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Funder: Max-Planck-Gesellschaft, Funder: Bundesministerium für Bildung und Forschung, Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (, Translational Psychiatry, 12 (1), ISSN:2158-3188

BackgroundLateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.MethodsWe studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.ResultsIn the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d= 0.19) and the pallidum (less leftward; d=-0.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.ConclusionsThe results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.

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

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker. ISSN:2158-3188

Objective: 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. Methods: 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. Results: 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. Conclusions: 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

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker., Translational Psychiatry, 10 (1), ISSN:2158-3188

ObjectiveNo diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Magnetic resonance imaging (MRI) studies have provided evidence for structural abnormalities in distinct brain regions, but effect sizes are small and have limited clinical relevance. To investigate whether individual patients can be distinguished from healthy controls, we performed multivariate analysis of structural neuroimaging data from the ENIGMA-OCD consortium.MethodWe included 46 data sets with neuroimaging and clinical data from adult (≥18 years) and pediatric (1images from 2,304 OCD patients and 2,068 healthy controls were analyzed using standardized processing to extract regional measures of cortical thickness, surface area and subcortical volume. Machine learning classification performance was tested using cross-validation, and possible effects of clinical variables were investigated by stratification.ResultsClassification performance for OCD versus controls using the complete sample with different classifiers and cross-validation strategies was poor (AUC—0.57 (standard deviation (SD)=0.02;Pcorr=0.19) to 0.62 (SD=0.03;Pcorrcorr>.99) to 0.54 (SD=0.08;Pcorr>.99)). In contrast, good classification performance (>0.8 AUC) was achieved within subgroups of patients split according to their medication status.ConclusionsParcellated structural MRI data do not enable good distinction between patients with OCD and controls. However, classifying subgroups of patients based on medication status enables good identification at the individual subject level. This underlines the need for longitudinal studies on the short- and long-term effects of medication on brain structure.

Accepted Manuscript, BACKGROUND: Lateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.METHODS: We studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.RESULTS: In the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -20.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.CONCLUSIONS: The results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD., This research was funded by the Max Planck Society (Germany). Additional funding was from the Japan Society for the Promotion of Science (KAKENHI Grant No. 18K15523 [to YA], KAKENHI Grant No. 16K04344 [to YH], KAKENHI Grant Nos. 16K19778 and 18K07608 [to TNakam], and KAKENHI Grant No. 26461762 [to AN]); the Carlos III Health Institute (Grant No. PI14/00419 [to PA], Grant No. PI040829 cofunded by European Regional Development Fund [to LL], Grant No. FI17/00294 [to IM-Z], Grant No. PI16/00950 [to JMM], and Grant Nos. CPII16/00048, PI13/01958, and PI16/00889 cofunded by European Regional Development Funds [to CS-M]); the Ontario Mental Health Foundation (Research Training Fellowship [to SHA]); Alberta Innovates Translational Health Chair in Child and Youth Mental Health (to PDA), the Ontario Brain Institute (to PDA); the National Institute of Mental Health (Grant No. K23MH104515 [to JTB], Grant No. K23-MH092397 [to BPB], Grant No. K23MH082176 [to KDF), Grant No. R21MH101441 [to RM], Grant No. R01MH081864 [to JO and JP], and Grant No. R01MH085900 [to JO and JF], Grant No. R21MH093889 [to HBS]); Fundação de Amparo à Pesquisa do Estado de São Paulo (Grant No. 2011/21357–9 [to MCB], Grant No. 2011/21357–9 [to GFB], Grant No. 2011/21357–9 [to MQH], and Grant No. 2011/21357–9 [to ECM]); the Swiss National Science Foundation (Grant No. 320030_130237 [to SB; principal investigator, Susanne Walitza]); the Hartmann Müller Foundation (Grant No. 1460 [to SB]); the David Judah Fund at the Massachusetts General Hospital (to BPB); EU FP7 Project TACTICS (Grant No. 278948 [to JB]); the National Natural Science Foundation of China (Grant No. 81560233 [to YC] and Grant No. 81371340 [to ZW]); the International OCD Foundation (Grant No. K23 MH115206 [to PG]); the Wellcome Sir Henry Dale Fellowship (Grant No. 211155/Z/18/Z [to TUH]); the Jacobs Foundation (to TUH); the Brain and Behavior Research Foundation (2018 NARSAD Young Investigator Grant No. 27023 [to TUH]); the Agency for Medical Research and Development (Grant No. JP18dm0307002 [to YH]); the Michael Smith Foundation for Health Research (to FJ-F); the Federal Ministry of Education and Research of Germany (Grant No. BMBF-01GW0724 [to NK]); the Deutsche Forschungsgemeinschaft (Grant No. KO 3744/7–1 [to KK]); the Helse Vest Health Authority (Grant Nos. 911754 and 911880 [to GK]); the Norwegian Research Council (Grant No. HELSEFORSK 243675 [to GK]); the Marató TV3 Foundation (Grant Nos. 01/2010 and 091710 [to LL]); the Agency for Management of University and Research Grants (Grant No. 2017 SGR 881 [to LL] and 2017 SGR 1247 from the Generalitat de Catalunya [to JMM]); Fundação para a Ciência e a Tecnologia (Grant No. PDE/BDE/113604/2015 from the PhD-iHES Program [to RM], Grant No. PDE/BDE/113601/2015 from the PhD-iHES Program [to PSM]); the Japanese Ministry of Education, Culture, Sports, Science and Technology (Grant-in-Aid for Scientific Research (Grant Nos. 22591262, 25461732, and 16K10253 [to TNakao]); the Government of India Department of Science and Technology (DST INSPIRE Faculty Grant No. -IFA12-LSBM-26 [to JCN] and Grant No. SR/S0/HS/0016/2011 [to YCJR]); the Government of India Department of Biotechnology (Grant No. BT/06/IYBA/2012 [to JCN] and Grant No. BT/PR13334/Med/30/259/2009 [to YCJR]); the New York State Office of Mental Health (to HBS); the Italian Ministry of Health (Grant No. RC13-14-15-16A [to GS]); the National Center for Advancing Translational Sciences (Grant No. UL1TR000067/KL2TR00069 [to ERS]); the Canadian Institutes of Health Research (to SES); the Michael Smith Foundation for Health Research (to SES); the British Columbia Provincial Health Services Authority (to SES); the Netherlands Organization for Scientific Research (Grant No. NWO/ZonMW Vidi 917.15.318 [to GAvW]); the Wellcome-DBT India Alliance (Grant No. 500236/Z/11/Z [to GV]); the Shanghai Key Laboratory of Psychotic Disorders (Grant No. 13dz2260500 [to ZW]).