Long-term grey matter changes in first episode psychosis: a systematic review

Objective: To determine possible progressive changes of the grey matter at the first stages of the schizophrenia spectrum disorders, and to determine what regions are involved in these changes. Methods: We searched the literature concerning studies on longitudinal changes in grey matter in first-episode psychosis using magnetic resonance imaging, especially studies with an interval between scans of more than a year. Only articles published before 2018 were searched. We selected 19 magnetic resonance imaging longitudinal studies that used different neuroimaging analysis techniques to study changes in cerebral grey matter in a group of patients with a first episode of psychosis. Results: Patients with first episode of psychosis showed a decrease over time in cortical grey matter compared with a group of control subjects in frontal, temporal (specifically in superior regions), parietal, and subcortical regions. In addition to the above, studies indicate that patients showed a grey matter decrease in cerebellum and lateral ventricles volume. Conclusion: The results suggest a decrease in grey matter in the years after the first episode of psychosis. Furthermore, the results of the studies showed consistency, regardless of the methods used in their analyses, as well as the time intervals between image collections

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Schizophrenia is associated with widespread alterations in subcortical brain structure.While analytic methods have enabled more detailed morphometric characterization,findings are often equivocal. In this meta-analysis, we employed the harmonizedENIGMA shape analysis protocols to collaboratively investigate subcortical brainstructure shape differences between individuals with schizophrenia and healthy con-trol participants. The study analyzed data from 2,833 individuals with schizophreniaand 3,929 healthy control participants contributed by 21 worldwide research groupsparticipating in the ENIGMA Schizophrenia Working Group. Harmonized shape analy-sis protocols were applied to each site's data independently for bilateral hippocam-pus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained fromT1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens,and thalamus in individuals with schizophrenia compared with control participants,more-convex-than-concave shape differences in the putamen and pallidum, and bothconcave and convex shape differences in the caudate. Patterns of exaggerated asym-metry were observed across the hippocampus, amygdala, and thalamus in individualswith schizophrenia compared to control participants, while diminished asymmetryencompassed ventral striatum and ventral and dorsal thalamus. Our analyses also rev-ealed that higher chlorpromazine dose equivalents and increased positive symptomlevels were associated with patterns of contiguous convex shape differences acrossmultiple subcortical structures. Findings from our shape meta-analysis suggest thatcommon neurobiological mechanisms may contribute to gray matter reduction acrossmultiple subcortical regions, thus enhancing our understanding of the nature of net-work disorganization in schizophrenia

Multimodal integration of brain images for MRI-based diagnosis in schizophrenia

Magnetic resonance imaging (MRI) has been proposed as a source of information for automatic prediction of individual diagnosis in schizophrenia. Optimal integration of data from different MRI modalities is an active area of research aimed at increasing diagnostic accuracy. Based on a sample of 96 patients with schizophrenia and a matched sample of 115 healthy controls that had undergone a single multimodal MRI session, we generated individual brain maps of gray matter vbm, 1back, and 2back levels of activation (nback fMRI), maps of amplitude of low-frequency fluctuations (resting-state fMRI), and maps of weighted global brain connectivity (resting-state fMRI). Four unimodal classifiers (Ridge, Lasso, Random Forests, and Gradient boosting) were applied to these maps to evaluate their classification accuracies. Based on the assignments made by the algorithms on test individuals, we quantified the amount of predictive information shared between maps (what we call redundancy analysis). Finally, we explored the added accuracy provided by a set of multimodal strategies that included post-classification integration based on probabilities, two-step sequential integration, and voxel-level multimodal integration through one-dimensional-convolutional neural networks (1D-CNNs). All four unimodal classifiers showed the highest test accuracies with the 2back maps (80% on average) achieving a maximum of 84% with the Lasso. Redundancy levels between brain maps were generally low (overall mean redundancy score of 0.14 in a 0-1 range), indicating that each brain map contained differential predictive information. The highest multimodal accuracy was delivered by the two-step Ridge classifier (87%) followed by the Ridge maximum and mean probability classifiers (both with 85% accuracy) and by the 1D-CNN, which achieved the same accuracy as the best unimodal classifier (84%). From these results, we conclude that from all MRI modalities evaluated task-based fMRI may be the best unimodal diagnostic option in schizophrenia. Low redundancy values point to ample potential for accuracy improvements through multimodal integration, with the two-step Ridge emerging as a suitable strategy.This work was supported by several grants funded by the Instituto de Salud Carlos III (co-funded by the European Regional Development Fund/European Social Fund “Investing in your future”): Miguel Servet Research Contract (CPII16/00018 to EP-C), Sara Borrell Research Contract (CD18/00029 to EC-R), Research Projects (PI14/01151 and PI18/00877 to RS, PI14/01148 to EP-C, PI18/00880 to PM, and PI17/01056 to DT-G), and an intramural grant (SAM18PI13 from CIBERSAM). It was also supported by the Generalitat de Catalunya: 2014SGR1573 to EP-C and SLT006/17/357 from the Departament de Salut to RS. The funding organizations played no role in the study design, data collection and analysis, or manuscript approval

A Disrupted-in-Schizophrenia 1 Gene Variant is Associated with Clinical Symptomatology in Patients with First-Episode Psychosis

OBJECTIVE: DISC1 gene is one of the main candidate genes for schizophrenia since it has been associated to the illness in several populations. Moreover, variations in several DISC1 polymorphisms, and in particular Ser704Cys SNP, have been associated in schizophrenic patients to structural and functional modifications in two brain areas (pre-frontal cortex and hippocampus) that play a central role in the genesis of psychotic symptoms. This study tested the association between Ser704Cys DISC1 polymorphism and the clinical onset of psychosis. METHODS: Two hundred and thirteen Caucasian drug-naive patients experiencing a first episode of non-affective psychosis were genotyped for rs821616 (Ser704Cys) SNP of the DISC1 gene. The clinical severity of the illness was assessed using SAPS and SANS scales. Other clinical and socio-demographic variables were recorded to rule out possible confounding effects. RESULTS: Patients homozygous for the Ser allele of the Ser704Cys DISC1 SNP had significantly (p<0.05) higher rates at the positive symptoms dimension (SAPS-SANS scales) and hallucinations item, compared to Cys carriers. CONCLUSION: DISC1 gene variations may modulate the clinical severity of the psychosis at the onset of the disorde

Pattern of long-Term weight and metabolic changes after a first-episode of psychosis: Results from a 10-years prospective follow-up of the PAFIP cohort

Background: People with psychosis are at higher risk of cardiovascular events, partly explained by a higher predisposition to gain weight. This has been observed in studies on individuals with a first-episode psychosis (FEP) at short and long term (mainly up to 1 year) and transversally at longer term in people with chronic schizophrenia. However, there is scarcity of data regarding longer-term (above 3-year follow-up) weight progression in FEP from longitudinal studies. The aim of this study is to evaluate the longer-term (10 years) progression of weight changes and related metabolic disturbances in people with FEP. Methods: Two hundred and nine people with FEP and 57 healthy participants (controls) were evaluated at study entry and prospectively at 10-year follow-up. Anthropometric, clinical, and sociodemographic data were collected. Results: People with FEP presented a significant and rapid increase in mean body weight during the first year of treatment, followed by less pronounced but sustained weight gain over the study period (?15.2 kg; SD 12.3 kg). This early increment in weight predicted longer-term changes, which were significantly greater than in healthy controls (?2.9 kg; SD 7.3 kg). Weight gain correlated with alterations in lipid and glycemic variables, leading to clinical repercussion such as increments in the rates of obesity and metabolic disturbances. Sex differences were observed, with women presenting higher increments in body mass index than men. Conclusions: This study confirms that the first year after initiating antipsychotic treatment is the critical one for weight gain in psychosis. Besides, it provides evidence that weight gain keep progressing even in the longer term (10 years), causing relevant metabolic disturbances

Understanding sex differences in long-term outcomes after a first episode of psychosis

While sex differences in schizophrenia have long been reported and discussed, long-term sex differences in outcomes among first episode of psychosis (FEP) patients in terms of the efficacy of Early Intervention Services (EIS) has been an under-explored area. A total of 209 FEP patients (95 females and 114 males) were reassessed after a time window ranging from 8 to 16 years after their first contact with an EIS program (PAFIP) that we will call the 10-year PAFIP cohort. Multiple clinical, cognitive, functioning, premorbid, and sociodemographic variables were explored at 1-year, 3-year and 10-year follow-ups. At first contact, females were older at illness onset, had higher premorbid adjustment and IQ, and were more frequently employed, living independently, and accompanied by a partner and/or children. Existence of a schizophrenia diagnosis, and cannabis and alcohol consumption were more probable among men. During the first 3 years, women showed a significantly better response to minimal antipsychotic dosages and higher rates of recovery than men (50% vs. 30.8%). Ten years later, more females continued living independently and had partners, while schizophrenia diagnoses and cannabis consumption continued to be more frequent among men. Females also presented a lower severity of negative symptoms; however, functionality and recovery differences did not show significant differences (46.7% vs. 34.4%). Between the 3- and 10-year follow-up sessions, an increase in dosage of antipsychotics was observed. These results suggest that the better outcomes seen among women during the first 3 years (while they were treated in an EIS) were in the presence of more favourable premorbid and baseline characteristics. After an average period of 10 years, with the only difference being in negative symptoms course, outcomes for women approximated those of men, drawing particular attention to the increase in dosage of antipsychotic medication once FEP patients were discharged from the EIS program towards community-based services. These findings help to pose the question of whether it is advisable to target sexes and lengthen EIS interventions

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

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3?90?years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.This study presents independent research funded by multiple agen-cies. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data. The views expressed inthe manuscript are those of the authors and do not necessarily repre-sent those of any of the funding agencies. Dr. Dima received fundingfrom the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS FoundationTrust and King's College London, the Psychiatry Research Trust and2014 NARSAD Young Investigator Award. Dr. Frangou received sup-port from the National Institutes of Health (R01 MH104284,R01MH113619, R01 MH116147), the European Community's Sev-enth Framework Programme (FP7/2007–2013) (grant agreementn 602450). This work was supported in part through the computa-tional resources and staff expertise provided by Scientific Computingat the Icahn School of Medicine at Mount Sinai, USA. Dr. Agartz wassupported by the Swedish Research Council (grant numbers:521-2014-3487 and 2017-00949). Dr. Alnæs was supported by theSouth Eastern Norway Regional Health Authority (grant number:2019107). Dr. O Andreasen was supported by the Research Councilof Norway (grant number: 223273) and South-Eastern Norway HealthAuthority (grant number: 2017-112). Dr. Cervenka was supported bythe Swedish Research Council (grant number 523-2014-3467).Dr. Crespo-Facorro was supported by the IDIVAL Neuroimaging Unitfor imaging acquisition; Instituto de Salud Carlos III (grant numbers:PI020499, PI050427, PI060507, PI14/00639 and PI14/00918) andthe Fundación Instituto de Investigación Marqués de Valdecilla (grantnumbers: NCT0235832, NCT02534363, and API07/011). Dr. Gurwas supported by the National Institute of Mental Health (grant num-bers: R01MH042191 and R01MH117014). Dr. James was supportedby the Medical Research Council (grant no G0500092). Dr. Saykinreceived support from U.S. National Institutes of Health grants R01AG19771, P30 AG10133 and R01 CA101318. Dr. Thompson,Dr. Jahanshad, Dr. Wright, Dr. Medland, Dr. O Andreasen, Dr. Rinker,Dr. Schmaal, Dr. Veltam, Dr. van Erp, and D.P.H. were supported inpart by a Consortium grant (U54 EB020403 to P.M.T.) from the NIHInstitutes contributing to the Big Data to Knowledge (BD2K) Initiative.FBIRN sample: Data collection and analysis was supported by the National Center for Research Resources at the National Institutes ofHealth (grant numbers: NIH 1 U24 RR021992 (Function BiomedicalInformatics Research Network) and NIH 1 U24 RR025736-01(Biomedical Informatics Research Network Coordinating Center;http://www.birncommunity.org). FBIRN data was processed by theUCI High Performance Computing cluster supported by the NationalCenter for Research Resources and the National Center for AdvancingTranslational Sciences, National Institutes of Health, through GrantUL1 TR000153. Brainscale: This work was supported by NederlandseOrganisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 toH.H., NWO 51.02.062 to D.B., NWO- NIHC Programs of excellence433-09-220 to H.H., NWO-MagW 480-04-004 to D.B., andNWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European ResearchCouncil (ERC-230374 to D.B.); and Universiteit Utrecht (High Poten-tial Grant to H.H.). UMCU-1.5T: This study is partially funded throughthe Geestkracht Programme of the Dutch Health Research Council(Zon-Mw, grant No 10-000-1001), and matching funds from partici-pating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly,Janssen Cilag) and universities and mental health care organizations(Amsterdam: Academic Psychiatric Centre of the Academic MedicalCenter and the mental health institutions: GGZ Ingeest, Arkin, Dijk enDuin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Hol-land Noord. Groningen: University Medical Center Groningen and themental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dim-ence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassiapsycho-medical center The Hague. Maastricht: Maastricht UniversityMedical Centre and the mental health institutions: GGzE, GGZBreburg, GGZ Oost-Brabant, Vincent van Gogh voor GeestelijkeGezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz,Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Ant-werp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZOverpelt, OPZ Rekem. Utrecht: University Medical Center Utrechtand the mental health institutions Altrecht, GGZ Centraal and Delta.).UMCU-3T: This study was supported by NIMH grant number: R01MH090553 (to RAO). The NIMH had no further role in study design,in the collection, analysis and interpretation of the data, in the writingof the report, and in the decision to submit the paper for publication.Netherlands Twin Register: Funding was obtained from the Nether-lands Organization for Scientific Research (NWO) and The NetherlandsOrganization for Health Research and Development (ZonMW) grants904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004,463-06-001, 451-04-034, 400-05-717, 400-07-080, 31160008,016-115-035, 481-08-011, 056-32-010, 911-09-032, 024-001-003,480-15-001/674, Center for Medical Systems Biology (CSMB, NWOGenomics), Biobanking and Biomolecular Resources Research Infra-structure (BBMRI-NL, 184.021.007 and 184.033.111); Spinozapremie(NWO- 56-464-14192), and the Neuroscience Amsterdam researchinstitute (former NCA). The BIG database, established in Nijmegen in2007, is now part of Cognomics, a joint initiative by researchers of theDonders Centre for Cognitive Neuroimaging, the Human Genetics andCognitive Neuroscience departments of the Radboud University Medi-cal Centre, and the Max Planck Institute for Psycholinguistics. TheCognomics Initiative is supported by the participating departments and centers and by external grants, including grants from the Biobankingand Biomolecular Resources Research Infrastructure (Netherlands)(BBMRI-NL) and the Hersenstichting Nederland. The authors alsoacknowledge grants supporting their work from the Netherlands Orga-nization for Scientific Research (NWO), that is, the NWO Brain & Cog-nition Excellence Program (grant 433-09-229), the Vici InnovationProgram (grant 016-130-669 to BF) and #91619115. Additional sup-port is received from the European Community's Seventh FrameworkProgramme (FP7/2007–2013) under grant agreements n 602805(Aggressotype), n 603016 (MATRICS), n 602450 (IMAGEMEND), andn 278948 (TACTICS), and from the European Community's Horizon2020 Programme (H2020/2014–2020) under grant agreements n 643051 (MiND) and n 667302 (CoCA). Betula sample: Data collectionfor the BETULA sample was supported by a grant from Knut and AliceWallenberg Foundation (KAW); the Freesurfer segmentations wereperformed on resources provided by the Swedish National Infrastruc-ture for Computing (SNIC) at HPC2N in Umeå, Sweden. Indiana sample:This sample was supported in part by grants to BCM from SiemensMedical Solutions, from the members of the Partnership for PediatricEpilepsy Research, which includes the American Epilepsy Society, theEpilepsy Foundation, the Epilepsy Therapy Project, Fight Against Child-hood Epilepsy and Seizures (F.A.C.E.S.), and Parents Against ChildhoodEpilepsy (P.A.C.E.), from the Indiana State Department of Health SpinalCord and Brain Injury Fund Research Grant Program, and by a ProjectDevelopment Team within the ICTSI NIH/NCRR Grant NumberRR025761. MHRC study: It was supported in part by RFBR grant20-013-00748. PING study: Data collection and sharing for the Pediat-ric Imaging, Neurocognition and Genetics (PING) Study (National Insti-tutes of Health Grant RC2DA029475) were funded by the NationalInstitute on Drug Abuse and the Eunice Kennedy Shriver National Insti-tute of Child Health & Human Development. A full list of PING investi-gators is at http://pingstudy.ucsd.edu/investigators.html. QTIM sample:The authors are grateful to the twins for their generosity of time andwillingness to participate in our study and thank the many researchassistants, radiographers, and other staff at QIMR Berghofer MedicalResearch Institute and the Centre for Advanced Imaging, University ofQueensland. QTIM was funded by the Australian National Health andMedical Research Council (Project Grants No. 496682 and 1009064)and US National Institute of Child Health and Human Development(RO1HD050735). Lachlan Strike was supported by a University ofQueensland PhD scholarship. Study of Health in Pomerania (SHIP): thisis part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald,which is supported by the German Federal State of Mecklenburg- WestPomerania. MRI scans in SHIP and SHIP-TREND have been supportedby a joint grant from Siemens Healthineers, Erlangen, Germany and theFederal State of Mecklenburg-West Pomerania. This study was furthersupported by the DZHK (German Centre for Cardiovascular Research),the German Centre of Neurodegenerative Diseases (DZNE) and theEU-JPND Funding for BRIDGET (FKZ:01ED1615). TOP study: this wassupported by the European Community's Seventh Framework Pro-gramme (FP7/2007–2013), grant agreement n 602450. The Southernand Eastern Norway Regional Health Authority supported Lars T. Westlye (grant no. 2014-097) and STROKEMRI (grantno. 2013-054). HUBIN sample: HUBIN was supported by the SwedishResearch Council (K2007-62X-15077-04-1, K2008-62P-20597-01-3,K2010-62X-15078-07-2, K2012-61X-15078-09-3), the regional agree-ment on medical training and clinical research between StockholmCounty Council, and the Karolinska Institutet, and the Knut and AliceWallenberg Foundation. The BIG database: this was established in Nij-megen in 2007, is now part of Cognomics, a joint initiative byresearchers of the Donders Centre for Cognitive Neuroimaging, theHuman Genetics and Cognitive Neuroscience departments of theRadboud university medical centre, and the Max Planck Institute forPsycholinguistics. The Cognomics Initiative is supported by the partici-pating departments and centres and by external grants, including grantsfrom the Biobanking and Biomolecular Resources Research Infrastruc-ture (Netherlands) (BBMRI-NL) and the Hersenstichting Nederland. Theauthors also acknowledge grants supporting their work from the Neth-erlands Organization for Scientific Research (NWO), that is, the NWOBrain & Cognition Excellence Program (grant 433-09-229), the ViciInnovation Program (grant 016-130-669 to BF) and #91619115. Addi-tional support is received from the European Community's SeventhFramework Programme (FP7/2007–2013) under grant agreements n 602805 (Aggressotype), n 603016 (MATRICS), n 602450(IMAGEMEND), and n 278948 (TACTICS), and from the EuropeanCommunity's Horizon 2020 Programme (H2020/2014–2020) undergrant agreements n 643051 (MiND) and n 667302 (CoCA)

Neuroharmony: a new tool for harmonizing volumetric MRI data from unseen scanners

We present Neuroharmony, a harmonization tool for images from unseen scanners. We developed Neuroharmony using a total of 15,026 sMRI images. The tool was able to reduce scanner-related bias from unseen scans. Neuroharmony represents a significant step towards imaging-based clinical tools.This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust’s Innovator Award (208519/Z/17/Z) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong (81220108013, 8122010801, 81621003, 81761128023 and 81227002). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, under the following NIH grants: 1R43NS074540, 2R44NS074540, and 1U24EB023398and previously GSA Contract No. GS-00F-0034P, Order Number HHSN268200100090U. This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Repository Without Borders (ARWiBo – www.arwibo.it). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID (https://neugrid2.eu), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer’ and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/ P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network (www.mrn.org). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581. MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ‘Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis’, TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database (http://central.xnat.org/REST/projects/NUDataSharing) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]. Part of the data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435. Jo~ao Sato is supported by Sao Paulo Research Foundation (FAPESP, Brazil) Grants 2018/04654-9 and 2018/21934-5

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

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.Funding information: European Union's Horizon2020 Research and Innovation Programme, Grant/Award Number: CoMorMent project; Grant #847776; KG Jebsen Stiftelsen; National Institutes of Health, Grant/Award Number: U54 EB020403; Norges Forskningsråd, Grant/Award Number: #223273; South-Eastern Norway Regional Health Authority, Grant/Award Number: #2020060ACKNOWLEDGMENTS: The ENIGMA Consortium is supported by the NIH Big Data to Knowledge (BD2K) program under consortium grant number U54 EB020403 (PI: Thompson). OAA is supported by the Research Council of Norway, South East Norway Health Authority, KG Jebsen Stiftelsen, EU H2020. C. A. has been funded by the Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/ 024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7-4-HEALTH-2009-2.2.1-2-241,909 (Project EU-GEI), FP7- HEALTH-2013-2.2.1-2-603,196 (Project PSYSCAN) and FP7- HEALTH-2013- 2.2.1-2-602,478 (Project METSY); and European Union H2020 Program under the Innovative Medicines Initiative two Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso and Fundación Alicia Koplowitz. R. A-A is funded by a Miguel Servet contract from the Carlos III Health Institute (CP18/00003). G. B. is supported by the Dutch Organization for Health Research and Development ZonMw (grants 91112002 & 91712394). A. S. B. is supported by the Dalglish Family Chair in 22q11.2 Deletion Syndrome, Canadian Institutes of Health Research (CIHR) grants MOP-79518, MOP89066, MOP-97800 and MOP-111238, and NIMH grant number U01 MH101723–01(3/5). C. E. B. is also supported by the National Institute of Mental Health: RO1 MH085953, R01 MH100900 and 1U01MH119736. N. E. B. is granted the KNAW Academy Professor Award (PAH/6635). V. D. C. is supported by NIH R01 MH094524. S. C. is supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3); Helmholtz Initiative and Networking Fund. C. R. K. C. is supported by NIA T32AG058507. E. W. C. C. is supported by the Canadian Institutes of Health Research, Ontario Mental Health Foundation grant MOP-74631 and NIMH grant U01MH101723–01(3/5). S. Ci. has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). M. C. C. is supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. N. A. C. is supported by Agencia Nacional de Investigación y Desarrollo (ANID Chile) PIA ACT192064. GId. Z. is supported by the NHMRC. J. L. D. and D. E. J. L. are supported by the Wellcome Trust. T. B. C. is supported by NICHD grant PO1-HD070454, NIH grant UO1-MH191719, and NIMH grant R01 MH087636-01A1. AMD is supported by U24DA041147. B. D. is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant numbers 32003B_135679, 32003B_159780, 324730_192755 and CRSK3_190185), the Leenaards Foundation and the Roger De Spoelberch Foundation. SE is supported by the NARSAD-Young Investigator Grant “Epigenetic Regulation of Intermediate Phenotypes in Schizophrenia”. B. E. S. is supported by the NIH (NIMH). D. C. G. is supported by NIH grant numbers MH078143, MH083824, AG058464. W. R. K. is supported by NIH/MH R0106824. R. E. G. is supported by NIH/NIMH grant numbers MH087626, MH119737. DMMcD-McG is supported by National Institutes of Mental Health (NIMH), grant numbers MH119737-02; MH191719; and MH087636-01A1. S. E. M. is supported by NHMRC grants APP1103623; APP1158127; APP1172917. TM is supported by Research Council of Norway - grant number 273345. D. G. M. is supported by the National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and S (European Autism Interventions)/EU AIMS-2-TRIALS, a European Innovative Medicines Initiative Joint Undertaking under grant agreements 115300 and 777394. T. N. was supported by Stiftelsen KG Jebsen under grant number SKGJ-MED-021. R. A. O. is supported by NIMH R01 MH090553. S. Y. S. has been funded by the Canadain Institutes of Health Research. M. J. O. is supported by MRC Centre grant MR/L010305/1 and Wellcome Trust grant 100,202/Z/12/Z; Dr. Owen has received research support from Takeda. Z. P. is supported by CIHR, CFI, HSFC. B. G. P. is supported by CIHR FDN 143290 and CAIP Chair. G. M. R. is supported by Fondecyt-Chile #1171014 and ANID-Chile ACT192064. A. Re. was supported by a grant from the Swiss National Science Foundation (31003A_182632). DRR is supported by R01 MH120174 (PI: Roalf). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London (to J. J. R). PSS is supported by NHMRC (Australia) program grant 1093083. J. E. S. is supported by NIH K01-ES026840. S. M. S. is supported by the Epilepsy Society. T. J. S. is supported by NIH grants R01MH107108, R01HD042794, and HDU54079125. I. E. S. is supported by South-Eastern Norway Regional Health Authority (#2020060), European Union's Horizon2020 Research and Innovation Programme (CoMorMent project; grant #847776) and the KG Jebsen Foundation (SKGJ-MED-021). V. M. S. is supported by Research Council of Norway (CoE funding scheme, grant number 223273). D. J. S. is supported by the SA MRC. C. K. T. is supported by Research Council of Norway (#230345, #288083, #223273) and South-Eastern Norway Regional Health Authority (#2019069, #2021070, #500189). D. T.-G. was supported by the Instituto de Salud Carlos III (PI14/00639 and PI14/00918) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT02534363). Dvd. M. is supported by Research Council of Norway #276082. F. V. R. is supported by the Michael Smith Foundation for Health Research Scholar Award. deCODE genetics has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements' no. 115008 (NEWMEDS) and no. 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (EU-FP7/ 2007–2013). L. T. W. is supported by Research Council of Norway, European Research Council. The IDIVAL neuroimage unit is supported by Instituto de Salud Carlos III PI020499, research funding SCIII-INT13/0014, MICINN research funding SAF2010-20840-C02- 02, SAF2013-46292-R. The TOP/NORMENT study are supported by the Research Council of Norway (#223273). The GOBS study data collection was supported in part by the National Institutes of Health (NIH) grants: R01 MH078143, R01 MH078111, and R01 MH083824 with work conducted in part in facilities constructed under the support of NIH grant number C06 RR020547. The Sydney Memory and Ageing Study has been funded by three National Health & Medical Research Council (NHMRC) Program Grants (ID No. ID350833, ID568969, and APP1093083). We thank the participants and their informants for their time and generosity in contributing to this research. We also acknowledge the MAS research team: https://cheba.unsw.edu.au/researchprojects/sydney-memory-and-ageing-study. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/ project/older-australian-twins-study) to this study. The OATS study has been funded by a National Health & Medical Research Council (NHMRC) and Australian Research Council (ARC) Strategic Award Grant of the Aging Well, Aging Productively Program (ID No. 401162); NHMRC Project (seed) Grants (ID No. 1024224 and 1025243); NHMRC Project Grants (ID No. 1045325 and 1085606); and NHMRC Program Grants (ID No. 568969 and 1093083). We thank the participants for their time and generosity in contributing to this research. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID No. 1079102) from the National Health and Medical Research Council. The NCNG sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the KG Jebsen Foundation, the Research Council of Norway, to S. L. H., V. M. S., A. J. L., and T. E. The authors thank Dr. Eike Wehling for recruiting participants in Bergen, and Professor Jonn-Terje Geitung and Haraldplass Deaconess Hospital for access to the MRI facility. Additional support by RCN grants 177458/V50 and 231286/F20. The Betula study was supported by a Wallenberg Scholar Grant (KAW). The HUNT Study is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU—Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. Research for the GAP cohort was supported by the Department of Health via the National Institute for Health Research (NIHR) Specialist Biomedical Research Center for Mental Health award to South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry at King's College London, London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. S.J. is supported by Calcul Quebec (http:// www.calculquebec.ca), Compute Canada (http://www.computecanada. ca), the Brain Canada Multi investigator research initiative (MIRI), the Institute of Data Valorization (Canada First Research Excellence Fund), CHIR, Canada Research Chairs and the Jeanne et Jean Louis Levesque Foundation. The NTR cohort was supported by the Netherlands Organization for Scientific Research (NWO), MW904-61-193 (de Geus & Boomsma), MaGWnr: 400-07-080 (van 't Ent), MagW 480-04-004 (Boomsma), NWO/SPI 56-464-14,192 (Boomsma), the European Research Council, ERC-230374 (Boomsma), and Amsterdam Neuroscience. Funding for genotyping was obtained from the National Institutes of Health (NIMH U24 MH068457-06; Grand Opportunity grants 1RC2 MH089951, and 1RC2 MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. The Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Boomsma), 51.02.060 (Hilleke Hulshoff Pol), 668.772 (Boomsma & Hulshoff Pol); NWO/SPI 56-464-14192 (Boomsma), the European Research Council (ERC230374) (Boomsma), High Potential Grant Utrecht University (Hulshoff Pol), NWO Brain and Cognition 433-09-220 (Hulshoff Pol). SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide SNP typing in SHIP and MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The ENIGMA-22q11.2 Deletion Syndrome Working Group wishes to acknowledge our dear colleague Dr. Clodagh Murphy, who sadly passed away in April 2020. Open access funding enabled and organized by Projekt DEAL

Neuroanatomical abnormalities in first-episode psychosis across independent samples: a multi-centre mega-analysis

Background Neuroanatomical abnormalities in first-episode psychosis (FEP) tend to be subtle and widespread. The vast majority of previous studies have used small samples, and therefore may have been underpowered. In addition, most studies have examined participants at a single research site, and therefore the results may be specific to the local sample investigated. Consequently, the findings reported in the existing literature are highly heterogeneous. This study aimed to overcome these issues by testing for neuroanatomical abnormalities in individuals with FEP that are expressed consistently across several independent samples. Methods Structural Magnetic Resonance Imaging data were acquired from a total of 572 FEP and 502 age and gender comparable healthy controls at five sites. Voxel-based morphometry was used to investigate differences in grey matter volume (GMV) between the two groups. Statistical inferences were made at p < 0.05 after family-wise error correction for multiple comparisons. Results FEP showed a widespread pattern of decreased GMV in fronto-temporal, insular and occipital regions bilaterally; these decreases were not dependent on anti-psychotic medication. The region with the most pronounced decrease-gyrus rectus-was negatively correlated with the severity of positive and negative symptoms. Conclusions This study identified a consistent pattern of fronto-temporal, insular and occipital abnormalities in five independent FEP samples; furthermore, the extent of these alterations is dependent on the severity of symptoms and duration of illness. This provides evidence for reliable neuroanatomical alternations in FEP, expressed above and beyond site-related differences in anti-psychotic medication, scanning parameters and recruitment criteria.This study was supported by the European Commission (PSYSCAN – Translating neuroimaging findings from research into clinical practice) (P.M., grant number 603196); International Cooperation and Exchange of the National Natural Science Foundation of China (Q.G. and A.M., grant numbers 81220108013, 8122010801, 81621003, 81761128023 and 81227002); Wellcome Trusts Innovator Award (A.M., grant number 208519/ Z/17/Z) Italian Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) (C.R, grant number art.1, commi 314-337 legge 232/2016) and the Foundation for Science and Technology (FCT) (S.V., grant number SFRH/BD/ 103907/2014)