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      Age-Related Differences in Cortical Thickness Vary by Socioeconomic Status

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      1 , 2 , 3 , 4 , 1 , * , Pediatric Imaging, Neurocognition, Genetics Study
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          Abstract

          Recent findings indicate robust associations between socioeconomic status (SES) and brain structure in children, raising questions about the ways in which SES may modify structural brain development. In general, cortical thickness and surface area develop in nonlinear patterns across childhood and adolescence, with developmental patterns varying to some degree by cortical region. Here, we examined whether age-related nonlinear changes in cortical thickness and surface area varied by SES, as indexed by family income and parental education. We hypothesized that SES disparities in age-related change may be particularly evident for language- and literacy-supporting cortical regions. Participants were 1148 typically-developing individuals between 3 and 20 years of age. Results indicated that SES factors moderate patterns of age-associated change in cortical thickness but not surface area. Specifically, at lower levels of SES, associations between age and cortical thickness were curvilinear, with relatively steep age-related decreases in cortical thickness earlier in childhood, and subsequent leveling off during adolescence. In contrast, at high levels of SES, associations between age and cortical thickness were linear, with consistent reductions across the age range studied. Notably, this interaction was prominent in the left fusiform gyrus, a region that is critical for reading development. In a similar pattern, SES factors significantly moderated linear age-related change in left superior temporal gyrus, such that higher SES was linked with steeper age-related decreases in cortical thickness in this region. These findings suggest that SES may moderate patterns of age-related cortical thinning, especially in language- and literacy-supporting cortical regions.

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          Most cited references47

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          Distinct genetic influences on cortical surface area and cortical thickness.

          Neuroimaging studies examining the effects of aging and neuropsychiatric disorders on the cerebral cortex have largely been based on measures of cortical volume. Given that cortical volume is a product of thickness and surface area, it is plausible that measures of volume capture at least 2 distinct sets of genetic influences. The present study aims to examine the genetic relationships between measures of cortical surface area and thickness. Participants were men in the Vietnam Era Twin Study of Aging (110 monozygotic pairs and 92 dizygotic pairs). Mean age was 55.8 years (range: 51-59). Bivariate twin analyses were utilized in order to estimate the heritability of cortical surface area and thickness, as well as their degree of genetic overlap. Total cortical surface area and average cortical thickness were both highly heritable (0.89 and 0.81, respectively) but were essentially unrelated genetically (genetic correlation = 0.08). This pattern was similar at the lobar and regional levels of analysis. These results demonstrate that cortical volume measures combine at least 2 distinct sources of genetic influences. We conclude that using volume in a genetically informative study, or as an endophenotype for a disorder, may confound the underlying genetic architecture of brain structure.
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            Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies.

            Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. Copyright 2009 Elsevier Inc. All rights reserved.
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              Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation.

              There is controversy over the nature of the disturbance in brain development that underpins attention-deficit/hyperactivity disorder (ADHD). In particular, it is unclear whether the disorder results from a delay in brain maturation or whether it represents a complete deviation from the template of typical development. Using computational neuroanatomic techniques, we estimated cortical thickness at >40,000 cerebral points from 824 magnetic resonance scans acquired prospectively on 223 children with ADHD and 223 typically developing controls. With this sample size, we could define the growth trajectory of each cortical point, delineating a phase of childhood increase followed by adolescent decrease in cortical thickness (a quadratic growth model). From these trajectories, the age of attaining peak cortical thickness was derived and used as an index of cortical maturation. We found maturation to progress in a similar manner regionally in both children with and without ADHD, with primary sensory areas attaining peak cortical thickness before polymodal, high-order association areas. However, there was a marked delay in ADHD in attaining peak thickness throughout most of the cerebrum: the median age by which 50% of the cortical points attained peak thickness for this group was 10.5 years (SE 0.01), which was significantly later than the median age of 7.5 years (SE 0.02) for typically developing controls (log rank test chi(1)(2) = 5,609, P < 1.0 x 10(-20)). The delay was most prominent in prefrontal regions important for control of cognitive processes including attention and motor planning. Neuroanatomic documentation of a delay in regional cortical maturation in ADHD has not been previously reported.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 September 2016
                2016
                : 11
                : 9
                : e0162511
                Affiliations
                [1 ]Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, New York, United States of America
                [2 ]Department of Epidemiology, Columbia University Medical Center, New York, New York, United States of America
                [3 ]Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, New York, United States of America
                [4 ]Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
                Institute of Psychology, Chinese Academy of Sciences, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceived and designed the experiments: LRP ECM KGN.

                • Performed the experiments: LRP ECM XH.

                • Analyzed the data: LRP ECM XH.

                • Contributed reagents/materials/analysis tools: XH.

                • Wrote the paper: LRP ECM ERS XH KGN.

                ¶ Membership of the Pediatric Imaging, Neurocognition, and Genetics Study is listed in the Acknowledgments.

                Article
                PONE-D-16-17385
                10.1371/journal.pone.0162511
                5028041
                27644039
                4e81de9a-3381-4b8c-a624-056e96a8a5b2
                © 2016 Piccolo et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 April 2016
                : 15 August 2016
                Page count
                Figures: 3, Tables: 5, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: T32MH13043
                Funded by: Brazilian National Council for the Improvement of Higher Education
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: RC2DA029475
                Funded by: funder-id http://dx.doi.org/10.13039/100000931, Annie E. Casey Foundation;
                Award ID: None
                Award Recipient :
                Data collection and sharing for this project was funded by the Pediatric Imaging, Neurocognition, and Genetics Study (PING) (National Institutes of Health Grant RC2DA029475). PING is funded by the National Institute on Drug Abuse and the Eunice Kennedy Shriver National Institute of Child Health & Human Development. PING data are disseminated by the PING Coordinating Center at the Center for Human Development, University of California, San Diego. This work was also made possible by funding from the Annie E. Casey Foundation; Teachers College, Columbia University; a National Institute of Mental Health (NIMH) training grant (T32MH13043); and the Brazilian National Council for the Improvement of Higher Education (CAPES).
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                The data used in the study are publicly-available through the Pediatric Imaging, Neurocognition, and Genetics (PING) study: http://pingstudy.ucsd.edu/.

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