Received: 14 December 2019 Revised: 12 February 2020 Accepted: 16 February 2020 DOI: 10.1002/hbm.24972 REVIEW ARTICLE An overview of the first 5 years of the ENIGMA obsessive–compulsive disorder working group: The power of worldwide collaboration Odile A. van den Heuvel1,2 | Premika S.W. Boedhoe1 | Sara Bertolin3 | 4 5,6 Willem B. Bruin | Clyde Francks | Iliyan Ivanov | Neda Jahanshad8 | 7 Xiang-Zhen Kong5 | Jun Soo Kwon9,10 | Joseph O'Neill11 | Tomas Paus12 | Yash Patel12 | Fabrizio Piras13 | Lianne Schmaal14,15 | Carles Soriano-Mas3,16,17 | Gianfranco Spalletta13,18 | Guido A. van Wingen4 | Je-Yeon Yun19,20 | Chris Vriend1 | H. Blair Simpson21 | Daan van Rooij6 | Marcelo Q. Hoexter22 | Martine Hoogman6,23 | Jan K. Buitelaar6 | Paul Arnold24 | Jan C. Beucke25,26 | Francesco Benedetti27 | Irene Bollettini27 | Anushree Bose28 | Brian P. Brennan29 | Alessandro S. De Nadai30 | Kate Fitzgerald31 | Patricia Gruner32 | Edna Grünblatt33,34,35 | Yoshiyuki Hirano36 | Chaim Huyser37 | Anthony James38 | Kathrin Koch39 | Gerd Kvale2 | Luisa Lazaro40 | Christine Lochner41 | Rachel Marsh21 | David Mataix-Cols27 | Pedro Morgado42,43,44 | Takashi Nakamae45 | Tomohiro Nakao46 | Janardhanan C. Narayanaswamy28 | Erika Nurmi47 | Christopher Pittenger48 | Y.C. Janardhan Reddy26 | Jo~ao R. Sato49 | Noam Soreni50 | S. Evelyn Stewart51,52,53 | Stephan F. Taylor31 | David Tolin54 | Sophia I. Thomopoulos8 | Dick J. Veltman1 | Ganesan Venkatasubramanian28 | Susanne Walitza31 | Zhen Wang55,56 | 8 57 Paul M. Thompson | Dan J. Stein | on behalf of the ENIGMA-OCD working group 1 Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 2 Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway 3 Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain 4 Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands 5 Department of Language & Genetics, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands 6 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands 7 Icahn School of Medicine at Mount Sinai, New York, New York 8 Keck USC School of Medicine, Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Marina del Rey, California 9 Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea 10 Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea 11 Division of Child & Adolescent Psychiatry, UCLA Jane & Terry Semel Institute For Neuroscience, Los Angeles, California This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. Hum Brain Mapp. 2022;43:23–36. wileyonlinelibrary.com/journal/hbm 23 24 VAN DEN HEUVEL ET AL. 12 Holland Bloorview Kids Rehabilitation Hospital, Bloorview Research Institute, Toronto, Ontario, Canada 13 Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy 14 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia 15 Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia 16 Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain 17 Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain 18 Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texsas 19 Seoul National University Hospital, Seoul, Republic of Korea 20 Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea 21 Center for OC and Related Disorders at the New York State Psychiatric Institute and Columbia University Irving Medical Center, New York, New York 22 Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de S~ao Paulo, S~ ao Paulo, Brazil 23 Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands 24 Mathison Centre for Mental Health Research & Education and Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 25 Humboldt-Universität zu Berlin, Department of Psychology, Berlin, Germany 26 Karolinska Institutet, Department of Clinical Neuroscience, Stockholm, Sweden 27 Department of Psychiatry and Clinical Psychobiology, Scientific Institute Ospedale, Milan, Italy 28 Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India 29 McLean Hospital, Harvard Medical School, Belmont, Massachusetts 30 Department of Psychology, Texas State University, San Marcos, Texas 31 Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan 32 Department of Psychiatry, Yale University, New Haven, Connecticut 33 Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland 34 Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland 35 Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland 36 Research Center for Child Mental Development, Chiba University, Chiba, Japan 37 De Bascule, academic center child and adolescent psychiatry, Amsterdam, The Netherlands 38 Department of Psychiatry, University of Oxford, Oxford, UK 39 Department of Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany 40 Department of Child and Adolescent Psychiatry and Psychology, IDIBAPS, CIBERSAM, Department of Medicine, Faculty of Barcelona, Barcelona, Spain 41 SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Matieland, South Africa 42 Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal 43 ICVS/3B's, PT Government Associate Laboratory, Braga/Guimar~aes, Portugal 44 Clinical Academic Center–Braga, Braga, Portugal 45 Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan 46 Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Kyushu, Japan 47 Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California 48 OCD Research Clinic, Yale University, New Haven, Connecticut 49 Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil 50 Pediatric OCD Consultation Service, Anxiety Treatment and Research Center, McMaster University, Hamilton, Ontario, Canada 51 Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada 52 BC Mental Health and Addictions Research Institute, Vancouver, British Columbia, Canada 53 BC Children's Hospital, Vancouver, British Columbia, Canada 54 Anxiety Disorders Center, The Institute of Living, Hartford, Connecticut 55 Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China 56 Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China 57 SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa VAN DEN HEUVEL ET AL. 25 Correspondence Odile A. van den Heuvel, MD PhD, Abstract Department of Psychiatry, Department of Neuroimaging has played an important part in advancing our understanding of the Anatomy, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit neurobiology of obsessive–compulsive disorder (OCD). At the same time, neuroim- Amsterdam, Amsterdam, The Netherlands. aging studies of OCD have had notable limitations, including reliance on relatively Email: oa.vandenheuvel@amsterdamumc.nl small samples. International collaborative efforts to increase statistical power by Funding information combining samples from across sites have been bolstered by the ENIGMA consor- National Institute of Health, Grant/Award Number: U54 EB020403; Japanese Ministry of tium; this provides specific technical expertise for conducting multi-site analyses, as Education, Culture, Sports, Science, and well as access to a collaborative community of neuroimaging scientists. In this arti- Technology, Grant/Award Number: 18K07608; Shanghai Municipal Health cle, we outline the background to, development of, and initial findings from Commission, Grant/Award Number: ENIGMA's OCD working group, which currently consists of 47 samples from 2019ZB0201; National Natural Science Foundation of China, Grant/Award Number: 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD 81671340; Swiss National Science Foundation, patients and 2,325 healthy controls. Initial work has focused on studies of cortical Grant/Award Number: 320030_130237; Wellcome-DBT India Alliance, Grant/Award thickness and subcortical volumes, structural connectivity, and brain lateralization Number: 500236/Z/11/Z; Michael Smith in children, adolescents and adults with OCD, also including the study on the com- Foundation for Health Research; Canadian Institutes of Health Research; Ontario Brain monalities and distinctions across different neurodevelopment disorders. Additional Institute, Grant/Award Number: BT/06/ work is ongoing, employing machine learning techniques. Findings to date have IYBA/2012; Department of Biotechnology; DST, Grant/Award Number: IFA12-LSBM-26; contributed to the development of neurobiological models of OCD, have provided Marató TV3 Foundation, Grant/Award an important model of global scientific collaboration, and have had a number of clin- Numbers: 091710, 01/2010; Deutsche Forschungsgemeinschaft, Grant/Award ical implications. Importantly, our work has shed new light on questions about Number: KO 3744/7-1; Japan Society for the whether structural and functional alterations found in OCD reflect neu- Promotion of Science, Grant/Award Numbers: 19K03309, 16K04344; Japan Agency for rodevelopmental changes, effects of the disease process, or medication impacts. Medical Research and Development, Grant/ We conclude with a summary of ongoing work by ENIGMA-OCD, and a consider- Award Number: JP19dm0307002; Seventh Framework Programme, Grant/Award ation of future directions for neuroimaging research on OCD within and beyond Numbers: 278948, FP7/2007-2013; National ENIGMA. Research Foundation of Korea, Grant/Award Number: NRF-2017R1D1A1B03028464; Ministry of Health, Grant/Award Number: KEYWORDS RC13-14-15-16-17-18-19A; Carlos III Health cortical thickness, ENIGMA, mega-analysis, meta-analysis, MRI, obsessive–compulsive Institute, Grant/Award Numbers: disorder, surface area, volume CPII16/00048, PI16/00889; NHMRC Career Development Fellowship, Grant/Award Number: 1140764; National Institute of Mental Health, Grant/Award Numbers: R01MH081864, R01MH085900, K23 MH082176, R21 MH093889, R01 MH104648, R01MH117601; NIH, Grant/ Award Numbers: K23 MH115206, R01AG059874, R01MH117601; Max Planck Society; Netherlands Organization for Scientific Research, Grant/Award Numbers: 91619115, NWO/ZonMW Vidi 016.156.318, NWO/ZonMW Vidi 91717306 1 | B A CKG R O U N D based on clinical observations, including descriptions of OCD-like behaviors in patients with subcortical disorders, such as Since the 1980s, obsessive–compulsive disorder (OCD) researchers Huntington's disease (Cummings & Cunningham, 1992), Sydenham's worldwide have made extensive use of neuroimaging (Behar et al., chorea (Swedo, Rapoport, Cheslow, & Ayoub, 1989), and pallidal 1984; Luxenberg et al., 1988). OCD has a distinctive phenotype, (Laplane et al., 1989) and frontal lobe (Eslinger & Damasio, 1985) with characteristic repetitive thoughts and intrusions (obsessions) lesions. Data obtained with human brain imaging in the late 1980s and habitual behaviors (compulsions), and there was early evidence allowed detailed neurobiological models of OCD to be developed. of the involvement of specific brain circuits and systems (Cummings, These models initially focused on the striatum and orbitofrontal cor- 1995). Early hypotheses regarding brain systems involved were tex, but were later refined to include more broadly interacting 26 VAN DEN HEUVEL ET AL. fronto-striatal, fronto-parietal, fronto-limbic, and cerebellar circuits subtypes of OCD, specific phenotypes showed additional neural alter- (Stein et al., 2019; Van den Heuvel et al., 2016). ations: aggressive obsessions and checking compulsions were associ- The first brain imaging studies in OCD focused on brain mor- ated with smaller right amygdala. Subsequently, these effects were phometry and glucose metabolism, using computed tomography largely replicated by the symptom dimension findings of Van den (CT) (e.g., Behar et al., 1984), single photon emission computed Heuvel et al. (2009). tomography (SPECT) and positron emission tomography (PET) Multiple conventional meta-analyses of VBM studies of (e.g., Rubin, Anath, Villanueva-Meyer, Trajmar, & Mena, 1995). In the OCD were subsequently published (Radua & Mataix-Cols, 2009; 1990s, magnetic resonance imaging (MRI) began to dominate the Radua, van den Heuvel, Surguladze, & Mataix-Cols, 2010; Rotge field. High resolution structural MRI enabled morphometric analyses et al., 2010; Peng et al., 2012), using statistical methods to aggregate of subcortical volume and shape, cortical thickness and surface area, information on effect sizes, standard errors, and confidence intervals as well as cortical gyrification. Functional MRI (fMRI) began to be from primary published studies, in order to estimate overall effect used to visualize brain activation patterns during specific states rele- size. To meta-analyze voxel-based data, an often-used approach is vant to the disorder, using a range of emotional and cognitive para- the signed differential mapping (SDM) method, developed by Radua digms that may be crucial in the disease, for example, symptom (https://www.sdmproject.com). A meta-analysis using this method provocation (Jaspers-Fayer et al., 2019; Thorsen et al., 2018), fear showed that compared to healthy controls, OCD patients have, on and extinction learning (Milad et al., 2013), response inhibition average, lower volumes of the dorsomedial prefrontal cortex (Norman et al., 2019), planning (e.g., Van den Heuvel et al., 2005), (dmPFC)/dorsal anterior cingulate cortex (dACC) and greater volume working memory (e.g., De Vries et al., 2014), and reversal learning of the bilateral striatum (mainly anterior putamen, extending into the (e.g., Remijnse et al., 2006). caudate nucleus) (Radua & Mataix-Cols, 2009). In comparison to In the past decade, imaging designs began to shift from analyzing other anxiety disorders, smaller volume of the dmPFC/dACC was specific regions of interest to studying whole-brain networks. Net- found across disorders, whereas OCD patients had greater pallidal work approaches can be used to examine structural connectivity, volume than did patients with anxiety disorders (Radua et al., 2010). using diffusion weighted imaging (DTI; Piras, Piras, Caltagirone, & An even larger cross-disorder meta-analysis of brain volume across Spalletta, 2013; Radua et al., 2014) as well as structural covariance mental disorders, including 193 studies (15,892 subjects) in six diag- (Yun et al., 2020), and functional connectivity at rest (Gursel, Avram, nostic groups (schizophrenia, bipolar disorder, major depressive disor- Sorg, Brandl, & Koch, 2018) and during task performance (e.g., Douw der, substance use disorder, OCD, and anxiety disorders) showed et al., 2019). As with other neuroimaging studies, a key challenge has that smaller volume of the dACC and bilateral insula/operculum was been the reproducibility of research findings, as most single center not specific to OCD, but was consistently present across all these imaging studies have limited statistical power to detect effects of the psychiatric disorders (Goodkind et al., 2015). disorder, while adequately controlling for multiple comparisons and These aggregate meta-analyses, based on published data, were heterogeneity in demographic and clinical characteristics. Although highly informative, but still prone to publication bias, selective each individual study differs in the choice of paradigms, a structural reporting in primary studies, large variations in data processing and MRI scan is typically part of every study design, enabling meta- analyses across studies influencing the results, and insufficient power analyses of data across many samples, or mega-analyses of pooled to undertake detailed investigations of the associations between clini- raw MRI scans or MRI-extracted measures. Although technically more cal characteristics and neuroimaging findings (Boedhoe et al., 2019). challenging, meta-analyses and mega-analyses of resting state fMRI To address these limitations, the OCD Brain Imaging Consortium (rsfMRI), and even task-based fMRI, are also possible (Adhikari et al., (OBIC) was initiated in 2010; this initiative aimed to increase statisti- 2018; Adhikari et al., 2019; Yan et al., 2019). cal power by pooling raw MRI data from centers around the world. Harmonization of data quality control and data processing, all per- formed at a single center, would limit variation across data samples to 1.1 | The first worldwide data-sharing initiative: just the variation in inclusion criteria and data acquisition. Six aca- The OCD brain imaging consortium demic OCD centers (from Asia, Europe, and South America) provided high-quality data from 412 adult OCD patients and 368 healthy con- The first large single-center structural MRI study of OCD was publi- trols. Using VBM, the OBIC consortium mega-analysis showed that shed in 2004 by Pujol et al., based on MRI scans of 72 OCD patients compared to healthy controls, OCD patients had lower volume of the and 72 controls, using voxel-based morphometry (VBM) (Ashburner & dmPFC, dACC, and bilateral insula-operculum, largely replicating pre- Friston, 2000; Pujol et al., 2004). Compared to healthy controls, OCD vious meta-analytic findings (Goodkind et al., 2015; Radua et al., patients had, on average, smaller volumes for the medial frontal gyrus, 2010; Radua & Mataix-Cols, 2009), as well as greater volume of the medial orbitofrontal cortex and left insula-operculum, and greater vol- cerebellum. Group-by-age interaction effects illustrated that some umes for the ventral part of the putamen and anterior cerebellum. regions (e.g., putamen) show a relative preservation of volume in OCD The striatal finding was mainly driven by older patients and those with with increasing age while other regions (e.g., temporal regions) show a longer disease duration. Pujol et al. (2004) also found that although relative loss of volume in OCD with increasing age. These findings are these frontal-striatal abnormalities were present across the various consistent with the initial study by Pujol et al. (2004), and suggest that VAN DEN HEUVEL ET AL. 27 increased volume of the striatum/pallidum is most prominent in older lifespan), and 12 that develop and disseminate analysis pipelines, to OCD patients, perhaps related to disease chronicity. facilitate harmonization across working groups. Using the same OBIC dataset, structural covariance analysis of With the goals of obtaining larger sample sizes than represented four striatal regions and two amygdala regions was performed, show- in OBIC, increasing worldwide coverage, extending the samples to ing increased covariance between the volume of the ventral-rostral pediatric OCD, comparing OCD to other disorders, and eventually putamen and the left inferior frontal gyrus/operculum. Consistent combining imaging data and genetics, van den Heuvel and Stein pro- with the previous findings, this association was only significant in posed adding an ENIGMA-OCD working group to the ENIGMA con- older OCD patients, suggesting that these alterations may develop sortium in 2013. As of 2019, ENIGMA-OCD consists of 47 samples over the course of the disease (Subira et al., 2016). In addition to this from 34 institutes in 15 countries on 5 continents, with a total sample fronto-striatal finding, increased covariance was found between the of 4,648 with MRI data of 2,323 OCD patients (1,824 adults right centromedial-superficial part of the amygdala and the ventrome- (>18 years), 359 adolescents (12–17 years), 140 children (<12 years)) dial prefrontal cortex, consistent with the role of the limbic circuit in and 2,325 controls (1,724 adults, 325 adolescents, and 166 children; OCD, as evident in functional MRI studies on emotional processing in Figure 1). this disorder (Milad et al., 2013; Thorsen et al., 2018). The standard procedure in ENIGMA has been collation of Whereas VBM measures volume or gray matter density in cortical individual-participant data (IPD; e.g., value of CTh, SA, subcortical regions at the voxel level (Ashburner & Friston, 2000), surface-based volume, and intracranial volume) from multiple studies, without methods such as FreeSurfer calculate morphometric measures from requiring the sharing or centralization of the raw MRI data, as was geometric models of the cortical surface (Fischl & Dale, 2000). Abnor- done in OBIC. The advantage of meta-analysis on IPD without the malities in regional volume as measured with VBM can be the result need to exchange raw data is of particular relevance in the context of altered cortical thickness (CTh), surface area (SA), cortical folding, of the new European law, that is, the General Data Protection Reg- or a combination of these. Whereas cortical thickness changes ulation 2016/679, regulating data protection and privacy for all dynamically across the lifespan as a consequence of development, dis- individual citizens of the European Union (https://gdpr-info.eu/). ease and environmental factors (Eyler et al., 2011; Frye et al., 2010), Using standardized protocols for data processing and quality control surface area and cortical folding are more indicative of early neuro- (see also: http://enigma.ini.usc.edu/protocols/), all data are development (Mangin, Jouvent, & Cachia, 2010). Atlas-based processed at each site locally. Standardization of protocols, within approaches as used in FreeSurfer and voxel-based approaches as used working groups and across working groups, ensures low methodo- in VBM also differ in their approach to and interpretation of subcorti- logical heterogeneity across sites and working groups. IPD can sub- cal regions: Whereas VBM detects density differences at the voxel- sequently be used in two different statistical approaches: A two- level, global or regional differences in subcortical structures can be stage or a one-stage approach (Boedhoe et al., 2019). In the two- inferred from atlas-based FreeSurfer analyses. Using exactly the same stage approach, IPD are first analyzed for each sample separately to OBIC sample as was used in the VBM mega-analysis (De Wit et al., obtain summary results (e.g., effect size estimates, confidence inter- 2014), Fouche et al. (2017) used vertex-wise FreeSurfer to compare vals, and so on), which are then used for standard meta-analysis. In cortical thickness and subcortical volumes in patients versus controls, contrast, the one-stage approach analyzes all IPD in one statistical and showed that adult OCD patients have a thinner cortex in a num- model, while accounting for site effects, to estimate the overall ber of frontal, temporal, and parietal regions and smaller bilateral hip- effect. In the ENIGMA-OCD publications we call the two-stage pocampus. These findings partly overlap with the VBM-based results, approach meta-analysis, and the one-stage approach mega-analysis. but also show that results and their interpretation depend to some Whereas in OBIC we conducted mega-analyses on the raw MRI extent on the methods used. data—all processed at a single site—in ENIGMA-OCD we conducted both the two-stage aggregated data meta-analyses and the one- stage IPD-based mega-analyses. 1.2 | Extension to children and better worldwide Within ENIGMA-OCD, the meta- and mega-analyses revealed coverage: The ENIGMA-OCD consortium comparable findings for the subcortical regions (Boedhoe et al., 2017), but the mega-analytic approach appeared more sensitive for detecting In 2009, Paul Thompson initiated the ENIGMA (Enhancing Neuroim- subtle cortical abnormalities (Boedhoe et al., 2018). We recently aging and Genetics through Meta-analysis) consortium. ENIGMA has empirically evaluated whether a meta-analysis provides results com- now grown to a collaboration of more than 1,400 scientists from parable to a mega-analysis, and which of the two analytic frameworks 43 countries studying the human brain, both in health and disease (multiple linear regression mega-analysis model versus linear mixed- (Thompson et al., 2019). The initial aim was to perform large-scale effects random-intercept mega-analysis model) performs better. neuroimaging genetics analyses, by pooling worldwide data, resources Effect sizes and standard error estimates (and 95% confidence and expertise, to answer clinical and fundamental questions related to intervals), and (where possible) model fit, assessed using the Bayesian psychiatric and neurological disorders. The consortium currently has information criterion, were used to evaluate which of the methods 30 working groups on specific psychiatric and neurological disorders, performs best. Although effect sizes were similar for the meta-analysis a few on trans-diagnostic constructs (e.g., irritability, suicidality, and and linear regression mega-analysis, we showed that in the case of 28 VAN DEN HEUVEL ET AL. F I G U R E 1 World map showing the 34 institutes participating in the ENIGMA- OCD consortium cross-sectional structural MRI data a mega-analysis performs better altered neurodevelopment in children prone to develop OCD. Never- than a meta-analysis (lower standard errors and narrower confidence theless, the thalamus is not a uniform structure but rather consists of intervals), and in a multi-center study with moderate variation multiple subnuclei, each with its own cortical connectivity profile and between cohorts, a linear mixed-effects random-intercept mega- functions (Behrens et al., 2003). OCD-related enlargement of the analytical framework seems to lead to the best model fit (Boedhoe overall thalamus may therefore be driven by one or a few subnuclei. et al., 2019). Which subnuclei drive this effect is currently unknown, although shape analysis has revealed increased surface area in anterior and pulvinar nuclei in adult OCD patients (Shaw et al., 2015). In future 1.3 | First findings of ENIGMA-OCD: Cortical studies, we will employ the recently developed and validated algo- thickness, surface area and subcortical volume rithm from Iglesias et al. (2018) to segment the subnuclei of the thala- mus and study which nuclei drive the effect. When comparing OCD patients to healthy controls, we analyzed data Adult OCD patients showed smaller hippocampus (Cohen's d = from adult subjects (18 years and older) and pediatric/adolescent sub- −0.13), most pronounced in patients with an adult onset of OCD jects (below 18 years), separately. For the purpose of basic compari- (Cohen's d = −0.18), patients with a comorbid lifetime diagnosis of sons of subcortical volume and cortical thickness and surface area, we depression (Cohen's d = −0.27), and patients using medication pooled data from children (<12 years) and adolescents (12–17 years) (Cohen's d = −0.29). Adult OCD patients also showed larger pallidum in a pediatric/adolescent analysis (Boedhoe et al., 2017, 2018), but in (Cohen's d = 0.16), most pronounced in patients with a childhood the subsequent cross-disorder comparisons with other child-onset onset of OCD (Cohen's d = 0.25), and patients using medication disorders (autism spectrum disorder, ASD) and attention deficit hyper- (Cohen's d = 0.29). The hippocampal findings do not seem to be spe- activity disorder (ADHD), we analyzed children and adolescents sepa- cific to OCD, as similar results have been reported by the ENIGMA rately (Boedhoe et al., 2019). working groups for schizophrenia (Van Erp et al., 2016), bipolar disor- In the subcortical analyses, we found clear differences between der (Hibar et al., 2016), major depressive disorder (Schmaal et al., the pediatric OCD patients (compared with healthy controls, n = 335 2016), and posttraumatic stress disorder (Logue et al., 2018), and so and 287, respectively) and the adult OCD patients (compared with may be related to trans-diagnostic aspects of chronic distress. The pal- healthy controls, n = 1,495 and 1,472, respectively) (Boedhoe et al., lidum findings fit well with all prior meta- and mega-analyses (De Wit 2017). Pediatric OCD was associated with greater volume of the thal- et al., 2014; Peng et al., 2012; Radua et al., 2010; Radua & Mataix- amus (Cohen's d = 0.38), but this effect was only detectable in Cols, 2009; Rotge et al., 2010) and appear to be more specific to the comparison of unmedicated OCD versus healthy controls. OCD. Larger pallidum could be due to chronic compulsivity (Van den We recently replicated this finding with data from the Generation R Heuvel et al., 2016). At the same time, since larger pallidum has also cohort (Jaddoe et al., 2010), in which children with probable OCD been reported in schizophrenia (Van Erp et al., 2016), use of antipsy- (scoring above the cut-off on the short OCD screener) also had a chotic medication might partly explain the findings. Indeed, in our larger thalamus, on average (unpublished data). This may suggest analysis by age-group and medication class (Ivanov et al., 2019), a VAN DEN HEUVEL ET AL. 29 larger pallidum was seen particularly in adolescent and adult patients −0.10 and −0.26, see Figure 3). Unmedicated pediatric/adolescent taking antipsychotics. (n = 222) and adult (n = 831) OCD patients did not differ from healthy Analyses of alterations in brain asymmetry, in partnership with controls, on average, in cortical thickness and surface area. Medica- ENIGMA's Laterality working group, showed that children/adoles- tion effects were most pronounced in those patients using a combina- cents with OCD (n = 501) versus healthy controls (n = 439) have tion of antidepressants and antipsychotics, and persisted after altered asymmetry of the thalamus (more leftward; Cohen's d = 0.19) adjusting for disease severity and co-morbidity. Although these medi- and pallidum (less leftward, Cohen's d = −0.21), which was not detect- cation effects should be interpreted with caution due to the cross- able in adult OCD, possibly reflecting altered neurodevelopmental sectional nature of the study design and lack of information on dura- processes (Kong et al., 2019; Figure 2). tion and dosage of medication use, they may suggest neuroplastic The cortical analyses showed that pediatric/adolescent OCD effects of medication on the brain, both in children and adults. Longi- patients (n = 407) versus healthy control children/adolescents tudinal studies of the long-term protective and potentially negative (n = 324) have thinner left and right inferior parietal, left superior pari- effects of antidepressants on the developing brain are non-existent. etal and lateral occipital cortices (Cohen's d values between −0.24 This knowledge gap is remarkable in the context of more than and − 0.31). Adult OCD patients (n = = 1,498), in comparison to adult 60 years of antidepressant prescriptions. Solid conclusions on the healthy controls (n = 1,436), showed a lower surface area of the trans- impact of this medication on the brain await longitudinal studies, with verse temporal cortex (Cohen's d = −0.16) and a thinner bilateral infe- detailed assessment of symptom profile, duration without treatment, rior parietal cortex (Cohen's d = −0.14; Boedhoe et al., 2018). The medication dosage and duration of exposure, also taking into account involvement of the parietal cortex is consistent with the vertex-wise the exposure to other interventions (e.g., CBT) and environmental fac- FreeSurfer findings of the OBIC consortium (Fouche et al., 2017), and tors (e.g., early life stress). the effect was now further found to extend to children, suggesting Using the available cross-sectional data of ENIGMA-OCD, we altered maturation of the parietal cortex early in life that persists into studied medication effects in two additional ways. First, we used adulthood. One obvious discrepancy with previous meta- and mega- machine learning analysis of the previously analyzed cortical and sub- analysis of structural MRI in OCD is the lack of involvement of the cortical measures from 2,304 OCD patients and 2,068 controls to prefrontal regions; this may reflect differences in methods used: The assess whether anatomical group differences might be used to create atlas-based approach to cortical thickness and surface areas that has a neuroimaging biomarker for OCD (Bruin et al., 2019). Classification been used in ENIGMA (segmenting whole structures using coarse performance across 10 different machine and deep learning parcellation) can be less sensitive to subtle regional abnormalities than approaches was limited, in this initial analysis that only used extracted VBM's voxel-wise registration and vertex-based approach to cortical measures derived from anatomical MRI. With site-stratified cross-vali- thickness that was used by OBIC. The use of higher-resolution dation, the receiver operating characteristic area under the curve parcellation methods (e.g., Glasser et al., 2016) might give results that (ROC-AUC) ranged between 0.57 and 0.62, and the performance are more consistent with the voxel-wise and vertex-wise approaches. dropped to chance level (classification performance between 0.51 and Medication status appeared to be a strong confounder (Boedhoe 0.54) when leave-one-site-out cross-validation was used, indicating et al., 2018). Compared to healthy controls (n = 324), pediatric/adoles- that these anatomical brain features, on their own, are not suitable as cent OCD patients on medication (n = 183) had thinner inferior and a biomarker for OCD. However, when patients were stratified superior parietal and lateral occipital cortices (Cohen's d = −0.31) and according to whether they currently use medication, classification per- widespread surface area differences, mainly in frontal regions formance improved remarkably: Medicated OCD patients and healthy (Cohen's d values between −0.27 and −0.33). Medicated adult OCD controls could then be distinguished with a 0.73 AUC (SD = 0.03, patients (n = 646), in comparison to controls (n = 1,436), had wide- pcorr < .001) (in contrast to unmedicated OCD and healthy controls, spread abnormalities in cortical thickness, mainly in relation to frontal, with 0.61 AUC [SD = 0.02, pcorr = .03]), and medicated and temporal, parietal and occipital regions (Cohen's d values between unmedicated OCD patients with 0.86 AUC (SD = 0.02, pcorr < .001). F I G U R E 2 Summary of the subcortical volume effects in ENIGMA-OCD (based on Boedhoe et al., 2017 Am J Psychiatry) 30 VAN DEN HEUVEL ET AL. F I G U R E 3 Summary of the cortical thickness effects in adult OCD patients compared to healthy controls in ENIGMA-OCD, in relation to medication status (based on Boedhoe et al., 2018 Am J Psychiatry). Negative effect sizes d (ranging from light orange d = −0.05 to dark red d = −0.15) indicate thinner cortex in OCD compared to controls These multivariate results therefore mirror the univariate results, and disorder (Hunt et al., 2016). Large-scale investigation of structural highlight that medication use is associated with significant differences covariance networks are therefore of specific interest when studying in brain anatomy. neurodevelopmental disorders such as OCD that have chronic symp- In the second additional approach, we studied age-by-medication toms over the lifespan. interactions in 2,176 OCD patients (1,040 medicated/1,136 Using T1-weighted MRI derived measures of brain morphometry unmedicated) and 2,003 healthy controls, using a general linear model (bilaterally-averaged values of 33 cortical surface areas, 33 cortical (Ivanov et al., 2019). Medicated OCD patients, unmedicated OCD thickness values, and six subcortical volumes) from OCD patients patients, and healthy controls all showed decreasing brain-wide corti- (n = 1,616) and healthy controls (n = 1,463), we calculated intra- cal thickness, surface area, and subcortical volume with increasing age individual brain structural covariance networks, in which edge weights (p < .0005) in most regions. Effects of medication (p < .001–.0005) were proportional to the similarity between two brain morphological and age-by-medication interactions (p < .05–.0005) were detected in features in terms of deviation from controls (z-score transformed; Yun 46 cortical and 7 subcortical brain regions. Accordingly, in certain et al., 2020). We focused on measures of network segregation (clus- regions (e.g., supramarginal gyrus, lateral occipital cortex), child and/or tering and modularity), network integration (global efficiency), their adolescent medicated OCD patients had thicker cortex than balance (small-worldness), and community membership. We also stud- unmedicated patients. Adult medicated OCD patients, however, had ied hub profiling of regional brain areas using measures of between- thinner cortex than unmedicated OCD patients. Effects on cortical ness, closeness, and eigenvector centrality. Individually calculated thickness were strongest for tricyclic antidepressants, but were also network measures were integrated across the 37 ENIGMA-OCD present for serotonin reuptake inhibitors (SRIs) and benzodiazepines. datasets using a meta-analytic approach. At the global level, compared One (speculative) explanation of the differential effects of medication to healthy controls, OCD patients showed lower clustering on cortical thickness in pediatric versus adult patients with OCD is (p < .0001), modularity (p < .0001), small-worldness (p = .017), and the combination of treatment-associated slowing of the neuronal community membership, suggesting lower network segregation. At regressive changes (synapse loss and neurite pruning) and slowing the regional level, compared to healthy controls, OCD patients (in children) and acceleration (in adults) of the progressive white- showed lower (rank-transformed) centrality values for caudate and matter myelination of normal aging. thalamus volume, and surface area of paracentral cortex, suggesting an altered distribution of brain hubs. Centrality, mainly of the cingu- late and orbitofrontal areas, was associated with OCD disease dura- 1.4 | The first ENIGMA-OCD brain structural tion, indicative of greater involvement of these regions with connectivity results chronicity. Abnormalities in structural connectivity might be explained in After conducting the initial meta- and mega-analyses of regional brain part by microstructural alterations in white matter. A number of the volumetric abnormalities, we initiated analyses of structural brain ENIGMA-OCD sites (n = 19) also have diffusion tensor imaging connectivity, using both structural covariance graph analyses (DTI) scans of the same participants used in the analyses of the (Yun et al., 2020) and diffusion tensor imaging (Piras et al., 2019). Brain T1-weighted MRI measures. We compared DTI-derived values for structural covariance networks reflect covariation in morphology of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity different brain areas and are thought to reflect common trajectories in (RD), and axial diffusivity (AD) in 25 brain regions of OCD patients brain development and maturation, as well as common effects of a (n = 700 adults and 174 children/adolescents) and healthy controls VAN DEN HEUVEL ET AL. 31 (n = 645 adults and 144 children/adolescents) (Piras et al., 2019). therapy (CBT) has been linked to structural variability in the prefron- We meta-analyzed patient versus control differences across sites, tal cortex (Fullana et al., 2014; Hoexter et al., 2013) and activation adjusted for age and sex, and investigated potential associations and connectivity of the amygdala (Fullana et al., 2017; Göttlich, with clinical characteristics, such as medication status, age of dis- Krämer, Kordon, Hohagen, & Zurowski, 2015; Olatunji et al., 2014). ease onset, duration of illness, and disease severity. Adult OCD Prediction studies have been so far limited in sample size and there- patients showed significantly lower FA in the sagittal stratum fore replicability of findings has been poor. We have now started (Cohen's d = −0.21) and posterior thalamic radiation (Cohen's d = using data from ENIGMA-OCD samples with longitudinal clinical −0.26). Lower FA in the sagittal stratum was associated with a data of 170 children with OCD (from nine sites) and 315 adults with younger age of onset (z = 2.71), longer duration of illness (z = OCD (from seven sites), to study how variation in morphological −2.09), and a higher percentage of medicated patients in the values (cortical thickness, surface area, and subcortical volumetry) is cohorts studied (z = −1.98). No significant association with symptom related to variation in treatment response (Bertolin, Martinez- severity was detected. Pediatric OCD patients did not show any Zalacain, Boedhoe, & Alonso, 2019). Moreover, we expect that mul- detectable microstructural abnormalities compared to matched timodal imaging approaches, combining data on morphological fea- healthy controls. These results suggest that in adult OCD patients tures with measurements of white matter integrity and information the reported microstructural alterations in projection and association on network function using resting-state fMRI, will be more informa- fibers to posterior brain regions are partly related to disease course tive for predicting treatment response at the individual level using and medication status, and thus are likely more a consequence than machine learning analyses. a cause of disease. Since brain morphometry is plastic and changes under the influ- ence of daily behavior and network function, the next level of under- 1.6 | Ongoing and future analyses within standing is to link these morphometric features of disease to ENIGMA-OCD alterations in brain network function, using fMRI scans, both at rest and during specific cognitive or emotional states. As already shown in previous meta-analyses across related mental dis- orders, some neural correlates are common across mental disorders, whereas others are more disorder-specific (Goodkind et al., 2015; 1.5 | New perspectives with clinical relevance Radua et al., 2010). The ENIGMA consortium, with different working groups using the same pipelines for data processing and data analysis, The ENIGMA-OCD consortium has already demonstrated significant is an ideal setting for studying common and distinct features of mental scientific relevance by increasing our understanding of the disorder. disorders. Our first attempt to do so focused on neurodevelopmental Large-scale worldwide collaboration has also been relevant to thinking disorders in the impulsive-compulsive spectrum, comparing OCD with through more effective and efficient ways of doing science by bring- ASD and ADHD. Using structural T1-weighted MRI scans from ing together data, expertise, and a critical mass of investigators. 151 cohorts worldwide with data from patients with OCD (n = 2,323), Regarding clinical significance, the machine learning analyses of mor- ASD (n = 1,777), and ADHD (n = 2,271), and matched healthy controls phological measures of the brain are not yet valuable diagnostically (n = 5,827), we performed a mega-analysis of cortical thickness, sur- (Bruin et al., 2019), and we certainly do not need an MRI scan to diag- face area and subcortical volume across groups, with separate ana- nose OCD. However, performance is likely to improve with the inclu- lyses for children (<12 years), adolescents (12–17 years), and adults sion of multimodal features from diffusion-weighted and functional (>18 years) (Boedhoe et al., 2019). We found no shared alterations MRI. However, several important lessons with clinical relevance have among all three disorders (even with uncorrected analyses), while emerged. One example is the unexpected, but robust finding that shared alterations between any two disorders did not survive correc- medication status and disease chronicity both have a marked impact tion for multiple comparisons. Children with ADHD compared to on all brain measures analyzed to date. This has two direct implica- those with OCD had smaller hippocampal volumes. Children and ado- tions: First, it emphasizes the importance of establishing longitudinal lescents with ADHD also had smaller intracranial volume than control studies to examine the beneficial and potential harmful effects of children and those with OCD or ASD. Adults with ASD showed medication on the developing brain and; second, the fact that disease thicker frontal cortices compared to adult controls and other clinical chronicity is related to marked morphological alterations of the brain groups. OCD-specific alterations were not found across different age- might provide additional incentive to invest in the implementation of groups and no surface area alterations were found across the disor- state-of-the-art interventions (e.g., exposure treatment requires sig- ders. Based on the literature, future cross-disorder comparisons nificant scale up across the globe) and the development of innovative between OCD and other anxiety disorders, as previously done by treatments for those who do not respond to first line interventions. Radua et al. (2010), as well as comparison with schizophrenia, which is Another example of clinical relevance of ENIGMA-OCD is the also characterized by a bigger pallidum (Van Erp et al., 2016) may be potential for discovering prognostic biomarkers, that is, the predic- useful. tion of treatment response based on morphometric and/or func- Another direction of research that is currently in preparation is tional brain signatures. For instance, response to cognitive behavior the meta-analysis of resting-state functional MRI. Resting-state fMRI 32 VAN DEN HEUVEL ET AL. (rs-fMRI) can provide multiple measures of functional connectivity investigated nearly 40,000 individuals, and found that more than that are thought to index the communication between distinct brain 45 genetic loci were significantly associated with subcortical volumes. regions. A recent meta-analysis based on reported findings in the liter- Analyses indicated that associated genes are implicated in neuro- ature found that multiple resting-state networks are affected in OCD development, synaptic signaling, axonal transport, and other key pro- (Gursel et al., 2018). Moreover, a single-site study suggests that rs- cesses. Other work has focused on CNVs: in the largest CNV fMRI can predict the outcome of CBT (Reggente et al., 2018). To neuroimaging study to date, for example, 15q11.2 BP1-BP2 structural obtain robust estimations of functional connectivity abnormalities in variation was found associated with brain morphology and cognition, OCD, the consortium aims to perform functional connectivity ana- with deletion carriers being particularly affected (Writing Committee lyses using the fmriprep + pipeline that has been developed by Veer for the ENIGMA-CNV Working Group., 2019). There is a clear poten- and colleagues (Charité, Berlin). This pipeline uses the fMRI tial to extend such work to specific disorders such as OCD. In a proof preprocessing stages from fmriprep (Esteban et al., 2019), and outputs of principle investigation, ENIGMA-OCD has explored the overlap in various rs-fMRI measures such as resting-state networks based on genetic contributions to subcortical volumes and OCD (Hibar et al., independent components analysis and (partial) correlation matrices 2018); significant positive concordance was found between OCD risk between distinct brain regions. Initially, the consortium will analyze variants and variants associated with greater nucleus accumbens and average group differences in these measures between OCD patients putamen volumes. Further work is needed to expand on these prelimi- and controls, and subsequently use machine-learning methods to nary findings. While the combined imaging-genetics database is still determine whether rs-fMRI can provide a biomarker for OCD. Fur- small, a polygenic risk score approach may be a useful first step. thermore, the same methods will be used in order predict CBT out- come at the group and individual patient level. We hope that this endeavor will provide a biomarker for treatment outcome that gener- 1.7 | Limitations of the ENIGMA approach alizes to different sites, and thereby paves the way towards personal- ized treatment for OCD. In the future, we hope to extend rs-fMRI Three important methodological weaknesses of the ENIGMA-OCD meta-analysis in ENIGMA-OCD to task-based fMRI, focusing on fre- concept should be mentioned. First, the data collection was done in quently used paradigms such as emotional processing, response inhi- the past, at a time when there was no plan to use the data in world- bition (and other executive tasks), and decision-making. wide collaboration. Therefore, there has been no harmonization of Advances in large-scale neuroimaging studies have allowed the data acquisition, either with respect to imaging data or with respect to characterization of distinct group differences in the macroscopic inclusion criteria and clinical data. Various MRI machines (with differ- structure of the cerebral cortex, across several psychiatric disorders ent field strengths) and various scan sequences have been used with (Thompson et al., 2019). Using a new bioinformatics approach, named inevitable variations in data quality. In addition, the level of detail on virtual histology, we can now link these macroscopic differences with the clinical phenotypes (disease severity, comorbidity, disease course, microscopic histological features within the human cerebral cortex treatment history, etc.) is limited, so disallowing detailed analyses of (Patel, Shin, Gowland, Pausova, & Paus, 2018; Shin et al., 2018). In symptom profiles or cross-disorder characteristics. Second, the data brief, virtual histology relates variation in cell-specific gene expression collection has been mostly cross-sectional, limiting our ability to profiles across the 34 regions of the Desikan-Killiany atlas with pro- answer questions related to disease course, effects of medication, and files of group differences in cortical thickness across the same cause versus consequence of disease. Longitudinal data is highly 34 regions (Desikan et al., 2006). This approach combines gene warranted, and with the increase in pre-/post-treatment imaging stud- expression data from the Allen Human Brain Atlas (Hawrylycz et al., ies, we expect that more longitudinal data will soon become available. 2012), and cell-specific markers from single-cell RNA sequencing Family and particularly twin studies could go a long way to disentan- (Zeisel et al., 2015) with MRI derived measures. Profiles of group dif- gle vulnerability from disease chronicity and medication. Third, only ferences in cortical thickness are generated using a meta-analytic some of the imaging samples also have genotyping data, presenting a approach for each of six major psychiatric disorders (ASD, ADHD, challenge, since for imaging-genetic analyses large samples are bipolar disorder, major depressive disorder, OCD, and schizophrenia). needed. Currently, there are 12,006 cases and 14,842 controls contributing to these profiles. The goal of the project is to characterize cell types within the human cortex (neurons [pyramidal cells and interneurons] 1.8 | Summary and future directions in global and glia [astrocytes, microglia, oligodendrocytes]) in which gene collaborations expression is correlated with differences in cortical thickness across disorders, in the hope of illustrating common and/or unique neurobio- ENIGMA-OCD has brought together OCD experts from all over the logical correlates between these disorders. world to re-analyze historical MRI data using meta- and mega-ana- Ultimately we would like to combine imaging data with genetic lyses. This has not only resulted in important contributions to the lit- information. ENIGMA has developed pipelines for integrated analyses erature on brain alterations in OCD, but has also stimulated of both neuroimaging and genetic data. Some of this work has international collaboration and sharing of OCD imaging data from focused on common variants: Satizabal et al. (2019), for example, sites across the globe. While as outlined earlier, there are many VAN DEN HEUVEL ET AL. 33 questions still to address in the growing ENIGMA-OCD dataset, the Korea (NRF) funded by the Ministry of Education (NRF- findings and the successful model of international collaboration have 2017R1D1A1B03028464); H.B.S. is supported by National Institute of already spurred new studies. For example, to address the lack of har- Mental Health R01 MH104648 and R21 MH093889; M.H. is monization of data acquisition and the lack of detail on clinical profile, supported by a personal Veni grant of The Netherlands Organization and to address the confound of medication in the ENIGMA-OCD for Scientific Research (NWO, grant number 91619115); J.K.B. is dataset, five of the participating ENIGMA-OCD sites, representing supported by the European Community's Seventh Framework Pro- five continents, are now funded by NIMH (PI: H. Blair Simpson) to gramme (FP7/2007-2013) under grant agreement number 278948 conduct the largest neurocognitive and multimodal-imaging study in (TACTICS); P.A. is supported by Alberta Innovates Translational Health medication-free subjects with OCD to date. Specifically, we will Chair in Child and Youth Mental Health and the Ontario Brain Institute; recruit 250 medication-free adults with OCD, 100 unaffected adult K.F. is supported by National Institute of Mental Health K23 siblings of individuals with OCD, and 250 healthy control subjects. All MH082176 and Dana Foundation; P.G. is supported by NIH K23 will receive clinical evaluation, neurocognitive assessment, and MRI, MH115206; Y.H. is supported by Brain/MINDS Beyond program from focusing on morphometry (T1-weighted MRI), structural connectivity the Japan Agency for Medical Research and Development (AMED) (DTI), and functional connectivity (rs fMRI). To ensure harmonized (JP19dm0307002) and Japan Society for the Promotion of Science data collection, we have developed detailed protocols to ensure (JSPS) KAKENHI (16K04344 and 19K03309); K.K. is supported by a cross-site reliability on clinical measures, standardized delivery of Deutsche Forschungsgemeinschaft (DFG) grant (KO 3744/7-1); L.L. is neurocognitive tasks, and optimal MRI data acquisition for data supported by the Marató TV3 Foundation grants (01/2010 and pooling (see Simpson et al., 2020 for details). Following best-practices 091710); J.Y.C.R. is supported by DST INSPIRE faculty grant used in large-scale imaging consortia, the MRI quality procedures (IFA12-LSBM-26) of the Department of Science and Technology and include: Harmonization of scan sequences to optimize scan quality BT/06/IYBA/2012 of the Department of Biotechnology, Government across scanners; both human and physical phantom studies at study of India; E.N. is supported by National Institute of Mental Health start, with repeat phantom studies and preprocessing of all scans in R01MH085900 and R01MH081864; J.C.N. is supported by DST real-time to ensure maintenance of scan quality over the course of INSPIRE faculty grant (IFA12-LSBM-26) of the Department of Science study recruitment. Other ENIGMA-OCD sites that start new data col- and Technology and BT/06/IYBA/2012 of the Department of Biotech- lection might consider using similar methods, to increase the number nology, Goverment of India; N.S. is supported by the Ontario Brain of samples with higher quality harmonized data. Institute; S.E.S. is supported by Canadian Institutes of Health Research, To conclude, large-scale data-sharing as facilitated by ENIGMA- Michael Smith Foundation for Health Research, BC Children's Hospital OCD has accelerated discovery and generated novel hypotheses that Foundation; G.V. is supported by Wellcome-DBT India Alliance grant are now being pursued. It has also created a model for large-scale (500236/Z/11/Z); S.W. is supported by Swiss National Science Foun- global collaboration across OCD experts, and even more broadly, dation grant (320030_130237); Z.W. is supported by National Natural across working groups focused on a range of disorders and methodol- Science Foundation of China (No. 81671340) and grants from Shanghai ogies. By joining forces, we can help to shift the research model from Municipal Health Commission (2019ZB0201); P.M.T. is supported by local and mono-disciplinary to global and multidisciplinary, increase NIH U54 EB020403; T.N. is supported by the Japanese Ministry of rigor and transparency, and accelerate discovery. In the end, we share Education, Culture, Sports, Science, and Technology (MEXT KAKENHI a common goal: To develop a circuit-based approach to different cog- No. 18K07608). nitive and clinical dimensions of OCD that can help transform how OCD and related mental disorders are conceptualized, diagnosed, and DISCLOS URES ultimately treated around the world. O.A.vdH.: Lecture honorarium Benecke. II: Data safety monitoring committee Lundbeck. H.B.S.: Research Support from Biohaven Phar- ACKNOWLEDGMENTS maceuticals for mutisite industry-sponsored clinical trial, royalties O.A.vdH. is supported by The Netherlands Organization for Scientific from UpToDate Inc and Cambridge University Press, stipend from the Research (NWO/ZonMW Vidi 91717306); C.F. is supported by funding American Medical Association for servicing as Associate Editor of from the Max Planck Society (Germany); N.J. is supported by NIH JAMA Psychiatry. N.J.: MPI of a research related grant from Biogen R01MH117601 and R01AG059874; J.O. is supported by National Inc, for work unrelated to the contents of this manuscript. J.K.B.: has Institute of Mental Health R01MH085900 and R01MH081864; L.S. is been in the past 3 years a consultant to/member of advisory board supported by the National Institute of Mental Health of the National of/and/or speaker for Shire, Roche, Medice, and Servier—he is not an Institutes of Health (R01MH117601) and a NHMRC Career Develop- employee of any of these companies, and not a stock shareholder of ment Fellowship (1140764); C.S.M. is supported by Carlos III Health any of these companies—he has no other financial or material support, Institute (PI16/00889; CPII16/00048); G.A.vW. is supported by Neth- including expert testimony, patents, royalties. 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