Publications by authors named "Derrek P Hibar"

90 Publications

A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies.

Neuropathol Appl Neurobiol 2022 Feb 5;48(1):e12758. Epub 2021 Sep 5.

Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia" of Catanzaro, Catanzaro, Italy.

Aims: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis.

Methods: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy.

Results: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia.

Conclusions: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.
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http://dx.doi.org/10.1111/nan.12758DOI Listing
February 2022

1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Transl Psychiatry 2021 03 22;11(1):182. Epub 2021 Mar 22.

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
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http://dx.doi.org/10.1038/s41398-021-01213-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985307PMC
March 2021

Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs.

Hum Brain Mapp 2022 Jan 21;43(1):300-328. Epub 2021 Feb 21.

Center for Neuroimaging, Genetics and Genomics, School of Psychology, NUI Galway, Galway, Ireland.

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.
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http://dx.doi.org/10.1002/hbm.25354DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675420PMC
January 2022

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Hum Brain Mapp 2022 Jan 17;43(1):431-451. Epub 2021 Feb 17.

Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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http://dx.doi.org/10.1002/hbm.25364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675431PMC
January 2022

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Hum Brain Mapp 2022 Jan 11;43(1):452-469. Epub 2021 Feb 11.

Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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http://dx.doi.org/10.1002/hbm.25320DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675429PMC
January 2022

Ten years of enhancing neuro-imaging genetics through meta-analysis: An overview from the ENIGMA Genetics Working Group.

Hum Brain Mapp 2022 Jan 10;43(1):292-299. Epub 2020 Dec 10.

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA.

Here we review the motivation for creating the enhancing neuroimaging genetics through meta-analysis (ENIGMA) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings, and future directions of the genetics working group. A major goal of the working group is tackling the reproducibility crisis affecting "candidate gene" and genome-wide association analyses in neuroimaging. To address this, we developed harmonized analytic methods, and support their use in coordinated analyses across sites worldwide, which also makes it possible to understand heterogeneity in results across sites. These efforts have resulted in the identification of hundreds of common genomic loci robustly associated with brain structure. We have found both pleiotropic and specific genetic effects associated with brain structures, as well as genetic correlations with psychiatric and neurological diseases.
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http://dx.doi.org/10.1002/hbm.25311DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675405PMC
January 2022

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area.

Cereb Cortex 2021 03;31(4):1873-1887

Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.

Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000-3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure.
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http://dx.doi.org/10.1093/cercor/bhaa327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7945014PMC
March 2021

Analysis of Spatial Organization of Suppressive Myeloid Cells and Effector T Cells in Colorectal Cancer-A Potential Tool for Discovering Prognostic Biomarkers in Clinical Research.

Front Immunol 2020 29;11:550250. Epub 2020 Oct 29.

Early Biomarker Development Oncology, pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany.

The development and progression of solid tumors such as colorectal cancer (CRC) are known to be affected by the immune system and cell types such as T cells, natural killer (NK) cells, and natural killer T (NKT) cells are emerging as interesting targets for immunotherapy and clinical biomarker research. In addition, CD3 and CD8 T cell distribution in tumors has shown positive prognostic value in stage I-III CRC. Recent developments in digital computational pathology support not only classical cell density based tumor characterization, but also a more comprehensive analysis of the spatial cell organization in the tumor immune microenvironment (TiME). Leveraging that methodology in the current study, we tried to address the question of how the distribution of myeloid derived suppressor cells in TiME of primary CRC affects the function and location of cytotoxic T cells. We applied multicolored immunohistochemistry to identify monocytic (CD11bCD14) and granulocytic (CD11bCD15) myeloid cell populations together with proliferating and non-proliferating cytotoxic T cells (CD8Ki67). Through automated object detection and image registration using HALO software (IndicaLabs), we applied dedicated spatial statistics to measure the extent of overlap between the areas occupied by myeloid and T cells. With this approach, we observed distinct spatial organizational patterns of immune cells in tumors obtained from 74 treatment-naive CRC patients. Detailed analysis of inter-cell distances and myeloid-T cell spatial overlap combined with integrated gene expression data allowed to stratify patients irrespective of their mismatch repair (MMR) status or consensus molecular subgroups (CMS) classification. In addition, generation of cell distance-derived gene signatures and their mapping to the TCGA data set revealed associations between spatial immune cell distribution in TiME and certain subsets of CD8 and CD4 T cells. The presented study sheds a new light on myeloid and T cell interactions in TiME in CRC patients. Our results show that CRC tumors present distinct distribution patterns of not only T effector cells but also tumor resident myeloid cells, thus stressing the necessity of more comprehensive characterization of TiME in order to better predict cancer prognosis. This research emphasizes the importance of a multimodal approach by combining computational pathology with its detailed spatial statistics and gene expression profiling. Finally, our study presents a novel approach to cancer patients' characterization that can potentially be used to develop new immunotherapy strategies, not based on classical biomarkers related to CRC biology.
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http://dx.doi.org/10.3389/fimmu.2020.550250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658632PMC
May 2021

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Nat Commun 2020 09 22;11(1):4796. Epub 2020 Sep 22.

Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
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http://dx.doi.org/10.1038/s41467-020-18367-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508833PMC
September 2020

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

JAMA Psychiatry 2021 Jan;78(1):47-63

Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, the Netherlands.

Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.

Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.

Design, Setting, And Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244.

Main Outcomes And Measures: Interregional profiles of group difference in cortical thickness between cases and controls.

Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders.

Conclusions And Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.2694DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450410PMC
January 2021

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

Hum Brain Mapp 2022 Jan 29;43(1):56-82. Epub 2020 Jul 29.

Division of Mental Health and Addicition, Oslo University Hospital, Oslo, Norway.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.
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http://dx.doi.org/10.1002/hbm.25098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675426PMC
January 2022

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

Transl Psychiatry 2020 03 20;10(1):100. Epub 2020 Mar 20.

Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
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http://dx.doi.org/10.1038/s41398-020-0705-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083923PMC
March 2020

The genetic architecture of the human cerebral cortex.

Science 2020 03;367(6484)

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1126/science.aay6690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295264PMC
March 2020

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain 2020 02;143(2):684-700

Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.
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http://dx.doi.org/10.1093/brain/awaa001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7009583PMC
February 2020

Association of Copy Number Variation of the 15q11.2 BP1-BP2 Region With Cortical and Subcortical Morphology and Cognition.

JAMA Psychiatry 2020 04;77(4):420-430

Department of Biological Psychology and Netherlands Twin Register, VU University Amsterdam, Amsterdam, the Netherlands.

Importance: Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities.

Objective: To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance.

Design, Setting, And Participants: In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019.

Main Outcomes And Measures: The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort.

Results: Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = -0.41; SE, 0.08; P = 4.9 × 10-8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10-7), and a smaller nucleus accumbens (Cohen d = -0.27; SE, 0.07; P = 7.3 × 10-5). There was also a significant negative dose response on cortical thickness (β = -0.24; SE, 0.05; P = 6.8 × 10-7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks.

Conclusions And Relevance: These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders.
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http://dx.doi.org/10.1001/jamapsychiatry.2019.3779DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822096PMC
April 2020

Genetic architecture of subcortical brain structures in 38,851 individuals.

Nat Genet 2019 11 21;51(11):1624-1636. Epub 2019 Oct 21.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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http://dx.doi.org/10.1038/s41588-019-0511-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055269PMC
November 2019

A genome-wide association study identifies genetic loci associated with specific lobar brain volumes.

Commun Biol 2019 2;2:285. Epub 2019 Aug 2.

17Department of Biomedical Data Sciences, Statistical Genetics, Leiden University Medical Center, Leiden, 2333ZA the Netherlands.

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications ( and ), or close to genes causing Mendelian brain-related diseases ( and ). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.
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http://dx.doi.org/10.1038/s42003-019-0537-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677735PMC
April 2020

Multi-Site Meta-Analysis of Morphometry.

IEEE/ACM Trans Comput Biol Bioinform 2019 Sep-Oct;16(5):1508-1514. Epub 2019 May 23.

Genome-wide association studies (GWAS) link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses (e.g., tensor-based morphometry; TBM) may help better localize statistical effects. This can lead to $10^{13}$1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.
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http://dx.doi.org/10.1109/TCBB.2019.2914905DOI Listing
March 2020

Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort.

Psychol Med 2020 04 14;50(6):1020-1031. Epub 2019 May 14.

Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany.

Background: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age.

Methods: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer.

Results: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions.

Conclusions: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.
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http://dx.doi.org/10.1017/S003329171900093XDOI Listing
April 2020

Concordance of genetic variation that increases risk for tourette syndrome and that influences its underlying neurocircuitry.

Transl Psychiatry 2019 03 22;9(1):120. Epub 2019 Mar 22.

Department of Psychiatry and MRC Unit on Risk & Resilience, University of Cape Town, Cape Town, South Africa.

There have been considerable recent advances in understanding the genetic architecture of Tourette syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variation that increases risk for TS-and its main symptom dimensions-influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS. We obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4644 cases and 8695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP effect concordance analysis (SECA) was used to examine genetic pleiotropy-the same SNP affecting two traits-and concordance-the agreement in single nucelotide polymorphism (SNP) effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage disequilibrium score regression (LDSR) was used as validation of the SECA method. SECA revealed significant pleiotropy between TS and putamen (p = 2 × 10) and caudate (p = 4 × 10) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p = 1 × 10). LDSR lent additional support for the association between TS and thalamus volume (p = 5.85 × 10). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamus volume (p = 1 × 10), as well as concordance between symmetry behaviour and greater putamen volume (p = 7 × 10). Conditional FDR analysis further revealed novel variants significantly associated with TS (p < 8 × 10) when conditioning on intracranial (rs2708146, q = 0.046; and rs72853320, q = 0.035) and hippocampal (rs1922786, q = 0.001) volumes, respectively. These data indicate concordance for genetic variation involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.
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http://dx.doi.org/10.1038/s41398-019-0452-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430767PMC
March 2019

Genetic Markers of ADHD-Related Variations in Intracranial Volume.

Am J Psychiatry 2019 03;176(3):228-238

The Department of Human Genetics, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands (Klein, Bralten, Roth Mota, Arias-Vasquez, Franke); University Medical Center Utrecht, UMC Utrecht Brain Center, Department of Psychiatry, Utrecht, the Netherlands (Klein); the Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston (Walters, Neale); Program in Medical and Population Genetics (Walters) and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Mass (Walters, Neale); the Department of Biomedicine and the Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark (Demontis, Mattheisen, Børglum); the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Denmark (Demontis, Børglum); the Department of Genetics and the Neuroscience Center, University of North Carolina, Chapel Hill (Stein); the Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles (Hibar, Thompson); the Department of Epidemiology and the Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands (Adams); the Department of Psychiatry and the Research Institute, Hospital for Sick Children, University of Toronto, Toronto (Schachar); the Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Sonuga-Barke); the Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Wuerzburg, Germany (Mattheisen); the Department of Clinical Neuroscience, Center for Psychiatric Research, Karolinska Institute, Stockholm (Mattheisen); Stockholm Health Care Services, Stockholm County Council, Stockholm (Mattheisen); the Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles (Thompson); the Quantitative Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia (Medland); the Department of Psychiatry and the Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, N.Y. (Faraone); the K.G. Jebsen Center for Neuropsychiatric Disorders, University of Bergen, Bergen, Norway (Faraone); and the Department of Psychiatry, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands (Roth Mota, Arias-Vasquez, Franke).

Objective: Attention deficit hyperactivity disorder (ADHD) is a common and highly heritable neurodevelopmental disorder with a complex pathophysiology. Intracranial volume (ICV) and volumes of the nucleus accumbens, amygdala, caudate nucleus, hippocampus, and putamen are smaller in people with ADHD compared with healthy individuals. The authors investigated the overlap between common genetic variation associated with ADHD risk and these brain volume measures to identify underlying biological processes contributing to the disorder.

Methods: The authors combined genome-wide association results from the largest available studies of ADHD (N=55,374) and brain volumes (N=11,221-24,704), using a set of complementary methods to investigate overlap at the level of global common variant genetic architecture and at the single variant level.

Results: Analyses revealed a significant negative genetic correlation between ADHD and ICV (r=-0.22). Meta-analysis of single variants revealed two significant loci of interest associated with both ADHD risk and ICV; four additional loci were identified for ADHD and volumes of the amygdala, caudate nucleus, and putamen. Exploratory gene-based and gene-set analyses in the ADHD-ICV meta-analytic data showed association with variation in neurite outgrowth-related genes.

Conclusions: This is the first genome-wide study to show significant genetic overlap between brain volume measures and ADHD, both on the global and the single variant level. Variants linked to smaller ICV were associated with increased ADHD risk. These findings can help us develop new hypotheses about biological mechanisms by which brain structure alterations may be involved in ADHD disease etiology.
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http://dx.doi.org/10.1176/appi.ajp.2018.18020149DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780894PMC
March 2019

Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Mol Psychiatry 2020 Mar;25(3):692-695

Department of Psychiatry and Mental Health, Anzio Road, 7925, Cape Town, South Africa.

Prior to and following the publication of this article the authors noted that the complete list of authors was not included in the main article and was only present in Supplementary Table 1. The author list in the original article has now been updated to include all authors, and Supplementary Table 1 has been removed. All other supplementary files have now been updated accordingly. Furthermore, in Table 1 of this Article, the replication cohort for the row Close relative in data set, n (%) was incorrect. All values have now been corrected to 0(0%). The publishers would like to apologise for this error and the inconvenience it may have caused.
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http://dx.doi.org/10.1038/s41380-019-0358-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608381PMC
March 2020

Concordance of genetic variation that increases risk for anxiety disorders and posttraumatic stress disorders and that influences their underlying neurocircuitry.

J Affect Disord 2019 02 13;245:885-896. Epub 2018 Nov 13.

Department of Psychiatry and MRC Unit on Risk & Resilience. University of Cape Town. Cape Town. South Africa; Groote Schuur Hospital. Cape Town. South Africa. Electronic address:

Background: There have been considerable recent advances in understanding the genetic architecture of anxiety disorders and posttraumatic stress disorder (PTSD), as well as the underlying neurocircuitry of these disorders. However, there is little work on the concordance of genetic variations that increase risk for these conditions, and that influence subcortical brain structures. We undertook a genome-wide investigation of the overlap between the genetic influences from single nucleotide polymorphisms (SNPs) on volumes of subcortical brain structures and genetic risk for anxiety disorders and PTSD.

Method: We obtained summary statistics of genome-wide association studies (GWAS) of anxiety disorders (N= 7016, N= 14,745), PTSD (European sample; N= 2424, N= 7113) and of subcortical brain structures (N = 13,171). SNP Effect Concordance Analysis (SECA) and Linkage Disequilibrium (LD) Score Regression were used to examine genetic pleiotropy, concordance, and genome-wide correlations respectively. SECAs conditional false discovery was used to identify specific risk variants associated with anxiety disorders or PTSD when conditioning on brain related traits.

Results: For anxiety disorders, we found evidence of significant concordance between increased anxiety risk variants and variants associated with smaller amygdala volume. Further, by conditioning on brain volume GWAS, we identified novel variants that associate with smaller brain volumes and increase risk for disorders: rs56242606 was found to increase risk for anxiety disorders, while two variants (rs6470292 and rs683250) increase risk for PTSD, when conditioning on the GWAS of putamen volume.

Limitations: Despite using the largest available GWAS summary statistics, the analyses were limited by sample size.

Conclusions: These preliminary data indicate that there is genome wide concordance between genetic risk factors for anxiety disorders and those for smaller amygdala volume, which is consistent with research that supports the involvement of the amygdala in anxiety disorders. It is notable that a genetic variant that contributes to both reduced putamen volume and PTSD plays a key role in the glutamatergic system. Further work with GWAS summary statistics from larger samples, and a more extensive look at the genetics underlying brain circuits, is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.
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http://dx.doi.org/10.1016/j.jad.2018.11.082DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6519055PMC
February 2019

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group.

Front Neuroinform 2018 8;12:102. Epub 2019 Jan 8.

Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.
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http://dx.doi.org/10.3389/fninf.2018.00102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331928PMC
January 2019

Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects.

Am J Psychiatry 2019 02 19;176(2):119-128. Epub 2018 Oct 19.

From the Department of Psychiatry and the Department of Mathematics and Statistics, University of Vermont, Burlington; Orygen, the National Centre of Excellence in Youth Mental Health, Parkville, Australia; the Department of Psychology, University of Oregon, Eugene; the Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia; the Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York; the Department of Psychiatry and Psychology, University of Barcelona, Barcelona, Spain; the Department of Psychiatry, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands; the Department of Psychiatry, Yale University School of Medicine, New Haven, Conn; the Department of Psychiatry and the MRC Unit on Anxiety and Stress Disorders, University of Cape Town, Cape Town, South Africa; the Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore; the Monash Institute of Cognitive and Clinical Neurosciences and the School of Psychological Sciences, Monash University, Melbourne, Australia; the Departments of Developmental and Experimental Psychology, Utrecht University, Utrecht, the Netherlands; the Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; the Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London; the Department of Psychiatry, Oregon Health and Science University, Portland; the Department of Neuroscience and the Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, N.Y.; the Department of Psychiatry, University of Amsterdam, Amsterdam; the Amsterdam Institute for Addiction Research and Arkin Mental Health Care, Amsterdam; the Department of Psychiatry, University of Michigan, Ann Arbor; the Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia; the Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder; the Department of Psychiatry, Washington University School of Medicine, St. Louis; the David Geffen School of Medicine, University of California at Los Angeles, Los Angeles; the School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia; the Department of Psychological Sciences, University of Liverpool, Liverpool, U.K.; the Behavioral Science Institute, Radboud University, Nijmegen, the Netherlands; the Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego; the Clinical Neuroimaging Research Core, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Md.; the VA San Diego Healthcare System and the Department of Psychiatry, University of California San Diego, La Jolla; the Laureate Institute for Brain Research, Tulsa, Okla.; the Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia; the Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; the Institute of Psychology, Cognitive Psychology Unit, and the Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; the School of Psychology and the Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia; the Department of Psychiatry, University of California San Diego, La Jolla; the Department of Psychiatry, VU University Medical Center, Amsterdam; the Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia; the QIMR Berghofer Medical Research Institute, Brisbane, Australia; the Department of Psychiatry, University of Utah School of Medicine, Salt Lake City; the Imaging Genetics Center, Department of Neurology, Keck School of Medicine, University of Southern California, Marina del Rey; and the Department of Psychiatry, University of Montreal, CHU Sainte-Justine Hospital, Montreal.

Objective: Although lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes.

Method: Brain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings.

Results: Lower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects.

Conclusions: The results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.
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http://dx.doi.org/10.1176/appi.ajp.2018.17040415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427822PMC
February 2019

Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Mol Psychiatry 2020 03 3;25(3):584-602. Epub 2018 Oct 3.

Department of Psychiatry and Mental Health, Anzio Road, 7925, Cape Town, South Africa.

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10, 1.7 × 10, 3.5 × 10 and 1.0 × 10, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes.
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http://dx.doi.org/10.1038/s41380-018-0118-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042770PMC
March 2020

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Mol Psychiatry 2020 09 31;25(9):2130-2143. Epub 2018 Aug 31.

Department of Psychiatry, Yale University, New Haven, CT, USA.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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http://dx.doi.org/10.1038/s41380-018-0228-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473838PMC
September 2020

Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium.

Biol Psychiatry 2018 11 14;84(9):644-654. Epub 2018 May 14.

Division of Mental Health and Addiction, NORMENT, K.G. Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway.

Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.

Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide.

Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset.

Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.
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http://dx.doi.org/10.1016/j.biopsych.2018.04.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6177304PMC
November 2018

Significant concordance of genetic variation that increases both the risk for obsessive-compulsive disorder and the volumes of the nucleus accumbens and putamen.

Br J Psychiatry 2018 07;213(1):430-436

Imaging Genetics Center,Keck School of Medicine of the University of Southern California,Marina del Rey,USA.

Background: Many studies have identified changes in the brain associated with obsessive-compulsive disorder (OCD), but few have examined the relationship between genetic determinants of OCD and brain variation.AimsWe present the first genome-wide investigation of overlapping genetic risk for OCD and genetic influences on subcortical brain structures.

Method: Using single nucleotide polymorphism effect concordance analysis, we measured genetic overlap between the first genome-wide association study (GWAS) of OCD (1465 participants with OCD, 5557 controls) and recent GWASs of eight subcortical brain volumes (13 171 participants).

Results: We found evidence of significant positive concordance between OCD risk variants and variants associated with greater nucleus accumbens and putamen volumes. When conditioning OCD risk variants on brain volume, variants influencing putamen, amygdala and thalamus volumes were associated with risk for OCD.

Conclusions: These results are consistent with current OCD neurocircuitry models. Further evidence will clarify the relationship between putamen volume and OCD risk, and the roles of the detected variants in this disorder.Declaration of interestThe authors have declared that no competing interests exist.
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http://dx.doi.org/10.1192/bjp.2018.62DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6053271PMC
July 2018
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