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Rare genetic variants that confer large effects on neurodevelopment and behavioral phenotypes can reveal novel gene-brain-behavior relationships relevant to autism. Copy number variation at the 22q11.2 locus offer one compelling example, as both the 22q11.2 deletion (22qDel) and duplication (22qDup) confer increased likelihood of autism spectrum disorders (ASD) and cognitive deficits, but only 22qDel confers increased psychosis risk. Here, we used the Penn Computerized Neurocognitive Battery (Penn-CNB) to characterized neurocognitive profiles of 126 individuals: 55 22qDel carriers (MAge=19.2 years, 49.1% male), 30 22qDup carriers (MAge=17.3 years, 53.3 % male), and 41 typically developing (TD) subjects (MAge=17.3 years, 39.0 % male). We performed linear mixed models to assess group differences in overall neurocognitive profiles, domain scores, and individual test scores. We found all three groups exhibited distinct overall neurocognitive profiles. 22qDel and 22qDup carriers showed significant accuracy deficits across all domains relative to controls (Episodic Memory, Executive Function, Complex Cognition, Social Cognition, and Sensorimotor Speed), with 22qDel carriers exhibiting more severe accuracy deficits, particularly in Episodic Memory. However, 22qDup carriers generally showed greater slowing than 22qDel carriers. Notably, slower social cognition speed was uniquely associated with increased global psychopathology and poorer psychosocial functioning in 22qDup. Compared to TD, 22q11.2 CNV carriers failed to show age-associated improvements in multiple cognitive domains. Exploratory analyses revealed 22q11.2 CNV carriers with ASD exhibited differential neurocognitive profiles, based on 22q11.2 copy number. These results suggest that there are distinct neurocognitive profiles associated with either a loss or gain of genomic material at the 22q11.2 locus.

Social-isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a GWAS is carried out to identify genetic variants which contribute to Social-isolation behaviors in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP heritability estimate. Using the Social-isolation GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with friendship quality. At age 18, friendship scores were associated with the Social-isolation PRS, demonstrating that the genetic factors are able to predict related social traits. LD score regression using the GWAS demonstrated genetic correlation with autism spectrum disorder, schizophrenia, and major depressive disorder. However, no evidence of causality was found using a conservative Mendelian randomization approach other than that of autism spectrum disorder on Social-isolation. Our results show that Social-isolation has a small heritable component which may drive those behaviors which is associated genetically with other social traits such as friendship satisfaction as well as psychiatric disorders.

ObjectiveSchizophrenia is associated with widespread brain-morphological alterations, believed to be shaped by the underlying connectome architecture. This study tests whether large-scale structural reorganization in schizophrenia relates to normative network architecture, in particular regional centrality/hubness and connectivity patterns. We examine network effects in schizophrenia across different disease stages, and transdiagnostically explore consistency of such relationships in patients with bipolar and major depressive disorder.MethodsWe studied anatomical MRI scans from 2,439 adults with schizophrenia and 2,867 healthy controls from 26 ENIGMA sites. Case-control patterns of structural alterations were evaluated against two network susceptibility models: 1) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; 2) epicenter models, which identify regions whose typical connectivity profile most closely resembles the disease-related morphological alteration patterns. Both susceptibility models were tested across schizophrenia disease stages and compared to meta-analytic bipolar and major depressive disorder case-control maps.ResultsIn schizophrenia, regional gray matter reductions co-localized with interconnected hubs, in both the functional (r=0.58, pspinspin=0.01). Epicenters were identified in temporo-paralimbic regions, extending to frontal areas. We found unique epicenters for first-episode and early stages, and a shift from occipital to temporal-frontal epicenters in chronic stages. Transdiagnostic comparisons revealed shared epicenters in schizophrenia and bipolar, but not major depressive disorders.ConclusionsCortical reorganization over the course of schizophrenia closely reflects brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters. The observed overlapping epicenters for schizophrenia and bipolar disorder furthermore suggest shared pathophysiological processes within the schizophrenia-bipolar-spectrum.

SummaryMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

Autism spectrum disorder (ASD) is associated with high structural heterogeneity in magnetic resonance imaging (MRI). This work uncovers three neuroanatomical dimensions of ASD (N = 307) using machine learning methods and constructs their characteristic MRI signatures. The presence of these signatures, along with their clinical profiles and genetic architectures, are investigated in the general population. High expression of the first dimension (A1, “aging-related”) is associated with globally reduced brain volume, cognitive dysfunction, and aging-related genetic variants. The second dimension (A2, “schizophrenia-like”) is characterized by enlarged subcortical volume, antipsychotic medication use, and partially overlapping genetic underpinnings to schizophrenia. The third dimension (A3, “classical ASD”) is distinguished by enlarged cortical volume, high non-verbal cognitive performance, and genes and biological pathways implicating brain development and abnormal apoptosis. Thus, we propose a three-dimensional endophenotypic representation to construe the heterogeneity in ASD, which can support precision medicine and the discovery of the biological mechanisms of ASD.

Mechanisms underpinning neurotypical age-related variations in cortical thickness in the human brain remain insufficiently specified. Here we used cell-specific marker genes, followed by gene ontology and enrichment analyses, to quantify the association between gene-expression levels and inter-regional age-related variations in neurotypical cortical thinning using multicohort neuroimaging data from 14,248 individuals ages 4-89 years. We found that early-life (60 years) were associated with distinct patterns of association between inter-regional profiles of cortical thickness and expression profiles of markers genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated each of the three life-stages was associated with different biological processes and cellular components; these related to synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights on age-related cortical thinning during typical development and ageing.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to structural covariance patterns across brain regions and individuals. We present a mega-analysis of structural covariance with magnetic resonance imaging of 50,699 healthy and diseased individuals (12 studies, 130 sites, and 12 countries) over their lifespan (ages 5 through 97). Patterns of structural covariance (PSC) were highly heritable (0.05< h2 -log10[p-value] > 8.8): 1245 previously unreported, and 69% of them independently replicated (-log10[p-value] = 4.5). Associations revealed an imaging phenotypic landscape between 2003 PSCs and 49 clinical and cognitive traits at multiple scales. We constructed machine learning-derived individualized imaging signatures for various disease diagnoses using PSC features at multiple scales, suggesting that disease effects on the brain were better manifested in a multi-scale continuum than on any single scale. Experimental results were integrated into the Multi-scale Structural Imaging Covariance (MuSIC) atlas and made publicly accessible through the BRIDGEPORT web portal (https://www.cbica.upenn.edu/bridgeport/). Our results reveal strong associations between brain structural covariance, genetics, and clinical phenotypes, supporting that PSCs can serve as an endophenotypic anatomic dictionary in future research.