This report details progress towards our overarching aim of understanding the interplay between behavioral, social, genetic and brain factors in development. In 2018-2019, our group has focused on (1) examining the social factors that accompany remission from ADHD; (2) Mapping associations between polygenic risks for ADHD, its core symptoms, cognition, and the brain.; (3) using multicohort data to identify convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. 1. The social and familial factors that affect symptom change in childhood ADHD (Sharp et al, Social Science and Medicine, 2019) Many children do not simply outgrow ADHD. The disorder often persists and affects around one in 40 adults, presenting a major public health challenge. In this study we parsed the interplay between neighborhood and familial factors on age-related change in symptoms of inattention and hyperactivity-impulsivity. A cohort of 190 children (96 with ADHD) had a range of neighborhood and familial factors ascertained and had repeated clinical assessments over an average of 2.5years at a U.S. research center. We found an association between neighborhood wealth, but not the built environment, and the annual rate of change of inattentive but not hyperactive-impulsive symptoms. We further asked if familial processes explain (mediate), modify (moderate), or act alongside this effect of neighborhood wealth on the change in a child's symptoms of inattention with age. We found evidence for moderation. Specifically, several family level variables parental economic/education status and degree family conflict and order moderated the effects of neighborhood wealth on the change in a child's inattentive symptoms. Children living in relatively affluent neighborhoods showed improvement with age in inattention, largely independent of variation in a wide range of familial factors. By contrast, children living in less affluent neighborhoods showed clinical deterioration only if the family had high levels of conflict or if the parents were of lower economic/educational status. Such work might help identify children whose familial and neighborhood contexts place them at risk of having ADHD symptoms persist or increase with age. 2. Mapping associations between polygenic risks for ADHD, its core symptoms, cognition, and the brain (Sudre et al , Molecular Psychiatry, 2019). There are now large-scale data on which common genetic variants confer risk for ADHD. Here, we use mediation analyses to explore how cognitive and neural features might explain the association between common variant (polygenic) risk for ADHD and its core symptoms. In total, 544 participants participated (mean 21 years, 212 with ADHD), most with cognitive assessments, neuroanatomic imaging, and imaging of white matter tract microstructure. We found that polygenic risk for ADHD was associated with symptoms of hyperactivityimpulsivity but not inattention. This association was mediated across multiple PRS thresholds by white matter microstructure, specifically by axial diffusivity of the right corona radiata, by thickness of the left dorsomedial prefrontal and the area of the right lateral temporal cortex. In addition, modest serial mediation was found, mapping a pathway from polygenic risk, to white matter microstructure of the anterior corona radiata, then cognition (working memory, focused attention), and finally to hyperactivityimpulsivity. These mediation pathways were diagnostically specific and were not found for polygenic risk for ASD or schizophrenia. In conclusion, using a deeply phenotyped cohort, we delineate a pathway from polygenic risk for ADHD to hyperactiveimpulsive symptoms through white matter microstructure, cortical anatomy, and cognition. 3. Collaborative studies of the brain in ADHD (Park et al 2018, Hoogman et al 2019) There is evidence suggesting neuropsychiatric disorders share genomic, cognitive and clinical features. Here, we ask if autism-spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD) and schizophrenia share neuroanatomical variations. In a collaborative study, we asked if autism-spectrum disorders (ASD), ADHD and schizophrenia share neuroanatomical variations (Park et al 2018). We used measures of cortical anatomy to estimate the spatial overlap of neuroanatomical variation using traditional univariate methods and developed a novel methodology to determine whether cortical deficits specifically target or are enriched within functional resting-state networks. We found cortical anomalies were preferentially enriched across functional networks rather than clustering spatially. Specifically, cortical thickness showed significant enrichment between patients with Autism Spectrum Disorders (ASD) and those with ADHD in the default mode network, between patients with ASD and those with schizophrenia in the frontoparietal and limbic networks, and between patients with ADHD and those with schizophrenia in the ventral attention network. These findings suggest that common deficits across neuropsychiatric disorders cannot simply be characterized as arising out of local changes in cortical grey matter, but rather as entities of both local and systemic alterations targeting brain networks. Our data have also contributed to several international consortia. For example. we participated in a study that mapped the cortical changes associated with ADHD across thousands of children (Hoogman et al 2019, American Journal of Psychiatry).

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Park, Min Tae M; Raznahan, Armin; Shaw, Philip et al. (2018) Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. J Psychiatry Neurosci 43:170094
White, Tonya; Jansen, Philip R; Muetzel, Ryan L et al. (2018) Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface-based reconstruction. Hum Brain Mapp 39:1218-1231
Kong, Xiang-Zhen; Mathias, Samuel R; Guadalupe, Tulio et al. (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A 115:E5154-E5163
Chen, Y-C; Sudre, G; Sharp, W et al. (2018) Neuroanatomic, epigenetic and genetic differences in monozygotic twins discordant for attention deficit hyperactivity disorder. Mol Psychiatry 23:683-690
Shaw, Philip; Ishii-Takahashi, Ayaka; Park, Min Tae et al. (2018) A multicohort, longitudinal study of cerebellar development in attention deficit hyperactivity disorder. J Child Psychol Psychiatry 59:1114-1123
Szekely, Eszter; Sudre, Gustavo P; Sharp, Wendy et al. (2017) Defining the Neural Substrate of the Adult Outcome of Childhood ADHD: A Multimodal Neuroimaging Study of Response Inhibition. Am J Psychiatry 174:867-876
Shaw, Philip; Ahn, Kwangmi; Rapoport, Judith L (2017) Good News for Screening for Adult Attention-Deficit/Hyperactivity Disorder. JAMA Psychiatry 74:527
Sudre, Gustavo; Choudhuri, Saadia; Szekely, Eszter et al. (2017) Estimating the Heritability of Structural and Functional Brain Connectivity in Families Affected by Attention-Deficit/Hyperactivity Disorder. JAMA Psychiatry 74:76-84
Hoogman, Martine; Bralten, Janita; Hibar, Derrek P et al. (2017) Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4:310-319
Shaw, Philip (2016) Quantifying the Benefits and Risks of Methylphenidate as Treatment for Childhood Attention-Deficit/Hyperactivity Disorder. JAMA 315:1953-5

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