- Email: [email protected]
Longitudinal Cortical Thinning in Adolescents With Autism: Good or Bad?
EDITORIAL Longitudinal Cortical Thinning in Adolescents With Autism: Good or Bad? Kelly N. Botteron,
I
t is a fairly well-established fact that autism is associated with a general increase in total brain volume based on increased gray matter and white matter proliferation during infancy and toddlerhood.1 Despite the clear assumption that there are likely large deviations in specific areas of brain development, there are conflicting findings and much to be learned about the neural circuitry of autism. For example, several previous studies have demonstrated that in very early childhood, there are clear deviations in patterns of regional brain development in autism that persist and evolve during infancy, early childhood, and adolescence.1-4 Recent work has begun to more clearly identify specific neural networks that might be more significantly involved in the early development of autism spectrum disorder (ASD). However, it is only recently that the importance of specific cortical characterization, including the assessment of components that make up cortical structure, has been obtainable and advocated for in the neuroimaging research community.5-7 Previous studies would look at regional volumes or regional cortical thickness (CT) in association with specific characteristics or disease; however, recent evidence has repeatedly demonstrated that there is more biological validity and sensitivity when assessing the components of regional volume by quantifying more specific cortical parameters, including CT, quantification of cortical gyrification (the extent and degree of cortical folding patterns), and cortical surface area (CSA).7,8 This is supported by evidence from several lines of research, including recent studies demonstrating that when parameters of CSA are included in addition to regional volume and CT, there is improved sensitivity in relation to illness and a clear significant relation with higher indices of heritability.5,7 In this issue of the Journal, Wallace et al.9 adopted these principles of deconstructing components of cortical structure in a new investigation characterizing longitudinal cortical developmental changes associated with autism in late adolescence. They present findings related to developmental patterns of cortical characteristics during late adolescence in a small longitudinal study of participants with ASD (n ¼ 17) in contrast to typical healthy controls (n ¼ 18). The findings support that ASD is associated with more dramatic cortical thinning in several regions compared with healthy controls during the late adolescent to early adulthood period. Although there has been a handful of previous investigations looking at CSA and CT in ASD during childhood and adolescence, none of these publications were based on longitudinal data.10,11 This interesting study covers an important topic in a somewhat new and novel way. These investigators
442
www.jaacap.org
MD
previously published cross-sectional analyses with this sample, which demonstrated some regional differences in CT that also appeared to be developmentally related.12 They did not assess CSA in their previous study. For the present analysis, they hypothesized that there would be greater cortical thinning across this adolescent age range without much difference between groups in developmental change in surface area. They hypothesized a more rapid decrease in CT in a few key regions, including some areas of the parietal and temporal cortex. Their results are very consistent with these hypotheses. They also demonstrate and illustrate regionally specific areas with the most change. This project involved high-functioning adolescents with ASD (12 with Asperger syndrome, 4 with high-functioning ASD) and healthy controls matched for age, IQ, sex, handedness, and scanning interval. The average age was 17.4 years at baseline and 19.2 at the follow-up time point. Thirteen of the 17 adolescents with ASD were on psychotropic medications, including 40% on stimulants and 40% on selective serotonin reuptake inhibitors; other different medications also were represented. Many participants were on these medications at the 2 time points. Participants were diagnostically characterized in a standardized fashion, including a battery with the Autism Diagnostic Interview–Revised and the Autism Diagnostic Observation Schedule. In general, they were a very high-functioning group, with a mean IQ of 117. The investigators obtained typical structural research MRI scans based on T-1 weighted images from each participant at baseline and approximately 2 years later. Image analyses were based on Freesurfer segmentations, and definitions of CT and surface area were derived from this widely used and freely available image processing program. The reported findings demonstrate substantial differences with accelerated cortical thinning in the group with ASD compared with the typically developing group before correction for multiple comparisons. Two regions survived multiple corrections: a left posterior region of the ventral occipital temporal cortex and a region of the superior parietal cortex. These areas are thought to be involved in sensory processing, social cognition, and communication. There were no differences in longitudinal changes in surface area. As is generally the case in ASD, the sample was largely a male sample. Given the established differences in cortical maturation patterns between adolescent boys and girls, the analyses also were completed with just boys, and the findings remained. These regional areas of thinning also were correlated with behavioral measurements of executive function and ASD social symptom severity. JOURNAL
OF THE
AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 54 NUMBER 6 JUNE 2015
EDITORIAL
This study complements another recent study by Zielinski et al.,4 which also examined age-related change in CT across different regions of interest in the cortex. One major difference was that the participant group in the study by Zielinski et al. had a much wider age range, and the study had a cross-sectional design. They found that in younger children, there was increased regional CT in their population with ASD, and this progressed to a pattern of cortical thinning in participants during late adolescence and early adulthood. Although it is increasingly clear that our understanding of the underlying heterogeneous etiologies of ASD is going to depend heavily on the characterization of very early neurodevelopmental differences in infants, toddlers, and young children with ASD, there is still an important role for understanding the ongoing developmental changes associated with late childhood, adolescence, adulthood, and even the effects of aging associated with ASD. Understanding the neurobiological underpinnings associated with ongoing symptomatic progression, static functional capacity, or deterioration in function also is an important area of research. Pursuit of this knowledge will be needed to establish more effective interventions that target specific pathophysiology. There are several limitations to the present study, which are important to consider when evaluating how these findings might generalize. First, as is often the case in imaging studies of ASD, the participants tend to be very high functioning and therefore are not representative of the overall spectrum of individuals with autism. This is clearly related to the fact that there are difficulties scanning unsedated children with significant developmental disabilities. However, although it takes more effort, it is possible to scan lower-functioning, more clinically impaired participants, and sedation could be considered a potential option in the more severely impaired group.2 Second, current and long-term medication use is definitely a significant issue for interpreting the findings but very difficult to avoid in investigations of ASD. Some of the medications involved could clearly be associated with changes in developmental trajectories, including changes in CSA or CT. Additional research to address this topic with larger samples is needed. These recent publications demonstrate that there are significant differences in longitudinal structural brain trajectories associated with ASD in children and adolescents.3,4,8,9 Others also have demonstrated altered longitudinal trajectories, but of a very different character in younger infants, toddlers, and young school-age children.1,2 All these studies point to the need to pursue additional investigations including much larger longitudinal samples to increase power to definitively quantify some of these important issues that need to be addressed and cannot be avoided. For example, there are clearly going to be different developmental patterns during infancy, childhood, adolescence, and young adulthood, and the actual accurate detailed characterization of these developmental trajectories is going to require significantly larger samples followed longitudinally. These large JOURNAL OF THE AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 54 NUMBER 6 JUNE 2015
replication and extension samples also will be needed to clarify male and female developmental pattern differences and to begin to more carefully address the definition of genetically and biologically relevant subcategorization to more aggressively establish answers related to heterogeneity in ASD. Characterization of the diverse early developmental pathways that lead to common neurodevelopmental outcomes in childhood and adolescence will help to clarify heterogeneity. Studies examining individuals with fragile X syndrome (FSX) are a good example of how very different patterns of neural developmental trajectories can appear to have very similar outward behavioral characteristics. FXS is the most common known single-gene cause of autism. A recent investigation by Hoeft et al.11 demonstrated significant differences in patterns of cortical changes in young children with FXS compared with participants with ASD of a similar age and healthy controls. Despite sharing similar developmental delays, sensory sensitivity, decreased eye contact, and impairments in social skills, the underlying pattern of cortical change was quite discrepant in the 2 populations. The 2 groups had differences in cortical characteristics in specific frontal and temporal regions, often closely overlapping regions; however, the differences were generally in opposite directions. Although young children with ASD demonstrated increased cortical gray matter in these regions compared with healthy controls, those with FXS had decreased cortical gray matter in these regions compared with healthy controls. This type of investigation and these findings support how the explication of heterogeneity is critical. Examination and characterization of specific endophenotypes that are related to specific patterns of longitudinal developmental change has a very strong potential to substantially add to our understanding of diverse developmental trajectories. We are interested in which neurobiological factors and underlying neural circuitry might be associated with a decrease in function, such as increased isolation and self-stimulating or self-injurious behaviors, or a decrease in daily living skills compared with which factors might be associated with improvements in communication or socialization skills or improvements in achieving independent-living skills. This knowledge will help us to identify which factors might be associated with resilience so that resilience factors might be enhanced, if possible. Better characterization of these longitudinal developmental trajectories might be an important tool in solving the extensive heterogeneity issues in ASD. As an increasing number of genetic characteristics associated with ASD, such as alterations in copy number variants or de novo genetic mutations, are identified, understanding which longitudinal developmental trajectories are associated with different genetic variants will be critical to understanding the specific underlying neurobiology and establishing specially targeted interventions. This study helps us proceed one small step further in the direction of defining biological endophenotypes based on longitudinal cortical development in ASD. & www.jaacap.org
443
BOTTERON
Accepted March 30, 2015.
Correspondence to Kelly Botteron, MD, Department of Psychiatry, Washington University School of Medicine, 660 South Euclid, Box 8134, St. Louis, MO 63110; e-mail: [email protected]
Dr. Botteron is with Washington University School of Medicine in St. Louis. Disclosure: Dr. Botteron has received grant or research support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Mental Health, and Autism Speaks.
0890-8567/$36.00/ª2015 American Academy of Child and Adolescent Psychiatry http://dx.doi.org/10.1016/j.jaac.2015.03.017
REFERENCES
1. Hazlett HC, Poe MD, Gerig G, et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry. 2011;68:467-476. 2. Wolff JJ, Gu H, Gerig G, et al. Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry. 2012;169:589-600. 3. Schumann CM, Bloss CS, Barnes CC, et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neurosci. 2010;30:4419-4427. 4. Zielinski BA, Prigge MB, Nielsen JA, et al. Longitudinal changes in cortical thickness in autism and typical development. Brain. 2014;137: 1799-1812. 5. Eyler LT, Prom-Wormley E, Panizzon MS, et al. Genetic and environmental contributions to regional cortical surface area in humans: a magnetic resonance imaging twin study. Cereb Cortex. 2011;21:2313-2321. 6. Raznahan A, Shaw P, Lalonde F, et al. How does your cortex grow? J Neurosci. 2011;31:7174-7177. 7. Vuoksimaa E, Panizzon MS, Chen CH. The genetic association between neocortical volume and general cognitive ability is driven by global
444
www.jaacap.org
8.
9.
10.
11.
12.
surface area rather than thickness [published online ahead of print February 18, 2014]. Cereb Cortex. PMID:24554725. Nordahl CW, Dierker D, Mostafavi I, et al. Cortical folding abnormalities in autism revealed by surface-based morphometry. J Neurosci. 2007;27: 11725-11735. Wallace GL, Eisenberg IW, Robustelli B, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J. Am Acad Child Adolesc Psychiatry. 2015;54: 464-469. Raznahan A, Toro R, Daly E, et al. Cortical anatomy in autism spectrum disorder: an in vivo MRI study of on the effects of age. Cereb Cortex. 2010;20:1332-1340. Hoeft F, Walter E, Lightbody AA, et al. Neuroanatomical differences in toddler boys with fragile X syndrome and idiopathic autism. Arch Gen Psychiatry. 2011;68:295-305. Wallace GL, Shaw P, Lee NR, et al. Distinct cortical correlates of autistic versus antisocial traits in a longitudinal sample of typically developing youth. J Neurosci. 2012;32:4856-4860.
JOURNAL
OF THE
AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 54 NUMBER 6 JUNE 2015
I
t is a fairly well-established fact that autism is associated with a general increase in total brain volume based on increased gray matter and white matter proliferation during infancy and toddlerhood.1 Despite the clear assumption that there are likely large deviations in specific areas of brain development, there are conflicting findings and much to be learned about the neural circuitry of autism. For example, several previous studies have demonstrated that in very early childhood, there are clear deviations in patterns of regional brain development in autism that persist and evolve during infancy, early childhood, and adolescence.1-4 Recent work has begun to more clearly identify specific neural networks that might be more significantly involved in the early development of autism spectrum disorder (ASD). However, it is only recently that the importance of specific cortical characterization, including the assessment of components that make up cortical structure, has been obtainable and advocated for in the neuroimaging research community.5-7 Previous studies would look at regional volumes or regional cortical thickness (CT) in association with specific characteristics or disease; however, recent evidence has repeatedly demonstrated that there is more biological validity and sensitivity when assessing the components of regional volume by quantifying more specific cortical parameters, including CT, quantification of cortical gyrification (the extent and degree of cortical folding patterns), and cortical surface area (CSA).7,8 This is supported by evidence from several lines of research, including recent studies demonstrating that when parameters of CSA are included in addition to regional volume and CT, there is improved sensitivity in relation to illness and a clear significant relation with higher indices of heritability.5,7 In this issue of the Journal, Wallace et al.9 adopted these principles of deconstructing components of cortical structure in a new investigation characterizing longitudinal cortical developmental changes associated with autism in late adolescence. They present findings related to developmental patterns of cortical characteristics during late adolescence in a small longitudinal study of participants with ASD (n ¼ 17) in contrast to typical healthy controls (n ¼ 18). The findings support that ASD is associated with more dramatic cortical thinning in several regions compared with healthy controls during the late adolescent to early adulthood period. Although there has been a handful of previous investigations looking at CSA and CT in ASD during childhood and adolescence, none of these publications were based on longitudinal data.10,11 This interesting study covers an important topic in a somewhat new and novel way. These investigators
442
www.jaacap.org
MD
previously published cross-sectional analyses with this sample, which demonstrated some regional differences in CT that also appeared to be developmentally related.12 They did not assess CSA in their previous study. For the present analysis, they hypothesized that there would be greater cortical thinning across this adolescent age range without much difference between groups in developmental change in surface area. They hypothesized a more rapid decrease in CT in a few key regions, including some areas of the parietal and temporal cortex. Their results are very consistent with these hypotheses. They also demonstrate and illustrate regionally specific areas with the most change. This project involved high-functioning adolescents with ASD (12 with Asperger syndrome, 4 with high-functioning ASD) and healthy controls matched for age, IQ, sex, handedness, and scanning interval. The average age was 17.4 years at baseline and 19.2 at the follow-up time point. Thirteen of the 17 adolescents with ASD were on psychotropic medications, including 40% on stimulants and 40% on selective serotonin reuptake inhibitors; other different medications also were represented. Many participants were on these medications at the 2 time points. Participants were diagnostically characterized in a standardized fashion, including a battery with the Autism Diagnostic Interview–Revised and the Autism Diagnostic Observation Schedule. In general, they were a very high-functioning group, with a mean IQ of 117. The investigators obtained typical structural research MRI scans based on T-1 weighted images from each participant at baseline and approximately 2 years later. Image analyses were based on Freesurfer segmentations, and definitions of CT and surface area were derived from this widely used and freely available image processing program. The reported findings demonstrate substantial differences with accelerated cortical thinning in the group with ASD compared with the typically developing group before correction for multiple comparisons. Two regions survived multiple corrections: a left posterior region of the ventral occipital temporal cortex and a region of the superior parietal cortex. These areas are thought to be involved in sensory processing, social cognition, and communication. There were no differences in longitudinal changes in surface area. As is generally the case in ASD, the sample was largely a male sample. Given the established differences in cortical maturation patterns between adolescent boys and girls, the analyses also were completed with just boys, and the findings remained. These regional areas of thinning also were correlated with behavioral measurements of executive function and ASD social symptom severity. JOURNAL
OF THE
AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 54 NUMBER 6 JUNE 2015
EDITORIAL
This study complements another recent study by Zielinski et al.,4 which also examined age-related change in CT across different regions of interest in the cortex. One major difference was that the participant group in the study by Zielinski et al. had a much wider age range, and the study had a cross-sectional design. They found that in younger children, there was increased regional CT in their population with ASD, and this progressed to a pattern of cortical thinning in participants during late adolescence and early adulthood. Although it is increasingly clear that our understanding of the underlying heterogeneous etiologies of ASD is going to depend heavily on the characterization of very early neurodevelopmental differences in infants, toddlers, and young children with ASD, there is still an important role for understanding the ongoing developmental changes associated with late childhood, adolescence, adulthood, and even the effects of aging associated with ASD. Understanding the neurobiological underpinnings associated with ongoing symptomatic progression, static functional capacity, or deterioration in function also is an important area of research. Pursuit of this knowledge will be needed to establish more effective interventions that target specific pathophysiology. There are several limitations to the present study, which are important to consider when evaluating how these findings might generalize. First, as is often the case in imaging studies of ASD, the participants tend to be very high functioning and therefore are not representative of the overall spectrum of individuals with autism. This is clearly related to the fact that there are difficulties scanning unsedated children with significant developmental disabilities. However, although it takes more effort, it is possible to scan lower-functioning, more clinically impaired participants, and sedation could be considered a potential option in the more severely impaired group.2 Second, current and long-term medication use is definitely a significant issue for interpreting the findings but very difficult to avoid in investigations of ASD. Some of the medications involved could clearly be associated with changes in developmental trajectories, including changes in CSA or CT. Additional research to address this topic with larger samples is needed. These recent publications demonstrate that there are significant differences in longitudinal structural brain trajectories associated with ASD in children and adolescents.3,4,8,9 Others also have demonstrated altered longitudinal trajectories, but of a very different character in younger infants, toddlers, and young school-age children.1,2 All these studies point to the need to pursue additional investigations including much larger longitudinal samples to increase power to definitively quantify some of these important issues that need to be addressed and cannot be avoided. For example, there are clearly going to be different developmental patterns during infancy, childhood, adolescence, and young adulthood, and the actual accurate detailed characterization of these developmental trajectories is going to require significantly larger samples followed longitudinally. These large JOURNAL OF THE AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 54 NUMBER 6 JUNE 2015
replication and extension samples also will be needed to clarify male and female developmental pattern differences and to begin to more carefully address the definition of genetically and biologically relevant subcategorization to more aggressively establish answers related to heterogeneity in ASD. Characterization of the diverse early developmental pathways that lead to common neurodevelopmental outcomes in childhood and adolescence will help to clarify heterogeneity. Studies examining individuals with fragile X syndrome (FSX) are a good example of how very different patterns of neural developmental trajectories can appear to have very similar outward behavioral characteristics. FXS is the most common known single-gene cause of autism. A recent investigation by Hoeft et al.11 demonstrated significant differences in patterns of cortical changes in young children with FXS compared with participants with ASD of a similar age and healthy controls. Despite sharing similar developmental delays, sensory sensitivity, decreased eye contact, and impairments in social skills, the underlying pattern of cortical change was quite discrepant in the 2 populations. The 2 groups had differences in cortical characteristics in specific frontal and temporal regions, often closely overlapping regions; however, the differences were generally in opposite directions. Although young children with ASD demonstrated increased cortical gray matter in these regions compared with healthy controls, those with FXS had decreased cortical gray matter in these regions compared with healthy controls. This type of investigation and these findings support how the explication of heterogeneity is critical. Examination and characterization of specific endophenotypes that are related to specific patterns of longitudinal developmental change has a very strong potential to substantially add to our understanding of diverse developmental trajectories. We are interested in which neurobiological factors and underlying neural circuitry might be associated with a decrease in function, such as increased isolation and self-stimulating or self-injurious behaviors, or a decrease in daily living skills compared with which factors might be associated with improvements in communication or socialization skills or improvements in achieving independent-living skills. This knowledge will help us to identify which factors might be associated with resilience so that resilience factors might be enhanced, if possible. Better characterization of these longitudinal developmental trajectories might be an important tool in solving the extensive heterogeneity issues in ASD. As an increasing number of genetic characteristics associated with ASD, such as alterations in copy number variants or de novo genetic mutations, are identified, understanding which longitudinal developmental trajectories are associated with different genetic variants will be critical to understanding the specific underlying neurobiology and establishing specially targeted interventions. This study helps us proceed one small step further in the direction of defining biological endophenotypes based on longitudinal cortical development in ASD. & www.jaacap.org
443
BOTTERON
Accepted March 30, 2015.
Correspondence to Kelly Botteron, MD, Department of Psychiatry, Washington University School of Medicine, 660 South Euclid, Box 8134, St. Louis, MO 63110; e-mail: [email protected]
Dr. Botteron is with Washington University School of Medicine in St. Louis. Disclosure: Dr. Botteron has received grant or research support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Mental Health, and Autism Speaks.
0890-8567/$36.00/ª2015 American Academy of Child and Adolescent Psychiatry http://dx.doi.org/10.1016/j.jaac.2015.03.017
REFERENCES
1. Hazlett HC, Poe MD, Gerig G, et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry. 2011;68:467-476. 2. Wolff JJ, Gu H, Gerig G, et al. Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry. 2012;169:589-600. 3. Schumann CM, Bloss CS, Barnes CC, et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neurosci. 2010;30:4419-4427. 4. Zielinski BA, Prigge MB, Nielsen JA, et al. Longitudinal changes in cortical thickness in autism and typical development. Brain. 2014;137: 1799-1812. 5. Eyler LT, Prom-Wormley E, Panizzon MS, et al. Genetic and environmental contributions to regional cortical surface area in humans: a magnetic resonance imaging twin study. Cereb Cortex. 2011;21:2313-2321. 6. Raznahan A, Shaw P, Lalonde F, et al. How does your cortex grow? J Neurosci. 2011;31:7174-7177. 7. Vuoksimaa E, Panizzon MS, Chen CH. The genetic association between neocortical volume and general cognitive ability is driven by global
444
www.jaacap.org
8.
9.
10.
11.
12.
surface area rather than thickness [published online ahead of print February 18, 2014]. Cereb Cortex. PMID:24554725. Nordahl CW, Dierker D, Mostafavi I, et al. Cortical folding abnormalities in autism revealed by surface-based morphometry. J Neurosci. 2007;27: 11725-11735. Wallace GL, Eisenberg IW, Robustelli B, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J. Am Acad Child Adolesc Psychiatry. 2015;54: 464-469. Raznahan A, Toro R, Daly E, et al. Cortical anatomy in autism spectrum disorder: an in vivo MRI study of on the effects of age. Cereb Cortex. 2010;20:1332-1340. Hoeft F, Walter E, Lightbody AA, et al. Neuroanatomical differences in toddler boys with fragile X syndrome and idiopathic autism. Arch Gen Psychiatry. 2011;68:295-305. Wallace GL, Shaw P, Lee NR, et al. Distinct cortical correlates of autistic versus antisocial traits in a longitudinal sample of typically developing youth. J Neurosci. 2012;32:4856-4860.
JOURNAL
OF THE
AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 54 NUMBER 6 JUNE 2015