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Correction to: Molecular Psychiatry, published online 2 May 2023 In this article Honami Arai, Irene Bollettini, Rosa Calvo Escalona, Ana Coelho, Federica Colombo, Leila Darwich, Martine Fontaine, Toshikazu Ikuta, Jonathan C. Ipser, Asier Juaneda-Seguí, Hitomi Kitagawa, Gerd Kvale, Mafalda Machado-Sousa, Astrid Morer, Takashi Nakamae, Jin Narumoto, Joseph O’Neill, Sho Okawa, Eva Real, Veit Roessner, Joao R. Sato, Cinto Segalàs, Roseli G. Shavitt, Dick J. Veltman, Kei Yamada were missing from the author list indexed under the ENIGMA-OCD Working Group. Additionally, there was an error regarding Tokiko Yoshida’s name, where the first name and last name were written in the wrong order. The original article has been corrected.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

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.

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

Background: Decision-making alterations are present in psychiatric illnesses like major depressive disorder (MDD), obsessive–compulsive disorder (OCD), and schizophrenia, linked to symptoms of the respective disorders. We sought to analyze unique and shared decision-making alterations in these disorders, which is crucial for early diagnosis and treatment, especially given potential comorbidities. Methods: Using 2 computational modelling approaches — logistic regression and hierarchical Bayesian modelling — we analyzed alterations in model-based and model-free decision-making in a transdiagnostic cohort of patients with MDD, OCD, or schizophrenia. Our aim was to identify disorder-specific and shared alterations and their associations with symptoms. Results: We included 23 patients with MDD, 25 patients with OCD, 27 patients with schizophrenia, and 25 controls. Overall, participants of all groups relied on model-free decision-making. Patients with schizophrenia had the lowest learning rate and highest switching rate, indicating low perseverance. Furthermore, patients with OCD were more random in both task stages than controls and patients with MDD. All patient groups exhibited more randomness in responses than controls, with the schizophrenia group showing the highest levels. Increased model-free behaviour correlated with elevated depressive symptoms, and more model-based decision-making was linked to lower anhedonia levels across all patient groups. Limitations: The sample size in each group was small. Conclusion: This study highlights disorder-specific and shared decision-making alterations among people with MDD, OCD, or schizophrenia. Our findings suggest that anhedonia and depressive symptoms, which are present in all 3 disorders, share underlying behavioural mechanisms. Improving model-based behaviour may be a target for intervention and treatment. Furthermore, completely random behaviour in the 2-step task appears to distinctly differentiate patients with schizophrenia in remission. [ABSTRACT FROM AUTHOR]

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.

ObjectiveBrain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.MethodT1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.ResultsIn adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.ConclusionsThe parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

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.

Background: Resting-state functional MRI (fMRI) studies commonly report alterations in 3 core networks in obsessive-compulsive disorder (OCD)--the frontoparietal network, the default mode network and the salience network--defined by functionally connected infraslow oscillations in ongoing brain activity. However, most of these studies observed static functional connectivity in the brains of patients with OCD. Methods: To investigate dynamic functional connectivity alterations and widen the evidence base toward the triple network model in OCD, we performed group-based independent component and sliding time window analyses in 49 patients with OCD and 41 healthy controls. Results: The traditional independent component analysis showed alterations in the left frontoparietal network as well as between the left and right frontoparietal networks in patients with OCD compared with healthy controls. For dynamic functional connectivity, the sliding time window approach revealed peak dysconnectivity between the left and right frontoparietal networks and between the left frontoparietal network and the salience network. Limitations: The number of independent components, noise in the resting-state fMRI images, the heterogeneity of the OCD sample, and comorbidities and medication status in the patients could have biased the results. Conclusion: Disrupted modulation of these intrinsic brain networks may contribute to the pathophysiology of OCD., Introduction In recent decades, increased use of functional MRI (fMRI) has contributed to our understanding of the human brain and the pathophysiology of many psychiatric disorders. Studies typically measure brain [...]

Amyloid-β pathology (Aβ) and impaired cognition characterize Alzheimer's disease (AD); however, neural mechanisms that link Aβ-pathology with impaired cognition are incompletely understood. Large-scale intrinsic connectivity networks (ICNs) are potential candidates for this link: Aβ-pathology affects specific networks in early AD, these networks show disrupted connectivity, and they process specific cognitive functions impaired in AD, like memory or attention. We hypothesized that, in AD, regional changes of ICNs, which persist across rest- and cognitive task-states, might link Aβ-pathology with impaired cognition via impaired intrinsic connectivity. Pittsburgh compound B (PiB)-positron emission tomography reflecting in vivo Aβ-pathology, resting-state fMRI, task-fMRI, and cognitive testing were used in patients with prodromal AD and healthy controls. In patients, default mode network's (DMN) functional connectivity (FC) was reduced in the medial parietal cortex during rest relative to healthy controls, relatively increased in the same region during an attention-demanding task, and associated with patients' cognitive impairment. Local PiB-uptake correlated negatively with DMN connectivity. Importantly, corresponding results were found for the right lateral parietal region of an attentional network. Finally, structural equation modeling confirmed a direct influence of DMN resting-state FC on the association between Aβ-pathology and cognitive impairment. Data provide evidence that disrupted intrinsic network connectivity links Aβ-pathology with cognitive impairment in early AD.

There is striking overlap between the spatial distribution of amyloid-β pathology in patients with Alzheimer's disease and the spatial distribution of high intrinsic functional connectivity in healthy persons. This overlap suggests a mechanistic link between amyloid-β and intrinsic connectivity, and indeed there is evidence in patients for the detrimental effects of amyloid-β plaque accumulation on intrinsic connectivity in areas of high connectivity in heteromodal hubs, and particularly in the default mode network. However, the observed spatial extent of amyloid-β exceeds these tightly circumscribed areas, suggesting that previous studies may have underestimated the negative impact of amyloid-β on intrinsic connectivity. We hypothesized that the known positive baseline correlation between patterns of amyloid-β and intrinsic connectivity may mask the larger extent of the negative effects of amyloid-β on connectivity. Crucially, a test of this hypothesis requires the within-patient comparison of intrinsic connectivity and amyloid-β distributions. Here we compared spatial patterns of amyloid-β-plaques (measured by Pittsburgh compound B positron emission tomography) and intrinsic functional connectivity (measured by resting-state functional magnetic resonance imaging) in patients with prodromal Alzheimer's disease via spatial correlations in intrinsic networks covering fronto-parietal heteromodal cortices. At the global network level, we found that amyloid-β and intrinsic connectivity patterns were positively correlated in the default mode and several fronto-parietal attention networks, confirming that amyloid-β aggregates in areas of high intrinsic connectivity on a within-network basis. Further, we saw an internetwork gradient of the magnitude of correlation that depended on network plaque-load. After accounting for this globally positive correlation, local amyloid-β-plaque concentration in regions of high connectivity co-varied negatively with intrinsic connectivity, indicating that amyloid-β pathology adversely reduces connectivity anywhere in an affected network as a function of local amyloid-β-plaque concentration. The local negative association between amyloid-β and intrinsic connectivity was much more pronounced than conventional group comparisons of intrinsic connectivity would suggest. Our findings indicate that the negative impact of amyloid-β on intrinsic connectivity in heteromodal networks is underestimated by conventional analyses. Moreover, our results provide first within-patient evidence for correspondent patterns of amyloid-β and intrinsic connectivity, with the distribution of amyloid-β pathology following functional connectivity gradients within and across intrinsic networks.

Background: Obsessive-compulsive disorder (OCD) is characterized by anxiety-provoking, obsessive thoughts. Patients usually react to these thoughts with repetitive behaviours that reduce anxiety and are perceived as rewarding. Hence, reward plays a major role in the psychopathology of OCD. Previous studies showed altered activation in frontostriatal networks, among others, in association with the processing of reward in patients with OCD. Potential alterations in connectivity within these networks have, however, barely been explored. Methods: We investigated a sample of patients with OCD and healthy controls using functional MRI and a reward learning task presented in an event-related design. Dynamic causal modelling (DCM) was used to estimate effective connectivity. Results: Our sample included 37 patients with OCD and 39 healthy controls. Analyses of task-related changes in connectivity showed a significantly altered effective connectivity between the ventromedial prefrontal cortex (vmPFC) and the orbitofrontal cortex (OFC), among others, both in terms of endogenous connectivity as well as modulatory effects under positive feedback. Clinical measures of compulsion correlated with the effect of feedback input on visual sensory areas. Limitations: The reported alterations should be interpreted within the context of the task and the a priori-defined network considered in the analysis. Conclusion: This disrupted connectivity in parts of the default mode network and the frontostriatal network may indicate increased rumination and self-related processing impairing the responsiveness toward external rewards. This, in turn, may underlie the general urge for reinforcement accompanying compulsive behaviours., Introduction Obsessive-compulsive disorder (OCD) is characterized by anxiety-provoking, obsessive thoughts (i.e., obsessions), which patients react to with repetitive behaviours (i.e., compulsions) to counteract anxiety. Patients perceive their obsessive thoughts as [...]

Multiple sclerosis (MS) is a chronic inflammatory autoimmune disease causing axonal degeneration and demyelination. Exercise in mice with active monophasic experimental autoimmune encephalomyelitis (EAE) attenuates disease severity associated with diverse impacts on T cell-mediated immunity. However, studies have so far focused on preventive approaches. In this study, we investigated the impact of endurance exercise on established EAE disease in a model of secondary progressive MS. When the exercise program on motorized running wheels was started at disease manifestation, the disease course was significantly ameliorated. This was associated with a significant decrease in B cell, dendritic cell, and neutrophil cell counts in the central nervous system (CNS). Furthermore, we observed an increased expression of major histocompatibility complex class II (MHC-II) as well as alterations in costimulatory molecule expression in CNS B cells and dendritic cells. In contrast, T cell responses were not altered in the CNS or periphery. Thus, exercise training is capable of attenuating the disease course even in established secondary progressive EAE, potentially via modulation of the innate immune compartment. Further studies are warranted to corroborate our findings and assess the potential of this lifestyle intervention as a complementary therapeutic strategy in secondary progressive MS patients. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence indicates the presence of structural brain alterations in individuals with obsessive-compulsive disorder (OCD). Findings are, however, rather heterogeneous, which may be partly because of differences in methodological approaches or clinical sample characteristics. The aim of the present study was to analyze the whole brain cortical volume, surface area and thickness in a large sample of patients with OCD compared with age- and sex-matched healthy controls. Methods: We conducted whole brain surfacebased analyses of grey matter measures using the automated FreeSurfer software in patients with OCD and matched controls. Group analyses were performed and corrected for multiple testing using Monte Carlo simulations (p < 0.05). Altered brain regions and their average morphological values were associated to symptom severity and type (Yale-Brown Obsessive Compulsive Scale scores). Results: We included 75 patients and 75 controls in our analyses. Patients with OCD showed decreases in both volume and surface area compared with healthy controls in inferior-superior parieto-occipital regions. In addition, the precuneus, posterior cingulate areas, middle frontal and orbitofrontal areas, and middle inferior temporal areas extending to the fusiform gyrus were characterized by a reduced surface area only. There were no differences in grey matter thickness between the groups. Limitations: The presence of comorbidities, medication usage and the multisymptomatic feature of OCD could have influenced our results to a certain degree. Conclusion: Our results suggest decreased grey matter volume and surface area in several key regions in patients with OCD. Parietal regions showed reductions in both volume and surface area, which underlines the potential relevance of these regions for the pathophysiology of the disorder., Introduction Studying the brain anatomic structure offers a promising approach to improve our understanding of neural functional alterations often encountered in individuals with psychiatric disorders. In those with obsessive-compulsive disorder [...]

The formation of new perceptual categories involves learning to extract that information from a wide range of often noisy sensory inputs, which is critical for selecting between a limited number of responses. To identify brain regions involved in visual classification learning under noisy conditions, we developed a task on the basis of the classical dot pattern prototype distortion task [M. I. Posner, "Journal of Experimental Psychology," 68, 113-118, 1964]. Twenty-seven healthy young adults were required to assign distorted patterns of dots into one of two categories, each defined by its prototype. Categorization uncertainty was modulated parametrically by means of Shannon's entropy formula and set to the levels of 3, 7, and 8.5 bits/dot within subsets of the stimuli. Feedback was presented after each trial, and two parallel versions of the task were developed to contrast practiced and unpracticed performance within a single session. Using event-related fMRI, areas showing increasing activation with categorization uncertainty and decreasing activation with training were identified. Both networks largely overlapped and included areas involved in visuospatial processing (inferior temporal and posterior parietal areas), areas involved in cognitive processes requiring a high amount of cognitive control (posterior medial wall), and a cortico-striatal-thalamic loop through the body of the caudate nucleus. Activity in the medial prefrontal wall was increased when subjects received negative as compared with positive feedback, providing further evidence for its important role in mediating the error signal. This study characterizes the cortico-striatal network underlying the classification of distorted visual patterns that is directly related to decision uncertainty.