Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).

Cognitive deficits are common across many neurodevelopmental and psychiatric conditions, including those studied in the current set of PGC-CNV papers. How changes in regional gene expression across the cerebral cortex influence cognitive ability remains unknown. Population variation in gene dosage-which significantly impacts gene expression-represents a unique paradigm to address this question. We developed a cerebral-cortex gene-set burden analysis (CC-GSBA) to associate a trait with genomic deletions and duplications that disrupt genes with similar expression profiles across 180 cortical regions. We performed CC-GSBA across 180 cortical regions to test associations with cognitive ability in 260,000 individuals from general population cohorts. Most cortical gene sets were associated with a decrease in cognitive ability when deleted or duplicated, and this novel approach revealed opposing cortical patterns for the effect sizes of deletions and duplications. These cortical patterns of effect sizes followed the cortical gradient previously characterized at the molecular, cellular, and functional levels. We show that genes with preferential expression in sensorimotor regions demonstrated the largest effect on cognition when deleted. At the opposing end of the cortical gradient, genes with preferential expression in multimodal association regions affected cognition the most when duplicated. These two gene dosage cortical patterns could not be explained by particular cell types, developmental epochs, or genetic constraints, highlighting the fact that the macroscopic network organization of the cerebral cortex is key to understanding the effects of gene dosage on cognitive traits.

The development and organization of the human cortex are influenced by both common and rare variants. While genome-wide association studies (GWAS) have identified common variants that influence global and regional cortical phenotypes derived using magnetic resonance imaging (MRI), the rare variant genetic architecture of the human cortex has not yet been characterized, and the relationship between common and rare variant architectures remains unknown. We hypothesize that common and rare variants exert similar influences on cortical phenotypes. Our study aims to map the effects of rare variants on twelve MRI-derived phenotypes, measured globally and regionally.

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia’s alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

BackgroundAlterations in neurochemical levels are potential biomarkers of affective disorders and offer a window into illness etiology. Much of the research done to date limits focus to few metabolites and relies on dichotomous diagnoses. Here, using 1H magnetic resonance spectroscopy (MRS), we generate profiles of association between depressive and manic symptom rating scales and numerous metabolites.MethodsMRS data were collected in 30 individuals (7 = major depressive disorder; 5 bipolar disorder and 18 unaffected individuals) in the right anterior cingulate cortex (ACC), insula and hippocampus. All participants were administered mania and depression symptom scales.ResultsSmall to medium relationships were observed between total choline (tCho), N-acetylaspartate (NAA), myo-inositol (mIns) and depressive and manic symptoms. Associations between NAA and mania (r = -0.47, p = 8.88×10−03) in the hippocampus, and tCho (r = -0.48, p = 8.79×10−03) and NAA (r = -0.44, p = 0.01) and mania in the ACC withstood multiple testing correction.ConclusionsElevated levels of choline-containing compounds (tCho) and reduced NAA measured in the ACC and hippocampus are associated with mania indexed dimensionally. These metabolites may represent potential in vivo biomarkers for bipolar disorder symptomatology that warrant follow-up.

Worldwide efforts have led to large and inclusive imaging genetics datasets enabling examination of the contribution of genetic and environmental factors to development, clinical course and treatment effectiveness in psychiatric disorders. These datasets combine high-resolution neuroimaging and genetic data in large and inclusive samples. Classical genetic analyses can help to parse the variance in disorder-related brain patterns into additive genetic, specific SNP, household and environmental causes. Performing these inquiries at full imaging and genetic resolution is a formidable computational task where the computational complexity of classical genetic analyses rises as a square or cube of the sample size. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees derived in samples such as UKBB, HCP and ABCD. These approaches linearize computational effort while maintaining approximation fidelity (r∼0.95) with VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches can help to tract the nature vs. nurture interaction in the development of major depressive disorder and psychosis in the longitudinal datasets such as ABCD. We also show how specific genetic risk factors for Alzheimer disease can interact with environment leading to development of brain patterns that are predictive of the risk of development of dementia.

The treatment of bipolar disorder (BD) often involves administering multiple psychotropic medications, yet little research has examined how these medications, especially when used together, affect the brain's white matter in BD. We investigate how polypharmacy and the severity of symptoms are associated with white matter connectivity in BD, in the largest neuroimaging study of its kind.

Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.

INTRODUCTION:We conducted admixture mapping and fine-mapping analyses to identify ancestry-of-origin loci influencing cognitive abilities. METHODS:We estimated the association of local ancestry intervals across the genome with five neurocognitive measures in 7140 diverse Hispanic and Latino adults (mean age 55 years). We prioritized genetic variants in associated loci and tested them for replication in four independent cohorts. RESULTS:We identified nine local ancestry-associated regions for the five neurocognitive measures. There was strong biological support for the observed associations to cognitive function at all loci and there was statistical evidence of independent replication at 4q12, 9p22.1, and 13q12.13. DISCUSSION:Our study identified multiple novel loci harboring genes implicated in cognitive functioning and dementia, and uncovered ancestry-relevant genetic variants. It adds to our understanding of the genetic architecture of cognitive function in Hispanic and Latino adults and demonstrates the power of admixture mapping to discover unique haplotypes influencing cognitive function, complementing genome-wide association studies. HIGHLIGHTS:We identified nine ancestry-of-origin chromosomal regions associated with five neurocognitive traits. In each associated region, we identified single nucleotide polymorphisms (SNPs) that explained, at least in part, the admixture signal and were tested for replication in independent samples of Black, non-Hispanic White, and Hispanic/Latino adults with the same or similar neurocognitive tests. Statistical evidence of independent replication of the prioritized SNPs was observed for three of the nine associations, at chr4q12, chr9p22.1, and chr13q12.13. At all loci, there was strong biological support for the observed associations to cognitive function and dementia, prioritizing genes such as KIT, implicated in autophagic clearance of neurotoxic proteins and on mast cell and microglial-mediated inflammation; SLC24A2, implicated in synaptic plasticity associated with learning and memory; and MTMR6, implicated in phosphoinositide lipids metabolism.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca’s area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

The relationship between the gut-brain microbiome axis has gained prominence in the study of psychiatric disorders, as it may represent a new target for their treatment. To date, the available literature suggests that the microbiota may influence the pathophysiology of several diseases, including psychosis. However, studies on the composition of the fecal microbiota and the metabolic consequences in early psychosis are scarce. The aim of this study was to determine the composition of the fecal microbiota in adolescents with early psychosis and compare it with healthy subjects. We collected fecal samples and extracted the DNA. Then we performed next generation sequencing in subjects with early psychosis and controls. Finally, we used Qiime2 to obtain the alpha and beta diversity of the samples and performed a linear discriminant analysis (LDA) effect size (LEfSe) to detect which taxa had significantly different abundances between groups. There were no differences in alpha diversity, but there were differences in beta diversity. There were changes in taxa between patients with early psychosis and controls, and changes in metabolic pathways. Changes in the composition of the microbiota and the alterations in its metabolic pathways may be one of the possible explanations for early-onset psychosis. Further study of the microbiota of these patients may provide insight into new therapies involving probiotics or prebiotics that may help reduce symptoms in the early stages of the disease.

IntroductionThe cocktail-party problem refers to the difficulty listeners face when trying to attend to relevant sounds that are mixed with irrelevant ones. Previous studies have shown that solving these problems relies on perceptual as well as cognitive processes. Previously, we showed that speech-reception thresholds (SRTs) on a cocktail-party listening task were influenced by genetic factors. Here, we estimated the degree to which these genetic factors overlapped with those influencing cognitive abilities.MethodsWe measured SRTs and hearing thresholds (HTs) in 493 listeners, who ranged in age from 18 to 91 years old. The same individuals completed a cognitive test battery comprising 18 measures of various cognitive domains. Individuals belonged to large extended pedigrees, which allowed us to use variance component models to estimate the narrow-sense heritability of each trait, followed by phenotypic and genetic correlations between pairs of traits.ResultsAll traits were heritable. The phenotypic and genetic correlations between SRTs and HTs were modest, and only the phenotypic correlation was significant. By contrast, all genetic SRT–cognition correlations were strong and significantly different from 0. For some of these genetic correlations, the hypothesis of complete pleiotropy could not be rejected.DiscussionOverall, the results suggest that there was substantial genetic overlap between SRTs and a wide range of cognitive abilities, including abilities without a major auditory or verbal component. The findings highlight the important, yet sometimes overlooked, contribution of higher-order processes to solving the cocktail-party problem, raising an important caveat for future studies aiming to identify specific genetic factors that influence cocktail-party listening.

Magnetic Resonance Imaging provides unprecedented images of the brain. Unfortunately, scanners and acquisition protocols can significantly impact MRI scans. The development of statistical methods able to reduce this variability without altering the relevant information in the scans, often coined harmonization methods, has been the topic of an increasing research effort supported by the recent growth of publicly available neuroimaging data sets and new possibilities for combining them to achieve greater statistical power. In this work, we focus on the challenges specifically raised by the harmonization of resting-state functional MRI scans. We propose to harmonize resting-state fMRI scans by reducing the impact of covariates such as scanner differences and scanning protocols on their associated functional connectomes and then propagating the changes back to the rs-fMRI time series. We use Riemannian geometric frameworks to preserve the mathematical properties of functional connectomes during their harmonization, and we demonstrate how state-of-the-art harmonization methods can be embedded within these frameworks to reduce covariates effects while preserving the relevant clinical information associated with aging or brain disorders. During our experiments, a large set of synthetic data was generated and processed to compare eighty variants of the proposed approach. The framework achieving the best harmonization was then applied to three low-dimensional data sets made of 712 sets of fMRI time series provided by the ABIDE consortium and two high-dimensional data sets obtained by processing 1527 rs-fMRI scans provided by the Human Connectome Project, the Framingham Heart Study and the Genetics of Brain Structure and Function study. These experiments established that our new framework could successfully harmonize low-dimensional connectomes and voxelwise functional time series and confirmed the need for preserving connectomes properties during their harmonization.

Autism spectrum disorder (ASD) is characterised by large clinical, genetic and biological heterogeneity. The prevalence of ASD is 2% in the general population, which makes it an important public health issue. ASD often co-occur with many comorbidities including intellectual disability, motor and language skills. Moreover, 30% of autism patients have intellectual disability (ID). Among the variants associated with ASD, copy number variants (CNVs) are the most frequently identified in the clinic. CNV are deletion or duplication of genomic regions of more than 1000 nucleotides that may involve one or many genes. ASD-associated variants also have a significant impact on ID risk. To date, no study of rare variants has been able to clearly separate the effects on ASD risk and cognitive ability. This is primarily due to a lack of research investigating the impact of CNVs or other rare variants on both of these conditions simultaneously: controls in genetic studies rarely receive a cognitive assessment. Characterizing the impact of common and specific genetic variants associated with ASD and ID is crucial to better understand the biological mechanisms involved and to improve diagnosis. The hypothesis of this study is that the genes included in CNVs can estimate the risks associated with ASD and ID separately, based on their biological characteristics and pathways. We aim to identify rare CNVs that confer ASD risk, while carefully controlling for their effects on cognition in order to identify common and specific biological pathways associated with ASD and ID. We called CNVs and harmonized measures of cognitive ability in an aggregated dataset including 3 cohorts of participants with ASD n=8,408 and 6 control cohorts n=458,407. We performed a genome wide association study of genes within CNV (CNV-GWAS) of ∼466 000 individuals. For genome-wide significant CNVs (FDR) associated with ASD, we recomputed the association study after adjusting for cognitive ability. We replicated previous associations of 33 recurrent CNVs with ASD and show that these associations remain even after adjusting for their effects on IQ. We also identified 28 new regions (3 for deletion and 25 for duplication) and corresponding genes that were not previously associated with ASD. When adjusting for cognitive ability, 3 of the new regions (1 for deletion and 2 duplications) remain significant with ASD. At the CNV level, we observed a positive correlation between effect sizes on ASD and cognitive ability. These datasets allowed us to detect rare variants with better precision than before. This unique design including controls with cognitive assessments demonstrated that CNVs deletions and duplications remained associated with ASD even after adjusting for their effects on cognitive ability. The identification of the biological pathways associated with these genes will lead to a better understanding of the common and specific features of ASD and ID.

Genome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants. We sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n = 1,672). Our analysis revealed four genome-wide significant risk loci, all of which were associated with >2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n = 314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors, CUX1 and CNOT1. Our findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies.

BACKGROUND: Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits. METHODS: Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects). RESULTS: Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 x 10(-6)). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = 20.88, p = 8.78 x 10(-6)). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease. CONCLUSIONS: Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.