lateralization of spatial skills in the right hemisphere. Witelson (1990) suggested that “more focused representation of speech and language functions in the left frontal regions in women than men, and of greater bihemispheric representation of some language skills in posterior regions in women than men (Kimura, 1987), are compatible with the sex differences in callosal anatomy” (p. 178). These anatomical differences suggest that female brains have greater capacity to deal with early insults or developmental anomalies affecting the left hemisphere and recovery of language functions, and
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may be a partial explanation for gender differences in dyslexia (Hier, 1979). See chapter 6 for a more in-depth discussion on dyslexia. In a large-scale neuroimaging study with healthy adults (18–49 years of age), regions of the brain were found to undergo volume changes with age, where men showed greater accelerated cerebral aging as compared to women (Gur, GunningDixon, Turetsky, Bilker, & Gur, 2002). With age, both genders showed moderate reductions in total gray matter in the frontal and temporal regions and in the basal ganglia, with mild increases in cerebrospinal fluid. The losses in subcortical regions were less prominent. In general, gender differences were greater in the dorsolateral pre-frontal cortex where executive functions, including working memory, are regulated (Pliska, 2003).
Gender Differences in Cognitive, Neuropsychological Skills Despite evidence of gender differences in the brain structures, the degree to which these explain variations in the cognitive functions is still unresolved. Some studies report differences in cognitive skills, where females have been shown to be slightly better at language tasks than males, and males are better at spatial tasks than females (Bear et al., 2001; Witelson, 1990). However, data from other studies are less clear-cut. In a study measuring neuropsychological performance with the NIMH sample, Waber et al. (2007) report that previously cited gender differences in verbal and spatial abilities were less obvious in their sample. They found no gender differences in verbal fluency, and no discernable differences in math calculation skills. The differences across studies may in part be a function of how skills are measured. While there is growing evidence of sexual dimorphism that may help explain differences in male–female behaviors and cognitions, Bear et al. (2001) concludes, “the anatomical differences between the female and male central nervous systems are not readily apparent, as indeed most human behavior is not distinctly masculine or feminine. Where small brain differences between the sexes occur, any adaptive purpose they may serve is not clear. And in no case is the neurobiological basis for sex differences in cognition known” (pp. 577–578).
Summary and Conclusions In this chapter, we provided a brief overview of neurodevelopment and highlighted the major gender differences in brain development. Gender differences have been found in gray matter development, specifically in maximum cortical gray matter thickness in total brain volume, frontal lobe, parietal lobe, and caudate nucleus. Regional differences were not as pronounced in temporal gray matter development. Peak growth curves were also found in the basal ganglia, where female brains reach peak volume almost 3 years earlier than males.
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The extent to which gender differences in brain development can be linked to cognitive and behavioral differences is still under study. Future studies will no doubt explore this linkage with better neuroimaging technologies and measures of neuropsychological functioning. This avenue of neuroscience holds promise for educational practices, early interventions, and comprehensive treatment programs for children with biogenetic and acquired brain-related disorders.
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Linnet, K. M., Wisborg, K., Obel, C., Secher, N. J., Thomsen, P. H., Agerbo, E., et al., (2005). Smoking during pregnancy and the risk for hyperkinetic disorder in offspring. Pediatrics, 116, 462–467. Milberger, S. Biederman, J., Faraone, S. V., Guite, J., & Tsuang, M. T. (1997). Pregnancy, delivery and infancy complications and attention deficit hyperactivity disorder: Issues of geneenvironment interaction. Biological Psychiatry, 41, 65–75. Minkoff, H. Deepak, N., Menez, R., & Firkig, S. (1987). Pregnancies resulting in infants With acquired immunodeficiency syndrome of AIDS-related complex: follow-up of mothers, children, and subsequently born siblings. Obstetrics and Gynecology, 69, 288–291. Mirescu, C., Peters, J. D., & Gould, E. (2004). Early life experience alters response of adult neurogenesis to stress. Nature Neuroscience, 7(8), 841–846. National Institutes of Mental Health Authors. (2007). The National Institutes of Mental Health strategic plan. www.nimh.nih.gov/about/strategic-planning-reports/index.shtml. Nopoulos, P., Flaum, M., O’Leary, D., & Andreason, N. C. (2000). Sexual dimorphism in the human brain: Evaluation of tissue volume, tissue composition, and surface anatomy using magnetic resonance imaging. Psychiatric Research, 98, 1–13. Penner, J. D., & Brown, A. S. (2007). Premorbid anomalies and risk of schizophrenia and Depressive disorders in a birth cohort exposed to prenatal rubella. School Psychology Quarterly, 22, 58–73. Pliska, S. R. (2003). Neuroscience for the mental health clinician. New York: Guilford Press. Purpura, D. (1974). Dendritic spine “dysgenesis” and mental retardation. Science, 20, 1126–1128. Rakic, P. (1990). Principles of neural cell migration. Experientia, 46, 882–891. Rapin, I. (1999). Autism in search of a home in the brain. Neurology, 52, 902–904. Reitan, R. M., & Wolfson, D. (1985). Neuroanatomy and neuropathology: A clinical guide for neuropsychologists. Tucson: Neuropsychology Press. Riikonen, R., Nokelainen, P., Valkonen, K., Kolehmainen, A., Kumpulainen, K., Kononen, M., Vanninen, R., & Kuikka, J. (2005). Deep serotonergic and dopaminergic structures in fetal alcoholic syndrome: a study of nor-beta-CIT-single-photon emission tomography and magnetic resonance imaging volumetry. Biological Psychiatry, 57, 1565–1572. Schlaeper, T. E., Harris, G. J., Tien, A. Y., Peng, L., Lee, S., & Pearlson, G. D. (1995). Structural differences in the cerebral cortex of healthy female and male subjects: A magnetic resonance imaging study. Psychiatric Research: Neuroimaging, 61, 129–135. Shepherd, G. M. (1994). Neurobiology (3rd ed). New York: Oxford University Press. Sowell, E. R., Peterson, B. S., Kan, E., Woods, R. P., Yoshii, J., Bansal, R. Xu, D., Zhu, H., Thompson, P. M., & Toga, A. W. (2007). Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cerebral Cortex, 17(7), 1550–1560. Spear, L. P. (2007). Brain development and adolescent behavior. In D. Coch, K. Fischer, & G. Dawson (Eds.), Human behavior, learning, and the developing brain (pp. 362–396). New York: Guilford Press. Teeter, P. A., & Semrud-Clikeman, M. (2007). Child clinical neuropsychology: Assessment and interventions for neuropsychiatric and neurodevelopmental disorders for childhood. New York: Springer Science+Business Media, LCC. Waber, D.P., DeMoor, C., Forbes, P.W., Almi, C.R., Botteron, K.N., Leonard, G., Milovan, D., Paus, T., Rumsey, J., & the Brain Development Cooperation Group. (2007), The NIH study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of the International Neuropsychological Society, 13, 1–18. Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., et al. (2004). Epigenetic programming of maternal behavior. Nature Neuroscience, 7(8), 847–854. Wilkerson, D. S., Volpe, A. G., Dean, R. S., & Titus, J. B. (2002). Prenatal complications as predictors of infantile autism. International Journal of Neuroscience, 112, 1085–1109. Witelson, S. F. (1976). Sex and the single hemisphere: right hemisphere specialization for spatial processing. Science, 193, 425–427.
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Chapter 3
Neuroimaging in Women Margaret Semrud-Clikeman, Jodene Goldenring Fine, and Jesse Bledsoe
Introduction With the advent of new technology for imaging, studies have begun exploring differences between the genders in neuropsychology and brain structure (Cairns, Malone, Johnston, & Cammock, 1985; Gur et al., 1995; Kulynych, Vladar, Jones, & Weinberger, 1994; Schlaepfer et al., 1995; Witelson & McCulloch, 1991). Initially, the studies utilized samples of psychiatric and neurological patients, and the emphasis was on the disease rather than on gender. In other cases, most of the samples were males, and females were excluded due to concerns about reproductive safety. For this reason, many studies were helpful in understanding the male brain and physiology, but were not helpful for women. Initially, it was believed that the findings for men would map onto what was present in the female brain. Differences in metabolism, hormones, responses to medication, and structures of both heart and brain have found this assumption to be faulty (Witelson & McCulloch, 1991). For this reason, studying gender differences can be helpful not just for understanding differences between the genders but also for understanding appropriate treatments and interventions. Magnetic resonance imaging (MRI) and functional MRI (fMRI) require no radiation. These noninvasive and safe techniques have increased interest in studying gender differences in brain structure and function. The aim of this chapter is to discuss the extant literature on imaging in women. To that end, we will discuss differences that are found between genders from the standpoint of normal development as well as in commonly researched diseases in adults and children. We will also discuss developmental differences in imaging found between girls and boys and, where possible, how those differences follow into adulthood. Moreover, we will discuss the imaging findings in different neuropsychological domains, including memory, language, emotion, and visual-spatial. Finally, we will link the discussion to research that is needed in the future to more fully understand gender differences with an
M. Semrud-Clikeman (B) 321 A, West Fee Hall, MSU, E. Lansing, MI 48824, USA e-mail:
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eye to understanding how normal as well as disrupted development influences later functioning. In order to understand the imaging literature, it is first important to discuss what neuroimaging is, what it can tell us, and what are the technological difficulties in research.
Neuroimaging Techniques Computed tomography (CT) and MRI are techniques that allow for the visualization of structures in the brain. While CT requires radiation MRIs do not and can be repeated. Both techniques have been used in the past research. MRI is now favored due to the resolution of the pictures as well as the ability to visualize brain activity through the use of fMRI. Both these techniques will be briefly discussed below.
Computed Tomography Computed tomography provides a picture of the brain anatomy that determines whether there are lesions, structural deviations, or tumors. A CT uses a narrow X-ray beam that rotates 360◦ around the scanning area. Each slice is independently acquired, and if movement occurs, the individual slice can be repeated. A CT requires 15–20 minutes to complete. Figure 1 illustrates a CT image. The major advantages of CT are the short acquisition time and the ability to repeat single slices. As you can see, the structures are fairly indistinct using CT, and this is a limitation in studying structural differences. In addition, CT can only be obtained in the axial plane (the plane perpendicular to the ground), and thus do not allow good visualization of the temporal lobes and posterior fossa (Filipek & Blickman, 1992), the region that is susceptible to tumors in childhood. In addition, CTs use radiation which, while within acceptable ranges, restricts serial use in children and in females of childbearing age.
Magnetic Resonance Imaging Magnetic resonance imaging allows for the viewing of structures in the living brain and is similar to the clarity present in postmortem studies. Figure 2 shows an example of an MRI scan. The MRI is a large magnet with field strength up to 3 T for clinical use. The field strength indicates how fast the scanner is able to obtain a picture of good resolution. The brain has many hydrogen photons, and these photons align in the same direction as a magnetic field. The MRI has a radiofrequency pulse that deflects these protons in a predetermined angle to provide the best picture of the desired structure. When the pulse ceases, the photons return to the original alignment gradually. If you alter the rate, duration, and intensity of these radiofrequency
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Fig. 1 An example of a CT scanner and CT control room
pulses, you will obtain different visualizations of the brain. An MRI is a series of these sequences, each averaging 7–10 minutes to complete. Clinically, MRI uses T1 - and T2 -weighted sequences. A T1 -weighted scan produces a picture from the protons involved in the pulse and shows good anatomical details of the white and gray matters. Figure 3 illustrates the detail that can be obtained. T2 -weighted sequences show the interaction of protons that are adjacent to the hydrogen protons and are sensitive to water content. These scans are generally
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Fig. 2 An example of an MRI scanner
Fig. 3 An example of a structural anatomic scan
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used to determine the extent of a lesion or a tumor. When a tumor is suspected, the MRI scan is good at isolating the tumor from the surrounding swollen tissue (Filipek, Kennedy, & Caviness, 1992). One advantage of an MRI is that it does not use radiation and is noninvasive. However, it is fairly expensive (around $1000 to $1500 per scan).
Functional MRI Functional MRI allows for the visualization of brain activity by tracking cerebral blood flow deoxygenization. The signal produced is called the blood oxygen level dependent (BOLD) signal and is presumed to be correlated to the brain regions where increased metabolic activity is occurring. It is the change in the oxygen levels in the blood that creates the BOLD signal due to differences in the magnetic properties between oxygenated blood and oxygen-depleted blood. This change affects the magnetic property of blood and can be imaged (Luna & Sweeney, 2004). During cognitive, perceptual, and/or affective processing, the BOLD signal increases or decreases, providing a map to show what areas of the brain are presumed to be activated. This technique is noninvasive and can be used in children and adults. Figure 4 shows a map of functionality of an adult female watching a video of happy situations.
Fig. 4 An example of a functional scan
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Diffusion Tensor Imaging Diffusion tensor imaging (DTI) is a technique that allows for the visualization of the white matter tracts in the brain. Unlike fMRI, DTI provides a static picture and does not show activity. Rather, DTI is a noninvasive analysis of the perfusion of water along the long axons of the brain. Using at least six directions for each voxel (a small unit of area usually about 3 mm3 ), DTI can show the orientation and density of white matter in the brain. Recent software advances have allowed for tracing the axon fiber tracks. Figure 5 shows an example of DTI in a young adult female.
Methodological Issues in MRI Research As with any new technology, there are methodological issues of which the consumer needs to be aware when reading the research. One major issue is how to define the sample. Early MRI studies combined subjects of different ages and did not evaluate age effects (Castellanos et al., 1996). As explained later in this chapter, there are many structures that change over age, with some growing and others decreasing in volume. In addition, studies had mixed samples of gender and ethnicity, making it difficult to understand whether there were gender or ethnicity differences among participants (DeCarli et al., 2005). Other studies at times included samples that were not clearly defined for diagnosis or whose participants had more than one disorder (e.g., dyslexia and ADHD, depression and anxiety). Early studies in ADHD also included children who had a history of medication as well as those who were treatment na¨ıve (Filipek et al., 1997; Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopulos, 1990).
Fig. 5 Sagittal DTI image depicting white matter fiber tracks (orange projections)
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Too much heterogeneity in samples not only makes interpretation of the findings difficult, but may also obscure differences that are truly present. For example, differences in the anterior cingulate volume have been found in children with ADHD who were treated with stimulants and those who were not treated (SemrudClikeman, Pliszka, Lancaster, & Liotti, 2006). Other aspects that have not been well controlled in many MRI studies are handedness (people who are left-handed may have speech/language lateralized differently than those who are right-handed), family history of the disorder (for genetically based differences), and true normalcy (functioning may be normal but brain may be different). Figure 6 shows an MRI of a participant who did not have behavioral difficulties but, in fact, had an abnormal MRI scan. Note the large space filled with fluid in the posterior part of the brain. In addition to the difficulties with sample selection, there are issues related to MRI technology. It is important to be aware of how the MRI is obtained. Data obtained in different magnets can vary, and thus measurements can vary and produce spurious results. Collecting data quickly is an important aspect when working with children or adults with disorders to avoid excess movement. Movement artifacts can negatively affect the findings to the extent that data is lost. Generally, magnets with higher ratings of strength (Tesla) are faster and produce scans that are less likely to be affected by movement artifacts due to the shorter length of time in the scanner. In addition, it is important to evaluate whether the scans have been examined for problems with head position during scanning. If head is tilted
Fig. 6 An anomalous scan of a person without behavioral difficulties
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in scanning, measurements can be affected due to misalignment. Generally, scans are adjusted for tilting and then “normalized” to a standard size using the Talaraich atlas of the brain (Talairach & Tournoux, 1988). Normalization results in brain scans being oriented in the same direction and sized to fit a common grid of measurement. Finally, it is important that the data be hypothesis driven – that is, there should be a theoretical basis for the findings. When there can be over 128 slices for each brain and there are over a hundred structures that can be studied, it becomes obvious that statistical power cannot be sufficient for all of these comparisons. Such fishing expeditions can yield spurious results. At times, neuroimaging research has been faulted as being atheoretical or a new type of phrenology. Good research approaches the question by asking “what do we believe underlies this difficulty/variable” and “how do our findings relate to theory?” These questions are particularly germane to MRI research because many of the studies use very small samples – sometimes less than 10 participants per group. Gender differences in brain structures, or sexual dimorphism, are important for our understanding of brain organization as well as cognitive differences that are present between men and women. There is emerging evidence that hormones play a major role in the development of selected structures in some cases in utero (Rose et al., 2004; Witelson & McCulloch, 1991). Not surprisingly, postmortem and neuroimaging studies to date suggest that brain structures differ depending on gender, including the corpus callosum, gray matter, amygdala, and hippocampus. Some of these differences are present in childhood, and some change with development. Given the above cautions, the following section will review differences in brain structure throughout the lifespan.
Gender Differences in Childhood The brain develops dramatically during gestation and in the first 3 years of life. By early childhood, the functional and structural organization of the brain is established by about 90%, including the sulcal and gyral formation (valleys and ridges) and size (Armstrong, Schleicher, Omran, Curtis, & Zilles, 1995; Caviness, Kennedy, Bates, & Makris, 1996; Giedd et al., 2004). As the child develops, there is pruning back of synapses as the brain becomes more efficient. Processing and integrating information spurs the elaboration of the connections between neurons and the development of neuronal connections (Huttenlocher, 1990). With time, and increasingly in adolescence and young adulthood, the process of myelination occurs (Jernigan, Trauner, Hesselink, & Tallal, 1991; Pfefferbaum et al., 1994). Myelination is the formation of white, fatty sheath that covers the axon and speeds up the neuronal impulses. Neither myelination nor synaptic pruning significantly affects the overall brain anatomy; thus, brain weight does not change during adolescence and young adulthood (Luna & Sweeney, 2004). Myelination is heavily concentrated in the frontal lobes during this time period, corresponding to the improved ability to form action plans and the development of insight into one’s behavior.
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Sowell, Trauner, Gamst, and Jernigan (2002) studied cortical and subcortical brain development in children and adolescents using MRI in 35 typically developing participants between 7 and 16 years of age. There were 20 males and 15 females in the sample. Findings included age-related increase in total brain volume as well as white matter volume. Gray matter was found to decrease in volume between childhood and adolescence with increase in white matter found during that period. Cerebrospinal fluid (CSF) was found to also increase with age in older participants showing about 4% of the brain volume due to CSF, while younger participants had 2% of the total brain volume due to CSF. Additional age effects were found in the areas of the frontal lobe and anterior cingulate with increase in white matter volume. The caudate and thalamus were found to decrease in volume with age. Male brains have been found to be approximately 7–10% larger in volume than female brains during childhood (Giedd, Castellanos, Rajapakse, Vaituzis, & Rapoport, 1997; Giedd et al., 1996; Reiss, Abrams, Singer, Ross, & Denckla, 1996; Sowell et al., 2002). When brain size was controlled, girls were found to show larger volumes in the gray matter of the temporal cortex, caudate, thalamus, and regions deep inside the brain (i.e., hypothalamus). Giedd et al. (1996) found that the volumes of the caudate and putamen decreased with age for boys but not for girls. The caudate is an area deep inside the brain, and is implicated in impulse control and inhibition. Similarly, the cerebellum (the region of the brain responsible for fluid movement) was found to be approximately 8% larger in boys. The putamen and globus pallidus (other areas deep inside the brain and responsible for the input of motor information) were also larger in males as compared to females. In a subsequent study, Giedd et al. (1997) found that the amygdala (a structure involved in emotional processing) and the hippocampus (a structure involved in setting down memories) volumes increased for both genders with age. The amygdala was found to increase significantly more for males than for females, while the hippocampus increased in volume more for females than for males. These findings map on to hormonal differences between the genders. The amygdala has been found to have more androgen receptors (Clark, Maclusky, & Goldman-Rakic, 1988), while the hippocampus has more estrogen receptors (Sholl & Kim, 1989). Similarly, women with hypoplasia ovaries (or less estrogen) have been found to show smaller hippocampus volumes compared to controls (Murphy et al., 1993). Some researchers have speculated that the X chromosome is involved in dictating the volume of the caudate, thalamus, and gray matter of the cerebral cortex (Murphy et al., 1993). Others have suggested that hormonal effects act on the overall brain size and the asymmetry of the hemispheres and specific structures (Kelley, 1993). These hypotheses require further study. Boys and girls have been found to differ in developmental changes in the volumes of specific brain structures. Males have been found to show more decrement of gray matter and greater increase of cortical white matter and volume of the corpus callosum with age during adolescence as compared to females (de Bellis et al., 2001). These changes did not affect the total brain volume and were thought to be related to dendritic pruning for gray matter and an increase in myelination for white matter. These findings are important for our understanding of how boys and girls
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may experience hormonal fluctuations and their reactions to stress, which is linked to the amygdala and hippocampal regions. To study the hormonal effects on amygdala and hippocampus, Rose et al. (2004) studied children who had congenital disorders associated with hormonal disturbances. MRIs were obtained from 16 boys with congenital adrenal hyperplasia (CAH) (the condition of fewer androgens) and from 20 children with Klinefelter’s syndrome (XXY). There were 32 matched aged controls for the CAH group and 40 for the XXY group. Smaller amygdala volumes were found in boys with CAH or XXY. Boys in the XXY group showed smaller hippocampi volumes. These findings are interesting because the structures assisting stress-coping were smaller in these samples. The study did not relate these findings to adjustment or to the child’s ability to manage stressful situations. Further study is needed to determine how these hormonal differences and environmental stress may interact. At this point, it is only clear that there are differences in structures in these children with anomalous hormonal distribution. To examine whether gender and age effects are present in the corpus callosum (CC) in childhood, a longitudinal study of children and adolescents was conducted (Giedd et al., 1999). The children were scanned at 2-year intervals. The CC was found to be significantly larger in males in the genu (the area connecting the frontal lobes) compared to females. Age-related changes in the CC were found in the regions of the splenium and also in the total area between the ages of 5 and 18. In a longitudinal study of children using MRI, Giedd et al. (1999) found that the right hemisphere and right caudate volumes were larger than the left while the left lateral ventricles and putamen were larger than the right for both genders, showing consistent asymmetries for males and females. These differences are likely programmed through genetics as well as being affected by hormones and the environment. Many of these differences may differentiate children with various psychiatric disorders. The regions involved in attention, disinhibition, reading, and language are all areas that have been found to differ in males with ADHD and/or reading disabilities. The following section briefly discusses the work that has been completed in girls with these disorders. The interested reader is also referred to the chapters on learning disabilities and ADHD within this volume for further information.
Neuroimaging in ADHD and Dyslexia Neuroimaging in Girls with ADHD Many gender-specific research findings are of interest in psychiatric disorders. Boys with ADHD have been found to show smaller caudate volumes (Filipek et al., 1997; Semrud-Clikeman & Pliszka, 2006), and this disorder has been found to have a higher incidence in males. To date, a vast majority of neuroimaging studies have included only male subjects with ADHD. However, the only study using MRI to measure the anatomical variation among girls with ADHD
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was completed by Castellanos et al. (2001). The total cerebrum, frontal lobe, striatum, cerebellum, and cerebellar vermis volumes of the brain were compared for 50 girls with ADHD and 50 girls without ADHD aged 5 to 15 years. Consistent with their previous study on boys with ADHD (Castellanos et al., 1996), smaller total brain volume and smaller volumes of the structure that connects the posterior region of the cerebellum (vermis) were found in girls with ADHD compared to healthy controls even after controlling for total cerebral volume, ADHD symptom severity, and stimulant medication history. Previous studies on males with ADHD had found smaller volumes of the right frontal lobe, right caudate, right globus pallidus, and left cerebellum compared to healthy controls, a finding not present in girls (Castellanos et al., 1996). Thus, consistent differences have been found in the cerebellum and in total brain volume for both genders with ADHD. The failure to find differences in the cortico-striatal region in girls with ADHD is puzzling as this pathway has been implicated in attention and disinhibition. While the study suggests that anatomical differences may exist between males and females with ADHD, more research is needed in girls with ADHD to determine what, if any, anatomical abnormalities are present in girls and boys with ADHD. Positron emission tomography (PET) scans utilize radioactive isotopes to evaluate brain activity through metabolic changes. PET scans have indicated that girls with ADHD may show decreased cerebral glucose metabolism compared to girls without ADHD (Zametkin et al., 1993). The decreased metabolism in the left anterior frontal lobe was significantly correlated with ADHD symptom severity (p < 0.001). In addition, the results suggest that lower cerebral glucose metabolism may distinguish girls with ADHD from healthy controls, and provide evidence for gender differences in ADHD, although this finding should be interpreted with caution because the result was not statistically significant and a very small sample size (n = 6) was used. However, the findings and hypotheses derived from Zametkin et al. (1993) were replicated by Ernst et al. (1994). Adolescent girls with ADHD were found to show less activation compared to healthy control girls. In addition to the metabolic difference between ADHD and control girls, Ernst et al. (1994) also found gender differences within the ADHD sample. Specifically, girls with ADHD showed less global cerebral glucose metabolism than boys with ADHD. Given that two preceding PET studies, Ernst et al. (1994) and Zametkin et al. (1993) found patterns of low cerebral glucose metabolism in girls with ADHD, but were lacking in power due to a small sample size; Ernst, Cohen, Liebenauer, Jons, and Zametkin (1997) recruited a slightly larger sample of girls with ADHD (n = 10) to replicate the previous findings. Twenty-one girls (10 with ADHD and 11 controls with a mean age of 14.0 years) participated in the follow-up study. The study failed to replicate the past findings of decreased cerebral glucose metabolism in girls with ADHD vs. healthy controls matched with age, handedness, ADHD symptom severity, and comorbid disorder. Ernst et al. (1997) suggested that the replication study included developmentally younger girls (Tanner stage, p = 0.024) as compared to the 1994 study, and girls in the 1997 study had significantly lower scores on the WISC-R Block Design but not on the verbal task. The sexual maturation level predicted lower levels of cerebral glucose metabolism.
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In conclusion, preliminary findings suggest that lower glucose metabolism is characteristic of girls with ADHD. Follow-up studies are needed with increased sample sizes that control for sexual maturation, cognitive ability, and genetic relatives with ADHD in order to fully understand the relationship between glucose metabolism and ADHD in girls. In addition, it would be helpful to have information about the relationship of these differences to neuropsychological findings. Neuroimaging in Girls with Dyslexia Neuroimaging techniques have revealed brain morphometric differences in people with dyslexia, generally either with a mixed gender sample or with boys (Hynd & Semrud-Clikeman, 1989). Differences have been consistently found in the language regions of the brain, although the laterality and degree of difference varies among the studies due to methodological issues. In previous studies, gender has not been controlled, and there is very little information as to the possible variations in brain structure for girls and boys with dyslexia. One study did seek to evaluate gender differences in dyslexia (Schultz et al., 1994). In this study, no gender differences were found between girls and boys with dyslexia when whole brain volume was controlled. Dyslexia studies have implicated the planum temporale in the temporal lobes. This structure has been found to either be smaller or show deviations in asymmetry in children and adolescents with dyslexia. Witelson’s studies with adult females (Witelson, Glezer, & Kigar, 1995; Witelson & Kigar, 1988a, 1988b, 2004; Witelson & McCulloch, 1991) have shown that females have more cell density in this region than males. There is about a 2.5 to 3 to 1 gender difference in dyslexia between boys and girls. Given the lower incidence of dyslexia in girls, it may show that this cell density is protective for reading and written language problems in females. The in utero experience of testosterone also appears to affect the functional asymmetries and may be linked to some of the differences present in learning and achievement. These areas require additional study with larger numbers of males and females. In addition, the relationship of handedness and/or age effects in differences in area and volume of key structures are variables that require additional studies not only across subjects but also across genders. Finally, there are very few studies that include sufficient numbers of minorities in order to study the possible effects of ethnicity on brain development.
Gender Differences in Adulthood Men’s brains tend to be 10% larger than women’s (Dekaban & Sadowsky, 1978). It is not clear why this difference is present. Witelson et al. (1995) suggest that the volume disparity may be due to differences in the number of cortical neurons that survive initial pruning during gestation and early development. It was also suggested that sexual differentiation in the brain may selectively influence how many neurons are produced and how many survive this pruning (McEwen, 1983). It is
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unknown whether women have a similar number of neurons or fewer during gestation, whether there is more or less pruning during gestation, and whether this variation accounts for brain size differences. There are two different methods for assessing these differences. The first method uses autopsies of postmortem brains while the second method uses imaging. The results from both methods will be discussed below.
Postmortem and PET Studies Sandra Witelson was one of the earliest researchers to evaluate gender differences. Exploring gender and handedness, Witelson (1989) used postmortem brains to find that people who were not consistently right-handed showed larger areas in the isthmus of the corpus callosum. The isthmus connects the right and left hemispheres in the posterior parietal and superior temporal regions. Sex differences were also present in the corpus callosum area, and there was an interaction between handedness and the posterior regions of the corpus callosum. Left-handed males showed the largest posterior corpus callosal area, whereas females did not show this difference for handedness. It was suggested that this finding indicated less lateralization of functioning in females compared to males. In addition, females showed a larger proportional isthmus than males who were right-handed. The genu and the corpus callosal segment adjacent to the genu were largest in men. With age, the corpus callosum size decreased in men but not in women. Witelson and Kigar (2004) suggest that these findings indicate that for women motor and perceptual functioning are less connected abilities, and may be less lateralized than that for men. Witelson suggests that the interconnectedness of the parietal–temporal regions in men supports improved ability in visual-spatial functioning, while the disconnection present for women results in less facility on these types of tasks. Handedness and gender also appear to be related to the Sylvian fissure. The Sylvian fissure is involved in language abilities, particularly phonological processing and language comprehension. Postmortem analysis of the Sylvian fissure revealed a relationship of handedness with right-handed men showing longer horizontal segments bilaterally compared to men who were not consistently righthanded (Witelson & Kigar, 2004). In contrast, no association was found between handedness and the Sylvian fissure anatomy in women. Gender-based findings in the Sylvian fissure and corpus callosum are important neuropsychologically because women tend to show less lateralization for language skills compared to men. Thus, for men the interconnectedness of the corpus callosum provides support for stronger visual-spatial abilities, while for women, the lack of lateralization supports verbal abilities. These gender-linked differences are important for our understanding of how brains may differ in processing information as well as in the development of specific skills. In addition to the differences in the Sylvian fissure between the genders, postmortem studies have also examined the planum temporale (PT). The PT is a structure within the Sylvian fissure that is believed to be involved with phonological
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processing. In a study with five women and four men with a mean age of 50 years (Witelson et al., 1995), the participants were documented to be cognitively normal as well as right-handed. The number of cells within a cortical column was found to be 11% greater in women in the posterior portion of the PT bilaterally. Thus, this difference may be related to a gender difference in neural connectivity, and supports the idea that women’s brains may be more specialized for language bilaterally then those of men. Witelson et al. (1995) indicated that the 11% difference in the density of total cortex in women might offset the 10% larger volume in the brain size of men. Further, they speculate that the tighter packing of neurons may be due to mechanical compression as a result of smaller brain or cranial space. The finding of higher cortical metabolic rate for women through the use of PET (Hatazawa, Brooks, Di Chiro, & Campbell, 1987) was consistent with the finding of tighter cell density in women. This higher metabolic rate is likely related to the tighter packing of cells and the need for a stronger impetus for neuronal impulses to move through the denser regions. Similarly, in visualizing blood flow during a task (regional cerebral blood flow; rCBF), Gur et al. (1982) found that females had a higher rate of blood flow when solving verbal and line orientation tasks compared to males. Right-handed females showed a strong effect to laterality – that is, the left hemisphere showed more activity when solving a verbal task compared to the right, and the right showed more activity on the spatial task than the left. Gur et al. (1995) studied sex differences in metabolism using PET at rest cognitively. The findings indicated that there were gender differences, with women showing higher left hemispheric metabolic activity and higher activity in the cingulate region than men. Men were shown to have higher metabolism in the temporal-limbic system. Gur et al. (1995) suggest that these differences may be related to the variations in emotional and cognitive processing that are seen behaviorally between the genders. Using this reasoning, women may utilize more brain activity in order to inhibit and direct a behavior compared to men. Men may utilize more emotionally based reactions to stimuli and utilize fewer brain inhibitions. Further study is needed to understand how functional, structural, and behavioral aspects of cognitive processing are interwoven. Taking these findings as a whole, Witelson and McCulloch (1991) suggest that many of the neuroanatomical differences between the sexes are present in the temporo-parietal regions of the brain. These regions are specialized for language processing as well as visual-motor and visual-spatial functions. Witelson suggests that these differences may be related to the presence of sex hormones. For example, lower levels of androgens and androgenic receptors in the male brain during gestation and early development may result in less cell pruning and cell death during this time, which in turn results in a larger corpus callosum, a larger brain volume, less cell density, and specialization for visual-spatial and motor skills. Support for this hypothesis comes from a study by Moffat, Szekely, Zonderman, Kabani, and Resnick (2000). They studied 68 young adult males using MRI and with samples of saliva to test for testosterone. Findings indicated that higher levels of testosterone correlated with larger corpus callosal area particularly in the posterior aspects of the corpus callosum.
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Thus, with the findings from postmortem studies, Moffat et al. (2000) suggest that sexual hormones may change the volumes of structures and cell density in gestation and early development. These findings are of interest when considering sexual differences that are present between the genders of all ages. There is emerging evidence that prenatal exposure to testosterone may influence functional cerebral lateralization in girls (Cohen-Bendahan, Buitelaar, van Goozen, & Cohen-Kettenis, 2004). When same-sex twins were compared to opposite-sex twins, the female in the opposite-sex twin pair was found to show more lateralization of function compared to the female in the same-sex twin. This difference may be due to prenatal exposure of testosterone to the male twin, which altered the brain organization in utero and thus postnatal behaviors. In addition, no difference was found in the testosterone level for girls in either twin pair, indicating that testosterone originated from outside the female, which lends support to the hypothesis that the brain differences are a result of prenatal testosterone exposure.
MRI Findings Four areas of interest have been investigated with MRI, and the findings make up the remainder of this chapter. The first is a review of studies aimed at determining normal sexual dimorphism in the adult brain. Second, the effects of aging on human brain, including gender differences, will be discussed. The third area comprises a combination of studies on gender differences in structures related to functioning. Finally, a discussion is presented on data from MRI for the selected disorders of Alzheimer’s disease, schizophrenia, and multiple sclerosis.
Normal Sexual Dimorphism Brain Size and Gray and White Matters Postmortem studies have found larger brain sizes in males than in females in respect of some structures differing between the genders. MRI studies further evaluate these findings to provide additional information on the structures of a “living” brain. By definition, postmortem findings are complicated through the use of methods for fixation of the brain material, shrinkage, and storage of autopsied brains. MRIs provide the opportunity to obtain volume, area, and surface anatomy measurements despite the barriers in autopsied brains. Similar to the postmortem studies, MRI studies of the CC have sought to evaluate possible gender differences. Davatzikos and Resnick (1998) found gender differences in CC in women aged 56–85 years showing smaller splenial area than in men independent of age and brain size. All the subjects were defined as righthanded. Further, selected neuropsychological measures of spatial rotation, naming of pictures, verbal fluency, and memory tasks were related to larger posterior CC area in women but not in men. Salat, Ward, Kaye, and Janowsky (1997) found no significant overall size differences between the genders; yet there were significant
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differences in the posterior region with women showing larger areas than in men. No differences were found in the anterior regions or in the cerebellum or pons. The callosal differences may be related to interhemispheric connectivity in women and more lateralization in men. Westerhausen et al. (2004) studied the area of the corpus callosum in women and men and in left- and right-handers aged 19–34 years. The total callosal area was found to be larger, particularly in the most anterior aspect of the CC in right-handed participants as compared to left-handed ones. When the brain size and CC area were covaried, no gender difference was found. Diffusion tensor imaging was also used, which showed increased molecular diffusion in the left-handed subjects as well as in males. Such diffusion is related to more interhemispheric connectivity in lefthanded participants than in right-handed ones despite more structural area being present. For males, the DTI results may indicate the presence of fewer but thicker fiber tracts, a finding consistent with animal studies (Kim, Ellman, & Juraska, 1996). These findings are, on the face of them, somewhat contradictory. One of the drawbacks in these studies is the use of differing methodology and age groups. Using gross measures of area may not provide similar results as methods that utilize measurement of size as well as variability in shape. Controlling for total brain volume is also very important in these studies, and when such controls are present, many of the gender differences disappear. Further evaluation of shape typography as well as area may assist in our understanding of possible gender differences. The above findings do indicate possible differences in interhemispheric connectivity that may be related to improved functioning in women on specific tasks (verbal) and in men on other tasks (visual-spatial). Nopoulos, Flaum, O’Leary, and Andreasen (2000) studied brain tissue volume, gray–white differences, and cortical surface anatomy in 84 healthy adults (42 women, 42 men) using MRI. Consistent with postmortem findings, men were shown to have larger cerebrums but not cerebellums compared to women. All four cerebrum lobes were found to be larger in males compared to females. The proportion of gray–white matter was similar among the genders, except in the right parietal lobe where females showed more gray matter than males. The cortical surface anatomy was not found to differ between the genders. One of the difficulties with this study was the use of large areas for analysis rather than selected structures. For example, some studies have found more cell density in the region of the Sylvian fissure in the left hemisphere in females (Witelson et al., 1995). Specific regional measurements can provide more information as to the possible differences that underlie gender differences in behavior. Differences due to gender may not be widespread in the brain and may be region-specific. Early studies have found differences in brain size between the genders (Passe et al., 1997). Significant differences were present in white matter volume with smaller volumes found in females. However, there were no differences in gray matter volume between the genders. However, this study included only 13 males, so sufficient power may not have been present to find a significant difference. There may be a trend present with such a small sample (the p-value was at 0.11), but effect sizes were not reported; hence, the findings cannot be fully interpreted.
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No differences have been found in the volume of lateral ventricles (Erdogan, Dane, Dumlu Aydin, Ozdikici, & Diyarbakirli, 2004). Sex Hormones In a larger study of gender differences, Goldstein et al. (2001) hypothesized that regions of the brain that are sensitive to androgens or estrogen may differ between the genders due to the effect of sex hormones on brain development. Structures that have been found to be particularly sensitive to these hormones were selected based on animal literature. Brain regions were divided into two groups based on responsivity to sexual steroid hormones. Group 1 included structures that are particularly sensitive to these hormones, while group 2 has not been identified in the animal literature yet. When smaller regions were measured, males have been found to have larger volumes in the cerebrum with significantly larger lateral and third ventricular volumes and larger white matter volumes proportional to the overall brain size. Women were found to have more gray matter than men relative to their brain size with more gray matter in the frontal regions of the brain, particularly in the fronto-orbital cortices, including the cingulate, and in the precentral gyrus. In contrast, men were found to show larger volumes in the medial section of the frontal cortex, the hypothalamus, the amygdala, and the angular gyrus. Consistent with their hypothesis, areas that have more sex hormone receptors were found to differ between the genders. These findings replicated the findings of Schlaepfer et al. (1995), where women showed larger gray matter volumes in the regions of dorsolateral prefrontal cortex and language centers in the superior temporal gyrus. Sex hormones have been hypothesized to play a part in development prenatally in terms of adult structures. Kallai et al. (2005) studied the hippocampal volumes in males and females. The hippocampus has been found to have both estrogen and testosterone receptors, and it is believed that the way the cells form the hippocampus is partially determined by these steroids (O’Keefe, Pedersen, Castro, & Handa, 1993). Subjects were all right-handed females and were grouped by the ratio of the second finger to the fourth finger (2D:4D). This ratio has been hypothesized to be related to testosterone or estrogen exposure during gestation. Low ratios have been associated with higher rates of testosterone during gestation, while high ratios are associated with higher rates of estrogen (Manning, 2002). Overall analysis found that the volume of the right hippocampus is larger than that of the left across the subtypes based on finger length. In addition, the right posterior segment of the hippocampus was found to be smaller in females with a high ratio, while the right medial hippocampal volume was found to be larger. For females with a low ratio, the right posterior hippocampal volume was larger while the right middle hippocampus volume was smaller. Other structures were not found to be related to these ratios. No difference was found in circulating testosterone in this sample. Since females were the only gender included in this study, it is not known whether these findings would be present for males also. It is also not clear from the study whether these differences translated into more masculine or feminine behaviors.
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Parietal Lobes The posterior parietal cortex contains the angular gyrus and has been implicated in visual-spatial processing, a skill in which men have been found to be more adept (Frederikse, Lu, Aylward, Barta, & Pearlson, 1999). The right posterior parietal cortex has been implicated in spatial working memory and recognition of affect (Borod, Koff, Lorch, & Nicholas, 1986; Jonides, Smith, Koeppe, & Awh, 1993). The left posterior parietal cortex has been implicated in mental rotation, complex motor planning, and time estimation (Alivisatos & Petrides, 1997; Maquet et al., 1996; Winstein, Grafton, & Pohl, 1997). This region has also been hypothesized to be interconnected to the planum temporale (an area utilized for phonological decoding), the dorsolateral prefrontal cortex (an area involved in planning, organization, and attention), and Broca’s area (an area specialized for verbal output) (Mesulam, 1998). Gender differences have been evaluated in the parietal region of the brain (Frederikse et al., 1999). Males were found to have larger left posterior parietal gray matter volumes as compared to females with no difference present for the right posterior parietal gray matter volume. No measure of white matter volume was obtained. The authors suggest that these findings are consistent with the previous findings of male dominance on tasks of mental rotation as well as indicating more lateralization in male brains than in females. Further discussion of the relation between cognitive and brain functioning for the sexes is described later in this section.
Temporal Lobes Sex differences in limbic structures in the temporal lobe as well as frontal brain volumes were further studied with 57 men and 59 women using MRI (Gur et al., 2002). Volumes of the hippocampus and amygdala were not found to differ between the genders. Consistent with previous findings, women were found to have larger orbital-frontal volumes compared to men. This finding is important for our understanding of emotional processing. The orbital-frontal regions have been implicated in social behavior as well as executive functioning. The orbital-frontal region is important for controlling intense emotions and for insight. This region has been found to be compromised in men with higher levels of psychopathy (Matsui, Gur, Turetsky, Yan, & Gur, 2000; Raine, Lencz, Bihrle, LaCasse, & Colletti, 2000). Given the finding of no gender differences in amygdaloid volume, one might wonder whether the larger volumes of the orbital-frontal region provide additional control over emotional response. In this study, when the volume of the orbital-frontal region was compared to amygdaloid volumes, women were found to have a larger proportion indicating the presence of more tissue volume that may assist with modulating amygdaloid input. Gur et al. (2002) suggest that this finding may be related to differential responses to aggressive impulses between the genders. Further study is needed to link emotional responses to these neural substrates.
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Aging Many of the studies were either controlled for age by having samples younger than 50, or not controlled by age but the samples were heterogeneous in age (above 56). Researchers have been seeking information as to how our brains age as well as the possible gender differences in aging. Early studies on structures that change with age have found that elderly men showed atrophy in the left hemisphere predominately, while women’s atrophy was more symmetrical (Gur et al., 1991). In addition, other studies have found that the frontal lobe is more vulnerable to aging effects compared to the temporal lobe in both sexes, with males showing more decrement (Cowell et al., 1994). A large-scale study of organ systems evaluated brain morphology in 2200 male and female participants aged 34–97 years (DeCarli et al., 2005). As found in younger samples, the brain volumes of men were larger than that of women. An age effect was evident, with approximately 50% of the differences occurring after the age of 50. The greatest decline was found in the frontal lobes, and the least decline was found in the temporal lobes. Very little difference was found in the occipital and parietal lobes. A major gender difference was found in the frontal lobes, where men showed significantly smaller volumes after 50 years of age. Women were also found to have larger brain volumes across structures after the age of 50 compared to men. For this reason, male brains appear to be particularly sensitive to aging while females are less vulnerable to brain volume decrement due to age. Estrogen has been suggested in the protection of atrophy in other brain areas beyond the frontal lobes (Eberling et al., 2003). In a study of Mexican–American men and women aged 60–73 years, some of the women were on estrogen replacement therapy (ERT) (n = 13) while others were not (n = 46). Findings indicated that women on ERT showed larger right hippocampal volumes compared to women not on ERT. In addition, these women also showed larger anterior hippocampal volumes compared to the men and to the women not on ERT. These findings are similar to those with Alzheimer’s patients, where the anterior hippocampus was found to show volume loss compared to controls (de la Torre, 1997; Jack et al., 2002). The decrement in hippocampal volume was also found in a study evaluating age differences over a 5-year period using serial MRIs (Raz et al., 2005). The hippocampus and cerebellum were found to reduce with age due to the presence of hypertension. In this study, no gender differences were found in these regions. The visual cortex volume was found to be stable over age. Variation in brain structure volume of the cerebellum prefrontal white matter, fusiform gyrus, visual cortex, and inferior temporal cortex was found to differ with age. The age at which these findings became noticeable was the mid-fifties. It was also suggested that the regions that mature at old age (frontal lobe, anterior cingulate) are most vulnerable to decline. There was sex difference in caudate associated with age. Women showed a larger decrease in caudate volume over time. One finding that is particularly important is the relation of hypertension to these results. Hypertension appeared to be most influential in the decrease of volume in the frontal lobes, particularly in the prefrontal white matter and orbital-frontal
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cortex. It was not clear how well hypertension was controlled in this group. However, these findings are important given the higher rates of hypertension in older participants. It would be very interesting to note whether people with well-controlled hypertension (120/80 or lower) would show similar decrements. In summary, the frontal lobe appears to be particularly vulnerable to change during aging, with males showing the most decrement in structure compared to females. The temporal lobe is also vulnerable, particularly in regions responsible for setting down of new memories. Consistent with postmortem studies, MRI studies indicate that women have a relative sparing of the corpus callosum, possibly contributing to better interhemispheric transmission. In most cases, the parietal, occipital, and limbic systems appear to be similar by gender during the aging process. Further study is needed to evaluate possible complications from hypertension, particularly if uncontrolled. There was only one study that included minorities, which studied the effects of ERT on hippocampal functioning. Further investigation is needed to determine which other aspects may differ between the genders, ethnicity, and aging.
Sex Differences in Activation During Cognitive Tasks While some structures have been found to differ between the genders, an open question has been concerning the relationship of these structures to functioning. There have been three main areas that have been evaluated as possibly differentiating between the genders: language processing, visual-spatial processing, and emotional processing. While there are not many studies that have looked at these aspects from a gender point of view, the following section will briefly discuss the existing literature. Language The language areas of the female brain are generally thought to have been less lateralized than the male brain. Shaywitz et al. (1995) studied gender differences in language organization using fMRI in adults. Each participant was asked to complete tasks that involved letter recognition, rhyming, and determining if two words were in the same semantic category. In addition, each of the participants was asked to judge two sets of four lines to determine if they were the same. Males were found to show more lateralization in the language tasks compared to females. Males showed increased activation in the left inferior frontal region and in the temporal region responsible for decoding of sounds, while females showed bilateral activation. In contrast, the activation of the occipital region involved in visual discrimination was similar between the genders. Gur et al. (2000a) further studied language and spatial tasks in adult females and males. For tasks that are spatial in nature, previous findings had indicated that men show greater right hemispheric activation compared to women, and this rightsided activation correlated strongly with better performance on these tasks (Gur, Skolnick, & Gur, 1994; Wendt & Risberg, 1994). In this study, the participants were
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asked to complete easy and hard verbal analogies as well as easy and hard line orientation tasks. Areas that were evaluated using fMRI included the planum temporale as well as the inferior-parietal regions. When the tasks were compared, the verbal analogies showed more left-sided activation than line orientation. Men were found to show more right lateralized change compared to women, particularly in the inferior parietal region for easy line orientation task. Activation was lateralized to the left for both men and women for the verbal analogies task, and no gender differences were found among these types of tasks. Taken together, these findings indicate that men show lateralization for specific, but not all, language functions, while women show less asymmetry. In addition, it appears that language functions differ depending on the requirements present. For complex tasks such as analogies, which may recruit the frontal areas of the brain, lateralization does not appear to differ between the genders. However, for language tasks that require processing of sound-symbols as well as semantics, women show a differential activation pattern from males, favoring more bilateral activity. These findings are similar to those that suggest some language functions in women may be more widely distributed than men’s and thus less vulnerable to cardiovascular or traumatic insult. They also suggest that males are more successful on visual-spatial tasks possibly because they show more lateralization and possibly more specialization. Emotions Emotion processing has also been studied between the genders. Baron-Cohen et al. (2006) found that females show more activity in the visual discrimination regions of the occipital lobes compared to males when viewing a task requiring them to figure out embedded figures. In a task that required emotion recognition, males showed more activity in the left inferior frontal gyrus, while females showed more bilateral activity in this region. These differences may translate into variances in interest in emotional information. Further study is necessary to completely understand this finding, as the study involved only 6 males and 6 females. Even with such a small sample, however, these findings are quite robust. DeCarli (2003) also studied hemispheric asymmetry for emotional stimuli using fMRI in females. Positive pictures of emotional stimuli evoked more activity in the left hemisphere, while negative pictures show increased activation in the right hemisphere. Particular activation was found in the left middle frontal gyrus and the left middle and superior temporal gyri for positive pictures, and in the right inferior frontal regions for negative pictures. Comparisons were not present for males; so, these findings are suggestive only for females. Conclusion Further study is necessary to more fully understand how gender differences in structures may map onto functional differences. These studies are just beginning and will more fully help us to understand whether the structural differences identified
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have any relationship to neuropsychological differences frequently found between the sexes. Many studies have sought to isolate gender differences in brain structure without fully studying what these differences may mean to functioning. Hormonal influences to structural variations between genders, although of interest, are not informative if they do not correspond to differences in behaviors. In addition, there are very few studies of ethnic differences, and this is one area that requires much more study in order to further our understanding. The transitions between childhood and adolescence and between adolescence and adulthood are not fully understood for age or gender. Additional studies are needed to advance our understanding of brain functioning in these critical periods as well as tying this brain function to behavior. The following section expands the above discussion to three types of disorders – mental health (schizophrenia), aging (Alzheimer’s disease), and neurodegenerative (multiple sclerosis). The existing literature in these areas is fairly sparse for females, and will be briefly reviewed here. As a result, much of the literature is in the preliminary stage and is often contradictory. We will do our best to synthesize these findings, tie them to normal development, and point out areas that require further study.
Schizophrenia Differing symptomatic presentations of schizophrenia based on gender have stimulated considerable research on brain differences in patient and control populations for both males and females. Symptom onset and degree of severity (Craig, Cutter, Norbury, & Myrphy, 2004; Loranger, 1984), effects of language (Sommer, Ramsey, Mandl, & Kahn, 2003), facial emotion recognition (Kohler et al., 2003; Scholten, Aleman, Montagne, & Kahn, 2005), and other positive and negative disease expressions (World Health Organization, 1975) have been noted to be variable according to gender. Brain areas related to these symptoms have been the target of numerous research studies, although the cumulative results are somewhat inconsistent.
Volumetric MRI Volumetric investigations are by far the most numerous of the neuroimaging studies on schizophrenia. Some researchers have taken a “shotgun” method and scanned the brains of schizophrenics and controls with no apparent theoretical hypothesis. Others have focused their study on a specific region of the brain based on symptom-related hypotheses. As with other areas of neuroimaging, many studies have very small numbers of participants, raising a question of whether the findings can be generalized to the general population. Methodological differences may further obfuscate the results among the studies (Goldstein et al., 2002). For example, as mentioned earlier, there are known volumetric differences in aging for male and
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female brains, and this should be controlled for in well-designed studies of any disease in which brain changes are anticipated (Craig et al., 2004). Additionally, differences between total cerebral volumes among subjects should also be controlled, since brain volumes vary individually. Gray Matter, White Matter, and CSF Consistent with the general population, total brain volume in males with schizophrenia is larger than the brain volume of their female counterparts (Goldstein et al., 2002). With regard to differences in total brain volume, several studies have shown no differences between women with schizophrenia and non-patient controls (Goldstein et al., 2002; Molina et al., 2005). The absolute volume of whole brain was found to be lower in persons with schizophrenia than in controls overall (Collinson et al., 2003; Wright et al., 2000), and one study found the increase in ventricular CSF in females to be double that of males (Davatzikos et al., 2005). The white matter of the brain provides connectivity among different brain systems. In schizophrenia, study results have generally suggested no genderdependent differences between patient and non-patient samples, although persons with schizophrenia have been found to have some reduction in white matter in specific areas of the brain (Crow, 1998; Davatzikos et al., 2005; Wood, De Luca, Anderson, & Pantelis, 2004). Davatzikos et al. (2005) found a gender-dependent decrease in female white matter in the occipito-parietal region, with an increase in males relative to their non-patient counterparts. This finding mirrors a postmortem study that revealed female reduction and male increase in the density of the largest white matter bundle of the brain, the corpus callosum, in schizophrenic patients relative to healthy controls found by Highley et al. (1999). The literature is equivocal with some finding a increase in females and a decrease in males (e.g., Raine et al., 1990), a decrease in females but not in males (Hauser et al., 1989), and no difference in females but a increase in males (Narr et al., 2000). Increased CSF found in the ventricles as a marker for the reduction of cortical mass has been a focus of several studies. In a well-designed study that controlled for both age and total intracranial volume, it was found that males had significantly more left-prefrontal CSF than did non-patient males, but females did not (Molina et al., 2005). This finding indicates that males with schizophrenia had more frontal volume loss than non-patients, and that females did not have significant frontal volume loss. In addition, Molina et al. (2005) examined the correlation between increased CSF space and illness duration, finding that, for males only, increased CSF dilation was correlated with longer illness. Yotsutsuji et al. (2003) found a similar pattern of enlargement for males and females, with most substantial differences in the left temporal horn, but the females appeared to be less severely affected than the males. In general, it appears that cortical (gray) matter areas are more significantly affected in schizophrenia than are white matter connections. The cortex of the brain provides the area for “calculation,” while the white matter transfers information from place-to-place. Goldstein et al. (2002) suggest that the cortex, being more
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sexually dimorphic in general, is more susceptible to sex-specific differences in schizophrenia. Temporal Lobes – Memory and Language The left temporal horn enlargement is consistent with findings suggesting reduction in the temporal lobes of patients with schizophrenia. This area is of particular interest because of the language disturbances typically seen in this disease. Some researchers suggest that normal brain development in the left temporal lobe includes sexual dimorphisms, such that females have larger areas of the superior temporal gyrus than do men relative to total cerebral size (Goldstein et al., 2001; Harasty, Double, Halliday, Kril, & McRitchie, 1997). Others have suggested that such differences are not extant (Vadlamudi et al., 2006). Researchers comparing females with and without schizophrenia have observed no significant differences in volume, although the superior temporal lobes of men with schizophrenia have been found to be smaller than that of their non-patient counterparts (Bryant, Buchanan, Vladar, Breier, & Rothman, 1999; Gur, Turetsky, et al., 2000). Gur, Cowell, et al. (2000) found that larger cortical volume in the temporal area in women was associated with better memory performance, for both the patient and non-patient groups. Lower volumes in the hippocampal area, known to be involved in memory, was found to be related to poor memory in men with schizophrenia, but not in women (Gur, Turetsky, et al., 2000). Within the superior temporal gyri, the planum temporale (PT) is a known language center having lateral asymmetry in normally developing people such that the left planum is larger than the right (Shapleske, Rossell, Woodruff, & David, 1999). This finding has been observed in newborns (Witelson & Pallie, 1973). Again, the PT, as part of the superior temporal gyrus, has been variably measured due to differing methods of identifying the structure on scans (Shapleske et al., 1999). In a recent study that clearly defined state-of-the-art methods in high-resolution 3-D images, no differences between genders were found (Vadlamudi et al., 2006), although others have found larger PT areas in women (Goldstein et al., 2001). In the Goldstein et al. study (2002), female patients were found to have greater lateral symmetry than their non-patient counterparts, which would indicate a larger volume on the right side. Symptoms of language disturbance in women was found to be less severe than for men in a small subset of the Goldstein (2002) participant sample (Walder et al., 2007). The authors suggest that women effectively recruit more right hemisphere function in language than do men, who may be more bound to the left hemisphere for the same task. Frontal Lobes – Executive Functions The prefrontal cortex is thought to be involved in executive functioning and is of interest in schizophrenia because of reduced executive functioning in those with the disease. Executive functions refer to the abilities of organization, planning, attention, working memory, motivation, and emotional regulation. Findings regarding differences in volumes in this area have been inconsistent, and gender differences
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have not been widely studied. However, in a large-scale study involving 70 patients with schizophrenia (40 men and 30 women) and 81 healthy controls (34 men and 47 women), Gur, Cowell, et al. (2000) found that both men and women with schizophrenia had reductions in the volume dorsolateral prefrontal gray matter as compared to same gender controls, but in women, the reduction was smaller and mostly in the right hemisphere. In contrast, the same study indicated greater reduction in the orbital areas for women than for men relative to their healthy counterparts. Gur, Cowell, et al. (2000) suggest that the reduced orbital areas in women may be related to higher levels of affective disturbance in women patients because of the rich connections between the frontal lobes and the limbic system, an area related to emotions. The Limbic System – Emotions Disturbance in emotions being a hallmark indicator of schizophrenia, the limbic system has been an area of focus for research in the disease. The limbic system includes the cingulate cortex as well as subcortical areas, such as the hypothalamus and amygdala. In a study of females only, the volume of the anterior cingulate (ACC) was significantly reduced as compared to non-patient controls (Takahashi et al., 2002). The normal rightward asymmetry of this structure was disrupted, such that females with schizophrenia showed no significant asymmetry, and the same was found for the white matter within this structure. In a study comparing men and women, non-patient women had larger volumes of the ACC than non-patient men, controlled for overall cerebrum size. Conversely, for patients with schizophrenia, men showed larger volumes of ACC relative to women, controlling for brain size (Goldstein et al., 2002). This work suggests, like the Takahashi study above, that female patients lose volume in this region, and that they are more prone to loss in ACC than male patients. The hypothalamus, a complex assortment of nuclei affecting the endocrine system, has been shown to be relatively larger in healthy men relative to women (Goldstein et al., 2007). Goldstein et al. (2007) hypothesized that this structure may be involved in stress–response functions, including anxiety and the reward system. Little difference was observed between male schizophrenic patients and healthy controls, but women patients showed an enlargement of the hypothalamus. Regarding asymmetry, healthy controls showed rightward asymmetry regardless of gender, with similar observations in the patient sample. Goldstein and colleagues suggest that disruption of aromatization (conversion of testosterone to estrogen by the enzyme aromataze) may lead to higher levels of testosterone in females, which in turn leads to less cell pruning in this area (Goldstein et al., 2007). The Volumetric Literature, Conclusion The volumetric neuroimaging data on schizophrenic patients suggest gender differences. However, the data are sometimes confusing because of differences in measures, poor control, and small samples. The findings generally support the observation that men tend to have more severe forms of the disease, as differences
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between male patients and male non-patients appear larger and frequently more than do differences between women and their healthy counterparts. Interestingly, it is the endocrine and emotional areas that have yielded the most striking differences for females. However, because these differences are not tied to symptoms and behaviors, it is difficult to explain how, for example, a smaller ACC will affect the female with schizophrenia. Because analysis of structural MRI predates FMRI, differences in volume for particular areas of the brain have been the focus of most of the schizophrenia work. Functional MRI offers the advantage of tying brain activity with structure and/or performance, but this technique along with PET and DTI have been largely used in the studies of male and not female samples. Sometimes, although females are included, they are not analyzed separately. In our literature search, one fMRI study, one PET study, and one DTI study, having only five females included in the sample, were found. Several SPECT studies that included gender differences were also located.
Functional MRI, PET, SPECT, and DTI A single study found utilizing fMRI examined language lateralization in women. Sommer et al. (2003) recognized that differences in language lateralization in male schizophrenic patients could not be extrapolated to female subjects. As discussed above, in normal adults, the issue of language lateralization is unsettled, with the majority of findings suggesting that women have a more bilateral pattern of language activation, while in men language is more encapsulated in the left hemisphere (e.g. Shaywitz et al., 1995), and a few studies suggest no differences in lateralization (e.g. Frost et al., 1999). Studies of males with schizophrenia suggest that they recruited more of their right brain for language task than in normal controls (Artiges et al., 2000; Collinson et al., 2003). Sommer et al. (2003) tested this finding in a sample of 12 females and 12 males, finding that both sexes had decreased lateralization as seen from an increase in right hemispheric language activation. No differences between male and female patients were found. The authors pointed to a failure to inhibit non-dominant language areas in schizophrenia for both men and women (Sommer et al., 2003). Positron emission tomography was used in one study to examine the effects of neuroleptic treatment in patients with schizophrenia, which has been found to be different for men and women (Cohen, Nordahl, Semple, & Pickar, 1999). Metabolic changes in cerebral glucose were examined for differences between those treated with clozapine and/or fluphenazine. Fewer females were included in the study (clozapine-treated 7 females versus 17 males; fluphenazine-treated 6 females versus 22 males). An auditory discrimination task using three tones was used for the stimulus. Statistically significant differences by gender were not generally observed in this study; both men and women showed altered patterns of glucose metabolic rates compared to their healthy counterparts. However, two regional differences
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were observed in the limbic system and the basal ganglia in clozapine-treated females. The largest gender-based differences were in the left hemisphere, with females showing symmetry and males showing increased right-side metabolism, which appears to be consistent with volumetric data discussed earlier. One study used SPECT in analyzing gender differences. SPECT was used to investigate striatal dopamine D2 receptor binding in seven male and eight female patients with schizophrenia. No gender-based differences were found in laterality for the basal ganglia or frontal cortex areas. Neither there were correlations between the ratios and age of the subjects, duration of illness, or symptom severity (Parellada et al., 2004). Diffusion tensor imaging (DTI) was used in one study to examine abnormalities in the largest white matter tract of the brain, the corpus callosum. The schizophrenic group was found to have disruptions in the integrity of the splenium (posterior), but not the genu (anterior) parts of the corpus callosum as compared to non-patients. However, no gender-based differences were found in this study.
Conclusion Consistent with most neuroimaging research, the preponderance of studies utilize anatomical MRI to analyze differences in brain structure. Very few studies using other methods have included women, and so very little is known about gender differences from the standpoint of BOLD activation, glucose metabolism, neurotransmitter receptivity, or integrity of white matter tracts in the brain of women with schizophrenia compared to healthy women. Overall, the research, as it now stands, tends to argue for some level of neural protection against deviations from the normal brain in women with schizophrenia. Overall, such deviations are less drastic than for male patients.
Multiple Sclerosis Multiple sclerosis (MS), a progressive demyelinating disease of the brain, optic nerve, and spinal cord, is thought to be immunological in nature. Inflammation causes the erosion of the fatty insulation, known as myelin, on neurons. Lesions or “scars” form, lending the disease its name (Beaumont, Kenealy, & Rogers, 1999). Damage to the nerves can also occur. MS is known to affect more women than men by a ratio of almost 2:1 (c.f. Eikelenboom, Killestein, Uitdehaag, & Polman, 2005), yet there is evidence suggesting that men suffer more severe forms of the disease. Sex hormones are also considered to be salient to the course of MS because women with MS appear to have variations in the number and severity of relapses at specific times in relation to pregnancy, birth, and menses (Smith & Studd, 1992; Vukusic & Confavreux, 2006). Neuroimaging research usually includes both men and women participants, but most often, gender differences are not explored. Instead, the effect of gender is statistically removed from most studies.
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Of the few studies using diffusion tensor imaging, none examined females exclusively or compared males and females. Similar results were obtained for PET and SPECT studies. There are research studies that have used MRI techniques to identify MS lesions in the brain relative to gender. In general, T2 -weighted images have been largely unhelpful because the number of lesions seen on the images did not correlate with disease severity, although T1 images may better track the disease course (Pozzilli et al., 2003). One neuroimaging technique used in MS is known as “gadolinium-enhancing.” Gadolinium is injected intravenously prior to scanning. Because the inflammation that creates scars in MS can cause a breakdown of the blood-brain barrier, an increased level of inflammation is related to the presence of gadolinium-enhanced lesions seen in the scans (Weatherby et al., 2000). This type of scan, along with T2 - and T1 -weighted scans, has been used to better understand the relation between gender and MS. In a pilot study to examine the relationship between gadolinium-enhancing lesions and gender in MS, Weatherby et al. (2000) found more gadoliniumenhancing lesions in females than in males, indicating more inflammatory processes in women. This appeared paradoxical, since women tend to have a milder progression of the disease. Pozzilli et al. (2003) explored this paradox using T2 , T1 , and gadolinium images in a study with 413 patients, including 194 females and 72 males, with MS. Fewer men showed scans with at least one enhancing lesion, but again, women appeared to have more gadolinium-enhancing lesions. Pozzilli et al. found, however, that males have more hypointensities on T1 scans, known as “black holes,” which are believed to be related to axonal loss. The authors concluded that men have less inflammation, but show a greater number of areas where brain matter has been destroyed. Findings were linked to the role of sex hormones on repair of axon damage seen in patients with traumatic brain injury (Stein, 2001; Stein & Hoffman, 2003). Further exploring sex hormones in relation to lesion types in MS, Tomassini et al. (2005) investigated testosterone and oestradiol levels in patients with MS. Like Pozzilli and colleagues, Tomassini et al. found that all the 35 women in the study sample had a greater number of gadolinium-enhancing lesions than did the 25 men. It was also observed that women with low testosterone levels (2 SD below mean) had significantly more gadolinium-enhancing lesions than did women with normal testosterone levels. In the case of MS, neuroimaging techniques have begun to unravel not only the types of scans that can best predict clinical outcomes associated with sexual dimorphism but also the underlying mechanisms of the protective elements involved in the disease process.
Alzheimer’s Disease Gender-related differences in the onset, severity, and course of Alzheimer’s disease (AD) have long been suspected, although some issues remain controversial. The prevalence of AD is generally regarded as higher in women, although
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research suggests that this is due to the longer life expectancy of women in relation to men rather than greater susceptibility to the disease in women (Hebert, Scherr, McCann, Beckett, & Evans, 2001; Lindsay et al., 2002; Ruitenberg, Ott, van Swieten, Hofman, & Breteler, 2001). Because women live longer, they can be expected to develop more severe symptoms prior to death. Thus, when controlling for dementia severity and age, many of the gender differences do not hold up, such as greater language deficits in women (Hebert et al., 2000). Aggressiveness, abusiveness, and wandering have been found to be more prevalent in male patients (Lyketsos et al., 1999; Ott, Lapane, & Gambassi, 2000), but hallucinations, delusions, and psychosis appear to be equally prevalent in both genders (Paulsen et al., 2000). Few neuroimaging studies review the morphometric or functional differences between men and women with AD. However, fewer studies have contributed to the literature on the subject. In a large study involving nearly 400 participants, total intracranial volume of the brain was found to be not related to gender (Edland et al., 2002). However, specific areas of the brain have been found to be differentially associated with gender in patient populations. As in the MS research discussed above, the limbic area of the brain has been an area of focus. A small study involving 12 subjects found more volume loss in the limbic area in women than in men (Juottonen et al., 1998), although several other studies have found the contrary. In a SPECT study involving 20 men and 20 women with probable AD, loss of volume in many limbic areas was apparent in both male and female patients (Callen, Black, Caldwell, & Grady, 2004). However, women showed only anterior thalamic atrophy when compared to age- and sex-matched controls. Further, men with AD appeared to have significantly more atrophy of the posterior cingulate compared to women with AD. Greater loss of volume was also seen in the anterior cingulate for men with AD compared to both male controls and females with AD in a small study with 13 patients (Ballmaier et al., 2004). Differences in the temporal and parietal lobes have also been observed. Males with AD were found to have greater atrophy of the parietal lobes (based on ventricular enlargement) than in female patients (Kidron et al., 1997). In a large study with 220 patients, it was found that men had a SPECT pattern indicating parieto-temporal hypoperfusion in those with early onset AD (Nitrini et al., 2000). The pattern was seen independent of the severity or duration of AD symptoms.
Conclusion As seen in these three examples, schizophrenia, MS, and AD, neuroimaging is shedding light on the sexual dimorphism inherent in each disease. Substantially, more effort has been directed toward schizophrenia, and far less in the areas of MS and AD even though clinical gender differences are well documented. The preponderance of evidence, overall, suggests that women have a neurological protection against more severe forms of brain disease. The role of hormones in utero, as well
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as through out life, are beginning to be explored. Genetics, too, are beginning to be better understood as potentiators of disease. The research on females and disease has lagged far behind that of research on males, even in diseases to which women are more prone, such as MS. Further, such research is still in infancy. Linking neuroimaging results more convincingly to neuropsychological, genetic, and behavioral effects is necessary to make further steps in our understanding of gender-specific features of disease. Finally, treatment, it is hoped, will be better identified as our knowledge of the female brain increases.
Future of Neuroimaging for Women Similar to many areas of science and medicine, focus on issues specific to women in neuroimaging lags behind that of attention to men. Compounding this historic tendency is the recent and rapid growth in neuroimaging techniques and the high cost associated with doing this type of research. Neuroimaging, in general, is just growing out of a haphazard investigative phase, and so the literature for both men and women is often at odds. Small sample sizes, poor agreement on control variables, lack of hypothetical focus, and lack of image-gathering consistency between studies is a necessary phase in this nascent field. Moreover, even for those illnesses for which women are particularly targeted, the neuroimaging research has failed to account for gender despite known differences in brain anatomy between the genders. These laments aside, information gathered thus far points to exciting and important news with regard to brain structure, neural activation, disease processes, and natural aging. Women appear to be somewhat protected from the most serious forms of certain diseases by virtue of their more flexible and widely distributed neural networks, particularly in the area of language and emotion processing. Our brains mature earlier and retain more mass, especially in the frontal lobes, as we age. As the field progresses, we hope to see better ties between form and function. How is behavior linked to the differences we see in male and female brains? How do patterns of activation (more or less) tie into volumetric properties and behavioral observations? Further, the link between hormones and neural development, with neuroimaging as the lens, is an area that should yield exciting information in the years to come. Better samples, more clearly defined questions, and the prospect of ever-improving neuroimaging techniques make for an especially exciting time for neuroscientists interested in further exploring gender differences through neuroimaging.
References Alivisatos, B., & Petrides, M. (1997). Functional activation of the human brain during mental rotation. Neuropsychologia, 35, 111–118. Armstrong, E., Schleicher, A., Omran, H., Curtis, M., & Zilles, K. (1995). The ontogeny of human gyrification. Cerebral Cortex, 5, 56–63.
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Vadlamudi, L., Hatton, R., Byth, K., Harasty, J., Vogrin, S., Cook, M. J., et al. (2006). Volumetric analysis of a specific language region – the planum temporale. Journal of Clinical Neuroscience, 13, 206–213. Vukusic, S., & Confavreux, C. (2006). Pregnancy and multiple sclerosis: the children of PRIMS. Clinical Neurololgy and Neurosurgery, 108, 266–270. Walder, D. J., Seidman, L. J., Makris, N., Tsuang, M. T., Kennedy, D. N., & Goldstein, J. M. (2007). Neuroanatomic substrates of sex differences in language dysfunction in schizophrenia: a pilot study. Schizophrenia Research, 90, 295–301. Weatherby, S. J., Mann, C. L., Davies, M. B., Fryer, A. A., Haq, N., Strange, R. C., et al. (2000). A pilot study of the relationship between gadolinium-enhancing lesions, gender effect and polymorphisms of antioxidant enzymes in multiple sclerosis. Journal of Neurology, 247, 467–470. Wendt, P. E., & Risberg, J. (1994). Cortical activation during visual spatial processing: relation between hemispheric asymmetry of blood flow and performance. Brain and Cognition, 24, 87–103. Westerhausen, R., Kreuder, F., Dos Santos Sequeira, S., Walter, C., Woerner, W., Arne Wittling, R., et al. (2004). Effects of handedness and gender on macro and microstructure of the corpus callosum and its subregions: a combined high-resolution and diffusion-tensor MRI study. Cognitive Brain Research, 21, 418–426. Winstein, C. J., Grafton, S. T., & Pohl, P. S. (1997). Motor task difficulty and brain activity: investigation of goal-directed reciprocal aiming using positron emission tomography. Journal of Neurophysiology, 77, 1581–1594. Witelson, S.F. (1989). Hand and sex differences in the isthnws and gene of the human corpus callosum. Brain, 112, 799–835. Witelson, S. F., Glezer, I. I., & Kigar, D. L. (1995). Women have greater density of neurons in posterior temporal cortex. Journal of Neuroscience, 15, 3418–3428. Witelson, S. F., & Kigar, D. L. (1988a). Anatomical development of the corpus callosum in humans: a review with reference to sex and cognition. In D. L. Molfese & S. J. Segalowitz (Eds.), Brain lateralization in children (pp. 35–57). New York: Guilford Press. Witelson, S. F., & Kigar, D. L. (1988b). Aysmmetry in brain function follows asymmetry in anatomical form: gross, microscope, postmortem and imaging studies. In F. Boller & J. Grafman (Eds.), Handbook of neuropsychology (Vol. 1, pp. 111–142). Amsterdam: Elsevier Science Publishers. Witelson, S. F., & Kigar, D. L. (2004). Sylvian fissure morphology and asymmetry in men and women: bilateral differences in relation to handedness in men. Journal of Comparative Neurology, 323, 326–340. Witelson, S. F., & McCulloch, P. B. (1991). Premortem and postmortem measurement to study structure with function: a human brain collection. Schizophrenia Bulletin, 17, 583–591. Witelson, S. F., & Pallie, W. (1973). Left hemisphere specialization for language in the newborn: neuroanatomical evidence of asymmetry. Brain, 96, 641–646. Wood, S. J., De Luca, C. R., Anderson, V., & Pantelis, C. (2004). Cognitive development in adolescence: cerebral underpinnings, neural trajectories, and the impact of aberrations. In M. S. Keshavan & J. L. Kennedy (Eds.), Neurodevelopment and Schizophrenia (pp. 69–88). New York: Cambridge University Press. World Health Organization. (1975). Schizophrenia: a multinational study. Geneva: World Health Organization. Wright, I. C., Rabe-Hesketh, S., Woodruff, P. W. R., David, A. S., Murray, R. M., & Bullmore, E. T. (2000). Meta-Analysis of regional brain volumes in schizophrenia. American Journal of Psychiatry, 157, 16–25. Yotsutsuji, T., Saitoh, O., Suzuki, M., Hagino, H., Mori, K., Takahashi, T., et al. (2003). Quantification of lateral ventricular subdivisions in schizophrenia by high-resolution three-dimensional magnetic resonance imaging. Psychiatry Research: Neuroimaging, 122, 1–12. Zametkin, A. J., Liebenauer, L. L., Fitzgerald, G. A., King, A. C., Minkunas, D. V., Herscovitch, P., et al. (1993). Brain metabolism in teenagers with attention-deficit hyperactivity disorder. Archives of General Psychiatry, 50, 333–340.
Chapter 4
Women and Traumatic Brain Injury Elaine Clark and Janiece L. Pompa
Introduction In the past 30 years, numerous studies have been published on traumatic brain injury (TBI). Often termed “the silent epidemic,” approximately 1.25 million people in the US sustain a TBI each year (Langlois, Rutland-Brown, & Thomas, 2006). Brain injuries continue to be the leading cause of disability in the US among individuals who are 45 years of age or younger, and the leading cause of death of persons under the age of 34 (Kaplan & Thacker, 2000). Overall, males account for 62% of all TBIs, and females account for 38%. Researchers, however, have shown that the rate is similar for men and women between the age of 45 and 75 (Farace & Alves, 2000). Above the age of 75, there is a slightly higher increase in brain injuries among women (Croce, Fabian, Malhotra, Bee, & Miller, 2002; Holbrook & Hoyt, 2004). Although the Centers for Disease Control and prevention (CDC) have shown that females are as likely as males to be hospitalized for TBIs, some studies have shown that the length of hospital stay is longer for males (Coimbra, Hoyt, Potenza, Fortlage, & Hollingsworth-Fridlund, 2003; Mostafa et al., 2002; Napolitano et al., 2001). According to these researchers, males have more complications from infections, including sepsis and pneumonia. Data have further shown that the death rate for males who sustain a TBI is 4%, whereas for females it is 2%. Despite the lower mortality rate, reports from the National Center for Injury Prevention and Control estimate that 13,000 females and 37,000 males die each year from brain injuries (Langlois et al., 2006). For females, the risk of death is highest between early childhood and mid-30’s. Although little data are available for different ethnic groups, African–Americans, American–Indians, and native Alaskans are at unusually high risk of TBI-related death (Centers for Disease Control [CDC], 2007). Research has shown that falls are the most common cause and account for the highest number of TBIs, that is, 380,000 each year. This is followed by motor
E. Clark (B) Department of Educational Psychology, University of Utah, 1705 Campus Center Drive, #327 Salt Lake City, UT 84112-9255, USA e-mail:
[email protected] E. Fletcher-Janzen (ed.), The Neuropsychology of Women, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-76908-0 4,
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vehicle accidents (MVAs), accounting for 280,000 TBIs (CDC, 2007). While MVAs are more likely to be the cause of TBIs among adolescents of age 15–19 years, falls cause more TBIs in children from birth to 4 years as well as adults of age 75 years and older (Langlois et al., 2006). Data from the Trauma Recovery Project (see Holbrook & Hoyt, 2004), however, showed that females of age 18 years and older were more likely to sustain brain injuries from MVAs, auto-pedestrian accidents, and blunt force trauma, while motorcycle accidents and assaults caused the most TBIs in males. CDC data further show that African–Americans and American– Indians/native Alaskans are more likely to sustain TBIs from assault, at a rate four times higher than that of Caucasians. Sports and recreation-related TBIs are common for both sexes. The scant data available, however, indicate that females report more concussive symptoms, and may actually have more cognitive impairments than their male counterparts (Broshek et al., 2005). More recently, TBIs have been found to comprise a larger proportion of casualties in soldiers wounded in the wars in Iraq and Afghanistan compared to other American wars. Of more than 22,600 US soldiers wounded in these conflicts as of November 2006, blast injuries have been the most common. Fifty-nine percent of patients at Walter Reed Army Medical Center have been diagnosed with TBI (Okie, 2006), many as a result of blast injuries. Deborah L. Warden, national director of the Defense and Veterans Brain Injury Center, who is also a neurologist and psychiatrist at Walter Reed Army Medical Center, points out that the number of actual TBIs in this population is probably higher than statistics would suggest, since many closed brain injuries are difficult to diagnose. According to Zoroya (2007), researchers are finding that large numbers of soldiers exposed to bomb blasts in the wars are not being diagnosed with TBI right away, being overshadowed by more obvious injuries including shrapnel wounds, ruptured eardrums, and loss of vision. The report suggests that as many as 10–20% of the troops returning to Iraq may have sustained a concussion that was not initially diagnosed. Women now make up 15% of active duty forces, more than four times the number who served in the Gulf War in 1991. To date, at least 450 women have been wounded in Iraq, and 71 have died (Benedict, 2007). With more than 160,500 American female soldiers, or one of seven, having served in Iraq, Afghanistan, and the Middle East since the beginning of the war in 2003, a large number of women in the military are expected to sustain TBIs. As women soldiers serve more often in the combat zones, they are experiencing increasing numbers of injuries of all types, including brain injuries. Although the impact of TBIs on these women is not clear, research on the veterans of war, albeit mostly males, indicate that female soldiers are at a high risk of confounding psychological problems, i.e., stress-related disorders, which may actually exacerbate damage to the brain already caused by a TBI. Researchers have shown that in some cases of prolonged stress there are anatomical brain changes, including changes in the medial frontal cortex, amygdale, and hippocampus (e.g., reduced hippocampal volume) (Bremner & Vermetten, 2004).
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Clearly, females are not as well represented in scientific literature as males. Not surprisingly, the scant research that does exist is mixed. Whereas some studies have found a clear advantage for females compared to males in terms of functional outcomes (e.g., Groswasser, Cohen, & Keren, 1998), others have found that women do much worse (e.g., Kraus, Peek-Asa, & McArthur, 2000), even when similar outcome variables are assessed (such as the studies investigating vocational outcomes). In the late 1990s, the National Institutes of Health (NIH) convened a panel to evaluate the existing TBI research. The panel concluded from their review that the research is inadequate to address the issue of recovery, especially with regard to long-term outcomes. Not surprisingly, the panel recommended that more investigations be conducted with females who sustain brain injuries (National Institutes of Health [NIH], 1999). Since 1999, there has been an increase in systematic research on the effects of TBIs on sex differences, including sex differences in brain anatomy and physiology that might account for differences in post-injury outcomes in males and females.
Sex Differences in Brain Structure and Function There is a substantial literature on experimentally induced TBI in laboratory animals. This line of research has provided important information regarding anatomical and physiological differences in the brain and the ways in which brain structure and hormonal status affect the outcomes for humans. Although not directly translatable to humans, these animal studies suggest that females may be less susceptible to traumatic injury to the brain and ischemic damage as a result of neuroprotection from circulating estrogen and progesterone (e.g., Alkayed et al., 1998; Roof & Hall, 2000). Researchers working with animals are better able to control how hormones are assayed, which provides more reliable data than would be available from humans with regard to sex differences in outcomes related to hormones (DeGraba & Pettigrew, 2000). In human studies, hormonal status is often assumed, based on reports from the participants about their menstrual cycle or menopause status.
Studies on Anatomy and Physiology Animal Studies Sex differences in the gross and cellular anatomy of the brain of male and female rats in various stages of the estrous cycle have been fairly well documented in animal studies. According to one study, young male rats were shown to have greater dendritic spine density and arborization than female rats in the anterior cingulate cortex (Markham, McKian, Stroup, & Juraska, 2005). However, despite cellular deterioration in the medial frontal cortex due to aging in both sexes, the decline was less dramatic in the female rats. Markham et al. hypothesized that the reason for more gradual decline in females may have been related to the fact that estrogen is still produced in the aging female rat, and this might have prevented some cell loss.
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According to Stein (2007), there has also been evidence that aging female rats do not display as much dendritic spine loss in the motor cortex as do aging male rats. Studies have also demonstrated that female rats produce new myelin more rapidly than male rats following experimentally induced brain damage. Research by Cerghet et al. (2006) showed that female rats generated more new glial cells in the corpus callosum; however, male rats were found to have greater density of oligodendrocytes in the same region. The researchers, however, concluded that hormones in the female rats resulted in a more rapid turnover of oligodendrocytes, which caused greater synthesis of new myelin that promoted recovery from brain damage. In a study of astrocytes, the most active of all the glial cells in producing neurosteroids, Conejo et al. (2003) found that, of the four hippocampal subfields in Ammon’s horn (CA1, 2, 3, and 4), female rats have a significantly greater number of astrocytes in the CA1 region. Interestingly, cells in this region are the most likely hippocampal cells to die in conditions of mild anoxia (Kolb & Whishaw, 2003). Although male rats were shown to have a greater number of astrocytes in the CA3 region, the CA1 region has been found to be less susceptible to ischemic damage in females producing estrogen. Although results from these and other animal studies suggest that females may be at an advantage in terms of recovery from brain injury when compared to males, results from studies with humans are less clear, especially in adults. Researchers nonetheless have shown that there are sex differences in the gross and cellular anatomy of the human brain that might help to explain differences in brain injury recovery and longer-term outcomes in males and females. Human Studies According to a study by Goldstein et al. (2001) that matched women and men for age, education, intelligence, and socioeconomic status, the hippocampus was found to be proportionally larger in women when overall brain size was taken into consideration. Men, however, were found to have greater volume in the CA1 area of the hippocampus. Studies have also shown that men have greater neuronal densities than women, or neuronal synaptic connections per millimeter (Rabinowicz et al., 2002). Studies on individuals with unilateral strokes have suggested that women may have an advantage in terms of recovery of language function, given the fact that their brain organization may be less lateralized than that of males. According to Miller et al. (2005), females are more likely to have bilateral or right hemisphere speech lateralization, making it more likely that they can compensate through the use of the non-dominant hemisphere to process language following a cerebrovascular attack. Similarly, studies on female and male children with perinatal intracranial hemorrhage have shown that females score better on tests of verbal processing as well as general intelligence (Raz et al., 1995). According to Raz and colleagues, the findings are consistent with sex differences in anatomy and physiology of the brain, and indicate that males may be more susceptible to hypoxia and ischemia due to their requirement for greater cerebral oxygenation and possibly less elasticity of the cerebral vasculature.
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To what extent these sex differences in the brains of men and women affect the differences in recovery from TBI, however, is unclear. In fact, despite the results of studies showing that women with high levels of estrogen during menstrual cycles have a greater incidence of catamenial (menstrual) epilepsy, it is not clear if the epilepsy is due to heightened hippocampal sensitivity to glucocorticoids, alterations in neurotransmitters or NMDA receptor binding, or other neural changes (Stein, 2007). Although there are no data showing that women are more susceptible to posttraumatic seizures than men, there has been research showing that sex hormones are capable of activating or inhibiting cerebrocortical activity and blood flow. For example, research has shown that high levels of estrogen production during the follicular stage of menstrual cycle increased motor-evoked potentials, whereas peak levels of progesterone were found to decrease these potentials during the luteal phase (Smith, Adams, Schmidt, Rubinow, & Wassermann, 2002). Studies have also found that metabolic rates in women’s brain are much higher than in men’s brain (Ng, Lee, Lim, Wong, & Ya, 2006). Although no sex differences have been shown in regional blood flow, Ng and colleagues found that women have higher levels of mean hemispheric flow compared to men. Increased blood flow has been shown to help maintain cerebral perfusion; however, researchers found that, with high levels of estrogen and progesterone, women often retain fluids, an event associated with increased swelling and hypertension (Silberstein & Merriam, 2000).
Studies on Sex Hormones Hormonal effects are implicated in many studies found in the literature on anatomical and metabolic differences between males and females. This includes the impact of estrogen on the development of neurons, the sprouting of axons, and the formation of synaptic connections in the developing brain (Garcia-Segura, Azcoitia, & Don Carlos, 2001). However, there are many questions regarding how sex hormone levels actually impact the recovery process from TBI. Although there are no conclusive data showing females to be at greater risk for post-traumatic epilepsy, studies have shown that women with high levels of estrogen during menstrual cycles have a greater incidence of catamenial seizures. There are other studies that have shown that high levels of estrogen production during the follicular stage of the menstrual cycle increase motor-evoked potentials, whereas high levels of progesterone decrease these potentials during the luteal phase (Smith et al., 2002). Researchers, however, are still trying to understand the underlying mechanisms for these events, including any sex differences that would explain heightened hippocampal sensitivity to glucocorticoids, alterations in neurotransmitters, or NMDA receptor binding (Stein, 2007). Animal Studies Animal studies have examined hormonal effects on steroid synthesis in the brain and ways in which this might affect the neurophysiological cascade of events following
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trauma to the brain. Estrogen has been found to reduce cytotoxicity, inflammation, swelling, and cellular loss, and progesterone can help stabilize membranes following a injury so as to decrease the damage caused by lipid peroxidation (Roof & Hall, 2000; Stein, 2007). Animal models have also suggested that endogenous growthstimulating factors and exogenous agents might be able to promote central nervous system adaptation by stimulating the formation of new axons, dendrites, and synaptic connections (Taupin, 2006). Results of animal studies using rats have shown that females of reproductive age are at much less risk of death from experimentally induced brain injury than males or ovariectomized females (Roof & Hall, 2000), According to Roof and Hall’s study, in this population there was a significant reduction in the secondary effects of the trauma in terms of reduced cerebral blood flow and reduced ischemia. Rats administered both exogenous estrogen and progesterone also demonstrated an additive advantage in terms of recovery from brain injury. In an earlier study by Roof and colleagues, the results showed that estrogen was not as likely as progesterone to serve as a neuroprotective agent in reducing brain swelling following experimentally induced injury (Roof, Duvdevani, & Stein, 1993). These researchers demonstrated that during normal hormonal cycling, female rats suffered less cerebral edema than male rats following a brain injury. Nonetheless, in ovariectomized rats with circulating progesterone, brain swelling was reduced regardless of any subsequent hormone treatment administered. Interestingly, Lacreuse, Verreault, and Herndon (2001) found that rhesus monkeys performed more poorly on spatial recognition tasks when they were in the peri-ovulatory phase where their estrogen levels were the highest. When Clark and Goldman-Rakic (1989) studied rhesus monkeys with orbitofrontal cortex lesions, they found that the females outperformed the males on a cognitive discrimination learning task; however, when the injured females were given testosterone, their performance was similar to that of the injured males. Whether the results support the neuroprotective action of estrogen or the detrimental effects of testosterone on females is, however, not clear. Similarly, the underlying mechanism for estrogen’s action on the brain, including the reduction of excitotoxicity, is poorly understood. It is assumed, however, that one effect of estrogen is its antioxidant effect in limiting secondary injury to the brain by inhibiting the cascading effects of free radicalinduced lipid peroxidation, a process that destroys cell membranes. Human Studies Although studies have shown that exogenous administration of estrogen and progestins have improved outcomes following ischemia and traumatic injury to the brain in animals (Roof & Hall, 2000), the effects of hormone treatment on humans with TBIs are not yet clear. Farin and colleagues found that women older than 50, who were assumed to have lower levels of estrogen and progesterone, actually displayed less edema than younger women following severe TBI (Farin, Deutsch, Biegon, & Marshall, 2003). The role of hormones has also been questioned,
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given the fact that many studies have failed to demonstrate the effectiveness of hormone replacement therapy (HRT) in women with degenerative disorders such as Alzheimer’s disease and amyotrophic lateral sclerosis (ALS). Although HRT has been found to reduce symptoms of early-onset Parkinson’s disease, the treatment has been shown to exacerbate an earlier onset of ALS (Czlonkowska, Ciesielska, Gromadzka, & Kurkowska-Jastrzebska, 2005). Other studies have provided evidence of the neuroprotective action of sex hormones, showing that estrogen works to protect against immune suppression following trauma-related hemorrhage, thereby reducing post-injury infection such as sepsis and pneumonia in both men and women (Angele et al., 1998; Choudhry et al., 2005; Diodato, Knoferl, Schwacha, Bland, & Chaudry, 2001). Hurn and Brass (2003) also showed that pre-menopausal women were at less risk of stroke than post-menopausal women. Of those women who did have strokes, pre-menopausal women had less severe and persistent neurologic symptoms compared to males and post-menopausal women. Despite the fact that a number of studies suggest that men of all ages and post-menopausal women fare worse than premenopausal women following brain damage, research findings are still inconclusive in this area. Researchers have suggested that studies with adult humans do not lend themselves to the same precision as those with animals or, for that matter, children. The problem with adults is getting reliable hormone assessment and controlling for hormonal fluctuations during the administration of performance measures (Stein, 2007). As a result, besides animal experiments, some researchers are examining data from studies with children, including those who sustain TBIs in childhood. Donders and Hoffman (2002) studied children following TBIs from blunt trauma and found that females scored higher on verbal tasks, had better adaptive strategies, and completed tasks more rapidly than males. The researchers hypothesized that progesterone might have acted as a protective factor in females with TBIs. Other researchers have suggested that early gonadal hormone exposure may explain the more positive outcomes of females who sustained prenatal brain damage. Hindmarsh, O’Callahan, Mohay, and Rogers (2000), in their study with 2-year-olds of extremely low birth weight (ELBW), found that females outperformed males on all cognitive, language, and personal-social tasks. Raz et al. (1995) also studied children with prenatal damage in the form of intracranial hemorrhage, who were born no later than 37 weeks of gestation. Females performed much better than males on the cognitive outcome measures used in this study. Even research findings in children, however, are mixed. Some researchers have failed to find the advantage in females with brain injury, while some have found females to do worse on cognitive tasks than males with similar injuries (e.g., Morrison, Arbelaez, Fackler, deMaio, & Paidas, 2004). A review of the pediatric TBI literature, however, demonstrates that males may be at greater risk for poor outcomes. Although not accounted for by differences in sex hormone exposure, this result may be due to developmental variants that sometimes cannot be explained.
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Sex Differences in Outcomes There are surprisingly few publications that address sex differences in TBI outcomes. This section provides a brief review of some recent studies examining sex differences on concussion, cognitive performance, quality of life, and psychological health.
Post-concussive Symptoms and Cognitive Outcomes A study on sports-related concussion in 155 athletes (Broshek et al., 2005) showed that women were more likely than men to report concussive symptoms, and were 1.7 times more likely than males to display cognitive impairments following concussion, using simple and complex tests of reaction time. Some studies, however, have shown that females of age 30 and younger are not as impacted by TBIs as older females. Kirkness, Burr, Mitchell, and Newell (2004) examined the effects of gender and age at 3- and 6-month mark after injury. Data for 33 females and 124 males between the ages of 16 and 89, using the Extended Glasgow Outcome Scale and Functional Status Examination, showed a significant relationship between sex and age at 6-month mark following injury. Females older than 30 years of age were found to have a slower rate of recovery, and on average failed to show significant improvement after the 3-month mark. Having divided the sample into younger and older women, the researchers attributed differences in the two age groups to differences in the level of female sex hormones for women in each group. Although there are studies that have shown that females outperform males following TBI, these studies are often limited in terms of the measures used to assess functioning (e.g., using only the Wisconsin Card Sorting Test to assess executive functioning in males versus females with TBIs) (Niemeier, Marwitz, Lesher, Walker, & Bushnik, 2007). One study of particular importance is a meta-analysis by Farace and Alves (2000), in which 9822 studies on individuals with TBI were reviewed to determine which ones met the study criteria (e.g., separate sex data and age limit of 12 years). Eight studies met the criteria and were included in the final analysis. The results showed that females in these studies performed worse than males post-TBI on 85% of the variables except for return to work. Effect sizes (ES) ranged from –0.17 to –0.32, with the largest ES found in women who reported one or more post-concussive symptoms (PCS) 6 weeks post-injury. Small to medium ES were found for headache, dizziness, insomnia, number of days of post-traumatic amnesia, length of hospitalization, and return to previous employment. According to a study by Donders and Woodward (2003), data from 70 male and 70 female children aged 6–16 years with TBIs and their matched controls were evaluated 1 year post-injury. The researchers found that males with brain injuries performed worse than females and male control subjects on visual and verbal subtests of the Wide Range Assessment of Memory and Learning – Screening. No gender differences were found in the normal controls on memory testing. When the influence of low processing speed on the Wechster Intelligence scale for Childern-III
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(WISC-III) was partialled out of the memory test scores, sex differences between males and females post-TBI were no longer significant.
Quality of Life and Psychological Health Outcomes Quality of life (QOL) has been a major focus of the outcome literature on sex differences in adults. According to one study, females were found to report lower QOL than males following a brain injury (Seibert et al., 2002). However, sex differences in terms of QOL have been shown to diminish over time, as gender interacts with injury severity after a period of time following injury (Corrigan et al., 1998; Wood & Rutterford, 2006). For example, Wood and Rutterford examined the demographic variables such as age, sex, severity of injury, and cognitive functioning of 131 individuals to determine if any of these variables could predict psychosocial functioning 10 years after injury. While severity of injury predicted poorer outcomes over time, gender did not. However, females had less success in community integration when compared to males. Beside gender, the other variable that predicted poor community involvement post-TBI was being in a relationship. Other significant predictor variables were younger age at the time of injury and better working memory, which predicted greater life satisfaction. In another investigation of QOL and psychological health, Holbrook and Hoyt (2004) studied 313 women and 735 men 6–18 months after a injury. In addition, the researchers examined the effects of age, cause of brain injury, severity, neurologic status, extremity injury, and intentionality of injury. Findings on the Quality of Well Being scale, Center for Epidemiological Studies Depression scale, and Impact of Events scale showed that women were more likely than men to report poorer QOL at each of the three follow-up time periods regardless of age, injury severity, mechanism of injury, or extremity injury. The women also reported more symptoms of both depression and acute stress on measures administered when examined shortly after hospital discharge. At the time of follow-up 18 months later, women reported more persistent problems with depression than men. A greater percentage of females than males reported psychological symptoms post-TBI in a study by Bryant and Harvey (2003). The results showed that 1 month after an MVA-induced brain injury, 23% of females in the study were diagnosed with acute stress disorder, compared to 8% of males. Of those who were diagnosed with ASD, 98% of females and 57% of males were later diagnosed with post-traumatic stress disorder (PTSD). When re-evaluated at 6-month follow-up, 38% of females continued to meet the criteria for a diagnosis of PTSD as compared to 15% of males.
Vocational Outcomes Bounds, Schopp, Johnstone, Unger, and Goldman (2003) studied 23 females and 55 males with a diagnosis of TBI, who were receiving services from the Missouri Division of Vocational Rehabilitation (DVR). All the participants also completed
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a variety of neuropsychological measures following the acute stage of recovery in order to help in vocational planning. Information was also obtained from each subject regarding premorbid history of substance abuse, psychological status, seizure activity, and “other medical diagnoses.” However, medical records were not available regarding the circumstances surrounding subjects’ TBIs, so information regarding severity, loss of consciousness, and post-traumatic amnesia was obtained through subject self-report. Results indicated that, post-injury, there were gender differences on 5 of the 21 neuropsychological measures administered. Women’s performance was significantly better than men’s on a composite measure of written language (Woodcock-Johnson-Revised Broad Written Language), while men scored higher on measures of grip strength and speed of finger-tapping with either hand. With regard to employment, 74% of women and 56% of men in Bounds’ study, who had been approved for services from the DVR in Missouri had their cases closed before services were initiated. Interestingly, of this group, 73% of the women’s cases were closed because they refused services, while only 45% of men refused services. The authors hypothesized that this may have been due to role stereotypes which led women to choose to stay home as a parent or homemaker, whereas men were expected to obtain employment to support their families. Of the individuals who were assisted by the DVR, 24% of males were successfully employed, compared to only 4% of females (or one case). Although females received similar services in terms of vocational assessment, counseling, job placement, and on-the-job training, twice as many males received “maintenance” services or assistance with basic living expenses deemed necessary for them to derive full benefit from the vocational program (44% of males compared to 22% of females). The authors opined that this could have been due to the fact that men who sought DVR services were more likely to describe themselves as the head of the household than women.
Research Limitations This section describes a number of methodological issues in the scientific literature that limit conclusions that can be drawn about sex differences. This includes underrepresentation of females in the scientific literature, methodological weaknesses in design, and problems with data collection methods and measures used. Representation of Females Females have been under-represented not only in human studies, but also in animal research. According to Stein (2007), researchers often exclude female animals because of concerns that gonadal hormones may adversely affect the data, including results of clinical drug trials examining the effects of drugs on injury recovery. Human female subjects were also excluded from studies at times as a result of concerns about side effects of drugs affecting hormonal cycling and fecundity. Another concern that leads to the exclusion of females from drug treatment studies is the impact that gonadal hormones might have on drug metabolism. For example,
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research has shown that females metabolize certain drugs more rapidly than males, including drugs such as tirilazad used to protect against ischemia (Hulst et al., 1994). In the Hulst et al. study, rapid metabolism by females led to diminished effectiveness of the drug being studied. Although females are excluded from many TBI studies for the reasons previously cited, the problem is not planned exclusion, but rather the unavailability or lack of effort on the part of researchers to include females in these studies. A part of the difficulty is due to the fact that relatively fewer females sustain brain injuries in the first place. Given the disproportionately large number of males who suffer TBIs, it is not surprising to find more males included in the research. This, however, has led to inconclusive and oftentimes contradictory findings for females. Although some researchers have demonstrated a decided advantage for females over males in terms of recovery (Groswasser et al., 1998), others have shown that females do more poorly than males despite similar treatments (Bounds et al., 2003; Kraus, PeekAsa, & McArthur, 2000). Results from the Groswasser et al. study, for example, showed that women who participated in vocational rehabilitation were more successful than males in returning to work. Bounds and his colleagues, on the other hand, found that males in their study were more likely to be successful in securing employment after vocational training. Like many studies in the field, both of these studies were limited by a disproportionate number of males and females; for example, Groswasser et al. compared 262 males to 72 females. Bounds et al. (2003) had fewer participants in their study; however, they also had a disproportionate number of males to females. Unlike the males in their study, a few female subjects failed to complete the vocational program, ultimately declining vocational rehabilitation services. Adequacy of the Research Another problem with the literature is the fact researchers often rely on retrospective data collection methods to study brain injuries. Although this type of design often permits the collection of data on more individuals, it does not allow researchers to control for important moderating (or potentially confounding) variables. This includes various demographic characteristics such as gender, age, ethnicity, and for females information regarding menstrual cycle/menopausal status. Other data often missing in retrospective studies include information about the injury itself, such as etiology, mechanism, severity of injury, and complications such as infections and other damage to other body systems or organs. Even prospective designs, however, fail to include information about important, and potentially confounding, pre-injury variables, including those that have been shown to interact with gender (e.g., cognitive ability, socioeconomic status, educational background, medical insurance, employment history, and family functioning (Girard et al., 1996; Harrison-Felix et al., 1998; Rivara et al., 1996; Webb, Wrigley, Yoels, & Fine,1995). Although some of the aforementioned limitations can be corrected using prospective designs, such designs are often limited in terms of the conclusions that can be drawn given the limitation in methods used to collect data. For example, investigators often rely on self-report rather than using objective measures.
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Beside injury-related sequelae, patients are even asked to estimate the length of unconsciousness and post-traumatic amnesia. Relying solely on self-report may be particularly problematic if a particular group has a different response style. This includes females who have been shown to have a tendency to report more physical problems than males (e.g., female athletes reporting more post-concussive symptoms than male athletes) (Covassin et al., 2006). Investigators have also shown that women, in general, report more medical symptoms than men, and also visit physicians’ offices more often (see Farace & Alves, 2000), making it even more difficult to understand the TBI sequelae versus other physical, and psychological, conditions. Another problem in understanding TBI outcomes has to do with the interaction among different variables, including gender, injury severity, the particular outcome measured, and the amount of time since injury. There are studies, for example, that have shown gender to have a different impact over time and depending on the particular outcome studied. Donders and his colleagues, for example, found that 1 year after the injury, sex differences were no longer found for adaptive or behavioral outcomes; in fact, sex differences were only important for memory outcomes (Donders & Woodward, 2003; Woodward et al., 1999). There is also research that has shown time since injury interacts with injury severity; that is, the predictive strength of “severity” diminishes over time (Groswasser, Melamed, Agranov, & Keren, 1999). This is not surprising given research that has found a TBI to be a “cascading” event that can continue for months or even years after the initial injury (Stein 2007). Most researchers who conduct outcome studies, however, collect follow-up assessments within the first few months, or at the most first 2 years, following a injury. Although some outcome predictors, including severity of injury, can be assessed soon after the event, this variable has been shown to interact with time in a way that diminishes its predictive power. Some researchers, in fact, have shown that severity of injury does not predict social and occupational outcomes (Groswasser et al., 1999), mental health outcomes, or life satisfaction as well over time (Deb, Lyons, & Koutzoukis, 1999). Although some studies have failed to find similar results, including Wood and Rutterford’s (2006) 10-year follow-up study of life satisfaction showing that severity continues to predict the outcome (i.e., the more severely injured, the less satisfied with life), the results are often difficult to compare, given the differences in the characteristics of individuals included in different studies and the methods used to assess the outcomes. According to some researchers, the relationships found in the TBI research also depend on the skill or area being assessed. Rassovsky et al., for example, showed that severity of injury was able to predict cognitive outcomes but not emotional and behavioral outcomes (Rassovsky et al., 2006).
Conclusions Several animal studies have shown that female rodents do better in terms of outcomes from TBI’s than male rodents, and some human studies have found
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the same. However, there are not enough data as yet to draw firm conclusions. Epidemiological data, nonetheless, show that women are as likely as men to sustain severe brain injuries from MVAs, including being hit by a vehicle as a pedestrian, and from blunt trauma that is often caused by domestic violence. There is a need to investigate the mechanisms of injury in women suffering from TBI, given the differences between the primary causes of injury between men and women. This includes studying the roles of differing brain organization and morphology between men and women, regional versus hemispheric blood flow, and cortex volume differences in brain injury (Ng et al., 2006). Although some studies have shown that if a TBI results in a vegetative state in women, this will likely be more persistent than men, and the impairments more severe, for the most part, the studies reviewed in this chapter show that for premenopausal women who have adequate estrogen and progesterone levels, the outcomes are better. Research with older females, or and those with either low or excessively high estrogen and progesterone levels, has also found that they do more poorly in terms of TBI recovery when compared to same age men. Even young female children, however, with extremely low birthweight, or those who are premature and/or suffer intracranial hemorrhage, have been shown to have a potential advantage in terms of cognitive outcomes, again, thought to be due to early gonadal hormone exposure. Despite these findings, there are a number of important questions that still need to be answered, and many more female subjects need to be studied in order to draw any firm conclusions regarding sex differences and TBI outcomes.
Future Research and Practice Needs It is unclear if the same results will be found if larger numbers of women are evaluated, and included in studies. This, and the methodological difficulties that have been previously described, need to be addressed in future studies. Methodological issues include controlling for variables such as possible differences in symptomreporting between female and male TBI survivors. Future research should also account for pre-injury status (psychological, physical, cognitive, financial, work, and family); age at the time of injury; severity and mechanism of injury; occurrence of other types of injury with TBI, including psychological trauma; and time since injury. Interactions between age and sex, as well as between sex and course of recovery, should be studied, given the results that suggest that sex differences diminish over time. There should also be research studying treatment history postinjury, such as physical, cognitive, and vocational rehabilitation and its impact on the outcome for TBI survivors. It would be helpful to obtain information with regard to other outcome variables, such as educational and vocational attainment; extending the time of follow-up past the typical 12–18 months to give information with regard to long-term outcomes for TBI survivors; and the use of direct measurements rather than self-reporting of outcome measures. In addition, there is a pressing need
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to study more diverse groups of female TBI survivors. Ng et al. (2006) point out that most of the studies on TBI have used populations from the western hemisphere, primarily Caucasian subjects. The results of recent investigations with women have also raised questions as to whether they are, as animal studies suggest, protected at certain ages by estrogen and progesterone in terms of brain injury outcomes. Hopefully, improved research methods, and increased numbers of studies with larger human participants, will improve our understanding of the impact of gonadal hormones on TBI recovery. It may be true that women with “just the right amount” of estrogen and progesterone will have a better chance of recovery; however, the research is inconclusive as to what that amount would be, and how to insure that women are treated after TBI in order to maintain optimal hormonal levels for enhanced recovery. Improved methods to assay hormonal levels may be critical to developing appropriate treatment plans, rather than assuming differing levels of sex hormones in premenopausal and postmenopausal women. The effects of prescribing estrogen for the individual TBI survivor, if deemed medically appropriate, should be investigated. Since women have been found to metabolize drugs more rapidly in the brain, including some drugs that promote TBI recovery such as tirilizad, it is also critical that more female animals and female humans participate in drug studies. We need to clearly learn a lot more about how females metabolize certain drugs, including neuroprotective agents, as well as which drugs are most beneficial in enhancing recovery in women. Involving more females in drug studies will also help us better understand why it is that females who are in relationships, versus single, have been shown to participate less in brain injury rehabilitation treatment programs. Clearly, more attention needs to be paid to interventions specifically designed for women, and more research conducted that will examine a broad range of variables in recovery from TBI that may impact a female differently than a male. Further measures need to be developed that will insure that critical information is obtained about women and how they experience brain injuries and in what areas they are experiencing the most disruption and stress post-injury. Research has shown that persistent problems with depression often occur in females following TBI’s. More research is needed to understand how premorbid problems with depression or stress tolerance affects a woman’s recovery after a brain injury. More research needs to be done with regard to how to improve the ways that women’s needs can be effectively addressed, and their outcomes following a TBI. Obviously, this will mean that investigations should be designed in a way to insure that information about preinjury functioning is examined, and that outcomes are measured for several months and years after a brain injury. Given what we have learned about TBI’s, it is critical that methodologically sound prospective studies are designed to capture the cascade of events that are thought to occur following a TBI. This includes events that may not be readily observable, but which evolve over months, and in some cases even years, and affect a person’s life. Researchers also need to study the population of female soldiers returning from the Middle East, who may or may not have been diagnosed with TBIs prior to discharge. With more females than ever also participating in sports that
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may result in brain injuries, it is also imperative that efforts are made to work with schools and athletic organizations to not only help prevent concussions by using adequate head protection, but identifying individuals who might have sustained concussions and determining when they may safely return to play. Although it is often assumed that females do not account for as much income loss as males given the much greater number of males sustaining brain injuries, TBI’s in women also result in significant career and income changes. Nearly 70% of all adults who sustain severe brain injuries are unemployed for up to 1 year, and for those who eventually return to work, they do so at nearly half the previous wage (Johnstone, Mount, & Schopp, 2003). The cost of lost income, and associated lost tax dollars, alone is estimated to be 96 million dollars a year. This figure does not, however, include the high cost of public assistance and medical care (the latter estimated to be $33,000 per person) (McGarry et al., 2002). The CDC (2007) has estimated the annual cost of all brain injuries to be $6 billion. This number, however, pales in comparison to the cost of lost lives and lost quality of life for those who survive. Women, like men, are often injured at a time when they are involved in intimate relationships; raising children; enhancing their educations; building careers; and participating in important social, recreational, and community activities. As a result, more efforts need to be made to ensure that women receive comparable services, and that family members and other caregivers are educated about TBI’s and ways to help the female survivor recover soon. Practice guidelines need to be developed that will help professionals who work with these women survivors, and their families, to better understand rehabilitation and life care needs, and assist women to return home and integrate back into the community.
References Alkayed, H. J., Harukami, I., & Kimes, A. S. (1998). Gender-linked brain injury in experimental stroke. Stroke, 29, 159–166. Angele, M. K., Ayala, A., Monfils, B. A., Cioffi, W. G., Bland, K. I., & Chaudry, I. H. (1998). Testosterone and or low estradiol normally required but harmful immunologically for males after traumatic hemorrhage. Journal of Trauma, 44, 78–85. Benedict, H. (2007). The private war of women soldiers. http:www.salon.com/news/features/ 2007/03/07/women˙in˙military/. Bounds, T., Schopp, L., Johnstone, B., Unger, C., & Goldman, H. (2003). Gender differences in a sample of vocational rehabilitation clients with traumatic brain injury. NeuroRehabilitation, 18, 189–196. Bremner, J. D., & Vermetten, E. (2004). Neuroanatomical changes associated with pharmacotherapy in posttraumatic stress disorder. Annals of New York Academy of Sciences, 1032, 154–157. Broshek, D. K., Kaushik, T., Freeman, J. R., Erlanger, D., Webbe, F., & Barth, J. T. (2005). Sex differences in outcome following sports-related concussion. Journal of Neurosurgery, 102(5), 856–863. Bryant, P. A., & Marvey, A. G. (2003). Gender difference in the relationship between acute stress disorder and post traumatic stress disorder following motor vehicle accidents. Australian and New Zealand Journal of Psychiatry, 37, 226–229. Centers for Disease Control and Prevention. (2007). Traumatic Brain Injury. Retrieved July 28, 2007, from http://www.cdc.gov/ncipc/factsheets/tbi.htm.
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Cerghet, M., Skoff, R. P., Bessert, D., Zhang, Z., Mulins, C., & Ghandour, M. S. (2006). Proliferation and death of oligodendrocytes and myelin proteins are differentially regulated in male and female rodents. Journal of Neuroscience, 26, 1439–1447. Choudhry, M. A., Schwacha, M., Hubbard, W., Kerby, J., Rue, L. W., Bland, K., & Chaudry, I. H. (2005). Gender differences in acute response to trauma hemorrhage. Shock, 24(1), 101–106. Coimbra, R., Hoyt, D. B., Potenza, B. M., Fortlage, D., & Hollingsworth-Fridlund, P. (2003). Does sexual dimorphism influence outcome of traumatic brain injury patients? The answer is no! Journal of Trauma, 54, 689–700. Clark, A. S., Goldman-Rakic, P. S. (1989). Gonadal hormones influence the emergence of cortical function in nonhuman primates. Behavioural Neuroscience, 103(6), 1287–95. Corrigon, J. D., Smith-Knapp, K., Granger, C. V. (1998). Outcomes in the first 5 years after traumatic brain injury. Archives of physical medicine and rehabilitation, 79(3), 298–305. Conejo, N. M., Gonzalez-Pardo, H., Pedraza, C., Navarro, F., Vallejo, G., & Arias, J. L. (2003). Evidence for sexual differences in astrocytes of adult rate hippocampus. Neurosciences, 339, 119–122. Covassin, T., Swanik, C., Sachs, M., Kendrick, Z., Schatz, P., Zillmer, E., & Kaminaris, C. (2006). Sex differences in baseline neuropsychological function and concussive symptoms of collegiate athletes. British Journal of Sports Medicine, 40(11), 923–927. Croce, M. A., Fabian, T. C., Malhotra, A. K., Bee, T. K., & Miller, P. R. (2002). Does biological sex influence outcome? Journal of Trauma, 53, 889–894. Czlonkowska, A., Ciesielska, A., Gromadzka, G., & Kurkowska-Jastrzebska, I. (2005). Estrogen and cytokines production: possible cause of gender differences in neurological disease. Current Pharmaceutical Design, 11, 1017–1030. Deb, S., Lyons, I., & Koutzoukis, C. (1999). Neurobehavioral symptoms one year after a head injury. British Journal of Psychiatry, 174, 360–365. DeGraba, T. J., & Pettigrew, L. C. (2000). Why do neuroprotective drugs work in animals but not humans? Neurology Clinics, 18, 475–493. Diodato, M. D., Knoferl, M. W., Schwacha, M. G., Bland, K. I., & Chaudry, I. H. (2001). Biological sex differences in the inflammatory response and survival following hemorrhagic and subsequent sepsis. Cytokine, 14, 162–169. Donders, J., & Hoffman, N. M. (2002). Gender differences in learning and memory after pediatric traumatic brain injury. Neuropsychology, 16, 491–499. Donders, J., & Woodward, H. R. (2003). Gender as a moderator of memory after traumatic brain injury in children. Journal of Head Trauma Rehabilitation, 18(2), 106–115. Farace, E., & Alves, W. M. (2000). Do women fare worse? A metaanalysis of gender differences in outcome after traumatic brain injury. Neurosurgical Focus, 8(1), E6. Farin, A., Deutsch, Biegon, A., & Marshall, L. F. (2003). Sex-related differences in patients with severe head injury: greater susceptibility to brain swelling in female patients 50 years of age and younger. Journal of Neurosurgery, 98, 32–36. Garcia-Segura, L. M., Azcoitia, I., & Don Carlos, L. L. (2001). Neuroprotection by estradiol. Progress Neurobiology, 63, 29–60. Girard, D., Brown, J., Burnett-Stolnack, M., Hashimoto, N., Hier-Wellmer, S., Perlman, O. Z., & Seigerman, C.(1996). The relationship of neuropsychological status and productive outcomes following traumatic brain injury. Brain Injury, 10, 663–676. Goldstein, J. M., Seidman, L. J., Horton, N. S., Makris, N., Kennedy, D. N., Caviness, V. S., Faraone, S. V., & Tsuang, M. T (2001). Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. Cereb Cortex, 11, 490–497. Groswasser, Z., Cohen, M., & Keren, O. (1998). Female TBI patients recover better than males. Brain Injury, 12, 805–808. Groswasser, Z., Melamed, S., Agranov, E., & Keren, O. (1999). Return to work as an integrative outcome measure following traumatic brain injury. Neuropsychological Rehabilitation, 9, 493–504.
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Harrison-Felix, C., Zafonte, R., Mann, N.,Dijkers, M., Englander, J., Kreutzer, J. (1998). Brain injury as a result of violence: preliminary findings from the traumatic brain injury model system. Archives of Physical Medicine and Rehabilitation, 79, 730–737. Hindmarsh, G. J., O’Callahan, M. J., Mohay, H., & Rogers, Y. M. (2000).Gender differences in cognitive abilities at 2 years in ELBW infants. Early Infant Development, 60, 115–122. Holbrook, T. L., & Hoyt, D. B. (2004). The impact of major trauma: quality of life outcomes are worse in women than in men, independent of mechanism and injury severity. Journal of Trauma Injury, Infection, and Critical Care, 56(2), 284–290. Hulst, L. K., Fleishaker, J. C., Peters, G. R., Harry, J. D., Wright, D. M., & Ward, P. (1994). Effect of age and gender on tirilazad pharmacokinetics in humans. Clinical Pharmacologic Therapy, 55, 378–384. Hurn, P., & Brass, L. (2003). Estrogen and stroke. Stoke, 34, 338–343. Johnstone, B., Mount, D., & Schopp, L. H. (2003). Financial outcomes one year post traumatic brain injury. Archives of Physical Medicine and Rehabilitation, 84, 238–241. Kaplan, J., & Thacker, S. B. (2000). Working to prevent and control injuries in the year 2000: the injury fact book for the year 2000. Atlanta, GA: National Center for Injury Prevention and Control. Kirkness, C. J., Burr, R. L., Mitchell, P. H., & Newell, D. W. (2004). Is there a sex difference in the course following traumatic brain injury? Biological Research for Nursing, 5(4), 299–310. Kolb, B. & Whishaw, I. (2003). Fundamentals of human neuropsychology. New York, NY: Worth Publishers. Kraus, J. F., Peek-Asa, C., & McArthur, D. (2000). The independent effect of gender on outcomes following traumatic brain injury. Neurosurgery Focus, 8(1): e5. Lacreuse, A., Verreault, M., & Herndon, J. G. (2001). Fluctuations in spatial recognition memory across the menstrual cycle in female rhesus monkeys. Psychoneuroendrocrinology, 26, 623–639. Langlois, J. A., Rutland-Brown, W., & Thomas, K. E. (2006). Traumatic brain injuries in the United States. Washington, DC: U.S. Department of Health and Human Services. Markham, J. A., McKian, K. P., Stroup, T., & Juraska, J. M. (2005). Sexually dimorphic aging of dendritic morphology in CA1 hippocampus. Hippocampus, 15, 97–103. McGarry, L. J., Thompson, D., Millham, F. H., Cowell, L., Snyder, P. H., Lenderking, W. R., & Weinstein, M. C. (2002). Outcomes and costs of acute treatment of traumatic brain injury. Journal of Trauma Injury Infection and Critical Care, 53(6), 1152–1159. Miller, J., Jayadev, S., Dodrill, C., & Ojemann, G. (2005). Gender differences in handedness and speech lateralization related to early neurologic insults. Neurology, 65(12), 1974–1975. Morrison, W. E., Arbelaez, J., Fackler, J. C., deMaio, A., & Paidas, C. N. (2004). Gender and age effects on outcome after pediatric traumatic brain injury. Pediatric Critical Care Medicine, 5, 145–151. Mostafa, G., Huynh, T., Sing, R. F., Miles, W. S., Norton, J., & Thomason, M. H. (2002). Biological sex related outcomes in trauma. Journal of Trauma, 53, 430–435. Napolitano, L. M., Greco, M. E., Rodriguez, A., Kufera, J. A., West, R. S., & Scalea, T. M. (2001). Gender differences in adverse outcomes after blunt trauma. Journal of Trauma, 50, 274–280. National Institutes of Health. (1999). Rehabilitation of Persons with Traumatic Brain Injury Consensus Statement. Washington, DC: NIH. Niemeier, J. P., Marwitz, J. H., Lesher, K., Walker, W., & Bushnik, R. (2007). Gender differences in executive functioning following traumatic brain injury. Neuropsychological Rehabilitation, 17(3), 293–313. Ng, I., Lee, K. K., Lim, J. H. G., Wong, H. B., & Yan, X. Y. (2006). Investigating gender differences in outcome following severe traumatic brain injury in a predominantly Asian population. British Journal of Neurosurgery, 20(2), 73–78.
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Okie, S. (2007). Iraq vets falling through the mental health cracks. http:www.healthfinder.gov/ news/newstory.asp?docID-600320. Rabinowicz, T., Petetot, J. M., Gartside, P., Sheyn, D., Sheyn, T., & De, C. M. (2002). Structure of the cerebral cortex in men and women. Journal of Neuropathological Experimental Neurology, 61, 46–57. Rassovsky, Y., Satz, P., Alfano, M. S., Light, R. K., Zaucha, K., McArthus, D. L., & Hovada, D. (2006). Functional outcome in TBI: Neuropsychological, emotional, and behavioral mediators. Journal of Clinical and Experimental Neuropsychology, 28(4), 567–580. Raz, S., Lauterbach, M. D., Hopkins, T. L., Glogowski, B. K., Porter, C. L., Riggs, W. W., & Sander, C. J. (1995). A female advantage in cognitive recovery from early cerebral assault. Developmental Psychology, 31(6), 958–966. Rivara, J. M., Jaffe, K. M., Polissar, N. L., Fay, G. C., Liao, S., & Martin, K. M. (1996). Predictors of family functioning and change three years after traumatic brain injury in children. Archives of Physical Medicine and Rehabilitation, 77, 754–764. Roof, R. L., Duvdevani, R., & Stein, D. G. (1993). Gender influences outcome of brain injury: progesterone plays a protective role. Brain Research, 607, 333–336. Roof, R. L., & Hall, E. D. (2000). Gender differences in acute CNS trauma and stroke: neuroprotective effects of estrogen and progesterone. Journal of Neurotrauma, 17, 367–388. Seibert, P. S., Reedy, D. P., Hash, J., Webb, A., Stridh-igo, P., Basom, J., & Zimmerman, C. G. (2002). Brain injury: quality of life’s greatest challenges. Brain Injury, 16, 837–848. Silberstein, S. D., & Merriam, G. R. (2000). Physiology of the menstrual cycle. Cephalagia, 20, 148–154. Smith, M. J., Adams, L., Schmidt, P. J., Rubinow, D. R., & Wassermann, E. M. (2002). Effects of ovarian hormones on human cortical excitability. Annals of Neurology, 51, 599–603. Stein, D. G. (2007). Sex differences in brain damage and recovery of function: experimental and clinical findings. Progress in Brain Research, 161, 339–351. Taupin, P. (2006). Adult neurogenesis in mammals. Current Opinion in Molecular Therapy, 8, 345–351. Webb, C. R., Wrigley, M., Yoels, W., & Fine, P. R. (1995). Explaining quality of life for persons with traumatic brain injuries two years after injury. Archives of Physical Medicine and Rehabilitation, 76, 1113–1119. Wood, R. L., & Rutterford, N. A. (2006). Demographic and cognitive predictors of long term psychosocial outcome following traumatic brain injury. Journal of the International Neuropsychological Society, 12, 350–358. Woodward, H., Winterhalter, K., Donders. J., Hackbarth, R., Kuldanek, A., & Sanfilippo, D. (1999). Prediction of neurobehavioral outcome 1–5 years post pediatric traumatic head injury. Journal of Head Trauma Rehabilitation, 13, 351–359. Zoroya, G. (2007). Scientists: brain injuries from war worse than thought. USA Today, 8A.
Chapter 5
Attention-Deficit/Hyperactivity Disorder Nancy L. Nussbaum and Katherine N. Shepard
Introduction Attention-deficit/hyperactivity disorder (ADHD) is a neurologically based condition that often affects an individual across the lifespan. By definition, it begins in childhood, and includes symptoms of inattention, hyperactivity, and impulsivity that cause clinically significant impairment in multiple settings (American Psychiatric Association, 2002). In addition, there is behavioral, social, and neuropsychological dysfunction associated with this disorder. In this chapter, we will explore various facets of ADHD in relation to girls and women. In particular, we will include a description of the disorder, including diagnostic criteria, characteristics, prevalence, and neuropathology. The assessment of ADHD from a neuropsychological perspective will be discussed followed by a case example. Various aspects of intervention will be examined and critiqued. Finally, this information will be summarized and future directions in the field will be explored. Our goal is to examine these topics of discussion in relation to girls and women who have ADHD. For too many years, gender was considered to be an “extraneous” variable that needed to be controlled for by excluding females from the subject pool. This was no exception in the fields of ADHD and neuropsychology. Fortunately, there appears to be a growing realization by researchers and their funding sources that excluding 51% of the population from the subject pool is not good for science or for the public. One can now find a growing body of publications in the field of ADHD, where gender is considered as an independent variable to study versus an extraneous variable for which to control. We would like to begin our discussion by presenting a vignette of Mary, whose story is similar to many girls and women who have ADHD.
N.L. Nussbaum (B) Austin Neuropsychology, University of Texas at Austin, Austin, Texas 78705, USA e-mail:
[email protected] E. Fletcher-Janzen (ed.), The Neuropsychology of Women, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-76908-0 5,
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Vignette • Meet Mary at age 7 Mary follows class rules and never causes any behavioral problems in the classroom, but she is usually one of the last to finish her work. Mary seems to understand the work, but she makes a lot of “careless” mistakes that often bring her grades down. She does not always seem to be listening and says, “I get lost in my head.” At home, Mary has great concentration for things: At home, Mary has great concentration for things; she is interested in like art projects and pretend play, but it takes her forever to complete a 15-min homework assignment. Her room is a disaster zone. She frequently misplaces many of her things – completed homework, soccer socks, lunchbox. . .. • Mary at 15 Mary is stressed out and exhausted. Although an A/B student, she has to study 5 or 6 h at night with her regular classes to achieve these grades. She attempted an Advanced Placement English class but had to return to a regular class after falling too far behind. Her teachers have been saying, “there’s something there” since elementary school, but because she tried so hard and was so well-behaved, they had never pushed to have her evaluated. • Mary at 35 She has become steadily more depressed and overwhelmed since marrying and having 2 children. After her second child, Mary decided to quit work, so that she would have more time to get organized at home. It seemed she was able to actually function better when she was in college and afterwards in a job situation. Now she feels like she jumps from unfinished project to unfinished project, nothing is accomplished and she is exhausted, irritable, and frustrated at the end of most days. When her 8-year-old daughter began struggling in school and was diagnosed with ADHD-PI, Mary began wondering whether she also might have the disorder. Unfortunately, this is not an uncommon scenario for many undiagnosed women who have struggled with ADHD symptoms since childhood. As the literature suggests, they are at higher risk of depression and anxiety resulting from many years without help dealing with the primary attention symptoms.
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Definition/Diagnostic Criteria What Is Attention-Deficit/Hyperactivity Disorder? According to the Diagnostic and Statistical Manual of Mental Disorder–Fourth Edition–Text Revised (DSM-IV-TR), ADHD is characterized as a “persistent pattern of inattention and/or hyperactivity/impulsivity that is more frequently displayed than is typically observed in individuals at a comparable level of development” (American Psychiatric Association, 2002, p. 85). The DSM-IV-TR criterion further specifies that the onset of behavioral problems must occur before the age of 7 and that problematic behaviors must occur in at least two settings (i.e., work, home, or school). The current classification system specifies three subtypes of ADHD: Predominantly Inattentive type (ADHD-PI), Predominantly Hyperactive-Impulsive type (ADHD-HI), and Combined type (ADHD-C). The criteria for each of the three subtypes are presented in Fig. 1. I. Either A or B: A. Six or more of the following symptoms of inattention have been present for at least 6 months to a point that is disruptive and inappropriate for developmental level: Inattention 1. Often does not give close attention to details or makes careless mistakes in schoolwork, work, or other activities. 2. Often has trouble keeping attention on tasks or play activities. 3. Often does not seem to listen when spoken to directly. 4. Often does not follow instructions and fails to finish schoolwork, chores, or duties in the workplace (not due to oppositional behavior or failure to understand instructions). 5. Often has trouble organizing activities. 6. Often avoids, dislikes, or does not want to do things that take a lot of mental effort for a long period of time (such as schoolwork or homework). 7. Often loses things needed for tasks and activities (e.g., toys, school assignments, pencils, books, or tools). 8. Is often easily distracted. 9. Is often forgetful in daily activities. B. Six or more of the following symptoms of hyperactivity−impulsivity have been present for at least 6 months to an extent that is disruptive and inappropriate for developmental level: Fig. 1 DSM-IV criteria for ADHD
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Hyperactivity 1. Often fidgets with hands or feet or squirms in seat. 2. Often gets up from seat when remaining in seat is expected. 3. Often runs about or climbs when and where it is not appropriate (adolescents or adults may feel very restless). 4. Often has trouble playing or enjoying leisure activities quietly. 5. Is often “on the go” or often acts as if “driven by a motor”. 6. Often talks excessively. Impulsivity 1. Often blurts out answers before questions have been finished. 2. Often has trouble waiting one’s turn. 3. Often interrupts or intrudes on others (e.g., butts into conversations or games). II. Some symptoms that cause impairment were present before the age of 7 years. III. Some impairment from the symptoms is present in two or more settings (e.g., at schoolwork and at home). IV. There must be clear evidence of significant impairment in social, school, or work functioning. V. The symptoms do not happen only during the course of a pervasive developmental disorder, schizophrenia, or other psychotic disorder. The symptoms are not better accounted for by another mental disorder (e.g., Mood Disorder, Anxiety Disorder, Dissociative Disorder, or a Personality Disorder). Based on the above criteria, three types of ADHD are identified: 1. ADHD, Combined Type: if both criteria 1A and 1B are met for the past 6 months. 2. ADHD, Predominantly Inattentive Type: if criterion 1A is met but criterion 1B is not met for the past 6 months. 3. ADHD, Predominantly Hyperactive-Impulsive Type: if criterion 1B is met but criterion 1A is not met for the past six months. Fig. 1 (continued)
The primary strength of current DSM-IV-TR diagnostic criteria is that it was established using a rigorous empirical procedure. The items used to create the criteria were selected from the factor analysis of parent and teacher rating forms. A cutoff score of six or more symptoms was established utilizing a field study addressing the levels of impairment in children. The additional strengths of the current diagnostic criteria are that the criteria provide a tool for clinicians to assess the pervasiveness of symptoms (i.e., Global Assessment of Functioning Scale) and highlight the
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importance of significant impairment or disruption in major domains of functioning (i.e., home, work, or school) when making diagnoses. However, the current classification system is not without criticism. Perhaps, the most heated debate is the classification of ADHD-PI and ADHD-C as subtypes of the same disorder. An extensive review of research conducted by Milich, Balentine, and Lynam (2001) found that ADHD-PI can be distinguished from ADHD-C in terms of symptoms, common comorbidities, executive functioning weaknesses, and treatment outcomes. Individuals with ADHD-PI are characterized as being hypoactive and are at greater risk of developing co-morbid internalizing disorders (Barkley, DuPaul, & McMurray, 1990; Lahey, Schoughency, Hynd, Carlson, & Niever, 1987), whereas individuals with ADHD-C are more likely to be hyperactive, impulsive, and are at greater risk of co-morbid externalizing disorders (Barkley et al., 1990; Eiraldi, Powr, & Nezu, 1997; Gaub & Carlson, 1997; Lahey et al., 1987; Maedgen & Carlson, 2000; Morgan, Hynd, Riccio, & Hall, 1996). Differences in executive functioning between ADHD-PI and ADHD-C have also been demonstrated. Children with ADHD-PI and ADHD-C demonstrate different weaknesses of executive functioning. Children with ADHD-C demonstrate significant weaknesses on tests of visual perception, visual sequential memory, planning, and inhibition compared to children with ADHD-PI. In contrast, children with ADHD-PI tend to be more impaired on tasks of working memory when compared to children with ADHD-C and controls (Diamond, 2005). A cautionary note is warranted when drawing conclusions from the existing body of research as most studies examining executive functions utilize small sample sizes and yield mixed results (Nigg, Blaskey, Huang-Pollock, & Rappley, 2002). Also, Barkley and colleagues (1999) demonstrated that children with ADHD-PI and ADHD-C responded differently to common stimulant medications prescribed for ADHD. Children with ADHD-PI responded to low dosages of stimulant medication, whereas children with ADHD-C responded more favorably to high doses of medication. These findings have led many researchers and practitioners to question if ADHD-PI and ADHD-C should be classified as two separate disorders at all. The second issue that has been a source of debate was the exclusion of “sluggish cognitive tempo” (SCT) from the DSM-IV criteria. The term sluggish cognitive tempo refers to a constellation of symptoms that include sluggishness, drowsiness, lethargy, and apparent daydreaming. Although such symptoms were included in the field trials of DSM-IV and were found not to be significant predictors of ADHD, follow-up studies have demonstrated that SCT represents a unique subtype of ADHD-PI (Carlson & Mann, 2002; Hartman, Willcut, Rhee, & Pennington, 2004; McBurnett, Pfiffner, & Frick, 2001). Furthermore, the presence of SCT seems to have important ramifications on assessment, treatment, and the risk for co-morbid conditions. Third, the early age of onset (i.e., symptoms must be present at or before age 7) has been criticized for lack of empirical support. Several studies, including the field trials of DSM-IV, did not demonstrate clear discontinuities in the degree of ADHD or type of impairments between individuals meeting and individuals not meeting the 7-year age-of-onset criterion (Applegate et al., 1997; Barkley, 2006;
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McGee, Williams, & Feehan, 1992). Barkley and Biederman (1997) proposed that the DSM-IV-TR age requirement should be broadened to specify that symptoms must occur during childhood, for example by age 15. Such a revision to the current criteria still would be consistent with the conceptualization of ADHD as a disorder with childhood onset.
DSM-IV Criteria for Females Numerous issues have been raised about the appropriateness of the DSM-IV criteria for females (Arnold, 1996; Nadeau & Quinn, 2002; Ohan & Johnston, 1999). As a group, females have significantly lower levels of inattention and hyperactivity on both rating scales and lab measures than males (Achenbach, 1991; Arnold, 1996; Conners, 1994; Goyette, Conners, & Ulrich, 1978; Trites, Dugas, Lynch, & Ferguson, 1979). For example, Achenbach, Howell, Quay, and Conners (1996) found that in a clinically referred sample parents of 16-year-old females endorsed an average of approximately four items and parents of 16-year-old males endorsed an average of eight items on the Achenbach Conners and Quay Questionnaire. A similar ratio of 2:1 for parental endorsement of symptoms was observed in the clinically referred sample from the same study. Because females demonstrate significantly fewer attentional symptoms than males, in order to be identified as having ADHD, they must demonstrate significantly more attentional symptoms, compared to female peers, than males compared to male peers. As a result, it has been hypothesized that certain females, who are experiencing significant deficits in attention compared to other females, are not receiving diagnoses due to the high threshold set by the DSM-IV. This has resulted in the proposed “corrected” criteria, which would have sex-specific thresholds related to the degree of deviance from sex-referenced norms (McGee & Feehan, 1991). Concerns have been expressed that lowering the cut-off scores for females can result in the over-diagnosis of ADHD. A conference held at the National Institutes of Health in 1994 (summarized in Arnold, 1996) concluded that using separate sex-referenced criteria for females should not currently be conducted; however, they did propose that future research should examine the effects of separate sex-referenced cut-off scores. In addition, the conference noted that it was imperative to maintain the DSM criterion, which required that the individual demonstrate impairment in two or more settings, in order to reduce the risk of over-identification (Arnold, 1996). Second, concerns have been raised that because the DSM-IV-TR criteria appears to be more appropriate for males than females, females who have significant difficulties with attention are being misdiagnosed with other forms of psychopathology. More specifically, because women who have ADHD-PI tend to report low levels of arousal, they are often diagnosed with dysthymia, whereas women who have ADHD-C and display high energy levels, impulsivity, and verbal aggression are frequently misdiagnosed with bipolar disorder (Nadeau & Quinn, 2002a, 2002b). The result of misdiagnoses can result in females receiving inappropriate treatment.
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Prevalence Rates Attention-deficit/hyperactivity disorder is one of the most common forms of psychopathology with estimates ranging from 8% to 12% (Faraone, Sergeant, Gillberg, & Biederman, 2003). Lifetime prevalence estimates of ADHD subtypes are 4% for ADHD-PI, 2.2% for ADHD-HI, and 3.7% for ADHD-C (Staller & Faraone, 2006). Estimates of the prevalence of the subtypes of ADHD vary depending on the sampling methodology used. In community samples, ADHD-PI is the most prevalent of the subtypes (Gaub & Carlson, 1997), whereas in clinical samples, ADHD-C is the most prevalent subtype (Eiraldi et al., 1997; Faraone, Biederman, Weber, & Russell, 1998; Lahey et al., 1994). It has been hypothesized that the disparity between community and clinical sample estimates is related to the increased rate of externalizing symptoms that are evident to teachers and parents compared to less-apparent inattentive symptoms (Gershon, 2002b). Prevalence estimates indicate that ADHD occurs more frequently in males than in females during childhood, with gender ratios (M:F) ranging from 2:1 to 9:1 in clinical samples and 2:1 to 3:1 in epidemiological studies (Arcia & Conners, 1998; American Psychiatric Association, 1994; Carlson & Mann, 2002; Goodyear & Hynd, 1992; Lahey et al., 1987, 1994). For ADHD-PI, prevalence ratios are 2.3:1 in community samples and 3.7:1 in clinical samples. For ADHD-C, gender ratio is 3.2:1 in community samples and 4.1:1 in clinical samples (Milich et al., 2001). In comparison to pediatric samples, there is an increased representation of females in adult samples, with approximately an equal number of adult males and females meeting the diagnostic criteria for ADHD (Biederman, Faraone, Spencer, Wilens, Mick, & Lapey, 1994). Arcia and Conners (1998) reported a significant increase in the number of females who self-refer to mental health professionals for difficulties with attention. This significant increase in the help-seeking behavior of adult women potentially indicates that teachers and parents may not be attuned to earlier childhood symptoms. Alternatively, the increase in prevalence in adolescents and adulthood for women could be related to changes in the hormone levels that occur during this time period. However, no research has assessed this possible link. Further discussion on the relationship between attention and hormones is provided in the section “Hormonal Influences on Attention” in this chapter. The accuracy of prevalence rates and gender ratios has been questioned for several reasons (Biederman et al., 1994; Nadeau & Quinn, 2002; Staller & Faraone, 2006). First, as previously discussed, the current diagnostic criteria used to evaluate ADHD have been developed predominantly with male samples. Therefore, the generalization of these criteria to females is questionable (Nadeau & Quinn, 2002; Staller & Faraone, 2006). Second, because females are more prone to have inattentive symptoms, which are less disruptive than hyperactivity and impulsivity, it has been argued that they are less likely to be referred for services until later in life (Gaub & Carlson, 1997; Gershon, 2002a; Solden, 1995). Thus, the lower prevalence of females diagnosed with ADHD in childhood may be an artifact of he different manifestations of attentional differences observed in females and males.
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This under-identification has serious mental health and educational implications that necessitate a better understanding of the clinical indicators of ADHD in females (Biederman et al., 1994).
Developmental Course Onset The average age of onset of ADHD symptoms is often during preschool, between the ages of three to four years (Applegate et al., 1997; Loeber, 1990; Spira & Fischel, 2005; Taylor, Sandberg, Thorley, & Giles, 1991). Patterns of hyperactivity typically arise first and are apparent in preschool, whereas the onset of ADHD-C is typically seen in elementary school. The average age of onset of ADHD-PI is between 8 and 12 years (Barkley, 2003). Although ADHD was once thought of as a childhood disorder, research has clearly demonstrated that 50–80% of children diagnosed with this disorder will continue to meet the criteria in adolescence and adulthood (August, Stewart, & Holmes, 1983; Claude, & Firestone, 1995; Fischer, Barkley, & Fletcher, 1993; Gittelman, Mannuzza, Shenker, & Bonagura, 1985; Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1993). Preschool Through Elementary School ADHD type difficulties are fairly common in the preschool population, with 18.2% of preschool children reportedly experiencing some symptoms of ADHD (Gadow & Sprafkin, 1997). During these early years of development, the most frequently cited ADHD symptoms fall under hyperactive/impulsive category. A majority of children who experience symptoms at these early stages do not continue to have significant attentional difficulties, which necessitates caution when diagnosing ADHD in preschool populations. In order to differentiate developmentally typical attentional difficulties from clinically significant attentional difficulties, it is essential to understand the typical development of attention. Attention Development According to Ruff and Rothbart’s (1996) developmental theory of attention, two general systems of attention develop during the first 5 years of life: (1) the orienting and investigative system, and (2) the system of higher level controls. The first system develops during the first year of life and is primarily governed by novelty. The second system emerges during the end of the first year and gradually becomes more dominant during the preschool years. Empirical research supports this model of development. Levy (1980) found that while only 27% of three- and three-anda-half-year-olds complied with all trials of a signal detection task, 100% of all four-and-a-half-year-olds complied with all tasks. A follow-up study conducted by Ruff, Cappazzoli, and Weissberg (1998) supported Levy’s findings. In addition to
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the observations of increased attentional abilities by the age of 4, this developmental period also corresponds with neurological development that is hypothesized to affect attention. More specifically, Courchesne (1990) found that a visual-event-related potential in the parietal areas that is linked to attention (P3b) does not develop reliably until 3–3.7 years. During the same time period, typical preschoolers also develop self-control. By the age 3–4, response inhibition is well developed (Barkley, 1997). The increase in self-control is aided by the development of self-speech, which typically emerges around the age of 3 and is critical for tasks involving motivation, task persistence, following multi-step directions, and motoric responses. The observed changes in self-control have been linked to continued maturation of the frontal cortex (Spira & Fischel, 2005). In summary, by the age of 4, the development of the ability to voluntarily direct attention and inhibit responses is well underway. Therefore, a diagnosis of ADHD should be carefully considered for the 4-year-olds who demonstrate difficulty in these areas. During the first few grades, children who demonstrate hyperactive or disruptive behaviors are most frequently referred for assessment (Henker & Whalen, 1999). As children progress through elementary school to middle school, inattentive symptoms become more apparent as the demands of the school shift from self-regulation to include independent completion of work and organization (Barkley, 1997). Because females are more likely to demonstrate inattentive symptoms that are not disruptive to the classroom environment, a significant increase in the number of females referred for evaluation occurs at this age (Solden, 1995).
Middle School Through High School Age The pre-teen years and puberty are marked by significant development in the frontal cortex and prefrontal cortex that are hypothesized to have a major impact on attentional processes. During puberty, the synaptic density begins to decrease, which corresponds with increasing cognitive capacity (Bourgeois, Goldman-Rakic, & Rakic, 1994). “These cognitive and biological processes may represent the behavioral and ultimate physiological suppression of competing/irrelevant behaviors as appropriate behaviors are reinforced and enhanced (i.e., inhibitory control)” (Ellison, 2005, p. 467). In the frontal lobe, gray matter increases during pre-adolescence with a maximum size occurring at 12.1 years for males and 11.0 years for females, followed by a decline during post-adolescence, which results in a net decrease in volume across this age span (Giedd et al., 1999). Giedd and colleagues postulate that “if increase is related to a second wave of overproduction of synapses, it may herald a critical stage of development when environment or activities of the teenager may guide selective elimination during adolescence” (p. 863). This finding has implications on both intervention and diagnosis. Given the maturational processes, Ellison (2005) indicated that it is possible that children may “grow into deficits or they may manifest deficits at a later stage than were presented in the initial evaluation” (p. 468).
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Given the physiological changes that occur during adolescence, it is not surprising that a marked decline in inattention and hyperactive symptoms is observed in adolescence (Fischer et al., 1993). Children with ADHD, however, continue to demonstrate significant impairment with impulse control, hyperactivity, and inattention. Adolescent females diagnosed with ADHD show weaknesses in executive functions, cognitive abilities, and inattention compared to typically developing female adolescents (Hinshaw, Carte, Fan, Jassy, & Owens, 2007; Hinshaw, Owens, Sami, & Fargeon, 2006).
Comorbidity Approximately 45% of females diagnosed with ADHD also meet the criteria for another disorder (Biederman et al., 1999). The most common co-occurring disorders for females with ADHD are conduct disorder (CD), oppositional defiant disorder (ODD), depression, anxiety disorders, and learning disabilities.
Externalizing Disorders When compared to typically developing females, girls with ADHD are more likely to be diagnosed with ODD and CD (Biederman et al., 1994; Hartung et al., 2002; Staller & Faraone, 2006). Females with ADHD, however, have significantly lower levels of ODD and CD than males with ADHD (Biederman et al., 1994; Hartung et al., 2002). Although both males and females with ADHD demonstrate higher rates of CD and ODD when compared to typically developing children, the symptoms may manifest somewhat differently in boys and girls with ADHD. A study comparing overt symptoms (e.g., physical altercations) and covert behaviors (e.g., burglary and lying) found that girls with ADHD are more likely to exhibit covert behaviors, whereas males are more likely to demonstrate overt behaviors (Monuteaux, Fitzmaurice, Blacker, & Biederman, 2004).
Internalizing Disorders Females with ADHD are more likely to experience significant internalizing symptoms than males diagnosed with ADHD as well as typically developing females (Biederman et al., 1994; Hinshaw, 2002; Staller & Faraone, 2006). Issues with anxiety and depression are particularly salient in women diagnosed with ADHD as adults. It has been postulated that these women often experience secondary depressive symptoms as a result of difficulties that their undiagnosed ADHD caused in school and social settings. Because undiagnosed women cannot conceptualize their difficulties in terms of a disability, they often misattribute their difficulties to lack of ability or laziness (Rucklidge & Kaplan, 2002; Weiss, 2002).
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Substance Abuse Females with ADHD are also more prone to developing substance abuse disorders (Biederman, Faraone, & Spencer, 1993; Disney, Elkins, & McGue, 1999; Milberger, Biederman, Faraone, Wilens, & Chu, 1997; Richardson, 1997; Shekim, Asarnow, Hess, Zaucharek, & Musial, 1995; Wilens, Biederman, Spencer, & Frances, 1994). Undiagnosed females are particularly prone to develop substance abuse disorders due to high levels of chronic stress (Cook, 2000). In order to reduce stress, women with undiagnosed ADHD may turn to alcohol and other substances to self-medicate. Additional evidence of the self-medication hypothesis can be derived from research which demonstrates that children with ADHD who are given psychopharmacological treatments are less likely to develop substance abuse disorders in adolescents and adulthood (Barkley, Fischer, Smallish, & Fletcher, 2004). Although no research explores how psychopharmacological intervention decreases the risk of substance abuse disorders, it seems plausible that such treatment reduces stress, which in turn significantly reduces the need for self-medication.
Learning Disabilities The co-occurrence of learning disabilities and ADHD has been documented for both males and females (Barkley, 2006; Mayes & Calhoun, 2000; Safer & Allen, 1976; Semrud-Clikeman et al., 1992). For example, many college students who have been diagnosed with ADHD often reported as many symptoms of learning disabilities as students diagnosed with learning disabilities (Hoffman, 2002). Although academic difficulties are generally associated with ADHD, it does not imply that academic difficulties are necessary to warrant a diagnosis of ADHD. Staller and Faraone (2006) express caution that many girls with ADHD frequently perform at or above grade level due to extreme levels of effort. Oftentimes, the effort that must be put forth to obtain adequate performance is overlooked, which may result in the child not being referred for services. This pattern may contribute to the under-diagnosis of ADHD in females.
Neuropathology of Attention-Deficit/Hyperactivity Disorder Brain Regions There has been tremendous growth in understanding the neuropathology of ADHD in the past 10 years. Contributing to this growth has been the advancement in imaging technologies, such as functional magnetic resonance imaging (f-MRI), magnetoencephalography (MEG), event-related potential (ERP), and single photon emission computerized tomography (SPECT), which has fostered the investigation of the neurobiological bases for ADHD. Dysfunction or abnormalities in the frontostriatal region have been the most consistent finding across many studies using these techniques (see Fig. 2).
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Caudate n.
Frontal Lobe Putamen
Cerebellum
Fig. 2 Lateral view of the right hemisphere of the human brain, showing the location of the frontal lobe, striatum (caudate nucleus and putamen), and cerebellum Source: From the Digital Project by the University of Washington, retrieved September 25, 2007 from http://da.biosts.washington.edu/da.html.
The frontal lobe of the brain subserves numerous functions, including motor, planning, abstraction, processing speed, affective regulation, attention/arousal, and impulse control/inhibition. The prefrontal area, which is particularly involved in executive functions (planning, monitoring, self-regulation), is principally linked in ADHD. This area of the brain is one of the last structures to mature, with research indicating continued development until the early twenties (Giedd, 2004). The wider developmental window of the prefrontal region creates a greater vulnerability of the functions related to it. Therefore, executive functions, which are mediated by the frontal networks, seem to be particularly at risk during childhood development. In a milestone study by Zametkin and his colleagues at NIMH in 1990, reduced activity primarily in the frontal and striatal regions was demonstrated in a welldefined sample of adults with ADHD. In this study, positron emission tomography (PET), which employs a short-lived radioactive tracer isotope, was used to study brain functioning. Significantly reduced brain metabolic activity was found in the frontostriatal region of adults with ADHD relative to a control group (Zametkin et al., 1990) (see Fig. 3). Interestingly, a 1994 study with adolescents found similar results for girls with ADHD, but not for boys with ADHD (Ernst et al., 1994). A later replication study with ADHD girls and a control group found some regional left/right lateralization differences but no significant differences in total glucose utilization (Ernst et al., 1997). Differences between controls and ADHD girls may have been obscured by other confounding factors. Girls with ADHD were less sexually mature (significantly lower Tanner scores), they had lower Block Design scores
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Fig. 3 Left image shows a typical PET brain image while the right image illustrating cortical underactivity in an individual with ADHD, particularly in the frontal area underlying executive function (Zametkin et al., 1990)
than girls in a previous study, and control subjects who had first-degree relatives with ADHD were included. During the same time period as the PET research at NIMH, research was being carried out using magnetic resonance imaging (MRI) to evaluate structural differences in individuals with ADHD. Volumetric studies conducted by a team of researchers at the University of Georgia found a smaller right frontal region in children with ADHD, and these children lacked the typical right/left frontal asymmetry (Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopulos, 1990). Similar findings using MRI on a larger sample of children also documented significantly smaller right prefrontal cortex and striatal regions in children with ADHD (Castellanos et al., 1996). In contrast, Filipek et al. (1997) found the left striatal region to be smaller than the right. Although there have been some inconsistencies in the literature, the preponderance of studies have implicated the frontostriatal region, with the right prefrontal region being smaller than the left. The cerebellum has also been implicated in the neuropathology of ADHD. A volumetric study by Berquin and colleagues in 1998 reported the vermal region of the cerebellum to be significantly smaller in a sample of 46 boys with ADHD compared to 47 healthy controls. They hypothesized a cerebello-thalamo-prefrontal circuit dysfunction as the basis for the motor control, inhibition, and executive function deficits encountered in ADHD (Berquin, Giedd, & Jacobsen, 1998). Along these lines, a report from a 2002 NIMH consortium on ADHD cited cerebellum and its prefrontal-striatal connections as one of the strongest emerging anatomical findings in ADHD (NIMH, 2002). Similar findings with some important differences have been noted in girls with ADHD. In the first large-scale MRI study with female ADHD subjects, Castellanos and colleagues (2001) examined the volumetric data for 50 girls with ADHD and 50 female controls. ADHD girls had smaller volumes in the posterior-inferior vermis of the cerebellum. Also, it was found that brain size differences correlated significantly with several ratings of ADHD symptom severity. This study did not replicate the previous lateralized findings noted with male subjects. These earlier studies with
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boys found smaller volumes in right frontal lobe, right caudate, right globus pallidus, and left cerebellum. More recently, a number of investigations have been carried out using functional neuroimaging techniques, such as f-MRI. The f-MRI registers the blood flow to the functioning areas of the brain, such as during the performance of a cognitive task. Findings from these studies have implicated the frontal and striatal regions in individuals with ADHD. In a well-controlled study comparing ADHD and nonADHD subjects on a task requiring inhibition (i.e., Stop Signal Task), it was found that ADHD subjects showed less activation of the anterior cingulate cortex and the left ventrolateral prefrontal cortex (Pliszka et al., 2006). In other findings using fMRI, response inhibition in control subjects was mediated by the frontal-striatal regions, while the right superior temporal region was activated in children with ADHD (Vaidya et al., 2005). Additional brain regions have been implicated in neuroimaging studies. In the same study cited above (Vaidya et al., 2005), interference suppression in the ADHD group was characterized by reduced participation of the frontal-striatal-temporalparietal network. In a study on boys with ADHD who were medication-na¨ıve, Smith, Taylor, Brammer, Toone, and Rubia (2006) found decreased activation in the bilateral prefrontal and temporal lobes and the right parietal lobe during a cognitive flexibility task. During a cognitive interference inhibition task, the more commonly reported reduced activation of the frontal cortex was noted. Although the preponderance of evidence suggests a frontostriatal basis for ADHD, there are clearly other neuroanatomical regions involved in this disorder (see Table 1). A multitude of non-frontal areas have also been implicated in ADHD, including the regions of the temporal lobe, parietal lobe, cerebellum, and other subcortical structures. These findings make sense in light of the fact that attention is not a unitary concept; thus, it stands to reason that there can be multiple brain regions involved in the disorder. As our appreciation for the interactive nature of brain function increases, we will better understand the neural networks underlying ADHD. Table 1 Predominant neuroanatomical findings in ADHD Brain regions (predominant findings) Prefrontal cortex/striatum (particularly caudate region of striatum) Right frontal usually more implicated Limbic connection via striatum Cerebellum (particularly vermis) connection via striatum Frontal–striatal–temporal–parietal network
Neurologically Based Models of Attention There have been a number of models of attention that have been developed to conceptualize the multidimensional nature of this function. Based on a factor analysis
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of data derived from more than 600 children and adults, Mirsky and colleagues hypothesized five major attention elements with corresponding specific brain regions (Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991). Their model divides attention into a number of elements or factors, including the capacities of encoding, focusing, and executing responses, sustaining attention, shifting attention, as well as response stability. These various aspects of attention are assessed by measures derived from neuropsychological tests. For example, in their model, encoding is measured by the Wechsler Intelligence Scale for Children–III’s (WISC-III) Arithmetic and Digit Span subtests, while focus/execute responses are assessed by the Coding and Cancellation subtests. Also, Mirsky’s model posits a system of brain structures that maintain the elements of attention, each of which may be supported by a distinct cerebral region. For example, sustain and stabilize components are purported to correlate with brain stem and midline thalamic elements. Stuss and colleagues have developed a model of three distinguishable attentional components in anterior cerebral regions, supporting their theory with reaction time (RT) data (Stuss, Shallice, Alexander, & Picton, 1995; Stuss et al., 2005). One attentional component maintains alertness or a general readiness to respond and is associated with the superior medial frontal region. In their research, dysfunction in this area affected simple RT speed. A second attentional system sets a threshold of response to a target stimulus, establishing a bias for responsiveness. This component is linked to the left dorsolateral frontal region with lesions altering response bias. A third attentional component maintains the selection of a defined schema to permit consistent target selection. The authors proposed that this system of sustained attention was associated with right dorsolateral frontal areas. They also indicated that this cortical region is important for the inhibition of incorrect responses or irrelevant information. Summary In summary, the examples of the Mirsky and Stuss attentional models as well as the neuroimaging findings indicate the involvement of neural networks associated with various aspects of attention. Attention is not a unitary concept, as ADHD is not a unitary disorder. The current conceptualization of ADHD as a disorder with several subtypes is very likely related to the multidimensional nature of attention systems and associated neural networks. The nature of gender differences in ADHD subtypes and the neuroanatomical basis for this disorder awaits further elucidation.
Hormonal Influences on Attention The influence of hormones on brain functioning is an important factor to consider in ADHD. There are a number of hormones that are known to affect cognition, although their exact effect can often be difficult to pin down.
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Thyroid Hormone The thyroid hormone, which is produced in response to thyroid stimulating hormone (TSH) excreted by the pituitary, influences neurotransmitters among other biochemical interactions. Thyroid functioning and in particular thyroid hormone deficiency can affect memory, attention, and mood. There can be a number of causes for hypothyroidism, including genetic disorders, iodine deficiency, and autoimmune thyroid disease. Lack of iodine during pregnancy or other causes of maternal hypothyroidism can have a very negative impact on fetal brain development and subsequent cognitive functioning. Similarly, iodine-related thyroid deficiency might have a negative impact on children’s cognitive function, including processing speed, memory, and attention functioning (Phillips, 2001). An intervention study conducted in Albania with 310 school children found many to have low iodine and thyroid levels. After a double-blind intervention with iodine supplementation, significant improvements in fine motor skills, information processing, and visual problem-solving were found in iodine-treated children (Zimmerman et al., 2006). An f-MRI study conducted by Zhu and colleagues (2006) revealed a neural substrate for reversible working memory dysfunction in subclinical hypothyrodism. Their study found a frontoparietal connection in subclinical hypothyroid subjects. In these subjects, memory performance and frontal executive function were improved after thyroid replacement therapy. Interestingly, women have larger thyroids than men and are more prone to many types of thyroid disease. This may be related to the finding that the female hormone estrogen blocks the efficiency of thyroid hormone causing them to produce more thyroid hormone.
Estrogen The influence of estrogen on women’s cognitive functioning has been an area of significant debate in the last several years. Not in debate, is the finding that the brain is a target organ for estrogen, with hormone-related increases found in the concentration of neurotransmitters, such as serotonin, dopamine, and norepinephrine (Archer, 1999). Fink and colleagues (1996) found a significant increase in dopamine (D2) receptors in striatum in response to estrogen stimulation. Despite these findings, controversy has surrounded the exact influence of estrogen on women’s cognitive functioning, especially postmenopausal women and estrogen replacement therapy. A number of these studies conclude that estrogen has a beneficial effect on the aspects of cognitive functioning, such as verbal memory, word finding, attention, and conceptualization, while others find the opposite with increases noted in dementia and mild cognitive impairment. In a prospective observational study, enhanced verbal memory was found in nondemented postmenopausal women receiving estrogen replacement therapy. These findings were based on women who were receiving and who had never previously received estrogen, and who were matched pre-treatment on health and cognitive
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status. Similarly, the results of a meta-analysis of 17 randomized studies indicated that women who received hormone replacement therapy improved in verbal memory, vigilance, reasoning, and motor speed (LeBlanc, Janowsky, Chan, & Nelson, 2001). In contrast, a very large prospective study of over 4000 postmenopausal women conducted as part of the Women’s Health Initiative (WHI) concluded that HRT involving estrogen plus progestin actually significantly increased the risk of dementia and mild cognitive impairment. In this randomized, double-blind, placebocontrolled study using neuropsychological and observational measures, the risk of probable dementia for women in the HRT group was twice that of women in the placebo group (Rapp et al., 2003). This corresponds with an increase in cardiovascular risk, such as stroke. Given that this sample of women was an average of 65 years old and approximately 10 years postmenopausal at the time HRT was initiated, there is a question as to whether cardiovascular factors may have been significant in the observed increased dementia symptoms. Summary In summary, it is clear that hormones influence aspects of cognitive functioning. However, the exact nature of this influence and the interaction of hormones with other moderator variables, such as age and cardiovascular factors, still require further elucidation through research. It is interesting to note that, in the childhood literature on ADHD, a male-to-female ratio of 3:1 is reported, whereas in the adult literature, a ratio of 1:1 is noted. Perhaps, the differences in these ratios may be partially accounted for by hormonal influences.
Assessment of Attention-Deficit/Hyperactivity Disorder Accurate diagnosis of ADHD is the first step in the treatment process. In order to obtain an accurate diagnosis, individuals often must complete a fairly extensive evaluation, which may involve interviews, behavioral checklists, psycho-educational testing, neuropsychological testing, and a physical examination to rule out other possible medical conditions. Psychological testing often provides answers to the following questions: (1) Is the diagnosis of ADHD warranted? (2) If the diagnosis of ADHD is not warranted, are there other explanations that better account for the symptoms? (3) If an individual meets the diagnostic criteria for ADHD, does he or she also meet the criteria for a co-morbid condition? The following sections address the major components in the assessment process. Interview The first step in the process is often an interview with the parents of the patient. Although parent interviews have been criticized for being unreliable and subjective (Angold, Erklani, Costello, & Rutter, 1996), they offer the most ecologically
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valid information concerning a child’s difficulty (Barkley & Edwards, 2006). Areas of focus during the interview should include demographic information, clientrelated issues (such as childhood data, school, family, and treatment histories), and work/school-related information. During the interview, the client can also be given an opportunity to generate a list of questions that he or she would like to have answered in the assessment. Behavioral Questionnaires Behavioral questionnaires and self-report measures are also an important component in the assessment process as they provide information about the frequency and severity of symptoms as well as screen for co-morbid conditions. When assessing middle school children, questionnaires should be provided to the children’s parents, teachers and to the children themselves. Although research has shown low rates of agreement between raters (Achenbach, McConaughy, & Howell, 1987), having multiple reporters across multiple settings often provides the most cost- and time-efficient manner to collect data across settings. If significant disparity between teacher and parent rating forms occurs only on reports of attention, it may indicate that ADHD symptoms are situation-specific (Gomez, 2007). Table 2 provides a brief overview of commonly administered behavioral checklists. As gender differences in ADHD have taken a more central role, many behavioral questionnaires, such as the Achenbach Child Behavior Checklist, have created separate norms for males and females. In addition, Nadeau and Quinn (2002) developed the Women’s AD/HD Self-Assessment Symptom Inventory (SASI). The SASI assesses a broad range of symptoms, including childhood symptoms of ADHD, symptoms in the home and work environment, co-morbid conditions, and chronic health problems. Although this measure is in the early stages of development, and therefore no norms or research pertaining to its psychometric properties are available, it is a useful measure for screening ADHD symptoms in women. A full version of the questionnaire is published in Gender Issues and ADHD (Quinn & Nadeau, 2002). Intelligence/Achievement Tests Although measures of academic achievement and cognitive abilities cannot be used to make a definitive diagnosis of ADHD, they can contribute to establishing the diagnosis in more indirect ways (Gordon, Barkley, & Lovett, 2006). First, the argument that a child’s attentional difficulties significantly impair functioning can be bolstered by the evidence of problems acquiring age-appropriate skills. Performance on intelligence and achievement tests can provide evidence of impairment relative to the normative samples of these tests. Also, relative weaknesses within the individual’s intelligence and academic profiles may be evident. Second, the scores from IQ tests and achievement tests are useful tools for ruling out possible explanations of referral concerns. For example, such measures can be used to determine if the child is receiving instruction at the appropriate level and to rule out potential learning disabilities (Gordon et al., 2006). Finally, although
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Table 2 Behavior checklists/report forms Behavior checklist
Description
Adult Self-Report Scale (Adler, Kessler, & Spencer, 2003) Behavior Assessment Scale for Children–Second Edition (Reynolds & Kamphaus, 1996)
A brief self report-screener of hyperactivity, impulsivity, and inattention Comprehensive self, parent, and teacher reports that assess a broad range of externalizing and internalizing symptoms. The BASC Monitor is a brief measure of treatment effectiveness. Measures six areas of functioning: (1) organizing, prioritizing, and activating work; (2) focusing, sustaining, and shifting attention to tasks; (3) regulating alertness, sustaining effort, and processing speed; (4) managing frustration and modulating emotions; (5) utilizing working memory and accessing recall; (6) monitoring and self-regulating action. Comprehensive child, parent, and teacher reports that assess internalizing and externalizing symptoms, including attention problems. Parent, teacher, and adolescent forms that assesses hyperactivity, impulsivity, and inattention; includes a 12-item ADHD Index and an 18-item DSM-IV Symptom Checklist. Parent and teacher checklists of inattentive symptoms, hyperactive/impulsive symptoms, and oppositional defiant systems based on DMS-IV criteria. A self-report measure that assesses: (1) childhood ADHD patterns (including learning issues, social interpersonal issues, psychological issues, problematic behaviors); (2) adult ADHD patterns (including difficulties in areas of adult responsibilities, psychological issues, and other health issues)
Brown ADD scales (Brown, 1996)
Child Behavior Checklist (Achenbach, 2001) Conner’s Rating Scale–Revised (Conners, 1997)
SNAP-IV (Swanson, 1992)
Women’s ADHD Self-Assessment Symptom Inventory (SASI) (Nadeau & Quinn, 2002)
IQ tests cannot be used for a definitive diagnosis, certain patterns of cognitive functioning are associated with attentional problems. Research has demonstrated that weaknesses in working memory are commonly associated with ADHD in females (Rucklidge, 2006). In addition, low processing speed may be indicative of inattention (Rucklidge & Tannock, 2002; Shepard & Nussbaum, 2006, 2007). More research is needed to evaluate gender differences in processing speed. Neuropsychological Testing As with measures of cognitive ability and academic achievement, neuropsychological tests alone cannot provide a definitive diagnosis of ADHD. Rather, neuropsychological tests assess a broad range of functioning, including fine motor skills, memory, auditory processing, and executive functions. The client’s profile is then examined to determine if weaknesses commonly associated with attention are present, such as poor executive functioning, difficulty with working memory, and slow processing speed. Table 3 provides a list and brief description of measures
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Measure
Description
California Verbal Learning Test–Second A list learning measure of memory that involves short delayed recall, an interference list, long delayed Edition (CVLT-II) (Delis, Kramer, recall, and recognition recall. The CVLT also Kaplan & Ober, 1987)/California provides a measure of semantic clustering. The Verbal Learning Test for Children CVLT-C version is a downward extension of the (CVLT-C) (Delis, Kramer, Kaplan, & adult version and is nationally normed for children Ober, 1994) 5–16 years old. Connors Continuous Performance Test A computerized measure of concentration, sustained II (Conner’s CPT-II) (Conners & vigilance, attention to simple tasks over time MHS Staff, 2000) intervals. Controlled Oral Word Association A measure of verbal fluency in which the test taker (FAS) (Spreen and Benton, 1977) must generate a list of words beginning with three letters. Weaknesses in verbal fluency have been shown to be associated with weakness in attention. Normative data are available for children 6 years and older. d2 Test of Attention (Brickenkamp & Measures the ability to differentiate visual stimuli Zilmer, 1998) rapidly and accurately. It is a timed, visual search, attention test that measures both selective and sustained attention, visual perception, and visuo-motor skills. Normative data are available for children 7 years and older. Delis-Kaplan Executive Function A measure of executive functions that includes a color System (Delis, Kaplan, & Kramer, word identification test, design fluency test, sorting 2001) test, tower test, trail making test, and a verbal fluency test. Normative data are available for children 8 years and older. Gordon Diagnostic System (Gordon, Measures sustained attention, inhibition, and 1987) impulsivity. Paced Auditory Serial Addition Test A measure of sustained auditory attention, information (Gronwall, 1977) processing speed, and divided attention in which the test taker listens to a series of tape-recorded single-digit numbers and must add successive pairs of numbers. The PASAT is appropriate for adults and adolescents 16 years and older. Rey-Osterrieth Complex Figure Test A copying and memory task that measures visual (see Waber & Holmes, 1985) organizational skills, general planning ability, and memory for visual information. Norms are available for 10-year-old children through adulthood. Test of Memory and Learning-2 A measure of memory that assesses verbal memory, (TOMAL-2) non-verbal memory, and delayed recall. The TOMAL is nationally normed for children 5 years through 19–11. A timed paper-and-pencil test that measures visual Trails Making Test (Halstead-Reitan Neuropsychological Battery, Reitan, scanning, attention, motor speed, mental flexibility, 1979; Reitan & Wolfson, 1985) and ability to establish and change mental set.
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used in the neuropsychological assessment of children and adults with attentional difficulties. Given that research has demonstrated that weaknesses in executive functioning are strongly related to ADHD (Frazier, Demaree, & Youngstrom, 2004; Hinshaw et al., 2007), this is often one of the central areas of focus in neuropsychological evaluation. Although executive functions have been defined in numerous ways, it is generally agreed that they encompass the skills necessary for goal-directed behavior (Anderson, 1998). These higher order functions include attention, planning, working memory, strategizing, and self-regulation (Ellison, 2005; Gershon, 2002b). Nigg and colleagues (2002) found that children with both ADHD-C and ADHD-PI demonstrated weaknesses in behavioral inhibition, planning, interference, shifting sets, and response sets. A majority of research indicates that there is little or no evidence of gender differences in executive functioning (Gershon, 2002a; Rucklidge & Tannock, 2002). However, Newcorn et al. (2001) found that girls with ADHD made significantly less impulsive errors on the Conner’s Continuous Performance Test than males with ADHD. Comparing behavioral rating forms, intelligence tests, and measures of academic achievement, scientific literature on tests of executive functioning is scant (Gordon et al., 2006). For many measures of executive functioning, only one or two studies examined the psychometric properties of the measures. Therefore, measures of executive functioning should not be used for a definitive diagnosis but rather as a tool to further assess an individual’s strengths and weaknesses. In summary, there is no single test for ADHD. Rather, an analysis of the individual’s cognitive, academic, and neuropsychological functioning is utilized to establish a pattern of strengths and weaknesses. The most commonly observed weaknesses in individuals with ADHD include difficulties with processing speed and working memory. In addition to test results, a psychologist must also consider the impairment reported by parents, teachers, and the client when making a diagnosis.
Excerpted Neuropsychological Summary Name: Grace Age: 16 years
Reason for Referral • Grace is a 16-year-old, right-handed, Caucasian girl with possible problems of attention.
Relevant Background History • Prenatal, birth, developmental, and medical history are unremarkable. • Positive family history for academic and attention difficulties.
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Educational History • At the time of assessment, Grace was in the tenth grade. • Her mother reported that she is motivated to do well in school and typically achieves grades of A and B with a great deal of effort. • Grace and her mother indicated that reading has been problematic for her since grade school. Although early reading did not appear to be difficult for Grace, her fifth grade teacher reported some concerns. Grace and her mother reported that her word-reading is adequate, but the words do not “go in.” They added that she is much more successful when material is read to her. • Grace and her mother reported that she is a good writer but is slow at getting things down on paper. They stated that she also has difficulty with mechanics and spellings in context. • Both Grace and her mother reported significant problems with inattention. • Grace indicated that she often experiences difficulty focusing, staying on track, listening, and following instructions. She added that she has never been able to focus, but that this difficulty has increased in the last year. • Grace’s mother reported that she has noticed Grace procrastinating more often. She added that she often loses things.
Behavioral Observations/Clinical Interview • During a clinical interview, Grace indicated that her worst subject this year was English, adding that discussions and interactions were limited in classroom instructions. • Grace expressed difficulties starting and finishing work because she often sits and daydreams. She further noted that she has some difficulties following written and spoken directions, sticking to work, being bored easily, and making careless mistakes. • Grace indicated that she experiences mild anxiety and stress related to schoolwork. She noted recent moderate jaw pain diagnosed as TMJ.
Clinically Relevant Test Results Motor/Sensory Findings • Grace demonstrated mild, bilateral synkinesia during finger-thumb sequencing.
Auditory/Language Findings • Grace’s lexical knowledge was in the average to high-average range (75th percentile) on the Vocabulary subtest from the WISC-IV.
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• Her verbal fluency was in the impaired range, compared to adolescents at her age, on the Controlled Oral Word Association task (FAS). • She was administered the SCAN-A Test of Auditory Processing. Her performance on the Filtered Words subtest was significantly below average (ss = 4); she had significant difficulty in processing words that were presented somewhat muffled. Other subtest performance was within normal limits, and Grace’s overall performance on the SCAN-A was in the low-average range. These difficulties indicate potential problems with auditory processing or selective attention under certain conditions and have implications for Grace in the classroom environment. Grace may experience difficulties in processing or staying tuned into speech when the acoustics are poor, the speaker is not directly facing her, or the person does not speak distinctly.
Memory Findings • On the California Verbal Learning Test–C, Grace demonstrated a typical learning curve on trials one through five of this measure. She encoded and retrieved an average number of words on trial one and five. She showed a typical interference effect after the presentation of List B, recalling an average number of items from List A. Grace demonstrated low-average recall after the delay. Grace was inconsistent in her use of categorical cueing to recall the list items, which suggests that she was not employing a consistent organizational strategy for optimal encoding and recall. • On the Test of Memory and Learning (TOMAL), Grace demonstrated aboveaverage immediate and delayed recall of verbal information presented in a story format (Memory for Stories). On this task, Grace successfully encoded and retrieved significant themes and details of the stories, indicating a well-developed ability to process orally presented information. Grace’s relative weakness on the CVLT-C versus the TOMAL suggests that she may benefit relatively more when learning involves more meaningful narratively organized (TOMAL) versus rote (CVLT-C) content. • Grace’s immediate visual recall and delayed recall were in the high-average range on the Rey-Osterreith Complex Figure design. She showed good recall of the general gestalt of the figure as well as the internal details. It seemed the active process of copying the figure was helpful in encoding.
Attention/Executive Function Findings • Grace was administered the Conners’ Continuous Performance Test. The overall results of the discriminant function analysis were somewhat equivocal, matching to a degree with individuals with attention problems.
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• On the more cognitively demanding Paced Auditory Serial Addition Test (PASAT), Grace performed significantly below-average on each of the trials, indicating difficulties with sustained attention and processing speed. • Grace was also administered the Trail Making Test. She performed in the average range on Trails A and significantly below-average on Trails B. She made two errors on Trails B, and had difficulty, after correction, regaining focus on the task. Grace’s poor performance on Trails B suggests poor cognitive flexibility and attention shifting.
Cognitive/Intellectual Findings • Grace’s overall cognitive abilities were in the average range, as measured by the Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV). Grace demonstrated a fair amount of variability among index scores, which ranged from the 16th percentile for the Processing Speed Index to the 82nd percentile for the Verbal Comprehension Index. • This finding of relatively poor processing speed may have implications for Grace in the classroom. She may experience some difficulties with reading, writing, and taking notes efficiently during class.
Academic Functioning • Reading Grace’s reading decoding skills were determined to be fairly well developed on the Letter and Word Recognition subtest from the KTEA-II (75th percentile). Her reading rate, as measured by the Nelson-Denny Reading Test, was significantly below-average, compared to other 10th grade adolescents (6th percentile). Her slower reading rate is likely related to difficulty staying focused, which Grace reported was problematic in her schoolwork, and below-average processing speed abilities. Her reading comprehension, as measured by the standard time administration of the Nelson-Denny Reading Test, was below average, compared to her grademates (9th percentile). Performance on the Comprehension portion of the Nelson-Denny Reading Test is greatly affected by reading rate. Grace did not reach many of the items in the time allowed, and so she was not able to demonstrate comprehension in these items. On the extended time version of the Nelson-Denny Reading Test, Grace achieved a comprehension score in the average range (44th percentile). • Written Expression Grace’s spelling for individually dictated words was found to be in the average range on the KTEA-II (32nd percentile).
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Her overall performance on the Spontaneous Writing task from the TOWL-3 was found to be in the average range (50th percentile). She demonstrated above-average story construction (84th percentile), average language usage (50th percentile), and below-average conventions in print (16th percentile). Grace’s below-average score for Contextual Conventions was likely negatively affected by attentional lapses in her writing. She made several spelling errors in words she has surely mastered (e.g., “new” for knew), and she left out punctuation details, such as apostrophes in contractions. • Mathematics Grace’s score on the Math Concepts and Applications subtest from the KTEA-II was in the average range (58th percentile). Her performance on the Math Computation subtest was at grade level and in the average range (47th percentile). In this subtest, it is notable that Grace neglected to complete a block of six items, likely due to a lapse in attention. Since Grace completed many items correctly that were after these missed items, it was assumed that she had likely gotten them correct. If the skipped items were not scored as errors, her subtest score would have been five standard score points higher. This is an illustration of how Grace’s attention problems likely affected her academic performance. Grace’s score on the Math Fluency subtest from the WJ-III was below-average. This is most likely related to her weakness in processing speed. In addition, she was observed to make numerous errors as a result of sign confusion, which may indicate that lapses in attention hinder her ability to complete simple academic tasks efficiently.
Emotional/Behavioral Functioning • Parent Questionnaires On questionnaires completed by her mother, Grace was described as thoughtful and kind. She reported that her greatest concerns for Grace were her inability to stay on task and her procrastination. On the Achenbach CBCL, there was a clinically significant elevation on the Attention Problems scale. Grace’s mother also completed the SNAP-IV Behavior Checklist. She endorsed as clinically significant 8 of 9 Inattention items, no Hyperactive/Impulsive, or Oppositional Defiant items. • Teacher Questionnaires Grace’s Spanish II teacher completed the Achenbach Teacher Report Form (TRF). She stated that the best things about Grace were she was extremely polite, hardworking, and conscientious. She indicated that Grace seems to study very hard, but has difficulty connecting concepts and retaining
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information. There were no clinically significant elevations on any scales from the TRF, but a mild elevation was noted on the Anxious/Depressed scale, related mostly to anxious behaviors. Grace’s teacher’s responses on the SNAP-IV indicated some concerns about inattention, such as increased “careless” mistakes in work. • Self-Report Grace also completed the Youth Self-Report YSR Form. She indicated that the best things about her were she works really hard and compensates for everything she cannot do. She added that she is easy to get along with and she wants everybody to be happy. She stated that she does what people tell her to do and does not break rules. Grace reported concerns about her inability to concentrate, procrastination, and stress. Her responses to individual items on the YSR resulted in a slightly elevated score on the Anxious/Depressed scale. Grace completed the Adult Self-Report Scale (ASRS) Symptom Checklist. On this measure, she endorsed a significant number of items related to inattention, including difficulty starting work and forgetfulness, further indicating concerns in this area.
Clinical Impression Grace is a pleasant young woman, who was found to have both strengths and weaknesses. She demonstrated strengths in visuospatial skills, aspects of memory, and verbal comprehension. Grace was cooperative and enjoyable to interact with in the one-to-one testing situation. Results from the current neuropsychological evaluation are indicative of several areas of weaknesses that may create challenges for Grace, particularly in the school environment. She exhibited weaknesses in attention, cognitive flexibility, processing speed, reading rate, and aspects of written language. Results from the neuropsychological evaluation are indicative of significant problems with attention. Grace demonstrated difficulty on Trails B, suggesting difficulties with cognitive flexibility and attention shifting. She also had difficulty on the PASAT, another measure of sustained attention and processing speed. Grace’s low processing speed, as measured by the WISC-IV, is also likely related to inattention. Further, Grace’s slow reading rate and errors in writing and spelling also may be attributable to problems with sustained attention. Finally, Grace and her mother report significant problems with attention, both in interviews and report forms, as well as a positive family history of attention problems. Grace’s problems with inattention significantly affected her ability to function well in the school environment. More specifically, the effects of Grace’s inattention on math, reading, spelling, and writing performances were apparent in testing.
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Grace made “careless” mistakes when her math computation skills were tested. She failed to attend sufficiently to the task and neglected several items. Grace’s reading rate was significantly below average for her age and grade level. The effect this had on her ability to complete a multiple-choice test, given a certain amount of time, was demonstrated with the Nelson-Denny Reading Test. Her achievement score significantly improved when she was given more time to read the passages and answer the questions. Also, careless mistakes were noticeable in Grace’s writing, which further suggests lapses in attention. In addition to the test results that indicate problems with attention, Grace’s personal account of her school difficulties further indicates that this is a significant problem. She reported problems in focusing, listening to and following instructions, being easily distracted, procrastinating, working slowly, and having to reread her work. In addition to her school achievement, there is evidence that Grace’s inattention is causing her to be anxious about school and to worry about her grades, a common experience for children with significant attention problems that are affecting their achievement. Grace reports these feelings, and her teacher’s responses in the rating form further support this concern. Grace’s attention problems are clinically significant and are negatively affecting her functioning in school and at home. She has shown these symptoms to some degree since elementary school age. The diagnosis of ADHD-PI is appropriate. Also, Grace is experiencing some symptoms of stress and anxiety that are likely secondary to the stress created by ADHD.
Recommendations Given the results of the neuropsychological evaluation, the following recommendations can be made: 1. Given Grace’s auditory processing and attention difficulties, she should get preferential seating in classroom and lecture situations so that auditory distractions are minimized. 2. Given that Grace has lapses in attention and takes more effort to process information, she would benefit from receiving a study guide when beginning a new chapter. She can then fill in the outline while reading the chapter and then study it later. 3. Grace may benefit from a well-monitored trial period on medication to manage her problems with inattention and distractibility. This option should be discussed with a physician. 4. Grace will need to keep a calendar or PDA with all relevant dates, assignments, and appointments. 5. Because of Grace’s disability (ADHD), she will have a tendency to work more slowly and make more attentional errors in her work. She will need more time to complete exams and carefully check her responses. Thus, she should be allowed
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to have extended time (50% additional time) in classroom and standardized tests, such as the SAT and ACT, in order to level the playing field with other normal students. 6. Grace’s parents should seek 504 designation for Grace in regard to her ADHD diagnosis. This may make relevant resources available to Grace, including extended time, note-taking assistance, and organizational skills assistance. 7. Grace would likely benefit from weekly individual therapy to improve coping skills to deal with stress and anxiety (Table 4).
Table 4 Case summary: In this case illustration, the diagnosis of ADHD-PI is the most parsimonious explanation of historical factors, laboratory results, qualitative data, and behavioral observations. The goal of evaluation is to help this young woman and her family better understand her strengths and weaknesses, and to develop an effective treatment plan Neuropsychological summary of case example History Family history of attention problems Reported difficulty when reading Adequate grades with Herculean effort Poor writing mechanics relative to content Procrastination and losing things Increasing problems as school demands increase Findings on neuropsychological measures Poor verbal fluency Poor selective auditory attention Relatively poor list learning versus narrative memory Inconsistent use of categorical organization in list learning Poor processing speed Poor sustained attention Poor shifting attention/cognitive flexibility Findings manifested on academic measures Poor reading rate Reading comprehension improves with extended time Poor contextual conventions (i.e., mechanics) in written expression Major “careless” mistakes on a math computation measure Behavioral reports Mother Inability to stay on task Procrastination Endorsed a significant number of DSM-IV inattention symptoms Teacher Mild symptoms of inattention noted Mild symptoms of anxiety noted Self-report Works very hard to compensate Procrastination Stress Endorsed a significant number of inattention symptoms
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Intervention Ninety percent of the world s woe comes from people not knowing themselves, their abilities, their frailties, and even their real virtues. Most of us go almost all the way through life as complete strangers to ourselves. Sidney J. Harris, 1917–1986, American Journalist and Author
Truly, the beginning of intervention for ADHD starts with the assessment process, when the individual presenting for evaluation is asked the question, “What brings you here today?” The neuropsychologist, the patient, and sometimes the patient’s family together define the questions the patient may have about herself. At this time, the patient begins to tell her story, which is the initiation of the process of making sense out of her history through the assessment procedure. As noted in the evaluation section of this chapter, the assessment procedure involves formal and informal measures that provide information about the patient’s strengths and weaknesses. When appropriate, a formal diagnosis of ADHD is made, subtype is specified, and other possible diagnostic issues are defined. These results are then usually communicated through one or more feedback sessions and a written report. Through the assessment process, the patient gains a better and more accurate understanding of herself and her history. Oftentimes, misperceptions and inaccurate attributions are corrected, such as, “I’m not very smart” or “I’m just a ditzy airhead.” This greater self-understanding sets the stage for intervention to address the identified ADHD and related weaknesses.
Symptom-Related Gender Differences Although there appear to be more similarities than differences in ADHD symptoms between females and males, some of the dissimilarities noted may suggest alternative treatment approaches, or at least a different emphasis in treatment. Metaanalyses indicate that girls may manifest somewhat lower symptom levels of hyperactivity and inattention than boys (Gaub & Carlson, 1997; Gershon, 2002). They tend to be less likely to manifest aggressive, ODD, or CD behaviors than males, but do show more overt and relational aggression than control girls (Zalecki & Hinshaw, 2004). There has been very little research examining the possible gender differences in ADHD subtypes. One study by Wolraich and colleagues (1996) using a large community-based sample did find that females tend to fall into the inattentive subtype more often than the other two subtypes. Also, working memory problems have been reported as higher in women with ADHD than their male counterparts in a relatively small study (N = 51) (Schweitzer, Hanford, & Medoff, 2006). Working memory is the ability to hold information in one’s mind for subsequent use, and is thought to be a key component of other cognitive functions, such as reasoning skills and executive functioning.
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Medication There is no cure for the underlying brain dysfunction that causes ADHD. However, medication has been found to be quite effective in the treatment of ADHD symptomatology, with stimulants being the most common medication prescribed for this disorder. Numerous studies have found that psychostimulants decrease impulsive, aggressive, and hyperactive behaviors (Goldman, Genel, Bezman, & Slanetz, 1998); improve performance on measures of reaction time, vigilance, impulse control, and fine motor coordination (Barkley, 1998; Barkley, DuPaul, & Connor, 1999; Rapport & Kelly, 1991); and enhance social interactions (Danforth, Barkley, & Stokes, 1991; Faraone et al., 2000; Whalen, Henker, & Dotemoto, 1980). Connor (2006) cites a response rate of approximately 75% in children with ADHD initially treated with stimulants. Multimodal Treatment Study The National Institutes of Health’s collaborative multi-site Multimodal Treatment Study of Children with ADHD (MTA) is the most comprehensive treatment study to date (MTA Cooperative Group, 1999a, 1999b). In this study, ADHD children between 7 and 9.9 years of age were assigned to four treatment groups: medication alone, behavior modification alone, a combination of medication and behavior modification, and community comparison. Generally, results indicated that combined treatments demonstrated the greatest likelihood of managing symptoms of not only ADHD but also many of its comorbid conditions, such as oppositional/aggressive symptoms. Combined treatments also seemed to lead to a reduced need for dosage of medication. In terms of gender and the MTA study, unfortunately, only 20% of the sample were females, and the study only included ADHD-C. In the sample of females, some differences were noted in their responses to treatment. Although boys showed better response to a combined behavioral and medication protocol or medication only protocol compared to community care, only the combined treatment was more effective than community care for girls. Thus, while medication alone may be an effective treatment for boys, girls seem to need behavioral interventions as well.
Stimulant Treatment for ADHD in Women There have been a number of studies examining the efficacy of stimulant treatment for ADHD symptoms in women. A large variation has been reported in response to treatment with efficacy ranging from 25% (Mattes, Boswell, & Oliver, 1984) to 78% (Spencer et al., 1995), which is much different than the fairly consistently reported 70% range for response rate in children. The wider response rate reported in women appears to be related to differences among the studies in diagnostic criteria, dosage levels, co-occuring disorders, and methodology for measuring responses to treatment (Quinn & Nadeau, 2002). This greater variability is likely not due to gender,
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given that stimulant studies comparing responses in women and men with ADHD have not found a difference in efficacy, side-effects, or safety (Patterson, Douglas, Hallmayer, Hagan, & Krupenia, 1999; Spencer et al., 2001). Literature on childhood is consistent with these findings, with boys and girls showing a similar response to basic stimulant treatment (Barkley, 1989; Pelham, Walker, Sturges, & Hoza, 1989), although, as noted in the previous section, girls seem to respond optimally when medication is combined with behavioral intervention. Non-Stimulant Medications Not all individuals with ADHD respond to stimulant medications, and some show co-occurring symptoms, such as anxiety, insomnia, or hypertension, which may contraindicate stimulant treatment. Although the stimulants have been found to be the most efficacious for treating ADHD symptoms (Connor, 2006), a number of other medication options have shown varying degrees of positive effects. The most promising results have been reported on the efficacy, tolerance, and relative safety of noradrenergic antidepressants (e.g., Bupropion) and specific norepinephrine reuptake inhibitors (e.g., Atomoxetine) (Spencer, 2006). Other second-line medications, such as the wake-promoting agent Modafanil, or the antihypertensive Guanafacine, have shown some degree of effectiveness against certain ADHD symptoms (Connor, 2006). Hormonal Factors Since most ADHD medication studies have primarily involved boys, and too little attention has been devoted to the possible influence of hormones on ADHD symptoms and medication effects in adolescent and adult women. Theoretically, fluctuations in hormone levels, in particular estrogen, could have a significant effect on the symptoms of attention, working memory, and behavior. Research indications are that estrogen increases the concentration of neurotransmitters that have been implicated in ADHD. For example, Fink and colleagues (1996) found that estrogen stimulated a significant increase in dopamine (D2) receptors in the striatal area of the brain. The stimulant medications affect the ADHD symptoms by blocking the reuptake of dopamine and norepinephrine into the presynaptic neuron (Barkley et al., 1999; Pliszka, 2003; Wilens & Spencer, 1998). In addition, there are findings that suggest that different phases of a woman’s menstrual cycle may interact to either increase or decrease the effects of stimulants (Justice & Wit, 1999, 2000). This aspect of treatment definitely deserves greater attention as more and more girls and women are identified with ADHD. Psychosocial Strategies Intervention strategies other than medication may be required for a number of reasons. Some individuals may not be able to tolerate side effects of medication, or there may be other life circumstances, such as pregnancy, that may preclude the
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use of medication. In addition, many individuals with ADHD may wish to learn strategies that will help them cope more effectively with their ADHD symptoms. After all, although medication can be quite effective in treating many symptoms of ADHD, and it may help the person use strategies more effectively, it does not teach the individual how to listen better, be more organized, use a planner, take notes or a test more successfully. These are strategies that must be learned and practiced. Of course, in terms of efficacy in treating ADHD symptoms, research findings have been the most robust for medication. However, as noted previously, the psychosocial interventions combined with medication resulted in the best outcomes in some circumstances (MTA Cooperative Group, 1999b). Also, there has been some promising research for cognitive interventions, such as the software for training working memory developed at the Karolinska Institute, Sweden (Cogmed, 2007). In any case, effective treatment for ADHD symptoms may involve a combination of non-medication interventions, such as counseling, coaching, and cognitive training. Counseling Counseling individuals with ADHD might involve individual, group, and couple therapy. It is imperative that a person with ADHD develops an understanding of the disorder, what it is (i.e., a neurobiological disorder), and what it is not (i.e., a character defect), and that they receive information and support in coping with this lifelong disorder. Counseling would likely involve education, goal-setting, strategies to meet these goals, monitoring progress toward goals, understanding and managing conflicts, and problem-solving. Also, given the high co-occurrence of other psychiatric disorders, there may be other aspects of psychotherapy involved in the treatment. For example, if an individual is also struggling with depression, they may find treatment through cognitivebehavioral therapy to be effective in dealing with depression or anxiety symptoms. Coaching The concept of coaching for ADHD symptoms is borrowed from athletics, where a coach helps the athlete learn and develop skills to be successful on the field. Ned Hallowell and John Ratey (1994) first introduced the idea of coaching adults with ADHD in their book, Driven to Distraction. This approach is collaborative and very pragmatic with the purpose of addressing very specific situations and multi-step tasks, such as applying to college or graduate school, carrying out a job search, or completing a thesis or dissertation. The sessions are often carried out via 10- to 15-minute phone sessions or sometimes through e-mail. There is currently no licensure or certification for ADHD coaches, but there are some professional organizations that provide training, such as the American Coaching Association AD/HD Comprehensive Coach Training Program. Coaching often involves very specific strategies for organization, planning, and monitoring progress. For example, in collaboration with a coach, a client with ADHD may learn to effectively use daily planning time, “to do” lists, a PDA, the
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reminder function in a PDA or computer, a color-coded system for files, desk, closet, and reducing clutter. A coach can be helpful in developing these systems, and assisting the individual with monitoring and maintenance. These techniques can often be effective in helping the person with ADHD cope better with the executive function deficits that frequently make their daily life very stressful and overwhelming. Although we await empirical support for coaching as an intervention for ADHD symptoms, this approach has a great deal of appeal for many women who have been sidetracked by their ADHD symptoms. Anecdotally, coaching can be very empowering for women with ADHD who feel overwhelmed by their difficulties with planning, organizing, and carrying out long-term, multi-step tasks. Cognitive Intervention We are just in the beginning stages of developing effective treatments to address the cognitive deficits associated with ADHD. Research to support the use of cognitive rehabilitation approaches to ADHD has been notoriously lacking. Undoubtedly, it is phenomenally difficult to carry out large-scale intervention studies that are double-blinded and answer questions of generalizability and long-term effectiveness. Promising research into non-medication-based training programs is beginning to emerge, such as neurofeedback, interactive metronome, and working memory training. A study examining the effectiveness of working memory (WM) training in a sample of 7–12-year-old children (N = 50) with ADHD was published fairly recently in the Journal of the American Academy of Child and Adolescent Psychiatry (Klingberg et al., 2005). The rationale for WM training for individuals with ADHD is threefold. First, WM is thought to be one of the key underlying cognitive variables involved in executive functioning. Secondly, WM and executive functioning deficits in ADHD have been extensively documented (Barkley, 2006; Frazier et al., 2004; Hervey, Epstein, & Curry, 2004; Holdnack, Morberg, Arnold, Gur, & Gur, 1995). Finally, it is thought that WM can be improved with practice. In the study by Klingberg and colleagues, children were randomly assigned to either 20 sessions of high intensity (HI) training with graduated and individualized difficulty levels or 20 sessions of low intensity (LI) training where the difficulty level was not increased. Training was carried out via a computer program that presented both verbal and visuospatial training exercises. The children, their parents, teachers, and experimenters were blind to group membership. Positive findings were reported for the HI group in terms of WM, measures of executive functioning, and parents’ rating of oppositional behaviors, and inattentive and hyperactive/impulsive symptoms. Findings for WM were comparable to medication treatment reported in prior studies. Teachers’ rating of ADHD symptoms was not significantly different between the groups. The positive findings for WM, executive function, and parent rating of oppositional behavior and inattention (but not hyperactive/impulsive symptoms) remained evident at 3-month follow-up. Despite the promising nature of these findings, a number of limitations can be found, which may affect generalizability. Only 10% of the sample was females;
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teacher ratings were not significant; and ADHD children with commonly co-occuring conditions, such as oppositional defiant disorder and/or depression, were excluded. Nevertheless, these preliminary findings are encouraging, and will hopefully be followed by more extensive evidence for cognitively-based treatment approaches. Summary ADHD is a neurobiologic condition that is not curable. However, it is quite hopeful that many of the symptoms of this disorder can be effectively treated through a number of means, including medication, psychosocial, and cognitive intervention. We are beginning the discussion on how these types of interventions may be tailored to the needs of females who suffer with ADHD. In their book, Gender Issues and ADHD (2002), Patricia Quinn and Kathleen Nadeau provide the much-needed discussion of treatment as applied to women. It is hoped that research will follow to measure the efficacy of various treatment approaches for girls and women with ADHD.
Conclusion Current scientific evidence strongly suggests that ADHD has a biogenetic basis. More specifically, the frontal-striatal circuits with projections between the frontal region and the thalamic, limbic, and basal ganglia regions are implicated in ADHD. Once thought of as a predominantly male disorder, research has unequivocally demonstrated that ADHD affects a substantial number of females; however, significant differences exist in the prevalence and clinical course of ADHD in women. Compared to males with ADHD, females with ADHD are more prone to have difficulties with inattentive symptoms than hyperactive and impulsive symptoms. Females often receive a diagnosis of ADHD significantly later than males. While this may represent a true difference in the developmental course of ADHD in women, it seems more likely that females display significantly less “acting out” symptoms and are, therefore, not referred for services. It is important to note that this failure to identify ADHD in childhood has serious consequences for later academic and emotional functioning. The assessment and diagnosis of ADHD often involves self-reports and/or parent reports, clinical interviews, neuropsychological testing, and psycho-educational testing. Because a thorough assessment provides the client with feedback about both strengths and weaknesses, the assessment is often the first step in the intervention process as it enhances the understanding of current and past challenges. While there is no cure for ADHD, numerous interventions exist which have demonstrated promising evidence. Multimodal intervention, including medication, psychosocial coaching, and cognitive intervention, is often warranted and is the most effective treatment approach. At the current time, there is limited research available regarding
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gender differences in treatment. Thus, clinicians who work with female populations with ADHD must be sensitive to gender issues and adapt interventions accordingly. For example, some research suggests that peer problems have much more negative consequences in girls than in boys; therefore, social skills training may play a more central role in intervention for females than for males (Quinn & Nadeau, 2002). Key Clinical Points and Future Directions in Research 1. Unlike boys with ADHD, girls with ADHD are more likely to be quiet and shy. As a result, they are less frequently referred for services. It is important to note, however, that girls who are described as “spacey” or “lost in a fog” should receive further evaluation. This is especially important during the elementary school years, as this is the time period when many basic reading and math skills are acquired. 2. Adult diagnosis of ADHD is fairly prevalent among females. When adult diagnosis occurs, it is important that the client be provided with an opportunity to process emotions related to the diagnosis as well as be provided with a supportive environment in which to reframe past challenges. 3. The most effective treatment for ADHD in females involves medication as well as psychotherapy and coaching to develop skills. An important part of treatment is helping female clients with ADHD develop organizational strategies (such as time management skills, effective use of a PDA or calendar, and organizing paper work and other belongings). Once these strategies are established, a decline in secondary anxiety and depression is often reported. 4. Although research examining ADHD in females has increased significantly over the past two decades, there are still many under-explored topics. a. Even though numerous hypotheses exist that purport hormonal functioning may have an impact on both the onset of symptoms in puberty and affect the clinical course of ADHD in females, there is little research exploring the relation between attentional functioning and hormones. b. A topic that has remained largely unexplored for both males and females is the possibility that divergent neurological development during adolescence could result in an adolescent onset of the disorder. c. Although numerous studies exist that establish effectiveness of various interventions for ADHD, there is a paucity of research examining gender differences in interventions. Undoubtedly, this is an exciting time in the field of neuropsychology, as we combine theoretical developments in our field with more advanced technologies in order to study important areas such as ADHD. It is critical that, as we make advances theoretically and technologically, we also carefully consider gender in these undertakings.
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Chapter 6
The Neuropsychology of Dyslexia: Differences by Gender Amy Nelson and Phyllis Anne Teeter Ellison
Severe reading disability, also known as dyslexia, is one of the most common classified learning disabilities (LD) in the US (Fine, Semrud-Clikeman, Keith, Stapleton, & Hynd, 2007). Approximately 5–10% of Americans possess a reading disability (RD), which makes up 80% of LD classifications (Shaywitz, 1998; Shaywitz & Shaywitz, 2003, 2005). In community-based samples, up to 17.5% of participants exhibited a RD. According to the Nations Report Card for reading progress published by the US Department of Education in 2005, more than one-third of American 4th graders performed below the basic reading level expectations (US Department of Education). Thus, issues pertaining to reading have gained serious attention in American education.
What Is Dyslexia? Lyon, Shaywitz, and Shaywitz (2003) state that: Dyslexia is a specific learning disability that is neurobiological in origin. It is characterized by difficulties with accurate and/or fluent word recognition and by poor spelling and decoding abilities. These difficulties typically result from a deficit in the phonological component of language that is often unexpected in relation to other cognitive abilities and the provision of effective classroom instruction (p. 2).
In most cases, state departments of education take responsibility for the definition of RD. As a result, the definition of RD varies from state to state. The traditional definition of RD is a discrepancy between one’s IQ score and his or her score on a reading achievement measure (Liederman, Kantrowitz, & Flannery, 2005; Siegel, 2003). In other words, there must be a significant difference between one’s cognitive ability and one’s current reading achievement. The idea is that the student has the A. Nelson (B) University of Wisconsin-Milwaukee, Aiken, SC 29803, USA e-mail:
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cognitive ability to achieve; however, for some reasons he or she cannot achieve this potential. In essence, this difficulty is “unexpected” (Shaywitz & Shaywitz, 2003, 2005). Extensive research on the process of reading has discovered that those with RD show a deficit in three areas of cognition, all of which have been identified as important for reading comprehension (Siegel, 2003). These areas include phonological processing, syntactic awareness, and working memory. Individuals with RD may show difficulty in one or all of these areas. Interestingly, research also shows that those with RD do not exhibit difficulty with semantic or orthographic processes. However, those with RD tend to overly rely on these two processes when reading. It is the reliance on semantic and orthographic processing, rather than phonology, that may cause reading problems. With current changes in the classification of LD, some states are moving from categorical to non-categorical procedures for delivering special education services. School districts in those states are, therefore, not required to label a student as RD in order for that child to receive services. As a result, RD is no longer strictly defined by a discrepancy between one’s ability and reading achievement. A student with RD can also be someone who does not respond to appropriate reading interventions and requires more intense services. Revisions of the classification of LD have caused some serious debate about the exact definition of RD (Siegel & Smythe, 2005). In most cases, it is the difference in the use of terminology and assessments. One issue is the operational definition of “reading” (Siegel, 2003). There are many reading assessments that can be used in evaluating for possible RD. However, there are inconsistencies in how reading is defined across all of these assessments. These assessments commonly come in two forms. The first are timed reading comprehension tests, where one is asked to read a passage and then answer several multiple choice questions about that passage. The second are untimed word recognition tests, where one is asked to read a list of individual words. This assessment of reading comprehension becomes problematic because it is a complex process that can be affected by one or many aspects, including word recognition. Further, word decoding is viewed as the integral piece to developing appropriate reading comprehension skills. Thus, assessing word recognition has gained the most support when evaluating for RD.
Major Features of Dyslexia As previously mentioned, RD is considered a cognitive processing issue. Struggling readers have the potential to be appropriate readers, but possess some fundamental cognitive deficit that affects their ability to read. Evidence suggests that this cognitive deficit is genetic and neurobiological in nature (Fine et al., 2007; Shaywitz & Shaywitz, 2005; Simos et al., 2005). Essentially, despite adequate classroom reading
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instructions, children with RD still find reading a significant challenge. The process of reading can be broken down into two parts (Gough & Tunmer, 1986). First, the reader must decode the words they are reading into their underlying phonological elements. Second, they must understand the words they are reading and draw meaning from the text. If a reader cannot first perform the fundamental decoding skill, he or she is inhibited from reaching a more complex process of comprehending the text. Not surprisingly, a majority of students with dyslexia exhibit some phonological deficit (Shaywitz & Shaywitz, 2003). Shaywitz and Shaywitz (2003) have found that family history is one of the most influential risk factors in developing RD. They have found that approximately 23–65% of those with a parent diagnosed with dyslexia have reported developing it themselves, and that 40% of children with dyslexia also have a sibling with dyslexia. On a related note, five specific chromosomes have been identified as being influential in the development of phonological awareness and other reading skills (Shaywitz & Shaywitz, 2003, 2005). Unfortunately, the expression of these genes is not fully understood. In a study of family influence on the development of RD, 24 families of children diagnosed with RD – according to standards set by the state of Georgia – were examined (Fine et al., 2007). Any family with a history of ADHD was excluded from the study to control for confounding variables. Participants were asked to complete an oral reading fluency test and undergo magnetic resonance imaging (MRI) to examine the volume and area of their corpus callosum. The midsagittal area and five segmentations of the anterior and posterior sections of the corpus callosum were also studied. According to regression analysis, the area and volume of the corpus callosum, along with the volume of the midsagittal area, were found to be significant contributors to oral reading fluency scores when looking at within-family variance. In other words, these three variables combinely predicted reading achievement for each family. The midbody segment of the corpus callosum was also found to be most highly correlated to oral reading fluency. This area is allegedly involved in higher level auditory processing, which can affect one’s reading ability. Conclusively, this study suggests the heritability of RD in regard to the development of the corpus callosum. In a follow-up controlled study of children aged 12–13 with a family history of dyslexia, 70 children were assessed for possible reading-related impairments (Snowling, Muter, & Carroll, 2007). Twenty of the children had no family history of dyslexia, while 50 of the children had a family history according to parent interviews. Those adolescents with a family history were considered “at-risk” of developing the disability. In a previous study, the participants were examined for RD at age 8. Those from an at-risk family who were RD were classified as “at-risk impaired.” Those from an at-risk family who were not RD were classified as “at-risk unimpaired.” Measures were used to assess literacy skills, language skills, attention control, emotional and behavioral adjustment, and parental reading skill. A parental interview was also conducted to determine if there were environmental influences that could account for any impairment. To control for those
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participants who had less familiarity with reading materials, print exposure was also accounted for. On average, the control participants performed 10 points higher on reading and spelling measures when compared to their at-risk peers (Snowling et al., 2007). When comparing the at-risk impaired, at-risk unimpaired, and control groups on literacy and language skills, there were no significant differences between the at-risk unimpaired and control groups. One can conclude that the at-risk unimpaired participants performed nearly as well as the control participants. This was also the case for measures of attention control and emotional and behavioral adjustment. Further, there was a clear relationship between those participants who performed low on the literacy and language assessments and their parent’s ratings of their adjustment. The at-risk impaired group seemed to exhibit significantly more emotional symptoms and attentional problems when compared to the other two groups. This makes sense, considering the comorbidity of many behavioral disorders that commonly occur with LD. Finally, a significant relationship was found between the literacy skills of the mother and the father and the literacy outcomes of their child within the control group. This was not true for the at-risk group. It seems as though the atrisk participants’ performance was due to less print exposure, possibly explained by their parents’ dyslexia. In other words, since their parents could not read well, they were less likely to be exposed to reading materials, and their reading skills were underdeveloped. Other factors such as family structure and socioeconomic status were also found to have an impact. These two studies are examples of the complex influence genetics may have on RD. In one case, it may be neurobiological, meaning that certain brain structures linked to reading skill are underdeveloped. However, in another case, it may be that a child was underexposed to print early in development. So, as a result of multiple family environmental factors, they were unable to develop their reading skills. The question then becomes much like the chicken and the egg phenomenon – did the environment cause the brain abnormalities, or did the brain abnormalities cause the environment? In the next section, we will further discuss the neurobiological underpinnings of RD and current research findings.
Neurobiology of Dyslexia Early advances in the neurobiological study of the brain first understood that reading occurred in the posterior left hemisphere of the brain (Shaywitz et al., 2002; Shaywitz & Shaywitz, 2003). Consequently, a RD emerges as a result of an underdeveloped or damaged left hemisphere. Functional brain imaging studies have allowed us to conduct more specific investigations of the brain and its involvement in the development of disorders (Frith, 2006; Shaywitz & Shaywitz, 2003, 2005). Instead of conducting autopsies, neuroimaging technology also researches to examine the brain while it is still alive and performing cognitive. Functional magnetic resonance imaging (fMRI) studies now show converging evidence that dyslexics exhibit
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a dysfunction in the posterior left brain, compared to non-impaired readers, when performing reading-related tasks. Specifically, the temporo-parieto-occipital brain regions have been found to differ in dyslexics compared to the same brain regions of non-impaired readers. In a study of 144 classified dyslexic and non-impaired children, a visual computer program was designed through Psyscope to neurologically probe the different areas of processing that are necessary for reading (Shaywitz et al., 2002). This program included tasks such as identifying letters, sounding out letters, sounding out pseudowords, and sounding out and deciphering the meaning of a real word. While children were completing these tasks, fMRI was used to take 10 axial-oblique functional activation images from the front to the back of the brain. When not undergoing fMRI tests, children were administered the word attack subtest from the Woodcock Johnson Psycho-Education Test Battery, which also assesses the decoding skills. Results of the study found that the dyslexic children did not exhibit significant differences in brain activation from the non-impaired children during the computerized reading tasks when phonology was not assessed. During the computerized tasks that involved phonological processing, further analyses showed that the nonimpaired children exhibited more activation in the left hemisphere than the dyslexic children. Brain activations and performance on the word attack subtest were also correlated to examine if there was a relationship between reading achievement and brain activation in the posterior brain. Performance on this subtest was found to be positively correlated with activation in the left posterior brain regions, considering age as a covariate. In summary, Shaywitz and colleagues (2002) argue that these results lend supportive evidence to the neurobiological nature of dyslexia. Not only is this evidence that dyslexia is a phonological processing problem, but it suggests that dyslexia occurs largely in the left hemisphere of the brain. In a longitudinal study of kindergarten children through 1st grade, reading skills were assessed to predict later reading achievement (Simos et al., 2005). Children were divided into low- or high-risk groups depending on whether they had developed certain fundamental skills for reading. Previous studies using Magnetoencephalography (MSI) have found that adults with reading difficulties present with a certain neurological profile that began to form during the end of kindergarten. Thus, the study aimed to follow up these results with children to examine the profiles of emerging poor readers. All participants were administered a state-established reading inventory before entering into kindergarten, which was designed to predict the risk of later reading difficulty. This inventory includes phonological and phonemic awareness probes. Once the low- and high-risk groups were determined, the highrisk group received instructions utilizing specific reading interventions (enhanced classroom instruction or pull-out services). At the end of the study, the children who did not respond to the intervention were deemed “non-responders” and classified as RD. MSI scans were also taken before and after the intervention period. During MSI scans, children were asked to complete two naming tasks that involved decoding – letter sound and pseudoword reading. In studying the brain activity of the non-responders, it was found that they performed poorly during the pseudoword reading task in pre- and post-tests that require phonological processing.
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By 1st grade, non-responders showed less brain activity in the left posterior brain region, compared to those in the low- and high-risk groups who did respond to intervention. The authors suggest that non-responders had an unexplainable deficit in the left posterior brain region, which prevented the acquisition of fundamental reading skills. In a similar study, Simos and colleagues (2007) examined the use of interventions to train the brains of poor readers. Fifteen children with severe reading difficulties, subsequent to appropriate reading instruction in 1st grade, received a 16-week intensive reading intervention. A control group of 10 non-impaired readers was included to control for confounding factors such as maturation, overexposure to test stimuli, and habituation to test procedures. The first 8 weeks of the intervention focused on developing word decoding skills, and the second 8 weeks focused on rapid word recognition. Reading progress was measured using the Basic Reading Composite. Similar to the study mentioned above by Simos et al. (2005), children in the intervention group were divided into “responders” and “non-responders.” Additionally, on three occasions over a 6-month period, all children underwent MSI while completing a pseudoword decoding task. The spatiotemporal profiles of each child were examined. Results of this study suggest that the responder group exhibited increased activity in the temporoparietal regions of the left hemisphere while performing reading tasks that required phonological decoding skill, while the non-responder group did not (Simos et al., 2007). This study made a unique discovery in that both increased duration of specific regions in the posterior left hemisphere of the brain along with a change in sequence of activity from the left posterior to the frontal regions were found to contribute to the efficacy of the designed reading intervention. Specifically, the non-responders showed compensatory brain activity, in that their brain circuitry shifted to the frontal brain regions causing deficient brain functioning. The authors argue that, “dyslexia is associated with a functionally impaired brain circuit for reading, which in turn may be caused by inefficient or inadequate neuronal connections between key brain areas that normally participate in this circuit” (p. 495). Therefore, reading intervention was only successful with those children whose brains did not compensate. Understandings of the neurobiological underpinnings of dyslexia have advanced considerably since the use of fMRI and MSI procedures and control-trial studies in this area of research. Not only do we now know the specific brain regions responsible for reading skill, we can track changes in response to intervention and develop understanding as to how reading interventions become efficacious. Our understanding of dyslexia will only grow with this continued approach.
Gender Differences in Dyslexia The extent to which gender differences exist in individuals with dyslexia is still unresolved, as research shows similarities as well as differences between females and males. Current evidence of gender disparities in identification rates for special
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education show the greatest differences in LD and emotional disturbance where boys represent 73% and 76% of students in each category, respectively (Oswald, Best, Coutinho, & Nagle, 2003). The reasons for these disparities, particularly RD, are explored in the next section, including explanations emphasizing teacher-referral biases, statistical artifacts, and biogenetic and neurological differences. These are explored separately.
Diagnostic Criteria, Statistical and Referral Bias School-based referral rates for LD are three to four times higher for males (Miles, Haslum, & Wheeler, 1998; Oswald et al., 2003). Miles et al. (1998) report that gender ratios are more equivalent, from 1.6:1.0 (males to females) when more stringent criteria are applied. Oswald et al. (2003) argue that discrepancy definitions (i.e., achievement/ability and other exclusionary criteria) account for disparities and suggest that gender differences would “change significantly if the assessments used to diagnose LD instead measured intrinsic processing limitations (i.e., followed the traditional conceptualization of LD as disorders in psychological processes), or if other exclusionary criteria attached to the current LD definition were removed” (p. 233). Miles et al. (1998) suggest that confusion over the use of the term dyslexia and LD contribute to the problem. Siegel and Smythe (2005) further argue that inconsistent definitions and classification criteria result in accurate conclusions that males have higher rates of reading difficulties than girls. When investigating gender bias in IQ-discrepancy definitions of RD, Share and Silva (2003) found that “when the general distribution of reading scores for boys was lower on average than the distribution for girls, the IQ-discrepancy regression formula produced a preponderance of boys with RD due to the fact that reading scores were systematically overestimated for boys, thereby inflating the discrepancies” (p. 10). In their analyses, the authors concluded that gender bias need not be an outcome of IQ-discrepancy approaches except in extreme cut-offs especially when there are larger reading score variances. In this study, the reading score standard deviations for boys were greater than those reported for girls. When these factors co-occur, the gender imbalance is greater. These results also occurred when IQ was not included in the analysis. When RD was defined by reading scores alone, gender bias also resulted from the reading score distributions. Share and Silva (2003) offer preliminary comments about their findings, The fact that reading disability is conventionally defined in reference to a child’s age or current grade level recognizes both the reality of fundamental biological differences related to age and the social context of school-based learning and instruction. Gender, like age, might be considered a basic biological variable, engendering differential expectations (particularly in early language development) with respect to academic outcomes and their prediction in social and cultural contexts. However, whereas in-school learning is almost segregated by age, segregation by gender – in Western cultures at least – is far less common. When
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schooling is gender-segregated (e.g., for religious or ideological reasons), it would seem appropriate to define reading disability by reference to the specific context of gendersegregated learning (Share & Silva, 2003, p. 11–12).
Some researchers have suggested that teachers are more likely to refer boys to special education because of a combination of poor achievement and behavior problems (Shin, Tindall, & Spira, 1987). Challenging, disruptive and aggressive behaviors in addition to learning difficulties may be more obvious in the classroom, such that males may appear more impaired than girls who may display more “silent,” underlying learning difficulties that are as more easily managed. Oswald et al. (2003) explored gender disparities and reported that even though disproportionality has declined from 1976 to 1997, LD and emotional disturbance (ED) rates are still higher in males. While there are many environmental and experiential factors that may account for different rates of ED in males, there is some evidence that girls may have some neuroanatomical advantages that may help them control and process emotions. Gur, Gunning Dixon, Bilker, and Gur (2002) found that adult females have larger orbital frontal cortices relative to amygdala volume than adult men. The orbital frontal region plays a significant role in social behavior, emotional functioning, reasoning and decision-making involving personal risk and reward, consequential thinking, and rational cognitive processes, while the amygdala, within the temporal lobes, plays a significant role in expressing emotional responses, including fear, anxiety, anger, and conditioned responses (Carlson, 2004). The orbital prefrontal cortex exerts a regulatory influence and control over emotions. Thus, the increased orbital volume relative to amygdala volume in women compared with men supports the hypothesis that women have greater tissue volume available for modulating amygdala input. This finding may explain gender differences in emotional behavior, particularly aggression (Gur et al., 2002). It is likely that such a neuroanatomical difference will have functional significance. While environmental and cultural factors undoubtedly contribute to sex differences in aggression, the existence of such marked neuroanatomical differences in brain structure related to emotion regulation warrants systematic effort to link emotional behavior to neural substrates. (Gur et al., 2002, p. 1001)
Biogenetic Factors Influencing Gender Differences in Dyslexia Males appear to be at a higher risk of postnatal mortality, genetic complications during pregnancy and childbirth, and congenital abnormalities compared to females (Eme, 1984; see Oswald et al., 2003). Researchers have hypothesized that gender differences in reading ability may in part be due to X-linked recessive inheritance (Hawke, Wadsworth, Olson, & DeFries, 2006). Females may be less susceptible to X-linked disorders because they inherit two X-chromosomes, while males inherit only 1 X-chrmosome females have a better chance of inheriting an unaffected chromosome. Hawke et al. (2006) explored the genetic etiology of reading difficulties in males and females. They argue that the ratio of males to females vary depending on study methodology. For example, studies using clinical or referral rate methods
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produce ratios from 2:1 to 15:1 (males to females), while research-identified samples produce gender ratios close to 1:1 (Hawke et al., 2006). Some researchers have also argued that females with reading problems may be less susceptible to environmental factors such as teaching methods and socioeconomic status (Geschwind, 1981), and that genetic factors may be more important to girls than boys (DeFries & Gillis, 1993). In an effort to further explore gender issues, Hawke et al. (2006) studied the contribution of genetic and environmental factors in twins. The study did reveal that there were more males than females at all levels of reading difficulties, and that there was an increase in the ratio as severity increased. However, the study did reveal “that not only is the magnitude of genetic influences similar for males and females at all levels of severity, but that the same genetic and environmental influences contribute to reading difficulties in boys and girls, irrespective of severity” (Hawke et al., 2006, p. 23). In studies investigating risk factors for LD, there are a number of factors that have been identified, including pre-pregnancy, prenatal, and birth and delivery complications (Hill, Cawthorne, & Dean et al., 1998). Dean and Davis (2007) found that several other perinatal factors increase the relative risk for special education including mother’s weight before pregnancy, saddle block anesthesia, much stress throughout pregnancy, prematurity, maternal age over 35 years, prenatal care initiated after the first trimester, unplanned pregnancy, hypoxia, medications during pregnancy, and cigarette smoking during pregnancy. When exposed to these risk factors, particularly extremely low birth weight (ELBW) and prematurity, males seem to fare worse than females. On average, preterm, ELBW females have lower mortality rates (Cooper et al., 1993; Morse et al., 2006; Stevenson et al., 2000) and have fewer learning difficulties compared to males. It is unclear why girls seem to have an advantage in these adverse conditions. Future research will help answer important questions of how these risk factors impact dyslexia in males and females, and the extent to which gender plays a role in later outcome.
Neurological Differences There is evidence that cortical areas underlying language skills are proportionally larger in females compared to males. Harasty, Double, Halliday, Kril, and McRitchie et al. (1997) found that on average, females had larger cortical areas in the following language regions: superior temporal regions were 17% larger, including Wernicke’s area, Heschl’s gyrus, and the anterior, superior temporal lobes; the planum temporale was 29.8% larger; and Broca’s area, including the inferior frontal gyrus, was 20.4% larger. In a study investigating functional asymmetry, Witelson (1976) found that women had greater bi-hemispheric representation for some language skills compared to males. Kulynych, Vlader, Jones, and Weinberger (1994) also found that males had significantly larger left planum versus right planum regions, while the right–left differences were not found in females. Hier (1979) suggested that
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these anatomical differences may account for differences in recovery from insults or developmental anomalies, and may be a partial explanation for gender differences in dyslexia. Girls may be able to recover more readily from left hemisphere damage given the differences in functional asymmetry. Inconsistent findings are reported for differences in the corpus callosum, the interhemispheric pathways which connect the right and the left frontal and/or temporal-parietal regions (Filipek, 1996). Duara et al. (1991) found that the posterior region of the corpus callosum (splenium) was larger in both male and female children with dyslexia compared to control children. However, females with dyslexia had larger total callosal areas and anterior callosal (genu) regions compared to males with dyslexia. Other studies have not supported this finding (Larsen, Hoien, & Odegaard, 1992). Hynd et al. (1995) found that reading ability was related to regions of the callosum (genu and spelnium), but they did not find gender differences. There are several explanations for these inconsistencies, including variability from MRI methods, small sample sizes, and sample selection criteria for controls. While a direct link between gender differences in anatomical structures and differences in the rates of dyslexia has not been established, there are some noteworthy sex differences in brain activation patterns during word reading tasks. Wirth et al. (2007) measured event-related potentials for both higher and lower level semantic processing during a passive reading task. Males and females showed similar activation patterns during initial lexical-semantic access; however, females showed earlier and long-lasting N400 effects in the temporal network, and women appeared to spontaneously access deeper semantic processing. The depth of processing may be the key here – while faster processing of words is not likely to be reflected in the differences in functional organization but in the depth of cognitive processes during reading (see Wirth et al., 2007).
Summary and Implications The evidence for gender differences in dyslexia is mixed with both similarities and differences across genders. The genetic basis of the disorder and the cortical substrates – temporo-parieto-occipital brain regions – involved in the reading process are the same for both males and females. On average, females have larger cortical areas in regions underlying language, and women have greater bi-hemispheric representation for some language skills compared to males. Females may have some advantages that protect against early insult or neurodevelopmental anomalies affecting these brain regions. The extent to which these anatomical differences are protective needs further investigation. Several underlying factors may explain gender differences in RD, including connections between biology and environment. Specifically, differences in early literacy environment and instruction may ultimately affect the development of neural
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networks involved with reading accounting for gender differences. Although we have strong evidence of the genetic basis of dyslexia, “genes, at best, explain only a portion, although substantial, of the relevant variance” (Grigorenko, 2007, p. 123). There are a number of important environmental factors that explain individual differences in reading skills, including socioeconomic status of the child’s family, school and community, teachers and their pedagogical approach, and availability of materials to enhance early learning and literacy (Grigorenko, 2007).
References Carlson, N. R. (2004). Physiology of behavior (8th ed.). Boston, MA: Allyn and Bacon. Cooper, R. L., Goldenberg, R., Creasy, R., DuBard, M., Davis, R., Entman, S., et al. (1993). A multicenter study of preterm birth weight and gestational age – specific neonatal mortality. American Journal of Obstetrics and Gynecology, 168, 78–84. Dean, R., & Davis, A. (2007). Relative risk of perinatal complications in common childhood Disorders. School Psychology Quarterly, 22, 13–25. DeFries, J. C., & Gillis, J. J. (1993). Genetics of reading disabilities. In R. Plomin & G. E. McClearn (Eds.). Nature, nurture and psychology (pp.121–145). Washington, DC: American Psychological Association. Duara, R., Kushch, A., Gross-Glenn, K., Barker, W. W., Jallad, B., Pascal, S., et al. (1991). Neuroanatomic differences between dyslexic and normal readers on magnetic resonance imaging scans. Archives of Neurology, 48, 410–416. Eme, R. F. (1984). Sex-role stereotypes and the epidemiology of child psychopathology. In C. S. Widow (Ed.), Sex roles in psychopathology (pp. 279–316). New York: Plenum Press. Filipek, P. A. (1996). Structural variations in measures in the developmental disorders. In R. W. Thatcher, C. R. Lyon, J. Rumsey, & N. Krasnegor (Eds.), Developmental neuroimaging: mapping the development of brain and behavior (pp. 169–186). San Diego, CA: Academic Press. Fine, J. G., Semrud-Clikeman, M., Keith, T. Z., Stapleton, L. M., & Hynd, G. W. (2007). Reading and the corpus collosum: an MRI family study of volume and area. Neuropsychology, 21(2), 235–241. Frith, C. D. (2006). The value of brain imaging in the study of development and its disorders. Journal of Child Psychology and Psychiatry, 47(10), 979–982. Geschwind, N. (1981). A reaction to the conference on sex differences in dyslexia. In A. Ansara, N. Geschwind, A. Galaburda, M. Albert, & N. Gartell (Eds.), Sex differences in dyslexia (pp. 1–9). Townson, MD: Orton Dyslexia. Grigorenko, E. (2007). Triangulating developmental dyslexia. In D. Coch, K. Fischer, & G. Dawson (Eds.), Human behavior, learning, and the developing brain (pp.117–144). New York, NY: Guilford Press. Gough, P. B., & Tunmer, W. E. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7(1), 6–10. Gur, R., Gunning Dixon, F., Bilker, W., & Gur, R. E. (2002). Sex differences in temporo-Limbic and frontal volumes in healthy adults. Cerebral Cortex, 12, 998–1003. Harasty, J., Double, K. L., Halliday, G. M., Kril, J. J., & McRitchie, D. A. (1997). Languageassociated cortical regions are proportionally larger in the female brain. Archives of Neurology, 54, 171–176. Hawke, J. L., Wadsworth, S. J., Olson, R. K., & DeFries, J. C. (2006). Etiology of reading difficulties as a function of gender and severity. Reading and Writing, 20, 13–25. Hier, D. B. (1979). Sex differences in hemispheric specialization: Hypothesis of the excess of dyslexia in boys. Annals of Dyslexia, 29, 74–83.
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Hill, S. K., Cawthorne, V., & Dean, R. (1998). Utility of the maternal perinatal scale (MPS) in distinguishing normal from learning disabled children. International Journal of Neuroscience, 95, 141–154. Hynd, G. W., Hall, J., Novey, E. S., Eliopulos, D., Black, K., Gonzales, J. J., et al. (1995). Dyslexia and corpus callosum morphology. Archives of Neurology, 52, 32–38. Kulynych, J. J., Vlader, K., Jones, D. W., & Weinberger, D. R. (1994). Gender differences in the Normal lateralization of the supratemporal cortex: MRI surface-reading morphometry of Heschl’s gyrus and the planum temporale. Cerebral Cortex, 4, 107–118. Larsen, J. P., Hoien, T., & Odegaard, H. (1992). Magnetic resonance imaging of the corpus callosum in developmental dyslexia. Cognitive Neuroscience, 9, 123–134. Liederman, J., Kantrowitz, L., & Flannery, K. (2005). Male vulnerability to reading disability is not likely to be a myth: a call for new data. Journal of Learning Disabilities, 38, 109–129. Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). A definition of dyslexia. Annals of Dyslexia, 53, 1–14. Miles, T. R., Haslum, M. N., & Wheeler, T. J. (1998). Gender ratio in dyslexia. Annals of Dyslexia, 48, 27–55. Morse, S., Uru, S., Ma, C., Ariel, M., Resnick, M., & Roth, J. (2006). Racial and gender differences in the viability of extremely low birth weight infants: a population-base study. Pediatric, 117, e106–e112. Oswald, D. P., Best, A. M., Coutinho, M. J., & Nagle, H. A. L. (2003). Trends in special education identification rates of boys and girls: a call for research and change. Exceptionality, 11, 223–237. Senechal, M., & LeFevre, J. (2002). Parental involvement in the development of children’s Skill: a five-year longitudinal study. Child Development, 73, 445–460. Share, D. L., & Silva, P. A. (2003). Gebder bias in IQ-discrepancy and post-discrepancy definitions of reading disability. Journal of Learning Disabilities, 36, 4–14. Shaywitz, B. A., Shaywitz, S. E., Pugh, K. R., Mencl, W. E., Fulbright, R. K., Skudlarski, P., et al. (2002). Disruption of posterior brain systems for reading in children with developmental dyslexia. Biological Psychiatry, 52, 101–110. Shaywitz, S. E. (1998). Current concepts: dyslexia. New England Journal of Medicine, 338, 307–312. Shaywitz, S. E., & Shaywitz, B. A. (2003). Neurobiological indices of dyslexia. In L. Swanson, K. R. Harris, & S. Graham (Eds.), Handbook of learning disabilities (pp. 514–531). New York, NY: Guilford Press. Shaywitz, S. E., & Shaywitz, B. A. (2005). Dyslexia (specific reading disability). Biological Psychiatry, 57, 1301–1309. Shin, M. R., Tindall, G. A., & Spira, D. A. (1987). Special education referrals as an index of teacher tolerance: are teachers imperfect tests? Exceptional Children, 54, 32–40. Siegel, L. S. (2003). Basic cognitive processes and reading disabilities. In L. Swanson, K. R. Harris, & S. Graham (Eds.), Handbook of Learning Disabilities (pp. 158–181). New York, NY: Guilford Press. Siegel, L. S., & Smythe, I. S. (2005). Reflections on research on reading disability with special attention to gender issues. Journal of Learning Disabilities, 38(5), 473–477. Simos, P. G., Fletcher, J. M., Sarkari, S., Billingsley, R. L., Denton, C., & Papanicolaou, A. C. (2007). Altering the brain circuits for reading through intervention: a magnetic source imaging study. Neuropsychology, 21(4), 485–496. Simos, P. G., Fletcher, J. M., Sarkari, S., Billingsley, R. L., Francis, D. J., Castillo, E. M., et al. (2005). Early development of neurophysiolocial processes involved in normal reading and reading disability: a magnetic source imaging study. Neuropsychology, 19(6), 787–798. Snowling, M. J., Muter, V., & Carroll, J. (2007). Children at family risk of dyslexia: a follow-up in early adolescence. Journal of Child Psychology and Psychiatry, 48(6), 609–618.
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Stevenson, D., Verter, J., Fanaroff, A., Oh, W., Ehrenkranz, R., Shankaren, S., et al. (2000). Sex differences in outcomes of very low birth weight infants: the male newborn disadvantage. Archives of Disabled Childhood Fetal & Neonatal Edition, 83, F182–F185. United States Department of Education. (n.d). Nations report card data from 2004. Retrieved September 10, 2005, from http://nationsreportcard.gov/reading math 2005/s0005.asp? printver=. Wirth, M., Horn, H., Koenig, T., Stein, M., Federspiel, A., Meier, B., et al. (2007). Sex differences in semantic processing: event-related brain potentials distinguish between lower and higher order semantic analysis during word reading. Cerebral Cortex, 17, 1987–1997. Witelson, S. F. (1976). Sex and the single hemisphere: right hemisphere specialization for spatial processing. Science, 193, 425–427.
Chapter 7
Sex and Gender Differences in the Assessment, Treatment, and Management of Epilepsy Elaine Fletcher-Janzen
Introduction Epilepsy was reported as far back as 4000 years ago in Babylonia, and until the 19th century, it was shrouded in mystery and stigma (Reynolds, 2001). The development of scientific inquiry into the causes and conditions associated with epilepsy emerged with the fields of neurology and neuropsychology, and it has enjoyed a sustained inquiry ever since. However, it has only been in recent times that the unique interaction between ictal activity and female central nervous, endocrine, and reproductive systems has come to light in the recent times (Mead & Hampson, 1996). Epilepsy is a common neurological disorder affecting 1–3% of females (Jette & Morrell, 2005) and more than one million women and girls in the US (Epilepsy Foundation, 2007a). Prior to 1963, most states in the USt had legislation prohibiting marriage for individuals with epilepsy therefore preventing many women in this population from having children and reproductive choice. In the past few years, it is understood that epilepsy affects sexual development, menses, aspects of contraception, fertility, and pregnancy, and that treatment of women has special considerations for successful outcomes to be present (Crawford, 2005; Jette & Morrell, 2005). Most women with epilepsy can sustain normal pregnancy and delivery, and current conventional thought is that women with epilepsy should not be discouraged from becoming pregnant (Fletcher-Janzen, 2000; Genton, Semah, & Trinka, 2006; Yerby, 2007). Not only does the disease itself present problems for the women themselves, but also for their children. The teratogentic effects of antiepileptic drugs (AEDs) have been well studied, but are not conclusive; and, therefore, the dilemma for women with epilepsy who wish to have children lies in the level of seizure control versus potential fetal malformations due to AEDs (Bauer et al., 2002; Genton et al., 2006; Vajda et al., 2006). This is not an easy calibration for any woman with epilepsy to tolerate; however, perhaps, it is easier than being directed not to have children at all.
E. Fletcher-Janzen (B) Private Practice, Cleveland, Ohio e-mail:
[email protected] E. Fletcher-Janzen (ed.), The Neuropsychology of Women, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-76908-0 7,
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The biopsychosocial ramifications of having epilepsy are sharply divided by gender roles and expectations. Crawford (2005) states, “Being a woman with epilepsy is not the same as being a man with epilepsy. Epilepsy affects sexual development, menstrual cycle, aspects of contraception, fertility, and reproduction” (p. 117). Hence, many now talk about epilepsy in women in terms of life span (McAuley & Anderson, 2002; Morrell, 2007) or developmental markers such as menopause or adolescence (Epilepsy Foundation, 2007c; Herzog, 2000, 2007; National Institute of Neurological Disorders and Stroke, 2007). Surveys on treatments of women with epilepsy and their medical providers indicate little sensitivity to female perspectives on diagnosis and care (Kampman, Johansen, Stenvold, & Acharya, 2005; McAuley & Anderson, 2002). Many healthcare professionals do not address the issues specific to women with epilepsy, and therefore seizure control and quality of life suffers. The lack of system-wide practice guidelines in the medical and neuropsychological areas has prompted the development of discussion checklists for physicians/clinicians and female patients that outline the areas of treatment specific to and essential for women (Epilepsy.com, 2007). Reproducible and complete copies of these checklists are available in the Appendix to this chapter, and the reader is directed to the website Epilepsy.com for an online sample of the checklist and for further useful resources. The checklists demonstrate that there are numerous aspects of female epilepsy care that need to be assessed and discussed in detail because they have material effects on treatment outcomes. In addition, the checklists document (and are supported by numerous professional resources) that there needs to be a strong, detailed, and ongoing partnership between clinician and female patient. This chapter summarizes the recent surge in research literature surrounding the special neuropsychological diagnostic and intervention considerations for women who have epilepsy. Much of the research is medically oriented and has been conducted in only the past 10 years. Neuropsychological professionals need to be aware of the important research findings to date translate that directly into clinical practice. The ultimate goal of neuropsychological treatment is to optimize seizure control in the patient. This goal may take many forms and routes as some patients are beginning their life course with epilepsy, some are suffering cognitive and neuropsychological deficits that need to be assessed, and some are going through life changes (e.g., pregnancy) that require monitoring. The feminine aspects of epilepsy require a thorough understanding of the woman’s physical health, lifestyle, and life goals (Epilepsy Foundation, 2007b). Quality of life for a woman with epilepsy depends largely on her being able to understand, manage, and express the natural changes and responses in her body. This understanding can be initiated and supported by neuropsychology professionals.
Genetic Studies Epilepsy, in general, is a heterogeneous condition incorporating many epilepsy syndromes with different causes. Common epilepsy syndromes are polygenetic and
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complex in nature and also subject to a myriad of environmental influences (Tan, Mulley, & Berkovic, 2004). Tan et al. (2004, p. 1429) suggest that: When a genetic basis exists for epilepsy, defining the genetic contribution has proven to be a formidable task. Success has been achieved in some families with rare monogenic epilepsy syndromes, with mutations of large effect where concordance between genotype and phenotype is reasonably strong. In contrast, progress has been slow for most of the common epilepsy syndromes encountered in daily clinical practice.
Overall, prevalence and incidence rates of epilepsy are slightly in favor of men, although certain epilepsies such as childhood absence seizures may be more frequent in girls (Tettenborn, 2006). There is emerging research that indicates a gender susceptibility to the development of specific epilepsy subtypes (Christensen, Kjeldsen, Andersen, Friis, & Sidenius, 2005). Women tend to have idiopathic generalized epilepsy more frequently than men. The reason for this difference is not clear at this point, but is most likely due to sex hormones having an influence on neuronal excitability (Galimberti et al., 2005; Jette & Morrell, 2005). On the other hand, symptomatic localization-related epilepsy appears to be more frequent among men than women. This may reflect differences in risk or structural damage to the brain and subsequent seizures (Christensen et al., 2005). Many women with epilepsy fear passing their condition on to their children and inquire about the chances for this to happen. Most women can be assured that they are at low risk for this event. However, in specific cases, there may be an underlying genetic cause that has not been identified. For generalized (idiopathic) epilepsy, the risk of a child developing the disorder is 5–20% if there is one affected first-degree relative. If there are two affected relatives, the risk increases to 25% (Crawford, 2005). After taking into account heredity, life style, adventitious events, gender bias in behavior, responses to AEDs, and physical and hormonal gender differences, it is easy to understand that genotypical and phenotypical studies of epilepsy are easily plagued with methodological problems, and ecological validity confounds.
Epilepsy and the Female Life Span Puberty Onset of Menarch Many times there is a change in the phenotypic expression of epilepsy in young women and girls during puberty. Many of the genetic epilepsy syndromes appear to respond to puberty, either by remitting or manifesting. Sex steroid hormones have been shown to significantly alter neuronal excitability with estrogenlowering seizure threshold and progesterone-increasing seizure threshold (McAuley & Anderson, 2002). Primary generalized epilepsy syndromes that develop at puberty include juvenile myoclonic epilepsy and photosensitive epilepsy. Primary generalized epilepsy syndromes that remit at puberty are childhood absence epilepsy and benign rolandic
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epilepsy. A significant number of young women (approximately 30%) experience an exacerbation of seizures at menarche (assessed by increased seizure frequency or development of a new, more severe seizure type) (Herzog, 2006). Thirty-three percent of young women with primary generalized epilepsy have onset within 6 months of menarche (Jette & Morrell, 2005). Puberty is the perfect time for the young woman with epilepsy to become aware of the special gender-based aspects of her condition. It is very important that she feels empowered to be a part of her treatment so that she can go through identity formation with a realistic and optimistic attitude. For the teenage girl with epilepsy, there are special constraints surrounding her body changes and there are also special constraints about the parameters of reproductive health awareness (Crawford, 2005). Contraception and pregnancy for women with epilepsy (however young) must be planned and monitored closely. Risk of contraception failure is higher in women with epilepsy and, therefore, reproductive choices must start early and with medical supervision. Catameniel Epilepsy The word catamenial is of Greek origin and means “monthly” (Jette & Morrell, 2005). Catamenial epilepsy refers to seizures influenced by the menstrual cycle. There has been a range of reported incidence of catamenial seizures from 12% (Crawford, 2005) to instances of 50% (McAuley & Anderson, 2002). There are many definitions to catamenial epilepsy, and this affects the reported incidence (McAuley & Anderson, 2002). Indeed, in women with localization-related epilepsy, it is reported that catamenial seizure occurs in 70% of cases (Herzog, 2000). Penovich, Eck, and Economou (2004) have created a parsimonious definition of catamenial seizures as “a doubling of the baseline seizure frequency during hormonal changes in the menstrual cycle” (p. S50). Although it has been clearly established that the sex steroid hormones, estrogen and progesterone, have variable effects on neuronal excitability, the relationship in terms of managing catamenial epilepsy at this stage is still poorly understood (Epilepsy Foundation, 2007d; Herzog et al., 2004; McAuley & Anderson, 2002). Menstrual disorders are more common among women with interictal discharges as well as women with abnormal hormonal findings (Herzog et al., 2003). Causes of catamenial epilepsy are thought to be the natural premenstrual withdrawal of progesterone, hormonal imbalances with high estrogen-to-progesterone ratios prior to ovulation (Crawford, 2005), and decreased AED levels premenstrually (Epilepsy Foundation, 2007d; Herzog, 2000; Jette & Morrell, 2005). The interaction of hormones and AEDs provides an interesting and complicated scenario: The premenstrual exacerbation of seizures may be related to a decline in serum antiseizure medication levels. . .as well as to a withdrawal of the anticonvulsant effects of progesterone. Serum antiseizure medication levels generally decrease in the days prior to menstruation. This decline is significantly more marked in women who experience premenstrual worsening of seizures. Hepatic mechanisms are implicated. Specifically, antiseizure medications and gonadal steroids are metabolized by the same microsomal enzyme systems in hepatic cells. The premenstrual decline in gonadal steroid secretion, therefore, may permit increased metabolism of antiseizure mediations, resulting in lower serum levels. (p. 105).
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Increased seizure activity might also be present in the second half of anovulatory cyles when estrogen levels surge, and the balancing effects of adequate progesterone are not present. Most seizures that are related to monthly hormonal changes occur in the perimenstrual period (Epilepsy Foundation, 2007d; Herzog, 2007); however, some women experience patterns of seizures during ovulation and during an inadequate luteal phase. Herzog (2000) states “seizures do not occur entirely randomly in relation to physiological reproductive endocrine events” (p. 105). Herzog, Klein, and Ransil (1997) studied 184 women aged 18 to 45 years with complex partial seizures, and divided the women’s cycles into ovulatory and anovulatory cycles. Seventy percent of the subjects (of either category of cycle) had increased seizures, and approximately onethird experienced a doubling of seizure events. Three patterns of catamenial seizures were documented: premenstrual, periovulatory during normal ovulatory cycles, and the entire second half of the cycle in anovulatory cycles. From this study, a basic definition of catamenial epilepsy emerged as “a two-fold or greater increase in seizure exacerbation” (p. 105) (Herzog, 2000; Herzog et al., 1997). An expedient aspect of assessing catamenial epilepsy is the inclusion of data gathered by the patient, which has a direct input into diagnosis and treatment. This process is a marker of the patient–doctor cooperation in the ongoing understanding of the seizure condition. It is important that the patient make detailed notes about when seizures occur in terms of the day of her cycle (Yerby, 2001). Charts that number days 1 through 28 can be used to make notations about when seizures occured and also important aspects of how the patient experienced that day in terms of collateral symptoms or life events that were present. Changes in diet, stress level, sleep disturbance, presence of illness, and auxilliary medications are examples of events that should be reported as they can affect seizure threshold. If the patient and the clinician/physician create a numerical assignment for stress and symptom levels, these data can be plotted monthly as well. In turn, the data feed the assessment of seizure frequency and control. These data can also serve as a baseline for times when the patient goes through unique life events such as pregnancy, where hormonal changes are anticipated and any prediction of seizure response is appreciated. There have been some studies over the past decade that have manipulated the hormonal levels in women with catamenial seizures with the goal of converting anovulatory cycles to ovulatory cycles, intermittent perimenstrual progesterone supplementation, and the increase of serum AED levels (Crawford, 2005; Epilepsy Foundation, 2007d). In some instances, a combined contraceptive pill may be prescribed (Crawford, 2005). For women on AEDs, intermittent use of perimenstrual clobazam or acetazolamide is suggested when a seizure is anticipated (Crawford, 2005; Epilepsy Foundation, 2007d). For women who are not taking AEDs, the recommended treatment is intermittent perimenstrual clobazam, oral contraceptives, progestogen therapy, or perimenstrual progestogen. Natural progesterone therapy used during the second half of each cycle with gradual tapering and discontinuation by the end of the cycle may benefit some women (Epilepsy Foundation, 2007c). Other women may also experience decreased serum AED levels premenstrually, related to increased
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hepatic metabolism of AEDs (Herzog, 2000). At this time, phenytoin has been found to decrease serum concentrations during menses (McAuley & Anderson, 2002); similarly, lamotrigine is useful (Reimers, Helde, & Brodtkorb, 2005). Other AEDs apparently do not show similar results; however, not all AEDs have been tested, especially in randomized, controlled trials. It might be advisable to have AED levels drawn during midluteal phase and again at menstruation to help with documentation of fluctuations. Adjunctive therapy or dose adjustment may be helpful during this time (Epilepsy Foundation, 2007d).
Childbearing Years In the childbearingage, women with epilepsy have to make unique decisions about their reproductive options. They must decide on measures of hormonal birth control vis-`a-vis impact on seizure threshold (Yerby, Kaplan, & Tran, 2004); they must know what they can do if they become pregnant unexpectedly; they must be aware of the impact of certain AEDs on the fetus; and they must be psychologically prepared for a potential increase in seizure frequency (hormonally induced) during pregnancy and beyond. These issues should be discussed with a physician and/or clinician ahead of time and in detail (Crawford, 2005; Penovich et al., 2004; Yerby, Kaplan, & Tran, 2004). Fertility It is generally thought that women with epilepsy have lower fertility (Yerby, 2001); however, women who marry tend to have normal fertility rates (Tettenborn, 2005). Interestingly, a large survey in the UK revealed that 30% of women with epilepsy do not have children due to epilepsy (Tettenborn, 2005). This fact, plus the comorbidity of epilepsy with other disorders, may make accurate estimates of fertility linked to specific causes difficult. In terms of physical reasons for infertility, 39% of women with temporal lobe epilepsy and 2% with idiopathic generalized epilepsy have anovulatory cycles, compared with 8% and 10% of controls, respectively (Jette & Morrell, 2005). Another study that evaluated 101 women with epilepsy found inadequate luteal phase cycles in 30% of the women without a clear association with epilepsy type (Harden, 2005). Anovulatory cycles are more common with concurrent use of valproic acid derivatives (Crawford, 2005; Jette & Morrell, 2005; Morrell, 2007; Tettenborn, 2006); however, it is not clear as to other reasons for increased anovulatory cycles in women with epilepsy. Women with epilepsy are known to be at increased risk of developing polycystic ovary syndrome (PCOS) (Crawford, 2005; Tettenborn, 2006). The prevalence of PCOS in women with epilepsy is thought to be approximately 20%, and present particularly in women with temporal lobe seizures (Tettenborn, 2006). PCOS is defined by the National Institutes of Health with two main criteria for diagnosis (a) the presence of ovulatory dysfunction, such as polymenorrhea, oligomenorrhea,
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or amenorrhea; and (b) clinical evidence of hyperandrogenism or hyperandrogenemia, such as hirsutism. Other endocrinic disorders must also be ruled out, including Cushing syndrome or late-onset congenital adrenal hyperplasia (National Institutes of Health, 2007). PCOS may also have negative cosmetic effects, including weight gain, gum hypertrophy, hair loss, hirsutism, coarsening of facial features, and acne (Johnson & Leschziner, 2006). PCOS occurs in the general population at the rates of 4–22%. The prevalence of PCOS is usually reported as higher in women with epilepsy, ranging between 10% and 26% – it is not known if this number is statistically significantly different from the norm (Johnson & Leschziner, 2006). The higher prevalence of PCOS in women with epilepsy may be as a result of the epilepsy itself. Epileptic discharges emanating from the amygdala may affect the secretion of gonadotrophin-releasing hormone, which may in turn lead to an increased luteinizing hormone–follicle stimulation hormone ratio. In addition, limbic seizures may reduce serum dopamine levels, resulting in increased pituitary prolactin and LD secretion (Johnson & Leschziner, 2006). Symptoms of PCOS can include weight gain, hyperandrogenism, hyperinsulimea, hirsutism, alopecia, acne, polycystic ovaries, abnormal menstrual cycle, midcycle menstrual bleeding, and difficulty in conceiving (Crawford, 2005; Harden, 2005; Jette & Morrell, 2005; National Institutes of Health, 2007). There is a strong link between PCOS and women taking valproate sodium, especially those starting the medication before the age of 20 (Crawford, 2005; Harden, 2005). It is possible that neuropsychiatric illness (such as epilepsy) is a prerequiste for the clinical expression of endocrinopathy related to valproate (Harden, 2005). Indeed, valproate is specifically associated with elevated androstenedione levels in every subject using it (Harden, 2005). Valproate is associated with weight gain and increased androgen levels, which are two features of PCOS (Harden, 2005; National Institutes of Health, 2007). In summary, to date, studies in patients with epilepsy have not clarified the relationship between AEDs and PCOS (Johnson & Leschziner, 2006). In the absence of conclusive data, it seems prudent that a change in drug therapy should be considered for women who are taking valproate and who are concerned about fertility (especially those who have experienced weight gain or developed menstrual irregularities while taking valproate and those with PCOS who are receiving valproate) (Johnson & Leschziner, 2006). Switching from valproate to lamotrigine in a subset of patients resulted in a reduction of body mass index, reduced insulin and testosterone levels, and a reduction in the frequency of menstrual disturbance and polycystic ovaries (Johnson & Leschziner, 2006). In terms of treatment of the women with epilepsy who have problems with fertility, Harden (2005) suggests: It seems prudent that clinicians educate themselves on the prevalence of PCOS in epilepsy before recommending the use of valproate, or any AED, to female patients and then continue to monitor female patients from adolescence to menopause, looking vigilantly for signs of PCOS. PCOS is associated with many health risks including type 2 diabetes mellitus, dyslepidemia, cardiovascular disease, and endometrial cancer. (p. 145).
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Mikkonen et al. (2004) agree with Harden’s (2005) sentiments and add: The clinician’s challenge resides in identifying any endogenous or genetic predisposition to developing a reproductive endocrine disorder and in making a careful assessment of which AEDs might be likely to facilitate their occurrence. Cautious selection of an AED is particularly relevant in the case of PCOS, as the condition can eventually result in serious long-term medical complications, including type II diabetes mellitus, infertility, endometrial cancer, and mood disorders (p. 39, 40).
Monitoring a woman from adolescence to menopause will involve the patient knowing that this information is being collected and inspected by health providers. It will most likely require not only the patient’s awareness of the importance of the information but also vigilance on her part to sustain information gathering throughout the years as healthcare providers and circumstances change. Contraception There are no contraindications to the use of non-hormonal contraception (Crawford, 2005; Penovich et al., 2004). Advice for women with epilepsy to receive contraception and reproductive counseling is common and focuses on the patient’s understanding of the possibility of hormonal contraceptives interfering with seizure control and also the possibility that AED or contraception effectiveness may reduce (Morey, 1999). Antiepileptic agents such as phenobarbital, primidone, phenytoin, carbamazepine, and felbamate induce the cytochrome P450 enzyme system and pose an increased risk of contraceptive failure with hormonal agents (Morey, 1999; Penovich et al., 2004; Tettenborn, 2006). Non-enzyme-inducing AEDs (valproate, benzodiazepines, ethhosuximide, zonisamide, and levetiracetam) do not appear to interact with birth control pills (Crawford, 2005; McAuley & Anderson, 2002; Penovich et al., 2004). It seems that interactions between oral contraceptives and hepatic microsomal-inducing AEDs (pheytoin, barbiturates, carbamazepine, topiramate, and oxcarbazepine) lead to a potential failure of contraception (Crawford, 2005; Penovich et al., 2004), and therefore supplemental birth control methods should be suggested (McAuley & Anderson, 2002). There have also been reports of reduced effectiveness of birth control pills with lamotrigine (French, 2007) and with potential impaired seizure control (Tettenborn, 2006). There have been no contraindications to the Mirena coil IUD in women with epilepsy because progestogen acts by being released locally in the uterus (Crawford, 2005). Emergency contraceptive pill can also be used by women with epilepsy, but some researchers suggest a higher dose for women who are taking enzyme-inducing AEDs. For the most part, if a woman with epilepsy does not want to become pregnant, then contraception that has been carefully adapted to her AED treatment must be designed, approved, and monitored by patient and clinician. Preconception Counseling Tettenborn (2006) suggests that women with epilepsy should not be discouraged from having children “as the chances of having a healthy baby are very high and can
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even be improved with good planning and supervision of the pregnancy” (p. 377). Given the amount of unique factors that contribute to a successful pregnancy, pregnancies in women with epilepsy are still considered at risk because of the potential for an increase in seizures, poor seizure control due to endocrine changes or alterations in the disposition of AED, and increased negative outcomes for the child. However, pregnancy planning is much easier if a woman with epilepsy is experiencing optimal seizure control prior to conception (McAuley & Anderson, 2002). Preconception planning is considered to be the “cornerstone for epilepsy care in women with epilepsy” (Thomas, 2006, p. 57). Morey (1999) suggests that issues to discuss in prepregnancy counseling shouldinclude: • • • • • •
importance of seizure control treatment compliance need for folic acid supplementation risks of fetal loss and adverse fetal outcome need for prenatal diagnostic testing implications of child care responsibilities on seizure disorder, “for example, sleep deprivation from taking care of an infant may have an effect on seizure control” (p. 2563)
In instances where a woman has an identified hereditary form of epilepsy, it is advisable to obtain genetic counseling before becoming pregnant as many women are concerned about passing on epilepsy to their children (Crawford, 2005; McAuley & Anderson, 2002; Tettenborn, 2006). Folic acid should be initiated for all women 3 months prior to conception and maintained over the first trimester (Yerby, Kaplan, & Tran, 2004). Indeed, Penovich et al. (2004) suggest that all women with epilepsy begin taking folic acid from preadolescence to menopause. This supplementation is to counter the potential for AED-induced neural tube defects and is especially pertinent for women taking valproate. It is commonly thought that women with epilepsy should enter pregnancy with the best seizure control possible and on the least dosage of AED (to reduce the risk of teratogenic effects on the fetus) (Doggett-Jones, 2007). The most effective drug should be chosen before conception and prescribed at its lowest effective dose, ideally as monotherapy (WHO, 2006). Some clinicians suggest considering the withdrawal of AEDs if possible (Crawford, 2005; McAuley & Anderson, 2002). If AEDs are continued, then monotherapy is the best, and the avoidance of valproate sodium (especially in combination with lamotrigine) is desirable. For women with epilepsy who wish to become pregnant, AED selection is complicated and rests on three considerations: seizure classification, epilepsy syndrome, and known or estimated teratogenic risk of the AED (Penovich et al., 2004). Studies show that, in general, the risk of significant fetal malformation is 3% if one AED is taken and up to 17% if two or more AEDs are taken. It is reasonable to suggest that valproate should not be the first AED of consideration in this patient population. (Mikkonen et al., 2004). Before prescribing valproate, clinicians
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must also carefully investigate the presence of any risk factors of PCOS such as irregular menstrual cycles, increased body mass index, evidence of hyperandrogenism, diabetes mellitus, and family history of PCOS (Mikkonen et al., 2004). In addition, women should be asked to keep careful diaries of their menstrual cycles and monitor any weight changes. Serial measurement of serum androstenedione and testosterone as well as ultrasonography studies of the ovaries are usually not performed in women with epilepsy taking valproate, but they should be considered in women at higher risk (Mikkonen et al., 2004). The impact of valproate on reproductive endocrine disorders does appear to be reversible. In a 2-year follow-up study on women with epilepsy and a reproductive endocrine disorder that had been attrributed to valproate, the replacement of the drug with another non-enzyme-inducing AED, such as lamotrigine, has been found to lead to the normalization of endrocrine functions (Mikkonen et al., 2004). Many times, fetal malformations occur at the early stages of development when women with epilepsy do not know that they are pregnant (Crawford, 2005; National Institute of Neurological Disorders and Stroke, 2007; Penovich et al., 2004; Yerby, Kaplan, & Tran, 2004). Therefore, anticipatory counseling can do much for a better outcome for the mother and the child. In summary, the main aim of preconception counseling is to ensure that the patient enters pregnancy with a minimum amount of risk factors operating, aware of the risks and benefits of the treatment, and able to make informed decisions about conception and pregnancy (Crawford, 2005; Genton et al., 2006). Studies (particularly in the UK) show significant and positive outcomes when prepregnancy counseling is offered and maintained (McAuley & Anderson, 2002; Tettenborn, 2006). Pregnancy Epilepsy is the most common neurological disorder followed in pregnancy and puerperium (Gupta, Rohatgi, Sharma, & Gurtoo, 2006). Women of childbearing age account for 25% of cases with epilepsy (Doggett-Jones, 2007), and approximately 1 in 200 pregnancies is exposed to antiepileptic drugs (AEDs) (Adab, 2006). Nonetheless, 90% of women with epilepsy have successful pregnancies with healthy outcomes (Meador & Zupanc, 2004; Penovich et al., 2004; Yerby, Kaplan, & Tran, 2004). The most important treatment goal is to support effective seizure control during pregnancy (Genton et al., 2006; National Institute of Neurological Disorders and Stroke, 2007; Penovich et al., 2004; Tettenborn, 2006). Most women require medication throughout pregnancy because seizures themselves can be harmful not only to the mother but also to the developing fetus (Adab, 2006; Crawford, 2005; DoggettJones, 2007; McAuley & Anderson, 2002; Meador & Zupanc, 2004; National Institute of Neurological Disorders and Stroke, 2007; Penovich et al., 2004) and are thought to be of more potential harm than teratogenic effects of AEDs (Meador & Zupanc, 2004). Women with tonic-clonic seizures and their children, for example, can be injured via hypoxia, acidosis, and blunt trauma during episodes. These factors can create stillbirth or spontaneous abortion and must be prevented if at all possible (Penovich et al., 2004; Yerby, 2001; Yerby, Kaplan, & Tran, 2004).
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A dizzying array of studies have reported an increase in seizures during pregnancy; however, there is no consensus or meta-analysis of these reports in terms of range. Therefore, it has been reported that the number of women with epilepsy who experience increase in seizures during pregnancy can range between 8% and 46% depending on how the women were identified and the AEDs used (Crawford, 2005; National Institute of Neurological Disorders and Stroke, 2007; Tettenborn, 2006; Yerby, & Cawthon, 2007; Yerby, Kaplan, & Tran, 2004). In a study of 42 pregnant women with epilepsy taking older AEDs, 49% did not need a change in dosage, 38% needed an increase in dosage, and 6% required a decrease in dosage (McAuley & Anderson, 2002). The increase in seizures with pregnancy is not related to seizure type, duration of epilepsy, or seizure frequency of a previous pregnancy; therefore, a current pregnancy cannot (in terms of seizure increase or management) be expected to mimic the previous ones (Yerby, Kaplan, & Tran, 2004). For the most part, it is thought that the reason for seizure increase is because of hormonal factors and pharmacokinetic changes in the disposition of AEDs (National Institute of Neurological Disorders and Stroke, 2007; Tettenborn, 2006). Others believe that seizure increase may be due to additional pregnancy-related conditions, such as increased blood volume during pregnancy (which can dilute the effect of medication), vomiting, sleep deprivation, and treatment compliance (Crawford, 2005; Doggett-Jones, 2007; National Institute of Neurological Disorders and Stroke, 2007; Penovich et al., 2004; Tettenborn, 2006; Yerby, Kaplan, & Tran, 2004). Pharmacokinetic changes in pregnancy show interindividual variability and are not well understood for most newer AEDs (McAuley & Anderson, 2002; Penovich et al., 2004). Recent studies have shown that changes in lamotrigine clearance are particularly marked, with increases in each trimester and a significant fall in plasma concentrations, leading to consequent breakthrough seizures in some women (Adab, 2006). Of particular concern is that the concentration may then rise precipitously after delivery, leading to the symptoms of lamotrigine toxicity (Adab, 2006). Valproate above 1100 mg/day is associated with a significantly higher incidence of fetal malformation. Infants exposed to sodium valproate monotherapy during gestation had the highest frequency of major congenital malformation by 3 months of birth (6.2%), and the frequency becomes significantly higher in polytherapy with AEDs (WHO, 2006). Lamotrigine monotherapy has so far been free of malformation. However, more patients on lamotrigine than on valproate require dose adjustments to control seizures. The choice of AED for pregnant women with epilepsy, therefore, requires the assessment of balance of risks between teratogenicity and seizure control (Genton et al., 2006; McAuley & Anderson, 2002; National Institute of Neurological Disorders and Stroke, 2007; Penovich et al., 2004; Vajda et al., 2006). In general, there is no controlled and comparative study yet to indicate which AED is the safest for pregnant women with epilepsy. Overall, infants of women with epilepsy have a reported rate of congenital major malformation between 4% and 6%, which is twice the rate for infants born to women not on AEDs (Yerby, Kaplan, & Tran, 2004). Valproate and phenobarbital appear to have the highest risk of malformations (Meador & Zupanc, 2004; Penovich et al., 2004; Yerby, 2001). Some AEDs, particularly valproate, trimethidone, and phenytoin, are known to increase the risk of birth defects such as cleft palate, heart problems, or finger and
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toe malformations (National Institute of Neurological Disorders and Stroke, 2007; Penovich et al., 2004; Yerby, 2001, 2006). Pregnancy registries have been developed for the North American and European regions. All are prospective and collect information on all AEDs. Mothers with epilepsy are often encouraged to register by clinicians and information is provided in Appendix II for those readers who wish to have more information. Labor and delivery usually proceed normally for women with epilepsy, although there is a slightly increased risk of hemorrhage, eclampsia, premature labor, and cesarean section (National Institute of Neurological Disorders and Stroke, 2007). It has been reported that infants of mothers with epilepsy are at greater risk of neonatal hemorrhage, or “neonatal coagulopathy,” that occurs internally as a result of fetal exposure to AEDs (Yerby, 2006; Yerby, Kaplan, & Tran, 2004). Vitamin K is given after 34 weeks in gestation to reduce the risk of bloodclotting (National Institute of Neurological Disorders and Stroke, 2007; Yerby, 2006).
Breast feeding Breastfeeding is considered to be quite safe for term infants as they have been exposed to AED for 9 months and have induced hepatic microsomal enzyme systems (Crawford, 2005). AEDs are secreted in breast milk in inverse proportion to their protein binding (Penovich et al., 2004). Therefore, breastfeeding should be done cautiously by women receiving phenobarbital, ethosuximide, levetiracetam, or primidone due to the risk of infant sedation (Crawford, 2005; McAuley & Anderson, 2002; Tettenborn, 2006; Yerby, 2006). The National Institute of Neurological Disorders and Stroke (2007) suggest that breastfeeding is acceptable for women with epilepsy because only minor amounts of AEDs are secreted in breast milk. Indeed, the levels in breast milk are far lower than the levels in the womb to which the child has already been exposed. However, longitudinal studies of children exposed to AEDs in the womb and breastfeeding are rare; therefore, more subtle cognitive and neuropsychological effects have not probably been adequately studied.
Infant and Child Care There are concerns about the reliability of women with epilepsy in caring for children during seizure episodes. Dropping the baby during a seizure is the most obvious concern, and it is well documented (Penovich et al., 2004; Saramma, Thomas, & Sarma, 2006). Healthcare professionals give advice about safety precautions especially for new mothers, such as not bathing the baby without another person present, not sleeping with the baby in the same bed, and strapping the baby into a stroller when transporting (Crawford, 2005; Penovich et al., 2004). Probably the principle concern for mother’s health is that of preventing frequent sleep disturbances and deprivation. Lack of sleep and interrupted sleep has a direct effect on seizure control and social, psychological, and physical well-being (Crawford, 2005; Morey, 1999; Penovich et al., 2004).
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Menopause Seizure Management Menopause and its effects on women with epilepsy occur significantly earlier in women and often with a higher seizure frequency (Crawford, 2005; Herzog, 2006; Jette & Morrell, 2005). Seizure frequency in the menopause years is quite unpredictable, but it is suggested that seizure frequency increases in about 30–40% of cases (Jette & Morrell, 2005; Penovich et al., 2004). On the other hand, some patients report an improvement or reduction in seizure frequency (French, 2007), probably due to the natural decline of estrogen levels and the waning of progesterone surges (Jette & Morrell, 2005). Some suggest that hormone replacement therapy can be given to most women with epilepsy without a change in their seizure condition (Penovich et al., 2004). However, Crawford (2005) states that “Hormone replacement therapy is significantly associated with an increase in seizure frequency during menopause, and this more likely in women with a history of catamenial epilepsy” (p. 123). French (2007) suggests that estrogen supplementation (or unopposed estrogen) naturally decreases the seizure threshold, which would lead to an increase in seizures. The need for ongoing research on perimenopausal and menopausal women with epilepsy is quite obvious as there is a dearth of studies examining topics such as impact of antiseizure medication on menopause onset (Jette & Morrell, 2005). Bone Health Women with epilepsy on long-term AED therapy are recognized to be at risk of bone demineralization (Crawford, 2005; Johnson & Leschziner, 2006). Patients with epilepsy have a higher incidence of skeletal fractures due to multiple reasons. Postmenopausal women and elderly men are particularly vulnerable to osteoporosis. A major convulsive seizure can often lead to falls and may result in fractures. Anti-epileptic therapy may have seemingly contradictory effects on bone health. It can effectively reduce the incidence of major seizures and prevent the seizurerelated falls and fractures. However, the central nervous system effects of these drugs increase the risk of falls, especially in the vulnerable population (Vinayan & Nisha, 2006). Women with epilepsy should be altered to the risk factors that may make them susceptible to secondary osteoporosis (Meador & Zupanc, 2004). Nearly 10% of women with epilepsy experience premature bone demineralization, particularly if they take AEDs that include the hepatic cytochrome P450 enzyme system (Meador & Zupanc, 2004). Counseling about calcium and vitamin D supplementation and bone density scanning should be offered (Crawford, 2005).
Summary and Conclusions It seems that as soon as a girl with epilepsy enters the world of reproductive possibilities, her relationship with epilepsy will change and continue to fluctuate for the rest of her life. Her relationship with epilepsy becomes dynamic and fluid as it responds
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to life events, endocrine functioning, medications, psychosocial needs, and qualityof-life issues. Epilepsyin women is a completely different experience than epilepsy in men. While the etiology and symptoms of ictal and interictal activity are similar for both sexes, the interaction of hormones and body changes are quite different. Neuropsychology professionals should be sensitive to the gender differences presented by the individual at different times during the course of treatment. Initial neuropsychological assessment of a female epileptic patient must take hormonal fluctuations into account, for example, considering where the woman is situated in the monthly menstrual cycle. Women who experience catamenial epilepsy may perform quite differently on sensitive neuropsychological tests the week before menses as opposed to midcycle. The customary practice of gathering detailed information on previous seizure control is a good one. However, neuropsychology professionals must determine if female developmental issues are influencing seizure control or if they are likely to change in the near future. When following a young patient through the elementary school years, for example, it would be appropriate to prepare for further assessments if changes in seizure control are noted during puberty. Again, preventative or prescriptive treatment around contraception and pregnancy should also be anticipated. Neuropsychologists have a unique opportunity to assist in the complex task of understanding how a young woman can achieve good seizure control and quality of life. Knowing about the potential confounding variables associated with neuropsychological assessment of a female epileptic patient allows the clinician to place test results in context and hopefully enhance ecological validity of the treatment. The neuropsychologist is also in the unique position of assisting in the longitudinal measurement of functioning and in the difficult job of helping the patient maintain maximal quality of life as she passes from one developmental phase to another.
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National Institute of Neurological Disorders and Stroke. (2007). Seizures and epilepsy: hope through research. Retrieved from the World Wide Web on June 28, 2007, from http://www.ninds.nih.ogv/disorders/epilepsy/detail epilepsy. htm#92343109. Penovich, P. E., Eck, K. E., & Economou, V. V. (2004). Recommendations for the care of women with epilepsy. Cleveland Clinic Journal of Medicine, 71(Suppl. 2), S49–S57. Reimers, A., Helde, G., & Brodtkorb, E. (2005). Ethinyl estradiol, not progestogens, reduces lamotrigine serum concentrations. Epilepsia, 46, 1414–1417. Reynolds, E. H. (2001). /LAE/IBE/WHO Global Campaign “Out of the Shadows”: Global and regional developments. Epilepsia, 42, 1094–1100. Saramma, P. P., Thomas, S. V., & Sarma, P. S. (2006). Child rearing issues for mothers with epilepsy: a case control study. Annals of the Indian Academy of Neurology, 9, 158–162. Tan, N. C. K., Mulley, J. C., & Berkovic, S. F. (2004). Genetic associations studies in epilepsy: “The truth is out there”. Epilepsia, 45, 1429–1442. Tettenborn, B. (2006). Management of epilepsy in women of childbearing age: practical recommendations. CNS Drugs, 20, 373–387. Thomas, S. V. (2006). Management of epilepsy and pregnancy. Journal of Postgraduate Medicine, 52, 57–64. Vajda, F. J. E., Hitchcock, A., Graham, J., Solinas, C., O’Brien, T. J., Lander, C. M., & Eadie, M. J. (2006). Foetal malformations and seizure control: 52 months data of the Australian Pregnancy Registry. European Journal of Neurology, 13, 645–654. Vinayan, K. R., & Nisha, B. (2006). Epilepsy, antiepileptic drugs and bone health. Annuals of the Indian Academy of Neurology, 9, 90–97. World Health Organization. (2006). Safety and efficacy issues. WHO Drug Information, 20, 246–247. Yerby, M. S. (2001). Neurological management of women with epilepsy. Retrieved from the World Wide Web on June 28, 2007, from http://www.seizures.net/articles epilepsy/ Neonatal text.html. Yerby, M. S., Kaplan, P., Tran, T. (2004). Risks and Management of Pregnancy in women with epilepsy. Cleveland Clinic Journal of Medicine, 71, S25–S37. Yerby, M. S. (2006). Pregnancy and the mother with epilepsy. Retrieved from the World Wide Web on June 28, 2007, from http://www.seizures.net/articles epilepsy/PregMotherepilepsy.html. Yerby, M. S., & Cawthon, M. L. (2007). Infant mortality of infants of mothers with epilepsy. Retrieved from the World Wide Web on June 28, 2007, from http://www.seizures. net/articles epilepsy/Neonatal text.html.
Appendix 1 Physician’s Discussion Checklist for Women with Epilepsy
Appendix 2 AED Drug Pregnancy Registry The Antiepilepsy Drug (AED) Pregnancy Registry is the first North American registry for pregnant women who are taking anti-epileptic drugs. The registry is maintained out of Genetics and Teratology Unit of Massachusetts General Hospital in Boston. Women who enroll will be asked to provide information about the health
http://professionals.epilepsy.com/page/women˙checklist.html
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status of their children. (All information will be kept confidential). The findings will be analyzed to assess the fetal risk of AED use during pregnancy. The principal investigator for the registry is Lewis B. Holmes, M.D.
Women and physicians are urged to call the registry directly at the toll-free number, (888) 233-2334. For further information on the registry, please contact Massachusetts General Hospital (617) 726-
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Chapter 8
Critical Issues in Chronic Illnesses of Women LeAdelle Phelps
It is well known that gender is one of the most important determinants of physical health. This is for several reasons. First, the sex chromosomes (X, Y) may malfunction, causing a multitude of problems. For example, some disorders occur almost exclusively in males because of a faulty gene carried on the X chromosome (e.g., hemophilia, Lesch–Nyhan, Lowe syndrome). In order for a female to be affected by X-linked illnesses, she must inherit the faulty gene from both parents, which rarely happens. Likewise, some anomalies occur because of the complete absence (e.g., Turner syndrome, genetic designation XO) or extra copies of the X chromosome (Klinefelter syndrome, XXY, or XXXY). Second, some disorders occur far more commonly in one gender than the other, but these syndromes are not related directly to the X or Y chromosome. For example, externalizing disorders such as attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, and conduct disorder are far more prevalent in adolescent males, whereas internalizing disorders such as anxiety, mood, and eating disorders are diagnosed more frequently in adolescent females. Such gender differences are likely related to a combination of environmental factors (e.g., societal norms, role models) coupled with genetic susceptibility. Third, there are diseases that are directly related to the sex organs (e.g., ovarian cancer, prostrate cancer) and usually occur after puberty. Obviously, unless one is a hermaphrodite (i.e., possessing sex organs of both genders), diseases of the sex organs occur solely within the corresponding gender. Disorders that are unrelated to the sex chromosomes, the sex hormones, or the sex organs often occur at equal rates for females and males (e.g., Huntington’s disease, cystic fibrosis). This happens most frequently when a single gene that follows the simple rules of Mendelian heritability causes the ailment. However, there are illnesses that show significant sex differences for reasons that are yet unclear. It is assumed that the etiologies of these disorders reflect a complex interaction between multiple genes or alleles and the environment. For example, adult males are
L. Phelps (B) University at Buffalo, SUNY e-mail:
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significantly more prone to cardiovascular and infectious diseases, whereas females are far more at risk of autoimmune and pain disorders. This raises a question as to which physiological and ecological systems create such a disparity. The literature is replete with hypotheses regarding gender role expectations that place women at risk of many illnesses. By comparison, the search for possible physiological causes has received much less attention. The understanding of the physiological bases of sex differences is of paramount importance when dealing with diseases that have no clearly recognized etiology or easily confirmed diagnosis. Fibromyalgia and multiple sclerosis are two such syndromes. Both affect more females than males, are difficult to diagnosis, and have no clear biomedical cause. Given the elusive nature of these conditions, many physicians have historically responded with skepticism, and it was not uncommon for many to conclude that the etiology was psychological in nature. No doubt, many readers can recall women who were suffering from chronic pain, abnormal fatigue, muscular paralysis, or frequent vertigo who were told that their conditions were psychosomatic or were not given a diagnosis but prescribed anti-anxiety, hypnotic, or muscle-relaxant medications. To dispel doubts in the biological bases of these two chronic illnesses that affect women and result in significant quality-of-life issues, a critical review of fibromyalgia and multiple sclerosis is provided with focus on the probable etiology and biologically connected outcomes. Evidence-based treatments that have been validated with women patients are examined.
Fibromyalgia Fibromyalgia (FM) is a severe chronic pain disorder with the presence of specific multiple tender points located primarily on the head, neck, back, elbows, and knees. Key features include significant pain, fatigue, and sleep disturbance (Mease, 2005). It affects 2% of the adult population, with a female-to-male ratio of 9:1 (Shaver, 2004). In a large prospective study that followed 538 adults diagnosed with FM, most reported no resolution of the debilitating pain over a 7-year period (Wolfe et al., 1997). Historically, many physicians concluded that the syndrome was psychosomatic in nature because there was no definitive medical evidence of disease (e.g., blood work, CT scan). In the search for biological options, the hypothalamic-pituitaryadrenal axis (HPAA), the autonomic nervous system (ANS), and the related neurotransmitters are probable candidates, for each show consistent differences between females and males, with the menstrual cycle, pregnancy, and menopause having marked effects (Kajantie & Phillips, 2006). For example, adult men have higher HPAA and autonomic responses than women who are of childbearing age, but before puberty and after menopause the sex differences are small to non-existent (Otte et al., 2005). During pregnancy, there are notable alterations in the physiology of HPAA and ANS (Mastorakos & Ilias, 2003). It is likely that the differences are a result of estrogen, which attenuates sympathoadrenal responsiveness (Kajantie & Phillips, 2006). Much less is known about the effects of progesterone and testosterone.
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In women diagnosed with FM, there is an overactive sympathetic nervous system and a decreased level of serotonin, which result in less deep-restorative sleep, increased mood disturbances, and an amplification in sensitivity to pain (Russler & Nemeroff, 2000; Sarzi-Puttini, Atzeni, Diana, Doria, & Furlan, 2006). Second, the HPAA is altered, which induces low levels of norepinephrine and dopamine, affecting pain transmission, mood, motivation, and sleep (Dessein, Shipton, & Stanwix, 2000; Tsigos & Chrousos, 2002). Thus, the impaired neuroendocrine and ANS may explain the greater prevalence of FM among females, because women naturally have less activation of these components than men (Davis, Galassetti, Waserman, & Tate, 2000; Kirschbaum, Kudielka, Gaab, Schommer, & Hellhammer, 1999). It also clarifies that depression and anxiety, which are highly co-morbid with FM, emanate from a shared physiological basis.
Genetics The specific genetic factors that cause FM have yet to be identified. However, it is evident that the syndrome is likely related to a complex interaction of multiple factors. Genetics clearly have a role, as demonstrated by familial aggregation data (Arnold et al., 2004), but no definitive markers have been located. Because FM is associated with decreased levels of serotonin, attention has focused on the serotonin transporter (5-HTT) gene. Two studies have found this gene to be more common among patients with FM when compared to controls (Cohen, Hagit, Neumann, & Ebstein, 2002; Offenbaecher et al., 1999). Serotonin receptor genes HTR3A and HTR3B have also been investigated, but no significant variations between patients with FM and normal controls were evident (Frank et al., 2004). As mentioned earlier, decreased dopamine levels are linked with FM. Hence, the D2 and D4 dopamine receptors have been studied. Located on chromosome 11, the genes for these two receptors have been studied with inconclusive results (Ablin, Cohen, & Buskila, 2006). The search for genes related to FM will continue and likely yield results in the near future. The Human Genome Project has greatly facilitated the search for genes related to many disorders. Yet like many diseases, FM is almost certainly caused by multiple genes interacting with environmental risk factors, making the search much more difficult.
Treatment Only research designed to provide evidence-based evaluations of efficacy (e.g., pre–post, experimental-control, double-blinded) with predominantly women clients will be reviewed. First, pharmacological treatments are reported, followed by psychosocial interventions, and then combinations thereof. When feasible, the research design and results will also be reported. Of the antidepressants, tricyclics (TCAs) have produced significantly greater effects than placebos in mitigating pain and depression (Arnold, Keck, & Welge,
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2000). By comparison, the selective serotonin reuptake inhibitors (SSRIs) have shown disappointing results in FM. For example, in a randomized, double-blind, placebo-controlled, 4-month trial, there was no significant advantage in citalopram (Celexa) over the placebo in pain control; however, there was improvement in depression (Anderberg, Marteinsdottir, & Knorring, 2000). Two newer antidepressant medications, milnacipran (Ixel) and duloxetine (Cymbalta), which are classified as selective serotonin and norepinephrine reuptake inhibitors (SSNRIs), have undergone multicenter, placebo-controlled trials with documented improvement in physical functioning, level of fatigue, and degree of reported physical impairment (Goldenberg, Burckhardt, & Crofford, 2004). Pregabalin (Lyrica) is a new anticonvulsant medication that has shown to be highly effective in the treatment of pain. For example, in an 8-week, randomized, double-blind, placebo-controlled trial that included 529 adults, pregabalin was superior to the placebo in reducing pain, improving quality and quantity of sleep, decreasing fatigue levels, and lessening reports of anxiety (Crofford, Russell, & Mease, 2002). Non-pharmacological interventions have been studied with fewer rigors. Often the studies have not incorporated a control group or randomized assignment. Two studies that met these requirements (i.e., randomized control trials) demonstrated that low-intensity aerobic exercise decreased pain and fatigue as well as improved the general functioning of women with FM (Gandhi, DePauw, Dolny, & Freson, 2002; Richards & Scott, 2002). In a meta-analysis of 49 FM treatments that compared pharmacological and psychosocial treatments, a combination of antidepressants, moderated exercise, and cognitive behavioral therapy (CBT) resulted in higher levels of patient improvement than when each approach was used alone (Rossy et al., 1999). It should be noted, however, that the preponderance of studies included in this meta-analysis did not incorporate randomized designs. It would be very helpful if more randomized, control group studies of CBT were completed. In addition, most treatment teams reported using a combination of medication, exercise, and CBT (Friedberg & Jason, 2001). More evidence-based studies supporting such a multifaceted approach would be beneficial. Likewise, it is suggested that future studies incorporate outcome measures that evaluate positive quality-of-life issues (e.g., increased mobility, muscle flexibility, feeling of wellness) rather than focusing on relief of symptoms such as pain, fatigue, and sleep disturbances. Given the fact that FM is a disorder that lasts for years, such outcome measures may provide a better depiction of how women respond to various treatments over an extended period of time.
Multiple Sclerosis Multiple sclerosis (MS) is an autoimmune inflammatory disorder that causes demyelination of the central nervous system (i.e., brain and spinal cord). The prevalence rate is approximately one case per 1000 with a mean age of onset of 30 years.
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The female-to-male ratio is 2.6:1 in the US and 3.2:1 in Canada (Coyle et al., 2004). Aggregated data are being studied to determine what factors place the women in Canada at such a high risk level (Orton et al., 2006). There are a number of classifications of MS (e.g., acute progressive, chronic non-remitting, relapsing-remitting, secondary progressive), with the most common (85%) being relapsing-remitting wherein there are episodic, largely reversible neurologic difficulties in sensory, motor, balance, and visual functioning (Ransohoff, 2007). In 75% of the cases, the disorder advances slowly from relapsing-remitting to steady and irreversible loss of motor and sphincter control, sensory and visual disturbances as well as cognitive difficulties (secondary progressive form; Ransohoff, 2007). It is uncertain when neurodegeneration starts, but the clinical onset of MS is thought to occur more than a decade after the initiating event (Pohl, Krone, & Rostasy, 2006). A diagnosis is made when there is clear evidence of active lesions in the brain and/or spinal cord, which is determined by MRI gadolinium contrast studies. The progression of the disease is slower in females than in males, with pregnancy functioning as a protective measure (El-Etr et al., 2005).
Genetics Studies of half-siblings and adoptees performed in Canada as well as twin studies, which indicated a concordance rate of 30% for monozygotic and 5% for dizygotic twins, confirm that biological factors are primarily responsible for the disease (Oksenberg, Baranzini, Barcellos, & Hauser, 2001). Using these data, it has been estimated that the genetic susceptibility is between 20% and 50%. Yet it is also clear that the pathogenesis is complex and multifactorial, with a probable interaction of multiple genes or alleles with environmental risk factors, all of which have yet to be clearly identified (Baranzini & Oksenberg, 2005). Just recently, a genome-wide study identified alleles IL2RA, located at chromosome 10p15, and IL7RA, positioned at chromosome 5p13, as heritable risk factors for MS (International Multiple Sclerosis Genetics Consortium, 2008). (Using the agreed upon international nomenclature, a q indicates the long arm of the gene whereas a p designates the short arm.) The results provided support that MS is an autoimmune inflammatory disorder, and that other genetic loci with similar MS risk status are highly probable. Other sites of interest include chromosomes 6p21, 11q12, 17q21, 18q13.2, and 19q13 (Koch et al., 2005; Oksenberg & Barcellos, 2005; Transatlantic Multiple Sclerosis Genetics Cooperative, 2003).
Pregnancy, Breastfeeding, and Disease Outcomes The Pregnancy in Multiple Sclerosis (PRIMS) study was the first, and only, large prospective study that has assessed the influence of pregnancy, delivery, and breastfeeding in women diagnosed with MS (Vukusic et al., 2004). The researchers
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followed the clinical course of 254 women diagnosed as MS from pre-pregnancy to the end of the second year postpartum. The results indicated a significant reduction in disease activity during pregnancy, especially during the third trimester. This was followed by a notable increase in relapse rates in the first trimester postpartum, which stabilized to the pre-pregnancy rates in the second trimester postpartum. Active disease progression during the year before and during pregnancy, as well as longer duration from initial diagnosis, predicted higher relapse rates during the first trimester after delivery (Confavreux et al., 1998). Breastfeeding did not affect the postpartum relapse (Vukusic & Confavreux, 2006). The PRIMS researchers concluded that the physiological basis that provided protection from disease progression during pregnancy was the change in immunological helper T-cell ratios. Type 1 helper T-cells (Th1) are involved in cellular immunity, e.g., organ or skin graft rejections, whereas type 2 helper T-cells (Th2) deal with anti-inflammatory responses. The fetus and placenta secrete hormones that decrease the presence of Th1 cells and the related cellular immune responses by the mother (so that her body will not reject the fetus) while increasing the Th2 cells, which reduces inflammation. Delivery of the newborn is associated with a dramatic reverse of this balance, with Th1 cells significantly increasing and Th2 cells notably decreasing. Thus, the mother’s body quickly returns to the pre-pregnancy conditions where foreign substances will be actively rejected and inflammation levels may be higher (Vukusic & Confavreux, 2006). It is the presence of the sex hormones, estrogen and progesterone, during pregnancy that appears to alter the immunological response of women with MS. In fact, animal research suggests that progesterone may even enhance remyelination and protection of the myelin sheaths (Koenig et al., 1995). Likewise, estrogen has been recommended as a treatment in relapsing-remitting MS because of its ability to reduce inflammation by enhancing Th2 responses (El-Etr et al., 2005; Zhu, Lu, Huang, Link, & Xiao, 2007).
Treatment Because pregnancy significantly influences the clinical evaluation of MS, the efficacy of treatment with sex hormones is of considerable interest. At this time, a European, multicenter, randomized, double-blind, placebo-controlled clinical trial (POPARTMUS study) is evaluating the efficacy of progestin coupled with estradiol (a therapeutic form of estrogen) in reducing disease relapse after childbirth (Vukusic & Confavreux, 2006). The results are expected to be available in 2008. In addition, such treatment is being evaluated for the reduction of lesion development in people who have been definitively diagnosed with MS. One open, prospective trial of estrogen hormone treatment has been completed (Sicotte et al., 2002). There was a decrease in the volume and number of gadoliniumenhancing (denotes active status) lesions on brain MRIs in women diagnosed with the relapsing-remitting form of MS, but not secondary progressive MS. Because
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high doses of estrogen have serious health concerns (e.g., heavy menstrual bleeding, increased risk of breast cancer), it has been recommended that high doses of progestin be combined with low doses of estradiol (El-Etr et al., 2005). Although there has been an open study with a small cohort of women that documented significant associations between the decrease in CNS lesion size and high levels of progesterone (Pozzili et al., 1999), a prospective, placebo-controlled, double-blinded study evaluating the efficacy of combining progesterone and estrogen in treating non-pregnant women with MS (as in the POPARTMUS study) is expected. To reduce the CNS inflammation common in MS, corticosteroid infusion treatments are often prescribed. Not to be confused with anabolic-androgenic steroids, which are related to the male sex hormone testosterone and sometimes abused to enhance athletic performance, the corticosteroids, such as methylprednisolone, are a group of anti-inflammatory drugs similar to the natural hormones produced by the adrenal glands. Used to treat other inflammatory disorders such as asthma, eczema, and rheumatoid arthritis, the medication works by decreasing the production of lymphocytes. The reduction of pressure on the lesions and nerve cells often improves sensory, motor, balance, and visual functioning in cases of relapsing-remitting MS. Unfortunately, the results are temporary and must be repeated when symptoms worsen. Another treatment option is the use of interferons (e.g., Avonex, Betaseron, Rebif), which are similar to the naturally occurring proteins that are secreted in response to viral infections. These medications have been shown to decrease relapses, reduce the number of active lesions, and slow down the progression of the disease in the relapsing-remitting form of MS (Chofflon, 2005). The antiinflammatory corticosteroids are often added to the interferons during relapse occurrences (Stuart & Vermersch, 2004). Randomized, double-blind, placebo-controlled studies are currently being conducted to evaluate the efficacy of such concomitant prescriptions (Stuart & Vermersch, 2004). Finally, Copaxone and Navantrone, which alter T-cell activity, are also successful in treating relapsing-remitting cases (Chofflon, 2005). It has been recommended, however, that these medications be discontinued during pregnancy because there may be associated risks with immune suppression (Ferrero, Pretta, & Regani, 2004; Sandberg-Wollheim et al., 2005).
Future Directions on Chronic Illnesses in Women There are numerous questions related to genetic etiology, the identification of specific environmental risk and protective factors that affect disease onset, as well as treatments that are efficacious in delaying disease progression in chronic illnesses that predominantly affect women. It is essential that we identify risk catalysts that are associated with higher probability of onset, greater severity, and longer duration of such disorders. Likewise, it is equally important that we identify possible protective variables such as pregnancy, which are affiliated with improved resistance and resilience in women with MS. Treatment programs could then be developed that
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include highly specific strategies with the prevailing intent to reduce risk factors while enhancing protective factors. The search for heritability factors is vital, for such data can result in much earlier diagnosis and, hence, quicker treatment. As with cancer research, it is likely that gene-typing within disorders such as FM and MS could result in more individualized and directed treatments. Likewise, research that identifies the genetic–hormonal– societal–environmental interactions could result in possible secondary prevention (i.e., women who are at risk but do not show active symptoms), enhanced diagnostic tools, and more efficacious treatments. Teasing out these multiple factors will be time-consuming, expensive, and difficult, but it is nonetheless imperative. For example, why are Canadian women at a higher risk level of MS? What factors determine whether MS follows the acute progressive, chronic non-remitting, or relapsing-remitting pattern? Why does FM respond so poorly to treatment? What are the genetic factors of FM? It is crucial that more randomized, control/experimental group studies of psychosocial treatments be completed. The medical field is proficient in validating the efficacy of medications. By comparison, psychological interventions are seldom studied in such scrutiny. In addition, most chronic illnesses are treated with a combination of medication, exercise, and psychotherapy. Far more evidence-based studies supporting such a combined approach would be very beneficial. Finally, it is recommended that psychology be oriented toward positive outcomes, not just the amelioration of disease symptoms. As indicated earlier, it is important that future studies evaluating treatment efficacy incorporate outcome measures that appraise positive quality-of-life issues (e.g., increased mobility, feeling of wellness, improved self-efficacy, and sense of control) rather than focusing solely on symptoms of the disorder such as pain, fatigue, and muscle weakness. By doing so, we may improve the treatments for women with chronic illnesses.
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Otte, C., Hart, S., Neylan, T. C., Marmar, C. R., Yaffe, K., & Mohr, D. C. (2005). A meta-analysis of cortisol response to challenge in human aging: importance of gender. Psychoneuroendocrinology, 30, 80–91. Pohl, D., Krone, B., & Rostasy, K. (2006). High seroprevalence of Epstein-Barr virus in children with multiple sclerosis. Neurology, 67, 2063–2065. Pozzili, C., Falaschi, P., Mainero, C., Martocchia, A., D’Urso, R., & Poroietti, A. (1999). MRI in multiple sclerosis during the menstrual cycle: relationship with sex hormone patterns. Neurology, 53, 622–624. Ransohoff, R. M. (2007). Natalizumab for multiple sclerosis. New England Journal of Medicine, 356, 2622–2629. Richards, S. C., & Scott, D. L. (2002). Prescribed exercise in people with fibromyalgia: parallel group randomized control trail. British Medical Journal, 325, 185. Rossy, L. A., Bickelew, S. P., Dorr, N., Hagglund, K. J., Thayer, J. F., McIntosh, M. J., et al. (1999). A meta-analysis of fibromyalgia treatment interventions. Annals of Behavioral Medicine, 21, 180–191. Russler, K. J., & Nemeroff, C. B. (2000). Role of serotonergic and noradrenergic systems in the pathophysiology of depression and anxiety disorders. Depression and Anxiety, 12, 2–19. Sandberg-Wollheim, M., Frank, D., Goodwin, T. M., Gisser, B., Lopez-Bresnahan, M., StamMoraga, M., et al. (2005). Pregnancy outcomes during treatment with interferon beta-1a in patients with multiple sclerosis. Neurology, 65, 802–806. Sarzi-Puttini, P., Atzeni, F., Diana, A., Doria, A., & Furlan, R. (2006). Increased neural sympathetic activation in fibromyalgia syndrome. Annals of New York Academy of Sciences, 1069, 109–117. Shaver, J. L. (2004). Fibromyalgia in women. Nursing Clinics of North America, 39, 195–204. Sicotte, N., Liva, S., Klutch, R., Pfeiffer, P., Odesa, W., Wu, T. C., et al. (2002). Treatment of multiple sclerosis with the pregnancy hormone estriol. Annals of Neurology, 52, 421–428. Stuart, W. H., & Varmersch, P. (2004). Concomitant therapy for multiple Sclerosis. Neurology, 63, 528–534. Transatlantic Multiple Sclerosis Genetics Cooperative. (2003). A meta-analysis of whole genome linkage screens in multiple sclerosis. Journal of Neuroimmunology, 143, 39–46. Tsigos, C., & Chrousos, G. P. (2002). Hypothalamic-pituitary-adrenal axis, neuro-endocrine functions and stress. Journal of Psychosomatic Research, 53, 865–871. Vukusic, S., & Confavreux, C. (2006). Pregnancy and multiple sclerosis: the children of PRIMS. Clinical Neurology and Neurosurgery, 108, 206–270. Vukusic, S., Hutchinson, M., Hours, M., Moreau, T., Cortinovie-Tourniaire, P., Adeleine, P., & Confavreux, C. (2004). Pregnancy and the PRIMS study: clinical predictors of post-partum relapse. Brain, 127, 1353–1369. Wolfe, F., Anderson, J., Harkness, D., Bennett, R. M., Caro, X. J., Goldenberg, D. L., et al. (1997). Health status and disease severity in fibromyalgia: results of a six-center longitudinal study. Arthritis and Rheumatism, 40, 1571–1579. Zhu, W., Lu, C., Huang, Y., Link, H., & Xiao, B. (2007). A putative mechanism on remission of multiple sclerosis during pregnancy: estrogen-induced indoleamine, 2,3 dioxgenase by dendritic cells. Multiple Sclerosis, 13, 33–40.
Chapter 9
Neuropsychology of Eating Disorders Catherine P. Cook-Cottone
Eating disorders (EDs) are chronic clinical disorders and are difficult to treat (e.g., Hoeken, Seidell, & Hoek, 2003). Although not often noted, eating disorder prevalence rates have the highest female-to-male ratio among psychiatric disorders (see prevalence rates, The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision – DSM-IV-TR; American Psychiatric Association, 2000). They are known to occur most commonly among women in their late adolescence and adulthood (Herzog & Eddy, 2007). However, there have been reports of increasing rates of early onset symptomatology in prepubescent children and new-onset cases in midand late-life (Bulik, Reba, Siega, & Reichborn-Kjennerud, 2005). These disorders are characterized by a unique set of cognitive symptoms, such as intense preoccupation with being thin and pathological fear of gaining weight. The accompanying behavioral disturbances function to modify or control the body (e.g., food restriction, self-induced starvation, binge/purge cycles, and excessive exercise; Cook-Cottone & Phelps, 2006). Symptoms can manifest with frequent relapse, associated with psychiatric comorbidity, and result in significant medical complications (Herzog & Eddy, 2007). Distinct from other disorders and medical complications in which much of the research has focused primarily on male populations, eating disorder researchers have studied predominantly female populations (Cook-Cottone & Phelps, 2006; Dobson & Dozois, 2004). Interestingly, Tchanturia, Campbell, Morris, and Treasure (2005) reported that there have been fewer neuropsychological studies of eating disorders than any of the other major psychiatric disorders. Generally, the specific pathophysiology of eating disorders is considered unknown with many factors likely involved (Duchesne et al., 2004). There have been decades of research with only emerging convergence regarding causal factors. As with other complex disorders, the classification of psychiatric disorders on the basis of overt clinical phenotypes may not be the most effective way to explore specific etiology (e.g., genotypes; Holliday, Tchanturia, Landau, Collier, & Treasure, 2005; Steiger & Bruce, 2007). As a result, researchers have been looking for new C.P. Cook-Cottone (B) Director of School Psychology, Department of Counseling School and Educational Psychology, 409 Baldy Hall, University of Buffalo, State University at New York Buffalo, NY 14260 e-mail:
[email protected] E. Fletcher-Janzen (ed.), The Neuropsychology of Women, C Springer Science+Business Media, LLC 2009 DOI 10.1007/978-0-387-76908-0 9,
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ways to identify etiological factors (e.g., Sodersten, Bergh, & Zandian, 2006). The study of endophenotypes, the measurable disease-associated traits that demonstrate a more simple relationship with underlying genes and neuropsychological function, has been identified as a potential pathway to understanding eating disorders (i.e., Holliday et al., 2005; Steiger & Bruce, 2007). Accordingly, there is growing body of research focusing on the neuropsychological functioning of individuals with eating disorders, at-risk individuals, and unaffected first-degree relatives (Lena, Fiocco, & Leyenaar, 2004; Steiger & Bruce, 2007; Tchanturia et al., 2005). In the field of neuropsychology, the relationship between behavior and mental functioning is examined by means of psychometric tests and/or qualitative examinations of the sensory-motor, perceptual and emotional, and cognitive areas (Duchesne et al., 2004). Progress in this area of research has contributed substantially to the understanding of eating disorders (Tchanturia et al., 2005). To date, neuropsychological assessment has been used to explore the possibility that there may be dysfunction in the central nervous system contributing to the risk, etiology, maintenance, and/or intractability of symptoms of eating disordered behaviors (e.g., Duchesne et al., 2004). It is important to note that researchers such as Cavedini and colleagues (2004) argue that neuropsychological studies have yet to produce an explanatory neurofunctional model of eating disorders. Despite such concerns, Tchanturia and colleagues (2005) report that neuropsychological tests are now being used to assist with diagnosis, to obtain quantifiable data on the eating disorder condition, and to develop effective treatment plans.
Definition and Presentation The DSM-IV-TR (APA, 2000) currently lists both anorexia nervosa (AN) and bulimia nervosa (BN). Herzog and Eddy (2007) noted that other clinically significant eating disturbances have been recognized, although AN and BN have received the most attention from researchers.
Anorexia Nervosa According to DSM-IV-TR (APA, 2000), individuals diagnosed with AN refuse to maintain a minimally normal body weight (i.e., 85% of normal weight for a person’s age and height), are intensely afraid of gaining weight, and demonstrate a significant perceptual disturbance regarding the size or shape of their bodies. At clinical levels, low weight is accompanied by reproductive endocrine dysfunction and cessation of menstruation (McDowell et al., 2003; Sodersten et al., 2006). Primarily, individuals with AN pursue and/or maintain excessively low body weight through a reduction in food intake or food restriction. Other methods may include self-induced vomiting, misuse of laxatives or diuretics, and/or excessive exercise (Cook-Cottone & Phelps, 2006). The DSM-IV-TR provides diagnostic information on two subtypes: restricting type (weight loss is accomplished primarily through
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restriction) and binge-eating/purging type (regular episodes of purging with or without bingeing). Younger children may not experience loss of weight; rather, they fail to make expected weight gains as they increase in age and height (Cook-Cottone & Phelps, 2006). Child and adolescent patients may appear younger than their chronological age, and those with chronic AN may seem older (Herzog & Eddy, 2007). Other physical symptoms include breast atrophy, dry and yellow skin, bradycardia, hypotension, lanugo, alopecia, and edema (Herzog & Eddy, 2007). Complications include cold intolerance, constipation, abdominal discomfort, lethargy, fluid and electrolyte imbalances, low bone mineral density, infertility, and cardiovascular compromise (Herzog & Eddy, 2007). Despite many physical difficulties, individuals with AN often present as hyperactive (Herzog & Eddy, 2007). For a detailed list of medical complications, please see Gowers and Bryan-Waugh’s (2004) and Ebeling and colleagues’ (2003) practice guidelines.
Bulimia Nervosa Bulimia nervosa was identified as a disorder about two-and-a-half decades ago (Quadflieg & Fichter, 2003). Individuals with BN manifest recurrent episodes of binge eating and use inappropriate compensatory behaviors to prevent weight gain (e.g., self-induced vomiting, misuse of laxatives, diuretics, enemas, fasting, or obligatory exercise; Cook-Cottone & Phelps, 2006). Symptoms also include an excessive emphasis on body shape and weight in overall self-evaluation (APA, 2000). Diagnostic criteria allow for the identification of two subtypes: purging type (regular engagement in vomiting, laxatives, diuretics, or enemas) and non-purging type (use of other compensatory behaviors such as fasting or exercise; APA, 2000). Of importance, in 2007, Steiger and Bruce reported that several latent class studies do not support these subtypes of diagnostic distinctions. Individuals with BN are often within healthy weight ranges with a subset even falling into overweight and obese categories (Herzog & Eddy, 2007). Physical complications include peripheral edema, bloating, weakness, fatigue, dental problems, facial swelling, peliosis, and calluses and abrasions on hands from self-induced vomiting (Herzog & Eddy, 2007). Medical complications are often secondary to purging and malnutrition, and include hypokalemia, arrhythmias, electrolyte and acid–base complications, muscle weakness, tetany, metabolic alkalosis, muscle myopathy, fatal cardiomyopathy, gastrointestinal problems, esophageal disorders, etc. (Herzog & Eddy, 2007). For a detailed medical report, see Mehler, Crews, and Weiner’s (2004) extensive review of the medical complications associated with BN.
Incidence and Prevalence There is need for national and international representation of epidemiological research (Pearson, Goldklang, & Striegel-Moore, 2002). For many years, eating disorders were understood as disorders of the “young, rich, and white” (Pearson et al.,
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2002, p. 214). However, these findings may be a reflection of the empirical focus on clinic and college samples (e.g., Pearson et al., 2002). Further complicating the epidemiology studies is the fact that, in clinical populations, symptoms are usually present 6–24 months before a diagnosis is made (Lena et al., 2004).
Anorexia Nervosa Generally, the prevalence rate for AN is estimated at approximately 3–5 in 1000 young females (Hoek & Hoeken, 2003). Comparatively, the prevalence rate among young males is approximately 3 in 10,000 (APA, 2000). Further, the incidence rate was estimated at 8 per 100,000 population (Rastam, Gillberg, Hoeken, & Hoek, 2004). Rastam and colleagues (2004) suggested that these rates represent an increase in anorexia over the past 50 years. Mortality rates from 1953 to 1999 for those diagnosed with AN average around 5%, and of the survivors, less than 50% recover, 33% improve but are not considered recovered, and 20% remain chronically ill (Steinhausen, 2002). Keel and Herzog (2004) report that AN is associated with one of the highest risks for premature death among all the psychiatric disorders, with an estimated crude mortality rate of 5%–5.9% of those diagnosed. In a recent 21-year follow-up study of 103 patients with AN, the crude mortality rates amounted to 4.9% after 2 years, 5.8% after 6 years, and 6.8% after 12 years (Fichter, Quadflieg, & Hedlund, 2006). At a 12-year follow-up, the standardized mortality ratio was 8.85 (95% CI: 2.29–15.40). All deaths were in some way associated with AN diagnosis (Fichter et al., 2006). Overall, the statistics are not encouraging, and it is theorized that the social stigma and secrecy associated with eating disordered behavior may contribute to an underestimation of true prevalence rates (Cook-Cottone & Phelps, 2006; Ray, 2004).
Bulimia Nervosa Recent reviews indicate the average prevalence rates for BN to be 1% in females and 0.1% in males (Hoek & Hoeken, 2003; Rastam et al., 2004). Comparatively, the prevalence rate is estimated at 1 in 1000 for young men (Hoek & Hoeken, 2003). In contrast with AN, outcomes of BN have been relatively promising, with evidence of up to 75% of those diagnosed not meeting the criteria at 5 years (Ben-Tovim, 2003; Cook-Cottone & Phelps, 2006). A crude mortality rate of 0.3% for BN was reported with a warning that this value may be an underestimate given the short duration of the currently available follow-up studies. In an extensive review of the research, Quadflieg and Fichter (2003) found variable crude mortality rates, depending on sample size, at 2.8% after 2 years, 1.1% after 3.5 years, 2.1% after 4.5 years, 1.1 after 5 years, and 2.2% after 10 years. In all cases reported, there was no cause of death which could be attributed directly to the specific eating behaviors of an individual with BN. Causes of death included pneumonia, encephalitis with pneumonia
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combined with cardiac problems, drug overdose, multiple cases of suicide, multiple cases of traffic accidents, hypertensive reaction following the intake of medication, and consequences of very low body weight (Quadflieg & Fichter, 2003).
Complications in Neuropsychological Research of Eating Disorders The interpretation of neuropsychological findings in the area of eating disorders must be tempered by the consistent acknowledgment of limitations in the current state of research (e.g., Tchanturia et al., 2005). Notably, neuroimaging and neuropsychological studies of eating disorders have produced conflicting results due to many confounding variables (Key, O’Brien, Gordon, Christie, & Lask, 2006). These include comorbidity, psychotropic medications, age of onset, state of starvation, and the absence of adequately sized homogenous samples with suitable control and comparison groups (Key et al., 2006). Further, few studies have attempted to combine both imaging and neuropsychometric measures (Key et al., 2006). Researchers have identified several general areas of concern: (a) heterogeneity in classification systems, construct definition, and assessment tools; (b) problems with research methodology; and (c) conflicting opinions regarding the reliability in findings due to relationships between weight, symptoms, and neuropsychological outcomes.
Heterogeneity in Classification Systems, Construct Definition, and Assessment Tools The current state of the eating disorder diagnostic system does not promote valid, comparable research. For example, Duchesne and colleagues (2004) report that the analysis of cognitive functions among individuals with eating disorder is complicated by the use of different classification systems among researchers. Classification systems used for eating disorders (EDs) have included DSM-III-R, DSM-IV, and the criteria used by Russell (1979) for BN. In 2005, Tchanturia and colleagues noted that 44% of the studies that they reviewed were published before the last two revisions of the DSM (i.e., DSM-IV and DSM-IV-TR), which included the change in classification of diagnostic subtypes. Further, despite the revisions, researchers such as Steiger and Bruce (2007) have suggested that current diagnostic subtypes in BN may not be valid as they have not been supported by latent class studies. Other terms, such as recovery, lack universally accepted operational definitions (Bachner-Melman, Zohar, & Ebstein, 2006). For example, Duchesne and colleagues (2004) noted that the values of BMI deemed sufficient to evidence recovery varied from study to study. Bachner-Melman and colleagues (2006) noted that some studies have defined recovery using largely biological criteria (e.g., normal weight and regular menstruation), and others included behavioral criteria (e.g., lack of bingeing and purging symptoms and absence of restrictive eating). Further, researchers
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such as Bachner-Melman and colleagues (2006) suggested that full recovery might entail the absence of additional cognitive criteria (e.g., lack of body image distortion, lack of fear of weight gain, and the absence of vigilance over eating for weight control purposes). Researchers have warned that, even when a more precise classification system is used (i.e., the Psychiatric Status Rating Scale for Anorexia Nervosa; Herzog et al., 1993), it is often not uniformly implemented (Bachner-Melman et al., 2006). In a study comparing women who had recovered from AN behaviorally and cognitively and female controls, Bachner-Melman and colleagues (2006) found that women who had recovered both behaviorally and cognitively scored similarly to female controls. However, women who had recovered behaviorally but not cognitively scored significantly higher than both comparison groups on measures of body dissatisfaction, disordered eating, drive for thinness, general symptomatology, endorsement of the thin ideal, concern for appropriateness, fear of failure, harm avoidance, obsessiveness, and perfectionism, and significantly lower on drive for success and self-esteem (Bachner-Melman et al., 2006). The authors concluded that the personality profile of individuals who have recovered from AN appears to be highly sensitive to the presence versus absence of cognitive criteria (BachnerMelman et al., 2006). Further, as a result of their findings, the authors questioned the validity of the generalization that individuals who have recovered from AN continue to display both cognitive features of the disorder and personality features like perfectionism and obsessiveness. There are issues with the relevant nomenclature beyond the diagnostic debates. For example, the classification of cognitive functions differs from study to study (Duchesne et al., 2004). This is often further complicated by the use of different neuropsychological tests to assess a determined function (Duchesne et al., 2004). To illustrate, in their review of the literature Duchesne and colleagues (2004) indicated that some studies that used the digit symbol test conceptualized it as a test of psychomotor speed, while other researchers viewed it as a test of attention.
Problems in Methodology To date, there have been many methodological problems affecting the reliability and validity of findings. These problems have been related, in part, to epidemiological issues (e.g., prevalence rates), cultural attitudes toward mental illness, cultural acceptance of chronic dieting and extreme body change strategies, as well as individual characteristics of those suffering from eating disorders (e.g., tendency toward secrecy, denial, and relapse). Further, it can be very difficult for researchers to recruit adequate numbers of participants for statistical power and comparative control groups. When researchers recruit participants, there is a concern that those individuals do not fully or aptly represent the actual population suffering from eating disordered behaviors.
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Reviews of the literature validate these concerns (Tchanturia et al., 2005). To illustrate, Duchesne and colleagues (2004) found that many studies have relied upon very small sample sizes. Further, very few studies were found to have used adequate control groups (Duchesne et al., 2004). Duchesne et al. (2004) reported that most studies utilized patients who had been referred to specialized clinics, making generalization to other samples and the general population difficult. Tchanturia and colleagues (2005) reported a problem in consistency in terms of patient samples (e.g., case-controlled, cross-sectional, or longitudinal). Finally, published studies demonstrate a pattern of non-assessment of pre-existing brain lesions and of possible comorbidities that might be related to cognitive function findings (Duchesne et al., 2004). Other methodological concerns include the application and interpretation of assessment procedures. For example, Duchesne and colleagues (2004) noted that some studies utilized non-validated tests. Further, the manner in which assessments are used can vary. Tchanturia et al. (2005) reported that studies often use different tests to measure the same function. Also, in studies that evaluated the cognitive performance of patients with EDs after treatment, the treatment periods varied in length, and times between initial assessment and reassessment were different (Duchesne et al., 2004).
Deficits Related to Symptoms Studies of patients with clinical levels of symptoms can be difficult to interpret. With few follow-up studies, the relationship between weight gain effects and cognitive performance remains a point of debate (Tchanturia et al., 2005). For example, in an early study, Hamsher, Halmi, and Benton (1981) found that cognitive performance at the end of treatment was significantly associated with the maintenance of treatment outcomes. At that time, it was theorized that patients with cognitive deficits may present as a subgroup with poorer prognosis (Hamsher et al., 1981). However, there is some evidence that cognitive deficits found in studies may be reversed with proper nutrition, and that many early researchers did not adequately control for this issue (Duchesne et al., 2004; Lena et al., 2004). Accordingly, some researchers question early empirical findings citing the malnutrition hypothesis. Specifically, authors who have found evidence of particular cognitive deficits in individuals with eating disorders have hypothesized that deficits were observed as a consequence of starvation (Duchesne et al., 2004; Lena et al., 2004). Starvation involves a combination of food restriction and malnutrition, as well as excessive weight loss, all of which can be problematic. For example, individuals with normal weight who are subjected to a restricted diet may demonstrate decreased performance in areas such as sustained attention and short-term memory (Duchesne et al., 2004). It is important to note that these concerns are not evenly applied to all eating disorder diagnostic categories and subtypes. That is, there is controversy as to whether or not the malnutrition hypothesis can be applied to bulimic, binge eating, and subclinical groups (e.g., Lena et al.,
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2004). Further complicating the empirical exploration of the malnutrition hypothesis is an assumption that there may also be a time limit of duration of weight loss, beyond which the normalization of neuropsychological functioning would be more difficult to regain (Duchesne et al., 2004). There are many studies that can illustrate the complexity of these issues. It has been theorized that physiological complications, nutrition intake, and eating behaviors may interact, making a causal pattern difficult to interpret (Tannhauser, 2002). For example, there may be a causal pattern among the variables zinc deficiency, meat avoidance, and increased risk for AN (Tannhauser, 2002). Following the analysis of data from 45 patients with AN, Tannhauser (2002) makes a compelling case of early zinc deficiency creating an increased risk of the onset of eating disordered behavior. It was theorized that early zinc deficiencies are aggravated in puberty by a high energy and low zinc ratio in the diet and a developmentally unique set of social and psychological stressors. Together, these factors influence the physiological processes (e.g., maldigestion, catabolism, and amenorrhea) needed for the conversion of tryptophan in the serotonin pathway, and affect norepinephrine, leptin levels, macronutrient intake, and composition. It is believed that these changes result in the behavioral development of AN.
Course of Disorders and Cognitive Symptoms Both AN and BN have profound medical and psychological complications that may persist throughout life (e.g., Bulik et al., 2005). With the highest mortality rate among all of the psychiatric disorders, eating disorders remain the least studied area of neuropsychological research in all of the major psychiatric disorders (Tchanturia et al., 2005). This is especially concerning in light of the fact that among all of the psychiatric classifications, eating disorders have the highest ratio of female to male patients (APA, 2000). It is unclear why, with such strong medical, psychological, and functional implications, more neuropsychological work has not been completed.
Risk and Development The development of risk can be best conceptualized by a multifaceted developmental model in which individual factors (e.g., genetic, gender, emotional regulation, and specific physiological tendencies) and particular familial characteristics (e.g., lack of emotional validation, focus on image or appearance) interact, creating a vulnerability that is expressed, in predisposing cultures, as eating disordered behavior (Cook-Cottone, 2006; Herzog & Eddy, 2007). This pathogenic developmental trajectory involves a process in which individual vulnerabilities and experiences lead to difficulties with self-regulation and care (Cook-Cottone, 2006). Food intake and nutrition become the primary focus of regulatory behavior. Specific differences in
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psychopathological traits, such as tendencies toward dysregulation and compulsivity and overcontrol, have led theorists to believe that there are multiple pathways to AN and BN (Steiger & Bruce, 2007). To illustrate, for some the pathway to BN may be marked with overcontrol, dieting, and consequent counterregulation (i.e., binge eating; Steiger & Bruce, 2007). For others, that pathway involves a long-term experience of dysregulation that affects appetite, mood, and impulse control (Steiger & Bruce, 2007). For those with eating disorders, difficulties with regulation and control of food and the body manifest within the context of a limited and disordered experience of the self, wherein physical appearance is disproportionably attended to and simultaneously assessed based on a gradually internalized set of unattainable and unhealthy standards (Bulik et al., 2005; Cook-Cottone, 2006). Family influences can contribute (Bulik et al., 2005). For example, academic and extracurricular excellence may be revered, beauty idealized, meal times may be tense, or parents may engage in inappropriate mealtime behaviors. Together, these experiences create a selfperpetuating disorder. Self-regulation and dissatisfaction can become the central organizing features of the individual’s life (Cook-Cottone, 2006). In brief, the experience of the self is diminished almost solely to dissatisfaction with and control of the body (i.e., restriction, binging and/or purging; Cook-Cottone, 2006). Several key risk factors have been identified. Genetics appear to play an important role with studies of patients with AN showing a potentially stronger genetic contribution than studies of patients with BN (Bulik et al., 2005). Genes involved in the development of the brain, energy homeostasis, serotonergic processing and the dopamine system appear to particularly important (Klump, Bulik, Kaye, & Strober, 2002; Steiger & Bruce, 2007). Specifically, family and twin studies of AN have shown that genetic factors may contribute to up to 50% of the variance in AN (Klump & Gobrogge, 2005). Heritability estimates for BN from twin studies have varied greatly ranging from 28% to 83% (Steiger & Bruce, 2007). Gender is considered a strong risk factor. While a small proportion of clinical cases are male, 90% of individuals diagnosed with AN and BN are female (APA, 2000). The cultural context appears to be involved in gender differences as the media-propagated thin ideal targets mainly women, with men presented at more normal weights. Also, subcultural effects may be apparent as both females and males show increases in vulnerability and incidence in specific weight-sensitive athletic or social contexts (e.g., wrestling, boxing, ballet dancing, gymnastics, and possibly in the gay culture; e.g., Patel, Greydanus, Pratt, & Phillips, 2003). High risk populations include those who engage in activities that focus attention on weight, appearance, and performance (e.g., ballet, long-distance running, ice-skating, gymnastics, modeling, and crew; Herzog & Eddy, 2007). Other groups identified as at-risk include women with chronic illness (e.g., cystic fibrosis, diabetes, and mood disorders) as well as women with professions that involve high levels of achievement for success (Herzog & Eddy, 2007). Many believe that the trajectory toward risk begins quite early in development. There is some evidence to support this contention. To illustrate, Duchesne and colleagues (2004) and Uher et al. (2003) reported that, in some cases, evidence of
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pre-morbid, neurological lesions are associated with diagnosis of a more severe variant of AN. Similarly in a review of the literature, Lena and colleagues (2004) concluded that neuropsychological deficits preexist and underlie the etiology of eating disorders. Despite early etiological influences, initial diagnostic symptoms typically present in the early to mid-adolescent years and childhood onset is quite rare (APA, 2000). It appears that although many predisposing factors may be present for some time, the manifestation of the full clinical disorder requires, at least, later childhood cognitive and physiological development (Cook-Cottone & Phelps, 2006). To explain cognitive implications, the integration of more developmentally sophisticated cognitive processing is reflected in many of the diagnostic symptoms (Cook-Cottone & Phelps, 2006). For example, in AN (two of the four) and BN (one of the five), diagnostic criteria describe symptoms that involve evaluative and comparative thought (e.g., self-evaluation unduly influenced by weight, fear of becoming fat; APA, 2000). Moreover, other criteria allude to cognitively mediated experiences and/or behaviors as well as the internal representation of an abstract ideal (Cook-Cottone & Phelps, 2006). Such cognitive processes are characteristic of later concrete operational or formally operative thought (e.g., a sense of lack of control and compensatory behaviors [three of the five criteria for BN], and refusal to maintain body weight [one of the four criteria for AN]; APA, 2000). Physiological implications are twofold. First, as the young adolescent is becoming increasingly capable of comparative and evaluative thought, the physical changes associated with puberty are emerging, creating a developmental period of unique vulnerability (Williams & Currie, 2000). Second, the final diagnostic criterion for AN is entirely dependent on later childhood physiological development, amenorrhea in postmenarchal females (APA, 2000). The contributions of cognitive and physiological development change with age. That is, once an individual is capable of more advanced cognitive processes, time of clinical onset becomes less dependent on cognitive and psychological developmental issues and external influences weigh more heavily. For example, this earlier period of vulnerability is followed by a later window of risk during the transition from adolescence to young adulthood (e.g., the transition to college; Cook-Cottone & Phelps, 2003).
Recovery, Relapse, Migration, and Outcomes In an extensive review of the research, Quadfleig and Fichter (2003) found that for BN, good outcome was observed in 28%–79% after 0.5–1 year, 38%–69% after 1.5–2 years, 13%–77% after 3–6 years and 47%–73% after 9–11 years. Further, they reported that it appeared that no stable recovery rate could be expected after intake into a study on BN for the first 5–6 years. Also, after about 10 years, between two-thirds and three-quarters of women with BN showed at least partial recovery. Further, review of the literature suggested that in the long run, more than half of the women treated for BN experienced social adjustment, finding friends and partners
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and having leisure activities (Quadfleig & Fichter, 2003). However, a considerable percentage continued to present with severe social adjustment difficulties, especially with sexuality (Quadfleig & Fichter, 2003). For BN, data on the percentage of relapse and the chronicity of the disorder were far from conclusive (Quadfleig & Fichter, 2003). However, poor outcomes were observed in 3%–67% after 0.5–1 year, in 5%–50% after 1.5–2 years, in 19%–87% after 3–6 years, and in 9%–30% after 9–11 years. Chronicity appears to be significant in AN. In a 12-year course and outcome study of AN, Fichter et al. (2006) found that nearly 30% of their sample of 103 patients with AN still or again received a diagnosis of AN after 12 years. Herzog and Eddy (2007) described a phenomenon termed diagnostic migration in which individuals shifted symptomatically from one disorder to another. For example, a majority of individuals diagnosed with AN eventually engage in bingeing and purging behaviors during the course of their disorder (Herzog & Eddy, 2007). They suggested that, within the first five years of illness, patients may diagnostically migrate from AN-restricting type to AN binge-purging type to BN (Herzog & Eddy, 2007). Quadfleig and Fichter (2003) found that data also existed that suggested cross-over from BN to eating disorder not otherwise specified (ED-NOS). The rates vary greatly (Fichter et al., 2006). In their 12-year follow-up study, Fichter and colleagues (2006) found a crossover from AN to BN in 9.5% of patients. Of note, interpretation of symptoms as migration was difficult because presentation may reflect partial recovery from BN as well as a possible crossover to AN not yet meeting diagnostic criteria for AN (Quadfleig & Fichter, 2003). Researchers theorized that the greater the number of cognitive deficits, the poorer the diagnosis after treatment (Lena et al., 2004). With decreased cognitive abilities, the eating disorder symptoms were more significant and more difficult to treat with the standard eating disorder treatment protocol (Lena et al., 2004). These underlying deficits helped to explain the high rate of symptom exacerbation and relapse as many treatment programs addressed the eating disorder behaviors and not the underlying problems (i.e., the cognitive deficits; Lena et al., 2004). There is emerging evidence that brain abnormalities may be reversible after long-term recovery (i.e., >1 year no bingeing, purging, or restricting behaviors, normal weight, and menstrual cycles, and not on medication; Wagner et al., 2006). In a 2006 study of individuals in longterm recovery from AN-restricting type (N = 14), AN binge-purging type (N = 16), and BN (N = 10), magnetic resonance imaging indicated no significant differences when compared to controls (of similar age and body mass index) in gray matter volume, white mater volume, and cerebrospinal fluid.
Anorexia Nervosa According to several sources, the mortality rate for AN is the highest of all psychiatric disorders (e.g., Steinhausen, 2002). The symptoms of AN include a multifaceted syndrome of relatively homogeneous characteristic preoccupations and behaviors (Steinglass & Walsh, 2006). There is notable comorbidity with other
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psychiatric disorders such as anxiety, depression, and Obsessive Compulsive Disorder (OCD; Fassino et al., 2002; Steinglass & Walsh, 2006). Genetic studies have accounted for a high level of variance suggesting the probability of a neurobiological substrate (Key et al., 2006; Ozaki et al., 2003). Many AN symptoms suggest frontal lobe involvement (e.g., Fassino et al., 2002). Cognitive and behavioral symptoms are marked with a stereotyped, rigid, repetitive, and preservative quality common in psychobiologic disturbances (Steinglass & Walsh, 2006). Further, patients with AN have been described as obsessive, hypoaffective, aggressive, and inflexible (Fassino et al., 2002). Some researchers suggest that the obsessional, perfectionist, and compulsive nature of disorder-related thoughts and behaviors suggest similarities between AN and OCD (Fassino et al., 2002; Steinglass & Walsh, 2006). Also, a 10-year follow-up of 24 patients with AN identified a subgroup (
