Received: 4 June 2020 Revised: 10 August 2020 Accepted: 5 September 2020 DOI: 10.1002/hbm.25204 RESEARCH ARTICLE Greater male than female variability in regional brain structure across the lifespan Lara M Wierenga1,2 | Gaelle E Doucet3,4 | Danai Dima5,6 | Ingrid Agartz7,8,9 | Moji Aghajani10,11,12 | Theophilus N Akudjedu13,14 | Anton Albajes-Eizagirre15,16,17 | Dag Alnæs7,18 | Kathryn I Alpert19 | Ole A Andreassen7,18 | Alan Anticevic20 | Philip Asherson21 | Tobias Banaschewski22 | Nuria Bargallo23,24 | Sarah Baumeister22 | Ramona Baur-Streubel25 | Alessandro Bertolino26 | Aurora Bonvino26 | Dorret I Boomsma27 | Stefan Borgwardt28,29 | Josiane Bourque30,31 | Anouk den Braber27,32 | Daniel Brandeis22,33,34,35 | Alan Breier36 | Henry Brodaty37,38 | Rachel M Brouwer39 | Jan K Buitelaar40,41 | Geraldo F Busatto42 | Vince D Calhoun43 | Erick J Canales-Rodríguez15,16 | Dara M Cannon13 | Xavier Caseras44 | Francisco X Castellanos45,46 | Tiffany M Chaim-Avancini42 | Christopher RK Ching47 | Vincent P Clark48,49 | Patricia J Conrod31,50 | Annette Conzelmann51,52 | Fabrice Crivello53 | Christopher G Davey54,55 | Erin W Dickie56,57 | Stefan Ehrlich58 | Dennis van't Ent27 | Simon E Fisher59,60 | Jean-Paul Fouche61 | Barbara Franke60,62,63 | Paola Fuentes-Claramonte15,16 | Eco JC de Geus27 | Annabella Di Giorgio64 | David C Glahn65,66 | Ian H Gotlib67 | Hans J Grabe68,69 | Oliver Gruber70 | Patricia Gruner20 | Raquel E Gur30,71 | Ruben C Gur30 | Tiril P Gurholt7,18 | Lieuwe de Haan72 | Beathe Haatveit7,18 | Ben J Harrison73 | Catharina A Hartman74 | Sean N Hatton75,76 | Dirk J Heslenfeld77 | Odile A van den Heuvel10,78 | Ian B Hickie75 | Pieter J Hoekstra79 | Sarah Hohmann22 | Avram J Holmes20,80,81 | Martine Hoogman60,62 | Norbert Hosten82 | Fleur M Howells83,84 | Hilleke E Hulshoff Pol39 | Chaim Huyser85,86 | Neda Jahanshad47 | Anthony C James87,88 | Jiyang Jiang37 | Erik G Jönsson7,9 | John A Joska84 | Andrew J Kalnin89 | Karolinska Schizophrenia Project (KaSP) Consortium | Marieke Klein39,60,62 | Laura Koenders72 | Knut K Kolskår18,90,91 | Bernd Krämer70 | Jonna Kuntsi21 | Jim Lagopoulos92,93 | Luisa Lazaro16,94,95,96 | Irina S Lebedeva97 | Phil H Lee81,98 | Christine Lochner99 | Marise WJ Machielsen100 | Sophie Maingault101 | Nicholas G Martin102 | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. 470 wileyonlinelibrary.com/journal/hbm Hum Brain Mapp. 2022;43:470–499. WIERENGA ET AL. 471 Ignacio Martínez-Zalacaín103,104 | David Mataix-Cols9 | Bernard Mazoyer105,106 | 107 13 108 Brenna C McDonald | Colm McDonald | Andrew M McIntosh | Katie L McMahon109,110 | Genevieve McPhilemy13 | Dennis van der Meer7,18,111 | 16,103,104 40 112,113 José M Menchón | Jilly Naaijen | Lars Nyberg | Jaap Oosterlaan114,115 | Yannis Paloyelis6 | Paul Pauli116,117 | 26,118 15,16 Giulio Pergola | Edith Pomarol-Clotet | Maria J Portella16,119 | Joaquim Radua9,15,16,17,120 | Andreas Reif121 | Geneviève Richard7,18 | Joshua L Roffman122 | Pedro GP Rosa42 | Matthew D Sacchet123 | Perminder S Sachdev37,124 | Raymond Salvador15,16 | Salvador Sarró15,16 | 30 107,125 42 Theodore D Satterthwaite | Andrew J Saykin | Mauricio H Serpa | Kang Sim126,127 | Andrew Simmons128 | Jordan W Smoller81,129 | Iris E Sommer130 | Carles Soriano-Mas16,103,131 | Dan J Stein132 | Lachlan T Strike133 | Philip R Szeszko3,134 | Henk S Temmingh84 | Sophia I Thomopoulos47 | Alexander S Tomyshev97 | Julian N Trollor37 | Anne Uhlmann84,135 | Ilya M Veer136 | Dick J Veltman137 | Aristotle Voineskos56 | 138,139,140 136 19 141 Henry Völzke | Henrik Walter | Lei Wang | Yang Wang | Bernd Weber142 | Wei Wen37 | John D West107 | Lars T Westlye7,18,90 | 108,143 144 68,69 Heather C Whalley | Steven CR Williams | Katharina Wittfeld | Daniel H Wolf30 | Margaret J Wright133,145 | Yuliya N Yoncheva146 | Marcus V Zanetti42,147 | Georg C Ziegler148 | Greig I de Zubicaray110 | Paul M Thompson47 | Eveline A Crone1,2,149 | Sophia Frangou3,150 | Christian K Tamnes7,8,151 1 Institute of Psychology, Leiden University, Leiden, The Netherlands 2 Leiden Institute for Brain and Cognition, Leiden, The Netherlands 3 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York 4 Boys Town National Research Hospital, Omaha, Nebraska 5 Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK 6 Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK 7 Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway 8 Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway 9 Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden 10 Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands 11 Department of Research & Innovation, GGZ inGeest, Amsterdam, The Netherlands 12 Institute of Education and Child Studies, Forensic Family and Youth Care, Leiden University, Leiden, The Netherlands 13 Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland 14 Institute of Medical Imaging & Visualisation, Faculty of Health & Social Sciences, Bournemouth University, Bournemouth, UK 15 FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain 16 Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain 17 Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 472 WIERENGA ET AL. 18 Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway 19 Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois 20 Department of Psychiatry, Yale University, New Haven, Connecticut 21 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK 22 Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany 23 Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain 24 Magnetic Resonance Image Core Facility, IDIBAPS, Barcelona, Spain 25 Department for Clinical Psychology, Würzburg University, Margetshöchheim, Germany 26 Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy 27 Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands 28 Department of Psychiatry, University of Basel, Basel, Switzerland 29 Department of Psychiatry, University of Lübeck, Lübeck, Germany 30 Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania 31 CHU Sainte-Justine Research Center, Montreal, Quebec, Canada 32 Alzheimer Center, Amsterdam UMC, Location VUMC, Amsterdam, The Netherlands 33 Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland 34 Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland 35 Neuroscience Centre Zurich, University and ETH Zurich, Zurich, Switzerland 36 Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana 37 Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia 38 Dementia Centre for Research Collaboration, School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia 39 Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands 40 Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands 41 Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands 42 Laboratory of Psychiatric Neuroimaging (LIM-21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de S~ao Paulo, S~ao Paulo, Brazil 43 Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Atlanta, Georgia 44 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK 45 Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, New York 46 Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 47 Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California 48 Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, New Mexico 49 Mind Research Network, Albuquerque, New Mexico 50 Department of Psychiatry, University of Montreal, Montreal, Canada 51 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Tübingen, Tübingen, Germany 52 Department of Psychology (Clinical Psychology II), PFH – Private University of Applied Sciences, Göttingen, Germany 53 Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, Bordeaux, France 54 Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia 55 Orygen, Parkville, Victoria, Australia 56 Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, Canada 57 Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada 58 Division of Psychological & Social Medicine and Developmental Neurosciences; Technische Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany 59 Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands 60 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands 61 Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, Western Cape, South Africa 62 Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands 63 Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands WIERENGA ET AL. 473 64 IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy 65 Tommy Fuss Center for Neuropsychiatric Disease Research, Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts 66 Olin Center for Neuropsychiatric Research, Institute of Living, Hartford Hospital, Hartford, Connecticut 67 Department of Psychology, Stanford University, Stanford, California 68 Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany 69 German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany 70 Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University Hospital, Heidelberg, Germany 71 Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 72 Department of Early Psychosis, Amsterdam UMC, Amsterdam, The Netherlands 73 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia 74 Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands 75 Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia 76 Department of Neurosciences, University of California San Diego, La Jolla, California 77 Departments of Experimental and Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 78 Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 79 Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands 80 Department of Psychology, Yale University, New Haven, Connecticut 81 Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 82 Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany 83 Neuroscience Institute, University of Cape Town, Cape Town, Western Cape, South Africa 84 Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa 85 De Bascule, Academic center child and adolescent psychiatry, Duivendrecht, The Netherlands 86 Amsterdam UMC Department of Child and Adolescent Psychiatry, Amsterdam, The Netherlands 87 Department of Psychiatry, Warneford Hospital, Oxford, UK 88 Highfield Unit, Warneford Hospital, Oxford, UK 89 Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio 90 Department of Psychology, University of Oslo, Oslo, Norway 91 Sunnaas Rehabilitation Hospital HT, Nesodden, Norway 92 Sunshine Coast Mind and Neuroscience Thompson Institute, Birtinya, Queensland, Australia 93 University of the Sunshine Coast, Sunshine Coast, Queensland, Australia 94 Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic, Barcelona, Spain 95 August Pi i Sunyer Biomedical Research Institut (IDIBAPS), Barcelona, Spain 96 Department of Medicine, University of Barcelona, Barcelona, Spain 97 Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia 98 Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 99 SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, Western Cape, South Africa 100 Department of Psychiatry, Academic Medical Center, Amsterdam, The Netherlands 101 Institut des maladies neurodégénératives, Université de Bordeaux, Bordeaux, France 102 Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia 103 Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain 104 Department of Clinical Sciences, University of Barcelona, Barcelona, Spain 105 University of Bordeaux, Bordeaux, France 106 Bordeaux University Hospital, Bordeaux, France 107 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana 108 Division of Psychiatry, University of Edinburgh, Edinburgh, UK 109 Herston Imaging Research Facility and School of Clinical Sciences, Queensland University of Technology (QUT), Brisbane, Queensland, Australia 110 Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Queensland, Australia 111 School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands 474 WIERENGA ET AL. 112 Department of Radiation Sciences, Umeå University, Umeå, Sweden 113 Department of Integrative Medical Biology, Umeå University, Umeå, Sweden 114 Emma Children's Hospital, Amsterdam UMC University of Amsterdam and Vrije Universiteit Amsterdam, Emma Neuroscience Group, Department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands 115 Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 116 Department of Psychology, University of Würzburg, Würzburg, Germany 117 Centre of Mental Health, Medical Faculty, University of Würzburg, Würzburg, Germany 118 Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Mary Land 119 Department of Psychiatry, Institut d'Investigació Biomèdica Sant Pau, Barcelona, Spain 120 Early Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK 121 Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfur am Maint, Germany 122 Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts 123 Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, Belmont, Massachusetts 124 Neuropsychiatric Institute, The Prince of Wales Hospital, Randwick, New South Wales, Australia 125 Indiana Alzheimer Disease Center, Indianapolis, Indiana 126 West Region, Institute of Mental Health, Singapore, Singapore 127 Yong Loo Lin School of Medicine, National University of Singapore, Singapore 128 Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurology, King's College London, London, UK 129 Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 130 Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit Groningen, University Medical Center Groningen, Groningen, The Netherlands 131 Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain 132 SAMRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, Western Cape, South Africa 133 Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia 134 Mental Illness Research, Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, New York, New York 135 Department of Child and Adolescent Psychiatry and Psychotherapy, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany 136 Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany 137 Department of Psychiatry & Amsterdam Neuroscience, Amsterdam UMC, location VUMC, Amsterdam, The Netherlands 138 Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany 139 DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany 140 DZD (German Center for Diabetes Research), partner site Greifswald, Greifswald, Germany 141 Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin 142 Institute for Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany 143 Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK 144 Department of Neuroimaging, King's College London, London, UK 145 Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia 146 Department of Child and Adolescent Psychiatry, NYU Child Study Center, Hassenfeld Children's Hospital at NYU Langone, New York, New York, Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, S~ao Paulo, Brazil 147 148 Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany 149 Department of Psychology, Education and Child Studies (DPECS), Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, The Netherlands 150 Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada 151 PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway Correspondence Lara M. Wierenga, Leiden University, Leiden, Abstract The Netherlands. For many traits, males show greater variability than females, with possible implica- Email: l.m.wierenga@fsw.leidenuniv.nl tions for understanding sex differences in health and disease. Here, the ENIGMA WIERENGA ET AL. 475 Funding information BIG - Nijmegen 1.5T; Deutsche (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the Forschungsgemeinschaft, Grant/Award largest-ever mega-analysis of sex differences in variability of brain structure, based Numbers: KFO 125, TRR 58/A1, TRR 58/A5, SFB-TRR 58/B01, SFB-TRR 58/B06; Deutsche on international data spanning nine decades of life. Subcortical volumes, cortical sur- Forschungsgesellschaft collaborative research face area and cortical thickness were assessed in MRI data of 16,683 healthy individ- center, Grant/Award Number: 636; EU H2020, Grant/Award Number: #667302; European uals 1-90 years old (47% females). We observed significant patterns of greater male Community's Horizon 2020 Programme, than female between-subject variance for all subcortical volumetric measures, all cor- Grant/Award Numbers: 643051, 667302; European Community's Seventh Framework tical surface area measures, and 60% of cortical thickness measures. This pattern was Programme, Grant/Award Numbers: 602805, stable across the lifespan for 50% of the subcortical structures, 70% of the regional 603016, 602450, 278948; European Research Council, Grant/Award Numbers: ERC- area measures, and nearly all regions for thickness. Our findings that these sex differ- 2010-StG-263234, ERC-230374; German ences are present in childhood implicate early life genetic or gene-environment inter- Research Foundation, Grant/Award Number: KFO 125; KA Wallenberg Foundation; KNAW action mechanisms. The findings highlight the importance of individual differences Academy Professor Award, Grant/Award within the sexes, that may underpin sex-specific vulnerability to disorders. Number: PAH/6635; Miguel Servet Research Contract, Grant/Award Number: CPII16/0020; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: 51.02.061 to H.H., NWO 51.02.062 to D.B., NWO- NIH; Netherlands Brain Foundation grant, Grant/Award Number: 2010 (1)-50; NIA, Grant/Award Number: T32AG058507; NIH/NIMH, Grant/Award Numbers: 5T32MH073526, U54EB020403, U54 EB020403, R56 AG058; NIHR Biomedical Research Centre for Mental Health, Grant/ Award Number: NIHR/MRC (14/23/17); NIHR senior investigator award, Grant/Award Number: NF-SI-0616-10040; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09- 229; Research Council of Norway, Grant/Award Numbers: #223273, #288083, #230345; South London and Maudsley Trust, Grant/Award Number: 064846; South-Eastern Norway Regional Health Authority, Grant/Award Numbers: #2017112, #2019069; the Generalitat de Catalunya, Grant/Award Number: 2017SGR01343; the German Research Foundation, Grant/Award Numbers: WA 1539/4-1, SCHN 1205/3-1; The Marató TV3 Foundation, Grant/Award Numbers: #091710, #091710; UK Medical Research Council Grant, Grant/Award Number: G03001896 to J Kuntsi; Vici Innovation Program, Grant/Award Numbers: 016-130-669, 91619115; National Institute of Aging, Grant/Award Number: R03AG064001; National Institute of General Medical Sciences, Grant/Award Number: P20GM130447 1 | I N T RO DU CT I O N or their interactions. Lehre et al. (2009) found compelling evidence for an early genetic or in utero contribution, reporting greater male variabil- For a diverse set of human traits and behaviors, males are often reported ity in anthropometric traits (e.g. body weight and height, blood parame- to show greater variability than females (Hyde 2014). This sex difference ters) already detectable at birth. Recent studies suggest greater male has been noted for aspects of personality (Borkenau, McCrae, and Ter- variability also in brain structure and its development (Forde et al. 2020; racciano 2013), cognitive abilities (Arden and Plomin 2006; Johnson, Ritchie et al. 2018; Wierenga et al. 2018, 2019), but studies with larger Carothers, and Deary 2008; Roalf et al. 2014), and school achievement samples that cover both early childhood and old age are critically (Baye and Monseur 2016). A fundamental question is to what degree needed. Specifically, we do not know when sex differences in variability these sex differences are related to genetic mechanisms or social factors, in brain structure emerge and whether they change with development 476 WIERENGA ET AL. and throughout life. Yet, data on this could inform us on the origins and cortical surface area and thickness. Our first aim was to replicate previ- factors that influence this phenomenon. For this reason, we set out to ous findings of greater male variability in brain structure in a substantially analyze magnetic resonance imaging (MRI) data from a large sample larger sample. Based on prior studies (Forde et al. 2020; Ritchie of individuals across a very wide age range (n = 16,683, age 1-90) to et al. 2018; Wierenga et al. 2018, 2019) and reports of somewhat robustly characterize sex differences in variability of brain structure greater genetic effect on surface area than thickness (Eyler et al. 2011; and test how these differences interact with age. Kremen et al. 2013), we hypothesized that greater male variance would Many prior studies report sex differences in brain structure, but be more pronounced for subcortical volumes and cortical surface area the specificity, regional pattern and functional relevance of such than for cortical thickness, and that greater male variance would be effects are not clear (Herting et al. 2018; Koolschijn and Crone 2013; observed at both upper and lower ends of the distribution. Our second Marwha, Halari, and Eliot 2017; Ruigrok et al. 2014; Tan et al. 2016). aim was to test whether observed sex differences in variability of brain One reason could be that most studies have examined mean differ- structure are stable across the lifespan from birth until 90 years of age, ences between the sexes, while sex differences in variability remain or e.g. increase with the accumulation of experiences (Pfefferbaum, understudied (Del Giudice et al. 2016; Joel et al. 2015). As mean and Sullivan, and Carmelli 2004). Third, in line with the single X-chromosome variance measure two different aspects of the distribution (center and hypothesis, we aimed to replicate whether males show greater inter- spread), knowledge on variance effects may provide important insights regional anatomical correlations (i.e. within-subject homogeneity) across into sex differences in the brain. Recent studies observed greater male brain regions that show greater male compared to female variance variance for subcortical volumes and for cortical surface area to a (Wierenga et al. 2019). larger extent than for cortical thickness (Ritchie et al. 2018; Wierenga et al. 2018, 2019). However, further studies are needed to explore regional patterns of variance differences, and, critically, to test how 2 | METHODS sex differences in variability in the brain unfold across the lifespan. An important question pertains to the mechanisms involved in 2.1 | Participants sex differences in variability. It is hypothesized that the lack of two parental X-chromosomal copies in human males may directly relate to The datasets analyzed in the present study were from the Lifespan greater variability and vulnerability to developmental disorders in working group within the ENIGMA Consortium (Jahanshad and males compared to females (Arnold 2012). All cells in males express Thompson 2016). There were 78 independent samples with MRI data, an X-linked variant, while female brain tissues show two variants. In in total including 16,683 (7,966 males) healthy participants aged females, one of the X-chromosomes is randomly silenced, as such 1-90 years from diverse ethnic backgrounds (see detailed descriptions neighboring cells may have different X related genetic expression at the cohort level in Table 1). Samples were drawn from the general (Wu et al. 2014). Consequently, one could expect that in addition to population or were healthy controls in clinical studies. Screening pro- greater variability across the population, interregional anatomical cor- cedures and the eligibility criteria (e.g. head trauma, neurological his- relations may be stronger in male relative to female brains. This was tory) may be found in Supplemental Table 1. Participants in each indeed observed for a number of regional brain volumes in children cohort gave written informed consent at the local sites. Furthermore, and adolescents, showing greater within-subject homogeneity across at each site local research ethics committees or Institutional Review regions in males than females (Wierenga et al. 2018). These results Boards gave approval for the data collection, and all local institutional remain to be replicated in larger samples as they may provide clues review boards permitted the use of extracted measures of the about mechanisms and risk factors in neurodevelopmental disorders completely anonymized data that were used in the present study. (e.g. attention-deficit/hyperactivity disorder and autism spectrum dis- order) that show sex differences in prevalence (Bao and Swaab 2010), age of onset, heritability rates (Costello et al. 2003), or severity of 2.2 | Imaging data acquisition and processing symptoms and course (Goldstein, Seidman, and O'brien 2002). In the present study, we performed mega-analyses on data from For definition of all brain measures, whole-brain T1-weighted anatom- the enhancing neuroimaging genetics through meta-analysis (ENIGMA) ical scan were included. Detailed information on scanner model and Lifespan working group (Dima et al., 2020; Frangou et al., 2020; image acquisition parameters for each site can be found in Supple- Jahanshad and Thompson 2016). A mega-analysis allows for analyses of mental Table 1. T1 weighted scans were processed at the cohort level, data from multiple sites with a single statistical model that fits all data where subcortical segmentation and cortical parcellation were per- and simultaneously accounting for the effect of site. Successfully pooling formed by running the T1-weighted images in FreeSurfer using ver- lifespan data was recently shown in a study combining 18 datasets to sions 4.1, 5.1, 5.3 or 6.0 (see Supplemental Table 1 for specifications derive age trends of brain structure (Pomponio et al. 2020). This con- per site). This software suite is well validated and widely used, and trasts with meta-analysis where summary statistics are combined and documented and freely available online (surfer.nmr.mgh.harvard.edu). weighted from data that is analyzed at each site (van Erp et al. 2019). The technical details of the automated reconstruction scheme are MRI data from a large sample (n = 16,683) of participants aged 1 to described elsewhere (Dale, Fischl, and Sereno 1999; Fischl et al. 1999, 90 years was included. We investigated subcortical volumes and regional 2002). The outcome variables included volumes of seven subcortical WIERENGA ET AL. 477 TABLE 1 Sex distributions and age of subjects by sample Age Sample Total N Sex N Mean SD Range EDINBURGH 55 Male 20 23.9 2.5 18.5–28.4 Female 35 23.7 3.1 18.6–30.6 UNIBA 131 Male 67 30.3 10.0 18.0–63.0 Female 64 24.3 6.8 18.0–52.0 Tuebingen 50 Male 22 38.4 11.1 26.0–61.0 Female 28 42.2 12.5 24.0–61.0 GSP 2009 Male 894 27.8 16.8 18.0–90.0 Female 1115 26.7 16.2 18.0–89.0 Melbourne 102 Male 54 19.5 2.9 15.0–25.0 Female 48 19.6 3.1 15.0–26.0 HMS 55 Male 21 41.3 11.2 24.0–59.0 Female 34 38.5 12.8 19.0–64.0 ENIGMA-OCD (1) 66 Male 30 30.6 8.9 19.0–56.0 Female 36 35.1 10.9 18.0–61.0 NUIG 93 Male 54 34.1 11.6 18.0–57.0 Female 39 39.0 11.0 18.0–58.0 NeuroIMAGE 383 Male 177 16.8 3.6 7.7–28.5 Female 206 17.0 3.8 7.8–28.6 CAMH 141 Male 72 43.2 18.9 18.0–86.0 Female 69 44.1 19.8 18.0–82.0 Basel 44 Male 17 25.7 4.5 19.0–35.0 Female 27 25.3 4.2 19.0–39.0 Bordeaux 452 Male 220 26.9 7.8 18.0–57.0 Female 232 26.6 7.7 18.0–56.0 FBIRN 174 Male 124 37.6 11.3 19.0–60.0 Female 50 37.4 11.3 19.0–58.0 KaSP 32 Male 15 27.4 5.5 21.0–43.0 Female 17 27.6 5.9 20.0–37.0 CODE 72 Male 31 43.7 12.4 25.0–64.0 Female 41 36.6 13.4 20.0–63.0 Indiana (1) 49 Male 9 71.9 6.6 63.0–80.0 Female 40 60.4 11.6 37.0–84.0 COMPULS/TS EUROTRAIN 53 Male 36 10.8 1.0 8.7–12.9 Female 17 11.0 1.1 9.2–12.9 FIDMAG 123 Male 54 36.4 8.5 19.0–63.0 Female 69 38.4 11.2 19.0–64.0 NU 79 Male 46 31.6 14.5 14.6–66.3 Female 33 34.4 15.3 14.2–67.9 SHIP-TREND 818 Male 467 50.5 14.4 22.0–81.0 Female 351 49.6 14.0 21.0–81.0 SHIP-2 373 Male 207 55.6 12.8 31.0–84.0 Female 166 54.4 12.0 32.0–88.0 QTIM 340 Male 111 22.5 3.3 16.0–29.3 Female 229 22.7 3.4 16.1–30.0 Betula 287 Male 136 61.6 12.5 25.5–81.3 (Continues) 478 WIERENGA ET AL. TABLE 1 (Continued) Age Sample Total N Sex N Mean SD Range Female 151 64.1 13.1 TOP 303 Male 159 34.5 8.8 18.3–56.2 Female 144 36.3 10.9 19.3–73.4 HUBIN 102 Male 69 42.1 9.0 19.4–54.9 Female 33 41.7 8.5 19.9–56.2 StrokeMRI 52 Male 19 47.9 20.8 20.0–77.0 Female 33 43.6 23.0 18.0–78.0 AMC 99 Male 65 22.5 3.4 17.0–32.0 Female 34 23.6 3.3 18.0–29.0 NESDA 65 Male 23 40.7 9.7 23.0–56.0 Female 42 40.1 9.9 21.0–54.0 Barcelona (1) 30 Male 14 15.1 1.5 13.0–17.0 Female 16 14.9 2.1 11.0–17.0 Barcelona (2) 44 Male 24 14.4 1.8 11.0–17.0 Female 20 14.8 2.4 11.0–17.0 Stages-Dep 32 Male 9 46.6 8.4 37.0–58.0 Female 23 45.8 8.2 27.0–58.0 IMpACT 144 Male 57 34.2 11.0 19.0–62.0 Female 87 37.2 12.6 19.0–63.0 BIG 1319 Male 657 29.8 15.4 17.0–82.0 Female 662 26.9 12.9 13.0–79.0 IMH Stanford 56 Male 22 36.0 10.5 20.4–60.5 34 Female 34 37.5 10.8 18.9–56.3 MCIC (1) + (2) 93 Male 63 32.8 12.2 18.0–58.0 Female 30 32.5 11.9 19.0–60.0 OLIN 599 Male 237 36.3 13.3 22.0–86.5 Female 362 35.9 12.8 21.0–74.0 Neuroventure 137 Male 62 13.7 0.6 12.4–14.9 Female 75 13.6 0.7 12.3–14.9 CIAM 30 Male 16 27.1 5.9 19.0–40.0 Female 14 26.1 3.8 20.0–33.0 ENIGMA-HIV 31 Male 16 25.6 4.7 19.0–33.0 Female 15 23.9 4.1 20.0–32.0 Meth-CT 62 Female 13 26.1 4.1 19.0–34.0 Males 49 27.0 7.9 18.0–53.0 ENIGMA-OCD 26 Male 10 34.6 13.6 19.0–56.0 Female 16 28.8 7.8 20.0–46.0 Oxford 38 Male 18 16.5 1.6 14.1–18.9 Female 20 15.9 1.1 13.7–17.7 Yale 23 Male 12 14.4 2.4 10.3–17.5 Female 11 14.0 2.0 9.9–16.5 Sao Paulo-1 69 Male 45 27.1 5.6 18.0–42.0 Female 24 27.5 6.4 17.0–43.0 Sao Paulo-3 85 Male 45 28.2 7.3 18.0–43.0 Female 40 32.7 8.8 18.0–50.0 WIERENGA ET AL. 479 TABLE 1 (Continued) Age Sample Total N Sex N Mean SD Range ENIGMA-OCD (2) 49 Male 19 32.1 7.8 24.0–53.0 Female 30 31.3 7.7 21.0–50.0 ENIGMA-OCD (3) 35 Male 16 42.9 12.9 22.5–64.0 Female 19 36.0 8.8 21.5–49.3 ENIGMA-OCD (4) 23 Male 9 13.1 2.9 8.8–15.9 Female 14 13.8 2.4 8.7–16.8 ENIGMA-OCD (5) 33 Male 12 30.7 8.8 21.0–53.0 Female 21 39.2 11.5 24.0–63.0 SYDNEY 157 Male 65 42.0 22.4 12.0–84.0 Female 92 37.1 21.7 13.0–78.0 IMH 79 Male 50 30.7 8.3 23.0–53.9 Female 29 34.2 12.4 20.4–59.0 UPENN 187 Male 86 35.7 12.9 18.0–71.0 Female 101 35.8 14.7 16.0–85.0 ADHD-NF 13 Male 7 13.3 1.2 11.9–14.8 Female 6 13.4 0.8 12.1–14.2 Indiana (2) 66 Male 26 40.2 15.3 19.0–65.0 Female 40 39.4 14.1 20.0–65.0 Sydney MAS 523 Male 236 78.3 4.6 70.3–89.8 Female 287 78.5 4.7 70.5–90.1 OADS (1) 118 Male 39 73.8 5.5 65.0–84.0 Female 79 70.4 5.6 65.0–84.0 Cardiff 318 Male 89 28.1 7.8 19.0–57.0 Female 229 24.2 7.0 18.0–58.0 CEG 32 Male 32 15.6 1.7 13.0–19.0 NYU 51 Male 31 30.2 7.7 18.8–46.0 Female 20 31.4 10.3 19.8–51.9 CLiNG 321 Male 131 25.5 5.4 19.0–58.0 Female 190 24.9 5.1 18.0–57.0 NTR (1) 112 Male 42 28.5 8.0 19.0–56.0 Female 70 37.0 10.5 19.0–57.0 NTR (2) 30 Male 11 28.4 3.6 22.0–33.0 Female 19 28.6 9.8 1.0–42.0 NTR (3) 37 Male 14 15.1 1.5 12.0–17.0 Female 23 14.5 1.4 11.0–18.0 Indiana (2) + (3) 201 Male 97 21.6 14.4 6.0–79.0 Female 104 33.0 22.8 7.0–87.0 BIG 1291 Male 553 25.1 9.3 18.0–71.0 Female 738 23.3 6.9 18.0–66.0 OADS (2) 35 Male 15 70.1 5.7 65.0–81.0 Female 20 67.4 3.8 65.0–78.0 OADS (3) 153 Male 59 70.3 4.2 65.0–81.0 Female 94 69.7 4.6 65.0–81.0 OADS (4) 108 Male 30 69.8 4.5 65.0–85.0 Female 78 70.1 4.9 65.0–89.0 (Continues) 480 WIERENGA ET AL. TABLE 1 (Continued) Age Sample Total N Sex N Mean SD Range MHRC 52 Male 52 22.3 2.9 16.1–27.6 BRAINSCALE 277 Male 146 10.1 1.5 9.0–15.0 Female 131 9.9 1.2 9.0–14.1 Leiden 611 Male 299 16.2 4.7 8.3–28.1 Female 312 16.9 4.9 8.4–28.9 IMAGEN 1964 Male 952 14.5 0.4 13.2–15.7 Female 1012 14.5 0.4 13.3–16.0 ENIGMA-HIV 175 Male 175 38.8 6.5 29.0–50.0 UMCU 172 Male 84 40.2 16.5 18.0–80.0 Female 88 39.2 17.9 18.0–84.0 structures: accumbens, caudate, pallidum, putamen, amygdala, hippo- 2.5 | Variance ratio campus, and thalamus (Fischl et al. 2002), and cortical surface area and thickness measures (Dale et al. 1999; Fischl et al. 1999) of Variance differences between males and females were examined, 68 regions of the cerebral cortex (Desikan-Killiany atlas) (Desikan after accounting for age and other covariates as described above. et al. 2006). Quality control was also implemented at the cohort level Fisher's variance ratio (VR) was estimated by dividing variance mea- following detailed protocols (http://enigma.ini.usc.edu/protocols/ sures for males and females. VR was log transformed to account for imaging-protocols). The statistical analyses included 13,696 partici- VR bias (Katzman and Alliger 1992; Lehre et al. 2009). Letting yi pants for subcortical volumes, 11,338 for surface area measures, and denote the observed outcome for observation number i and y^i its 12,533 participants for cortical thickness analysis. predicted outcome, the residuals were then formed: ri = yi −y^i 2.3 | Statistical analysis The residual variance Varmales and Varfemales were computed sepa- rately for males and females, and used to form the test statistic Statistical analyses were performed using R Statistical Software. The T = Varmales =Varfemales complete scripts are available in the Appendix. In brief, we first adjusted all brain structure variables for cohort, field strength and FreeSurfer For each outcome, a permutation test of the hypothesis that the sex version effects. As age ranges differed for each cohort this was done in specific standard deviations were equal, was performed. This was done by two steps: initially, a linear model was used to account for cohort effects random permutation of the sex variable among the residuals. Using β per- and non-linear age effects, using a third-degree polynomial function. mutations, the p-value for the k-th outcome measure was computed as XB Next, random forest regression modelling (Breiman 2001) was used to pk = IðT b > T Þ=B b=1 additionally account for field strength and FreeSurfer version. See Supplemental Figure 1 for adjusted values. This was implemented in the where I(Tb ≥ T) is an indicator function that is 1 when Tb ≥ T, and R package randomForest, which can accommodate models with inter- 0 otherwise. Thus, the p-value is the proportion of permuted test sta- actions and non-linear effects. tistics (Tb) that were greater than the observed value T of the test sta- tistic above. Here B was set to 10,000. FDR corrected values are reported as significant. 2.4 | Mean differences Mean sex differences in brain structure variables were tested using 2.6 | Shift Function t-tests (FDR corrected, see (Benjamini and Hochberg 1995)) and effect sizes were estimated using Cohen's d-value. A negative effect To assess the nature of the variability difference between males and size indicates that the mean was higher in females, and a positive females, shift functions were estimated for each brain measure that effect size indicates it was higher in males. The brain structure vari- showed significant variance differences between males and females using ables were adjusted for age and covariates described above. Graphs quantile regression forests (Meinshausen 2006; Rousselet, Pernet, and were created with R package ggseg (Mowinckel and Vidal- Wilcox 2017), implemented in the R package quantregForest (see Pineiro, 2019). Wierenga et al. 2018) for a similar approach). First, as described above, WIERENGA ET AL. 481 brain measures were accounted for site, age, field strength and FreeSurfer produced two group correlation matrices Mij and Fij where i, j, = 1, 2, . version. Next, quantile distribution functions were estimated for males …, N, where N is the number of brain regions. and females separately after aligning the distribution means. Let q be a Sex specific means and standard deviations were removed by per- probability between 0 and 1. The quantile function specifies the values at forming sex specific standardization. The significance of the differ- which the volume of a brain measure will be at or below any given q. The ences between Mij and Fij was assessed by the difference in their quantile function for males is given as Q(qj males) and for females as Q Fisher's z-transformed values, and p-values were computed using per- (qjfemales). The quantile distance function is then defined as: mutations. Whether these significantly differed between the sexes was tested using a Chi-square test. DðqÞ = QðqjmalesÞ −QðqjfemalesÞ A bootstrap method was used to estimate the standard error of 3 | RE SU LT S the quantile difference functions, which was used to form approxi- mate 95% confidence intervals. If the quantile distance function is a 3.1 | Sex differences in mean and variance straight-line parallel to the x axis, this indicates a stable difference between the sexes across the distribution and thus no detectable dif- All brain measures were adjusted for cohort, field strength, FreeSurfer ference in variability. A positive slope indicates greater male variance. version and (non-linear) age. As a background analysis, we first More specifically, this would indicate that the males with the largest assessed whether brain structural measures showed mean differences values have relatively larger values than females with the largest between males and females to align our findings to previous reports values, and males with the smallest values are relatively smaller values (Figure 1, Table 2). All subcortical volumes were significantly larger in than the females with the smallest values. A negative slope of the males, with effect sizes (Cohen's d-values) ranging from 0.41 (left quantile distance function would indicate larger variability in females accumbens) to 0.92 (right thalamus), and an average effect size of 0.7. at both ends of the distribution. In follow-up analyses with total brain volume as an additional covari- ate we found a similar pattern, although effect sizes were smaller (Supplemental Table S2A). Also for cortical surface area, all regions 2.7 | Variance change with age showed significantly larger values in males than females, with effect sizes ranging from 0.42 (left caudal anterior cingulate area) to 0.97 To study whether the sex differences in variance are stable across the (left superior temporal area), on average 0.71. When total surface area age range we used the residuals of the predicted outcome measure was included as an additional covariate, a similar pattern was observed, and each individual i: although effect sizes were smaller (Supplemental Table S2B). Cortical thickness showed significant mean sex differences in 43 (out of 68) ri = j yi − y^i j regions, of which 38 regions showed larger thickness values in females than males. These were mostly frontal and parietal regions. The absolute value of ri was then used in a regression model. It was The largest effect size, however, was only 0.12 (right caudal anterior next explored whether there was a significant (FDR corrected) age by cingulate cortex). When total average cortical thickness was included sex interaction effect using a linear model 1 and quadratic model 2: as an additional covariate, nine regions showed a male advantage that was not observed in the raw data analysis, and six of the 38 regions yi = Agei  sexi + errori ðmodel 1Þ showing female advantage did not reach significance (Supplemental Table S2C). yi = Age2i  sexi + errori ðmodel 2Þ We then tested for sex differences in variance of brain structure, adjusted for cohort, field strength, FreeSurfer version and (non-linear) age (Figure 2, Tables 2). All subcortical volumes had significantly 2.8 | Anatomical correlation analysis greater variance in males than females. Log transformed variance ratios ranged from 0.12 (right accumbens) to 0.36 (right pallidum), Inter-regional anatomical associations were assessed by defining the indicating greater variance in males than females. Similar results were correlation between two brain structures, after accounting for age also observed when total brain volume was taken into account (Sup- and other covariates as described above. Anatomical correlation plemental Table S2A). Cortical surface area also showed significantly matrices were estimated as previously applied in several structural greater variance in males for all regions: variance ratios ranged from MRI studies for males and females separately (see e.g. Baaré 0.13 (left caudal anterior cingulate cortex) to 0.36 (right para- et al. 2001; Lerch et al. 2006). Next, the anatomical correlation matrix hippocampal cortex). This pattern was also observed when total sur- for females was subtracted from the anatomical correlation matrix for face area was included in the model (Supplemental Table S2B). males, yielding a difference matrix. Cortical thickness showed significantly greater male variance in Thus, the Pearson correlation coefficient between any two 41 out of 68 regions, with the greatest variance ratio being 0.11 (left regions i and j was assessed for males and females separately. This precentral cortex). Notably, 37 of these 41 regions did not show 482 WIERENGA ET AL. F I G U R E 1 Sex differences in volumetric measures of subcortical volumes (left), cortical surface area (center), and cortical thickness (right). Shown are effect sizes (Cohen's d-value) of FDR corrected mean sex differences. Greater mean values for males are displayed in blue, greater mean values for females are displayed in red. Darker colors indicate larger effect sizes significantly larger mean thickness values in males. When additionally (Table 3, Figure 5). Cortical surface area showed significant interac- accounting for total average thickness, we found greater male vari- tion effects in 30% of the cortical regions (Table 3, Figure 5). In both ance in 39 regions and greater females variance in 5 regions. Also cases, younger individuals tended to show greater sex differences in here, significant variance ratios were present in the absence of mean variance than older individuals. For cortical thickness, an interaction sex differences (Supplemental Table S2C). with age was detected only in the left insula (Table 3, Figure 5). This Next, we directly tested whether the regions showing larger vari- region showed greater male than female variance in the younger age ance effects were also those showing larger mean differences, by cor- group, whereas greater female variance was observed in older relating the variance ratios with the vector of d-values (Supplemental individuals. Figure 2). There was a significant association for subcortical volumes Next, these analyses were repeated using a quadratic age model (r (12) = 0.7, p-value = .005), but no significant relation for regional (Supplemental Tables 3A-C). None of the subcortical or cortical sur- cortical surface area (r (66) = 0.18, p-value = .14), or thickness (r (66) = face area measures showed quadratic age by sex interaction effects in -0.21, p-value = .09). variance. Cortical thickness showed significant quadratic age by sex effects in two regions; left superior frontal cortex and right lateral orbitofrontal cortex. 3.2 | Greater variance in males at upper and lower tails 3.4 | Sex differences in anatomical correlations In order to characterise how the distributions of males and females differ, quantiles were compared using a shift function (Rousselet Finally, we tested whether females showed greater diversity than et al. 2017). As in the previous models, brain measures were adjusted males in anatomical correlations by comparing inter-regional anatomi- for cohort, field strength, FreeSurfer version and age. In addition, the cal associations between males and females. Using permutation test- distribution means were aligned. Results showed greater male vari- ing (B = 10000), the significance of correlation differences between ance at both upper and lower tails for regions that showed significant males and females was assessed. variance differences between males and females. The top three vari- Of the 91 subcortical-subcortical correlation coefficients, 2% ance ratio effects for subcortical volume, cortical surface area and cor- showed significantly stronger correlations in males, while, unexpect- tical thickness are shown in Figure 3. edly, 19% showed stronger correlations in females (tested two-sided) (Figure 6A). A chi-square test of independence showed that this sig- nificantly differed between males and females, X2 (1, N = 18) 3.3 | Variance differences between sexes = 10.889, p < .001. For surface area, no significant difference between across age males and females were observed: significantly stronger male homo- geneity was observed in 4% of the 2,278 unique anatomical correla- We next tested whether the sex differences in variance interacted tions, and similarly females also showed significantly stronger with age (Figure 4 and supplemental Figure 3). In this set of analyses, correlations in 4% of the anatomical associations (Figure 6B). For brain measures were adjusted for cohort, field strength, and thickness, stronger male than female homogeneity was observed in FreeSurfer version. For 50% of the subcortical volume measures 21% of the correlations, while stronger female correlations were there was a significant interaction, specifically for the bilateral thal- observed in <1% of the correlations (Figure 6C). This difference was ami, bilateral putamen, bilateral pallidum and the left hippocampus significant, X2 (1, N = 484) = 460.300, p < .001. WIERENGA ET AL. 483 TABLE 2 Sex differences in mean and variance Mean difference test (a) Subcortical volume Female (n = 7141) Male (n = 6555) Variance Ratio test M M p Cohen's d VR p Left thal -328.287 357.024 ** 0.840 0.237 ** Right thal -317.358 345.963 ** 0.918 0.357 ** Left caud -139.573 152.488 ** 0.609 0.150 ** Right caud -147.366 160.706 ** 0.625 0.147 ** Left put -237.405 257.178 ** 0.757 0.197 ** Right put -233.415 252.623 ** 0.786 0.220 ** Left pal -86.166 93.761 ** 0.768 0.317 ** Right pal -74.910 81.507 ** 0.793 0.339 ** Left hippo -137.976 149.409 ** 0.673 0.173 ** Right hippo -134.745 145.724 ** 0.669 0.232 ** Left amyg -73.754 80.305 ** 0.765 0.154 ** Right amyg -80.242 87.372 ** 0.790 0.216 ** Left accumb -22.255 24.369 ** 0.414 0.168 ** Right accumb -22.755 24.685 ** 0.454 0.119 ** Mean difference test Female (n = 6243) Male (n = 5092) Variance Ratio test (b) Surface area M M p Cohen's d VR p Left bankssts -45.976 56.715 ** 0.596 0.282 ** Left caudalanteriorcingulate -25.875 31.956 ** 0.420 0.131 ** Left caudalmiddlefrontal -100.326 123.509 ** 0.589 0.163 ** Left cuneus -55.069 67.958 ** 0.605 0.188 ** Left entorhinal -19.379 23.824 ** 0.540 0.310 ** Left fusiform -142.081 174.977 ** 0.794 0.240 ** Left inferiorparietal -203.760 250.694 ** 0.751 0.288 ** Left inferiortemporal -158.709 195.821 ** 0.778 0.193 ** Left isthmuscingulate -54.544 67.228 ** 0.765 0.326 ** Left lateraloccipital -229.910 284.223 ** 0.893 0.240 ** Left lateralorbitofrontal -93.815 115.782 ** 0.771 0.194 ** Left lingual -114.132 141.130 ** 0.630 0.197 ** Left medialorbitofrontal -76.336 94.318 ** 0.741 0.288 ** Left middletemporal -139.909 172.666 ** 0.808 0.227 ** Left parahippocampal -24.273 30.139 ** 0.522 0.330 ** Left paracentral -46.588 57.790 ** 0.578 0.303 ** Left parsopercularis -63.862 78.461 ** 0.536 0.350 ** Left parsorbitalis -27.703 34.060 ** 0.755 0.223 ** Left parstriangularis -55.836 68.926 ** 0.633 0.262 ** Left pericalcarine -48.359 58.895 ** 0.485 0.151 ** Left postcentral -176.934 217.762 ** 0.867 0.286 ** Left posteriorcingulate -50.597 62.161 ** 0.651 0.253 ** Left precentral -207.652 255.826 ** 0.949 0.319 ** Left precuneus -163.276 200.728 ** 0.834 0.266 ** Left rostralanteriorcingulate -40.967 50.637 ** 0.619 0.160 ** Left rostralmiddlefrontal -297.267 365.653 ** 0.934 0.261 ** Left superiorfrontal -330.564 406.757 ** 0.962 0.269 ** Left superiorparietal -202.642 249.403 ** 0.730 0.241 ** Left superiortemporal -177.562 218.916 ** 0.970 0.262 ** (Continues) 484 WIERENGA ET AL. TABLE 2 (Continued) Mean difference test Female (n = 6243) Male (n = 5092) Variance Ratio test (b) Surface area M M p Cohen's d VR p Left supramarginal -205.547 254.230 ** 0.877 0.304 ** Left frontalpole -6.671 8.241 ** 0.439 0.249 ** Left temporalpole -15.185 18.664 ** 0.557 0.224 ** Left transversetemporal -19.898 24.463 ** 0.585 0.239 ** Left insula -84.765 104.782 ** 0.847 0.250 ** Right bankssts -42.654 52.655 ** 0.662 0.261 ** Right caudalanteriorcingulate -31.929 39.489 ** 0.465 0.275 ** Right caudalmiddlefrontal -95.924 117.705 ** 0.563 0.225 ** Right cuneus -61.606 75.541 ** 0.668 0.213 ** Right entorhinal -16.941 20.615 ** 0.467 0.339 ** Right fusiform -155.696 191.647 ** 0.900 0.225 ** Right inferiorparietal -278.411 342.870 ** 0.920 0.325 ** Right inferiortemporal -157.460 193.922 ** 0.827 0.187 ** Right isthmuscingulate -47.046 57.740 ** 0.723 0.314 ** Right lateraloccipital -227.765 282.023 ** 0.876 0.279 ** Right lateralorbitofrontal -99.594 122.823 ** 0.765 0.234 ** Right lingual -110.640 136.478 ** 0.644 0.225 ** Right medialorbitofrontal -70.180 86.695 ** 0.777 0.203 ** Right middletemporal -155.924 192.222 ** 0.857 0.224 ** Right parahippocampal -30.721 37.810 ** 0.708 0.357 ** Right paracentral -57.941 71.375 ** 0.609 0.349 ** Right parsopercularis -53.895 65.892 ** 0.506 0.312 ** Right parsorbitalis -35.086 43.159 ** 0.771 0.197 ** Right parstriangularis -69.557 85.138 ** 0.634 0.252 ** Right pericalcarine -56.327 68.894 ** 0.528 0.145 ** Right postcentral -168.595 208.307 ** 0.851 0.278 ** Right posteriorcingulate -52.836 65.327 ** 0.662 0.237 ** Right precentral -216.995 267.894 ** 0.950 0.341 ** Right precuneus -184.909 228.043 ** 0.878 0.248 ** Right rostralanteriorcingulate -33.179 41.005 ** 0.576 0.221 ** Right rostralmiddlefrontal -294.685 363.055 ** 0.898 0.228 ** Right superiorfrontal -325.198 400.002 ** 0.939 0.258 ** Right superiorparietal -205.624 252.962 ** 0.765 0.216 ** Right superiortemporal -132.506 163.787 ** 0.800 0.243 ** Right supramarginal -168.426 207.920 ** 0.754 0.285 ** Right frontalpole -9.712 11.996 ** 0.481 0.194 ** Right temporalpole -11.097 13.725 ** 0.422 0.228 ** Right transversetemporal -14.315 17.686 ** 0.564 0.194 ** Right insula -95.695 117.482 ** 0.863 0.238 ** Mean difference test Female (n = 6620) Male (n = 5913) Variance Ratio test (c) Thickness M M p Cohen's d VR p Left bankssts 0.001 -0.001 n.s. 0.011 0.039 ** Left caudalanteriorcingulate 0.026 -0.028 ** 0.213 -0.042 n.s. Left caudalmiddlefrontal 0.008 -0.008 ** 0.103 0.061 * Left cuneus 0.000 0.000 n.s. 0.001 0.050 * WIERENGA ET AL. 485 TABLE 2 (Continued) Mean difference test Female (n = 6620) Male (n = 5913) Variance Ratio test (c) Thickness M M p Cohen's d VR p Left entorhinal -0.013 0.015 ** 0.084 0.023 n.s. Left fusiform 0.001 -0.001 n.s. 0.016 0.022 n.s. Left inferiorparietal 0.009 -0.009 ** 0.128 0.092 ** Left inferiortemporal -0.002 0.003 n.s. 0.027 0.004 n.s. Left isthmuscingulate 0.009 -0.009 ** 0.088 -0.007 ** Left lateraloccipital 0.005 -0.005 ** 0.074 0.079 ** Left lateralorbitofrontal -0.002 0.003 n.s. 0.036 0.101 ** Left lingual -0.003 0.004 ** 0.058 0.040 n.s. Left medialorbitofrontal -0.004 0.006 ** 0.058 0.027 n.s. Left middletemporal -0.003 0.004 n.s. 0.037 0.093 * Left parahippocampal 0.015 -0.016 ** 0.098 0.016 n.s. Left paracentral 0.006 -0.005 ** 0.067 0.030 ** Left parsopercularis -0.002 0.003 n.s. 0.027 0.087 ** Left parsorbitalis 0.013 -0.014 ** 0.120 0.071 ** Left parstriangularis 0.004 -0.004 * 0.049 0.084 ** Left pericalcarine 0.000 0.001 n.s. 0.006 0.043 ** Left postcentral 0.008 -0.009 ** 0.133 0.078 ** Left posteriorcingulate 0.004 -0.004 ** 0.052 0.080 ** Left precentral 0.007 -0.007 ** 0.097 0.112 ** Left precuneus 0.000 0.000 n.s. 0.002 0.041 ** Left rostralanteriorcingulate 0.020 -0.021 ** 0.170 -0.046 n.s. Left rostralmiddlefrontal 0.005 -0.004 ** 0.061 0.112 ** Left superiorfrontal 0.013 -0.014 ** 0.168 0.048 n.s. Left superiorparietal 0.009 -0.009 ** 0.136 0.098 ** Left superiortemporal -0.001 0.001 n.s. 0.014 0.052 ** Left supramarginal 0.009 -0.009 ** 0.126 0.064 ** Left frontalpole 0.015 -0.016 ** 0.100 0.036 n.s. Left temporalpole 0.004 -0.004 n.s. 0.023 0.027 n.s. Left transversetemporal 0.020 -0.021 ** 0.177 0.018 n.s. Left insula -0.009 0.011 ** 0.121 0.049 n.s. Right bankssts -0.001 0.002 n.s. 0.016 0.064 ** Right caudalanteriorcingulate 0.027 -0.030 ** 0.242 -0.029 n.s. Right caudalmiddlefrontal 0.008 -0.009 ** 0.109 0.019 ** Right cuneus 0.003 -0.002 n.s. 0.034 0.027 * Right entorhinal 0.005 -0.005 n.s. 0.028 0.026 n.s. Right fusiform 0.001 0.000 n.s. 0.008 0.029 n.s. Right inferiorparietal 0.008 -0.008 ** 0.110 0.103 ** Right inferiortemporal 0.000 0.001 n.s. 0.003 0.032 n.s. Right isthmuscingulate 0.010 -0.010 ** 0.099 -0.038 ** Right lateraloccipital 0.004 -0.004 ** 0.057 0.078 ** Right lateralorbitofrontal 0.003 -0.003 n.s. 0.036 0.074 ** Right lingual -0.002 0.003 n.s. 0.036 0.036 n.s. Right medialorbitofrontal 0.003 -0.003 n.s. 0.033 0.056 n.s. Right middletemporal -0.003 0.004 * 0.047 0.065 ** Right parahippocampal 0.021 -0.023 ** 0.162 0.028 n.s. (Continues) 486 WIERENGA ET AL. TABLE 2 (Continued) Mean difference test Female (n = 6620) Male (n = 5913) Variance Ratio test (c) Thickness M M p Cohen's d VR p Right paracentral 0.004 -0.004 ** 0.055 0.065 ** Right parsopercularis 0.000 0.000 n.s. 0.001 0.037 ** Right parsorbitalis 0.018 -0.019 ** 0.164 0.026 n.s. Right parstriangularis 0.004 -0.004 ** 0.053 0.008 ** Right pericalcarine 0.001 -0.001 n.s. 0.017 0.020 n.s. Right postcentral 0.009 -0.009 ** 0.135 0.009 ** Right posteriorcingulate 0.007 -0.007 ** 0.082 0.013 ** Right precentral 0.008 -0.009 ** 0.119 0.084 ** Right precuneus -0.001 0.002 n.s. 0.018 0.063 ** Right rostralanteriorcingulate 0.009 -0.010 ** 0.080 0.055 n.s. Right rostralmiddlefrontal 0.006 -0.006 ** 0.078 0.085 ** Right superiorfrontal 0.013 -0.013 ** 0.165 0.065 * Right superiorparietal 0.008 -0.009 ** 0.132 0.065 ** Right superiortemporal -0.003 0.004 * 0.042 0.073 ** Right supramarginal 0.006 -0.007 ** 0.086 0.096 ** Right frontalpole 0.021 -0.022 ** 0.140 0.012 n.s. Right temporalpole -0.006 0.007 * 0.038 0.023 n.s. Right transversetemporal 0.011 -0.031 ** 0.095 0.101 * Right insula -0.008 0.010 ** 0.107 0.092 ** * p < 0.05, ** p < 0.01, both after FDR correction. F I G U R E 2 Sex differences in variance ratio for subcortical volumes (Left), cortical surface area (center), and cortical thickness (right). Shown are log transformed variance ratios, where significant larger variance ratio for males than females is displayed in blue ranging from 0 to 1. Darker colors indicate a larger variance ratio 4 | DISCUSSION brain measures (Wierenga et al. 2018). These variance effects seem to describe a unique aspect of sex differences in the brain that does not In this study, we analyzed a large lifespan sample of neuroimaging follow the regional pattern of mean sex differences. A novel finding data from 16,683 participants spanning nine decades of life starting at was that sex differences in variance appear stable across the lifespan birth. Results confirmed the hypothesis of greater male variability in for around 50% of subcortical volumes, 70% of cortical surface area brain structure (Forde et al. 2020; Ritchie et al. 2018; Wierenga measures and almost all cortical thickness measures. Unexpectedly, et al. 2018, 2019). Variance differences were more pronounced for regions with significant change in variance effects across the age subcortical volumes and regional cortical surface area than for range showed decreasing variance differences between the sexes regional cortical thickness. We also corroborated prior findings of with increasing age. Finally, we observed greater male inter-regional greater male brain structural variance at both upper and lower tails of homogeneity for cortical thickness, but not for surface area or WIERENGA ET AL. 487 F I G U R E 3 Jittered marginal distribution scatterplots are displayed together with their shift function for the top three variance ratio effects of subcortical volumes (top), cortical surface area (middle) and cortical thickness (right). The central, darkest line on each distribution is the median, note that main sex effects are removed. The other lines mark the deciles of each distribution. The shift values are included, which refer to the number of units that the male (upper) distribution would have to be shifted to match the female (lower) distribution. Confidence intervals are included for each of these shift values subcortical volumes, partly replicating prior results of greater within- may reduce in old age. Longitudinal designs are, however, needed to subject homogeneity in the male brain (Wierenga et al. 2018). Unex- address the mechanisms underlying this observation. pectedly, subcortical regions showed stronger interregional correla- The expression of greater male variability in both upper and lower tion in females than in males. tails of the distribution may be related to architectural and geometric Greater male variance was most pronounced in brain regions constraints that are critical for a delicate balance for effective local- involved in planning, regulation and inhibition of motor movements (pal- global communication. For example, neurons only partly regulate their lidum, right inferior parietal cortex and paracentral region), episodic size, and the number of neural connections does not vary strongly memory (hippocampus), and multimodal sensory integration (thalamus) with neocortical size across species (Stevens 1989). Although axon (Aron, Robbins, and Poldrack 2004; Burgess, Maguire, and O'Keefe size and myelin can compensate firing rates in larger brains by speed- 2002; Grillner et al. 2005). In addition, the early presence of sex differ- ing up conduction time, there is a limited energy budget to optimize ences in brain structural variability may be indicative of genetic effects, both volume and conduction time (Buzsáki, Logothetis, and in line with findings in a pediatric sample (Wierenga et al. 2018). We also Singer 2013). As such, extreme brain structure (in both directions) observed that sex differences in structural variation are either stable or may come at a cost. This is in line with recent findings that show that 488 WIERENGA ET AL. F I G U R E 4 Regions where sex differences in variability of brain structure interacted with age displayed for subcortical volumes (left), cortical surface area (center), and cortical thickness (right) extreme neural activity patterns may induce suboptimal expressions mechanism may lead to increased male vulnerability, as is also seen of mental states (Northoff and Tumati 2019). Interestingly, it has been for a number of rare X-linked genetic mutations (Chen et al. 2008; found that individuals with autism spectrum disorder show atypical Craig, Haworth, and Plomin 2009; Johnson, Carothers, and patterns of brain structure and development in both the upper and Deary 2009; Reinhold and Engqvist 2013; Ryan et al. 1997). None of lower range (Zabihi et al. 2019), suggesting a possible link between the other sex effects mentioned above predict these specific inter and greater male variability and vulnerability for developmental disorders intra-individual sex differences in brain patterns. Future studies are, (see also Alnæs et al. 2019)). Together with our findings, this opens up however, needed to directly test these different mechanisms. Further- new approaches to understanding sex biased developmental disor- more, the observation that greater male homogeneity was only ders, beyond group-level mean differences. observed in cortical thickness, but not cortical surface area or subcor- Although most results showed stable sex differences with tical volumes, may speculatively indicate that X-chromosome related increasing age, half of the subcortical regions and a quarter of the cor- genetic mechanisms may have the largest effect on cortical thickness tical surface area measures showed decreasing sex differences in vari- measures. ance. What stands out is that in all these regions, sex differences in This paper has several strengths including its sample size, the age variance were largest in young compared to older age. This is indica- range spanning nine decades, the inclusion of different structural mea- tive of early mechanisms being involved. Furthermore, for subcortical sures (subcortical volumes and cortical surface area and thickness) regions, the patterns showed larger volumetric increases in females and the investigation of variance effects. These points are important, then in males. For surface area, interaction effects showed mostly sta- as most observed mean sex differences in the brain are modest in size ble variance across age in females, but decreases in variability in (Joel and Fausto-Sterling 2016). We were able to analyze data from a males. The observation that there were no significant quadratic inter- far larger sample than those included in recent meta-analyses of mean actions makes it unlikely that pubertal hormones may affect greater sex differences (Marwha et al. 2017; Ruigrok et al. 2014; Tan male variance. Yet, the decrease in male variance in older age, may be et al. 2016), and a very wide age range covering childhood, adoles- indicative of environmental effects later in life. Alternative explanation cence, adulthood and senescence. The results of this study may have may be the larger number of clinical or even death rates in males that important implications for studies on mean sex differences in brain may lead to some sex difference in survival (Chen et al. 2008; Ryan structure, as analyses in such studies typically assume that group vari- et al. 1997). ances are equal, which the present study shows might not be tenable. Factors underlying or influencing sex differences in the brain may This can be particularly problematic for studies with small sample sizes include sex chromosomes, sex steroids (both perinatal or pubertal), (Rousselet et al. 2017). and the neural embedding of social influences during the life span The current study has some limitations. First, the multi-site sam- (Dawson, Ashman, and Carver 2000). Although we could not directly ple was heterogeneous and specific samples were recruited in differ- test these mechanisms, our findings of greater male variance, that are ent ways, not always representative of the entire population. mostly stable across age, together with the greater male inter-regional Furthermore, although structural measures may be quite stable across homogeneity for cortical thickness are most in line with the single X- different scanners, the large number of sites may increase the vari- chromosome expression in males compared to the mosaic pattern of ance in observed MRI measures, but this would be unlikely to be sys- X-inactivation in females (Arnold 2012). Whereas female brain tissue tematically biased with respect to age or sex. In addition, variance shows two variants of X-linked genes, males only show one. This effects may change in non-linear ways across the age-range. This may WIERENGA ET AL. 489 TABLE 3 Variance differences between sexes across age (a) Subcortical Intercept SE p Age SE p Sex SE P Sex by age SE p Left thal 587.987 6.178 ** 9398.523 652.185 ** 60.310 9.199 ** -3107.885 979.201 ** Right thal 515.416 5.524 ** 6424.232 583.119 ** 82.380 8.225 ** -3102.267 875.503 ** Left caud 361.790 3.729 ** 879.545 393.693 * 28.152 5.553 ** 270.769 591.096 n.s. Right caud 371.773 3.785 ** 1290.352 399.567 ** 31.395 5.636 ** -561.719 599.915 n.s. Left put 495.399 5.150 ** 4435.730 543.701 ** 54.586 7.669 ** -2966.533 816.321 ** Right put 460.842 4.887 ** 5622.177 515.939 ** 51.687 7.277 ** -3853.454 774.638 ** Left pal 165.039 1.816 ** 837.030 191.768 ** 26.852 2.705 ** -784.363 287.923 * Right pal 140.799 1.598 ** 910.463 168.695 ** 26.247 2.379 ** -850.994 253.281 ** Left hippo 309.722 3.308 ** 2755.892 349.231 ** 31.626 4.926 ** -1375.500 524.341 * Right hippo 305.607 3.264 ** 2615.969 344.571 ** 35.732 4.860 ** -890.970 517.345 n.s. Left amyg 148.932 1.598 ** 1378.267 168.734 ** 13.800 2.380 ** -233.236 253.340 n.s. Right amyg 154.218 1.645 ** 1621.298 173.675 ** 16.477 2.450 ** -540.141 260.758 n.s. Left accumb 82.473 0.875 ** 442.922 92.410 ** 7.382 1.303 ** -136.472 138.746 n.s. Right accumb 78.541 0.823 ** 539.975 86.850 ** 7.412 1.225 ** -106.522 130.398 n.s. Surface area Intercept SE p Age SE p Sex SE p Sex by age SE p Left bankssts 127.133 1.376 ** -437.616 142.554 ** 16.563 2.056 ** -574.105 219.785 * Left caudalanteriorcingulate 104.209 1.113 ** -302.669 115.254 ** 4.299 1.663 ** -277.614 177.695 n.s. Left caudalmiddlefrontal 293.750 2.943 ** -1359.284 304.791 ** 21.272 4.397 ** -660.300 469.918 n.s. Left cuneus 154.129 1.607 ** -360.698 166.430 * 13.158 2.401 ** -330.457 256.596 n.s. Left entorhinal 57.126 0.651 ** -458.398 67.397 ** 9.241 0.972 ** 1.893 103.911 n.s. Left fusiform 305.090 3.105 ** 250.591 321.575 n.s. 35.738 4.639 ** -2446.584 495.794 ** Left inferiorparietal 454.916 4.708 ** -614.521 487.682 n.s. 63.459 7.035 ** -2243.805 751.894 * Left inferiortemporal 352.394 3.540 ** -353.703 366.628 n.s. 31.482 5.289 ** -1652.239 565.256 * Left isthmuscingulate 116.771 1.249 ** -32.188 129.411 n.s. 19.544 1.867 ** -204.545 199.522 n.s. Left lateraloccipital 438.089 4.474 ** -1416.631 463.377 ** 50.571 6.685 ** -813.654 714.421 n.s. Left lateralorbitofrontal 208.173 2.120 ** 204.108 219.597 n.s. 20.633 3.168 ** -1428.745 338.567 ** Left lingual 310.573 3.141 ** -234.334 325.364 n.s. 29.898 4.694 ** -1268.288 501.636 * Left medialorbitofrontal 172.506 1.795 ** 3.188 185.938 n.s. 23.450 2.682 ** -213.946 286.673 n.s. Left middletemporal 296.794 2.997 ** -421.492 310.480 n.s. 31.627 4.479 ** -1014.822 478.689 n.s. Left parahippocampal 72.669 0.887 ** -211.577 91.839 * 10.825 1.325 ** -241.097 141.595 n.s. Left paracentral 133.446 1.419 ** -195.857 147.019 n.s. 19.139 2.121 ** -171.708 226.670 n.s. Left parsopercularis 193.582 2.113 ** -540.023 218.880 * 31.583 3.158 ** -459.911 337.462 n.s. Left parsorbitalis 61.886 0.643 ** -172.940 66.566 ** 7.120 0.960 ** -131.612 102.629 n.s. Left parstriangularis 148.566 1.524 ** -644.966 157.820 ** 19.173 2.277 ** -546.829 243.322 n.s. Left pericalcarine 171.607 1.690 ** -245.127 175.004 n.s. 13.803 2.525 ** -283.583 269.815 n.s. Left postcentral 340.927 3.572 ** -1033.492 370.007 ** 46.097 5.338 ** -1240.366 570.466 n.s. Left posteriorcingulate 130.459 1.363 ** -176.189 141.217 n.s. 13.905 2.037 ** -400.954 217.724 n.s. Left precentral 360.893 3.926 ** -1088.967 406.693 ** 47.580 5.867 ** -876.707 627.028 n.s. Left precuneus 329.439 3.386 ** -444.670 350.720 n.s. 44.718 5.060 ** -1691.713 540.730 * Left rostralanteriorcingulate 113.700 1.156 ** -6.807 119.754 n.s. 7.691 1.728 ** -80.447 184.632 n.s. Left rostralmiddlefrontal 541.319 5.553 ** -1574.677 575.208 ** 63.888 8.298 ** -2391.074 886.838 * Left superiorfrontal 577.465 6.015 ** -1306.494 623.063 * 75.007 8.988 ** -2320.740 960.620 n.s. Left superiorparietal 471.735 4.793 ** -1198.240 496.487 * 57.076 7.162 ** -2051.708 765.468 * Left superiortemporal 308.552 3.215 ** -864.236 333.037 ** 40.486 4.804 ** -1222.034 513.467 n.s. Left supramarginal 392.296 4.082 ** -1937.799 422.787 ** 58.041 6.099 ** -775.470 651.841 n.s. Left frontalpole 25.431 0.265 ** -114.432 27.425 ** 3.212 0.396 ** -7.992 42.283 n.s. (Continues) 490 WIERENGA ET AL. TABLE 3 (Continued) Surface area Intercept SE p Age SE p Sex SE p Sex by age SE p Left temporalpole 45.410 0.478 ** -173.235 49.555 ** 5.115 0.715 ** -59.323 76.403 n.s. Left transversetemporal 56.992 0.594 ** -201.824 61.535 ** 6.690 0.888 ** -81.655 94.872 n.s. Left insula 164.339 1.842 ** -460.767 190.830 * 17.215 2.753 ** 6.824 294.215 n.s. Right bankssts 107.290 1.139 ** -392.600 117.986 ** 13.575 1.702 ** -493.453 181.908 * Right caudalanteriorcingulate 114.549 1.199 ** -266.524 124.192 * 14.948 1.792 ** -8.218 191.475 n.s. Right caudalmiddlefrontal 288.671 2.929 ** -1415.348 303.395 ** 30.576 4.377 ** -360.883 467.765 n.s. Right cuneus 152.647 1.656 ** -146.322 171.565 n.s. 16.151 2.475 ** -436.462 264.513 n.s. Right entorhinal 57.865 0.641 ** -455.979 66.351 ** 10.302 0.957 ** -50.231 102.298 n.s. Right fusiform 295.259 3.000 ** 43.695 310.723 n.s. 32.408 4.483 ** -1812.528 479.064 ** Right inferiorparietal 504.767 5.239 ** -577.142 542.646 n.s. 82.015 7.828 ** -2767.949 836.635 ** Right inferiortemporal 327.236 3.331 ** -482.481 345.043 n.s. 28.512 4.978 ** -1116.568 531.977 n.s. Right isthmuscingulate 105.700 1.157 ** -228.263 119.818 n.s. 16.311 1.729 ** -192.830 184.732 n.s. Right lateraloccipital 436.925 4.537 ** -1283.916 469.975 ** 58.726 6.780 ** -1927.057 724.593 * Right lateralorbitofrontal 220.527 2.284 ** 236.472 236.616 n.s. 24.442 3.413 ** -1470.759 364.808 ** Right lingual 289.568 3.001 ** -299.806 310.855 n.s. 34.596 4.484 ** -1128.138 479.266 n.s. Right medialorbitofrontal 154.743 1.568 ** 74.312 162.424 n.s. 15.452 2.343 ** -964.430 250.420 ** Right middletemporal 309.733 3.171 ** -517.078 328.408 n.s. 34.194 4.738 ** -1188.068 506.329 n.s. Right parahippocampal 70.171 0.781 ** -155.100 80.940 n.s. 11.822 1.168 ** -420.498 124.790 ** Right paracentral 156.024 1.669 ** -273.907 172.868 n.s. 25.570 2.494 ** -271.297 266.523 n.s. Right parsopercularis 174.570 1.866 ** -1036.595 193.296 ** 25.454 2.789 ** -231.029 298.018 n.s. Right parsorbitalis 77.607 0.794 ** -103.424 82.287 n.s. 7.160 1.187 ** -311.879 126.867 * Right parstriangularis 184.989 1.887 ** -925.697 195.494 ** 21.344 2.820 ** -662.628 301.407 n.s. Right pericalcarine 184.490 1.818 ** -314.748 188.350 n.s. 13.276 2.717 ** -264.356 290.392 n.s. Right postcentral 330.886 3.494 ** -1175.639 361.875 ** 44.061 5.220 ** -907.204 557.928 n.s. Right posteriorcingulate 133.953 1.413 ** 42.583 146.371 n.s. 14.739 2.112 ** -695.150 225.670 * Right precentral 374.619 4.131 ** -1039.063 427.849 * 53.576 6.172 ** -579.997 659.645 n.s. Right precuneus 355.783 3.685 ** -894.373 381.705 * 42.292 5.507 ** -1788.652 588.501 * Right rostralanteriorcingulate 97.009 1.005 ** 198.486 104.078 n.s. 10.668 1.501 ** -140.756 160.464 n.s. Right rostralmiddlefrontal 560.924 5.691 ** -2015.333 589.514 ** 60.682 8.504 ** -1467.830 908.895 n.s. Right superiorfrontal 586.059 6.054 ** -748.583 627.121 n.s. 72.274 9.047 ** -3613.685 966.876 ** Right superiorparietal 453.081 4.716 ** -1983.725 488.528 ** 49.530 7.048 ** 42.170 753.197 n.s. Right superiortemporal 281.023 2.898 ** -481.481 300.133 n.s. 31.844 4.330 ** -1005.995 462.736 n.s. Right supramarginal 376.538 3.839 ** -1315.029 397.627 ** 51.001 5.736 ** -1362.209 613.049 n.s. Right frontalpole 34.322 0.352 ** -93.541 36.451 * 2.974 0.526 ** -112.046 56.199 n.s. Right temporalpole 44.173 0.457 ** -144.791 47.330 ** 5.067 0.683 ** -32.370 72.972 n.s. Right transversetemporal 43.342 0.436 ** -122.601 45.112 ** 4.348 0.651 ** -76.872 69.553 n.s. Right insula 185.386 1.947 ** 167.564 201.684 n.s. 22.970 2.910 ** -270.419 310.950 n.s. Thickness Intercept SE p Age SE p Sex SE p Sex by age SE p Left bankssts 0.138 0.001 ** 0.012 0.150 n.s. 0.002 0.002 n.s. 0.345 0.217 n.s. Left caudalanteriorcingulate 0.204 0.002 ** 1.405 0.217 ** -0.005 0.003 n.s. 0.207 0.314 n.s. Left caudalmiddlefrontal 0.119 0.001 ** 0.375 0.131 ** 0.002 0.002 n.s. -0.108 0.190 n.s. Left cuneus 0.108 0.001 ** -0.194 0.118 n.s. 0.003 0.002 n.s. -0.386 0.171 n.s. Left entorhinal 0.263 0.003 ** 0.348 0.288 n.s. 0.001 0.004 n.s. -0.414 0.417 n.s. Left fusiform 0.114 0.001 ** 0.484 0.125 ** 0.000 0.002 n.s. -0.340 0.181 n.s. Left inferiorparietal 0.109 0.001 ** 0.329 0.122 ** 0.005 0.002 ** 0.023 0.176 n.s. Left inferiortemporal 0.128 0.001 ** 0.515 0.138 ** 0.000 0.002 n.s. -0.327 0.199 n.s. WIERENGA ET AL. 491 TABLE 3 (Continued) Thickness Intercept SE p Age SE p Sex SE p Sex by age SE p Left isthmuscingulate 0.165 0.002 ** 0.491 0.175 ** -0.003 0.002 n.s. -0.076 0.254 n.s. Left lateraloccipital 0.096 0.001 ** 0.132 0.106 n.s. 0.004 0.001 ** 0.057 0.154 n.s. Left lateralorbitofrontal 0.124 0.001 ** 0.212 0.138 n.s. 0.006 0.002 ** -0.438 0.201 n.s. Left lingual 0.099 0.001 ** 0.343 0.109 ** 0.001 0.001 n.s. -0.308 0.157 n.s. Left medialorbitofrontal 0.135 0.001 ** 0.067 0.150 n.s. 0.004 0.002 n.s. -0.425 0.217 n.s. Left middletemporal 0.129 0.001 ** 0.493 0.140 ** 0.004 0.002 * -0.012 0.203 n.s. Left parahippocampal 0.248 0.002 ** 0.441 0.254 n.s. 0.002 0.003 n.s. -0.372 0.368 n.s. Left paracentral 0.126 0.001 ** 0.321 0.138 * 0.003 0.002 n.s. -0.017 0.199 n.s. Left parsopercularis 0.123 0.001 ** 0.497 0.134 ** 0.005 0.002 ** -0.358 0.194 n.s. Left parsorbitalis 0.178 0.002 ** -0.413 0.192 * 0.004 0.003 n.s. 0.266 0.278 n.s. Left parstriangularis 0.134 0.001 ** 0.145 0.144 n.s. 0.004 0.002 * -0.073 0.209 n.s. Left pericalcarine 0.101 0.001 ** 0.202 0.114 n.s. 0.001 0.002 n.s. -0.325 0.165 n.s. Left postcentral 0.097 0.001 ** 0.340 0.106 ** 0.004 0.001 ** 0.222 0.154 n.s. Left posteriorcingulate 0.131 0.001 ** 0.308 0.142 * 0.005 0.002 ** -0.236 0.205 n.s. Left precentral 0.110 0.001 ** 1.223 0.122 ** 0.004 0.002 * 0.181 0.177 n.s. Left precuneus 0.111 0.001 ** 0.521 0.121 ** 0.003 0.002 n.s. -0.056 0.176 n.s. Left rostralanteriorcingulate 0.193 0.002 ** 0.470 0.205 * -0.005 0.003 n.s. -0.378 0.298 n.s. Left rostralmiddlefrontal 0.109 0.001 ** 0.153 0.122 n.s. 0.005 0.002 ** 0.039 0.177 n.s. Left superiorfrontal 0.124 0.001 ** 0.505 0.137 ** 0.002 0.002 n.s. 0.083 0.198 n.s. Left superiorparietal 0.099 0.001 ** 0.158 0.109 n.s. 0.004 0.001 ** 0.224 0.158 n.s. Left superiortemporal 0.129 0.001 ** 0.832 0.139 ** 0.004 0.002 * -0.123 0.201 n.s. Left supramarginal 0.114 0.001 ** 0.396 0.122 ** 0.005 0.002 ** 0.063 0.177 n.s. Left frontalpole 0.241 0.002 ** -1.236 0.266 ** 0.004 0.004 n.s. 0.112 0.386 n.s. Left temporalpole 0.268 0.003 ** -2.010 0.301 ** 0.006 0.004 n.s. -0.518 0.436 n.s. Left transversetemporal 0.182 0.002 ** 0.027 0.194 n.s. -0.001 0.003 n.s. -0.168 0.281 n.s. Left insula 0.125 0.001 ** 1.184 0.135 ** 0.002 0.002 n.s. -0.700 0.195 * Right bankssts 0.146 0.001 ** -0.094 0.157 n.s. 0.003 0.002 n.s. 0.217 0.228 n.s. Right caudalanteriorcingulate 0.186 0.002 ** 0.936 0.198 ** -0.008 0.003 ** -0.105 0.288 n.s. Right caudalmiddlefrontal 0.120 0.001 ** 0.226 0.130 n.s. 0.002 0.002 n.s. 0.179 0.189 n.s. Right cuneus 0.110 0.001 ** 0.037 0.118 n.s. 0.001 0.002 n.s. -0.334 0.170 n.s. Right entorhinal 0.288 0.003 ** 0.122 0.310 n.s. 0.004 0.004 n.s. -0.746 0.449 n.s. Right fusiform 0.114 0.001 ** 0.657 0.125 ** 0.001 0.002 n.s. -0.171 0.181 n.s. Right inferiorparietal 0.109 0.001 ** 0.390 0.120 ** 0.005 0.002 ** 0.233 0.174 n.s. Right inferiortemporal 0.124 0.001 ** 0.539 0.135 ** 0.003 0.002 n.s. -0.132 0.196 n.s. Right isthmuscingulate 0.162 0.002 ** 0.401 0.172 * -0.002 0.002 n.s. 0.223 0.249 n.s. Right lateraloccipital 0.101 0.001 ** 0.280 0.110 * 0.005 0.001 ** 0.023 0.159 n.s. Right lateralorbitofrontal 0.129 0.001 ** -0.174 0.144 n.s. 0.004 0.002 * -0.110 0.208 n.s. Right lingual 0.102 0.001 ** 0.172 0.111 n.s. 0.000 0.002 n.s. -0.201 0.161 n.s. Right medialorbitofrontal 0.142 0.001 ** -0.424 0.156 ** 0.003 0.002 n.s. -0.201 0.227 n.s. Right middletemporal 0.123 0.001 ** 0.067 0.137 n.s. 0.006 0.002 ** 0.400 0.198 n.s. Right parahippocampal 0.207 0.002 ** 0.554 0.224 * 0.005 0.003 n.s. -0.115 0.325 n.s. Right paracentral 0.124 0.001 ** 0.492 0.134 ** 0.002 0.002 n.s. -0.050 0.194 n.s. Right parsopercularis 0.131 0.001 ** 0.330 0.139 * 0.001 0.002 n.s. -0.056 0.201 n.s. Right parsorbitalis 0.175 0.002 ** -0.470 0.188 * 0.002 0.003 n.s. 0.159 0.273 n.s. Right parstriangularis 0.131 0.001 ** -0.016 0.141 n.s. 0.002 0.002 n.s. 0.052 0.204 n.s. Right pericalcarine 0.102 0.001 ** 0.199 0.112 n.s. 0.002 0.002 n.s. -0.336 0.163 n.s. (Continues) 492 WIERENGA ET AL. TABLE 3 (Continued) Thickness Intercept SE p Age SE p Sex SE p Sex by age SE p Right postcentral 0.102 0.001 ** 0.121 0.111 n.s. 0.002 0.002 n.s. 0.251 0.161 n.s. Right posteriorcingulate 0.129 0.001 ** 0.442 0.139 ** 0.000 0.002 n.s. -0.014 0.202 n.s. Right precentral 0.110 0.001 ** 0.992 0.124 ** 0.005 0.002 ** 0.411 0.179 n.s. Right precuneus 0.110 0.001 ** 0.473 0.121 ** 0.004 0.002 * -0.148 0.176 n.s. Right rostralanteriorcingulate 0.185 0.002 ** 0.390 0.205 n.s. 0.009 0.003 ** -0.713 0.298 n.s. Right rostralmiddlefrontal 0.108 0.001 ** 0.084 0.120 n.s. 0.003 0.002 n.s. -0.162 0.174 n.s. Right superiorfrontal 0.120 0.001 ** 0.499 0.131 ** 0.003 0.002 n.s. -0.189 0.190 n.s. Right superiorparietal 0.099 0.001 ** 0.231 0.110 * 0.003 0.002 * 0.154 0.160 n.s. Right superiortemporal 0.127 0.001 ** 0.738 0.138 ** 0.005 0.002 * 0.153 0.201 n.s. Right supramarginal 0.117 0.001 ** 0.723 0.127 ** 0.004 0.002 * -0.037 0.184 n.s. Right frontalpole 0.236 0.002 ** -0.642 0.255 * 0.002 0.003 n.s. -0.248 0.369 n.s. Right temporalpole 0.274 0.003 ** -2.088 0.317 ** 0.007 0.004 n.s. 0.219 0.459 n.s. Right transversetemporal 0.181 0.002 ** 0.511 0.198 * 0.010 0.003 ** -0.175 0.287 n.s. Right insula 0.130 0.001 ** 1.079 0.146 ** 0.005 0.002 * -0.468 0.211 n.s. * p < 0.05, ** p < 0.01, both after FDR correction. be particularly apparent for surface area and subcortical volume mea- results have important implications for the interpretation of studies sures, as these showed pronounced non-linear developmental pat- on (mean) sex differences in brain structure. Furthermore, the results terns through childhood and adolescence (Tamnes et al. 2017; of decreasing sex differences in variance across age opens a new Wierenga et al. 2018). Also, the imbalanced number of subjects across direction for research focusing on lifespan changes in variability within the age range may have diminished variability effects in the older part sexes. Our findings of sex differences in regional brain structure being of the age range. The present study has a cross-sectional design. present already in childhood may suggest early genetic or gene- Future studies including longitudinal data are warranted to further environment interaction mechanisms. Further insights into the ontog- explore the lifespan dynamics of sex differences in variability in the eny and causes of variability differences in the brain may provide brain. Last, one caveat may be the effect of movement on data quality clues for understanding male biased neurodevelopmental disorders. and morphometric measures. As males have been shown to move more than females in the scanner (Pardoe, Kucharsky Hiess, and AC KNOWLEDG EME NT S Kuzniecky 2016), this may have resulted in slight under estimations of ADHD NF-Study: The Neurofeedback study was partly funded by the brain volume and thickness measures for males (Reuter et al. 2015). project D8 of the Deutsche Forschungsgesellschaft collaborative Although quality control was conducted at each site using the stan- research center 636. Barcelona 1.5T, Barcelona 3T: The Marató TV3 dardized ENIGMA cortical and subcortical quality control protocols Foundation (#01/2010, #091710). Barcelona-Sant Pau: Miguel Servet (http://enigma.ini.usc.edu/protocols/imaging-protocols/), which Research Contract CPII16/0020 (Spanish Government, National Insti- involve a combination of statistical outlier detection and visual quality tute of Health, Carlos III); the Generalitat de Catalunya checks and a similar number of males and females had partially miss- (2017SGR01343). Betula - Umea University: KA Wallenberg Founda- ing data (52.4% males), we cannot exclude the possibility that in- tion to LN. BIG - Nijmegen 1.5T; BIG - Nijmegen 3T: The BIG database, scanner subject movement may have affected the results. Neverthe- established in Nijmegen in 2007, is now part of Cognomics, a joint ini- less, we do not think this can explain our finding of greater male vari- tiative by researchers of the Donders Centre of Cognitive Neuroimag- ance in brain morphometry measures, as this was seen at both the ing, the Human Genetics and Cognitive Neuroscience departments of upper and lower ends of the distributions. the Radboud university medical centre, and the Max Planck Institute for Psycholinguistics. The Cognomics Initiative is supported by the par- ticipating departments and centres and by external grants, including 5 | C O N CL U S I O N S grants from the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL) and the Hersenstichting The present study included a large lifespan sample and robustly con- Nederland. The authors also acknowledge grants supporting their work firmed previous findings of greater male variance in brain structure in from the Netherlands Organization for Scientific Research (NWO), humans. We found greater male variance in all brain measures, includ- i.e. the NWO Brain & Cognition Excellence Program (grant 433-09- ing subcortical volumes and regional cortical surface area and thick- 229), the Vici Innovation Program (grant 016–130-669 to BF) and ness, at both the upper and the lower end of the distributions. The #91619115. Additional support is received from the European WIERENGA ET AL. 493 F I G U R E 5 Sex differences in variability interacted with age in 50% of the subcortical volumes, 30% of the surface area measures, and only one thickness measure. Three representative results are shown: right thalamus volume (top left), surface area of the right parahippocampal gyrus (top right) and thickness of the left insula (bottom center). Absolute residual values are modeled across the age range. Effects showed larger male than female variance in the younger age group, this effect attenuated with increasing age Community's Seventh Framework Programme (FP7/2007 – 2013) under excellence 433-09-220 to H.H., NWO-MagW 480-04-004 to D.B., and grant agreements n 602805 (Aggressotype), n 603016 (MATRICS), n NWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European Research Council 602450 (IMAGEMEND), and n 278948 (TACTICS), and from the (ERC-230374 to D.B. Universiteit Utrecht (High Potential Grant to H.H.); European Community's Horizon 2020 Programme (H2020/2014 – KNAW Academy Professor Award (PAH/6635). BRCATLAS: National   2020) under grant agreements n 643051 (MiND) and n 667302 Institute for Health Research (NIHR) Biomedical Research Centre at South (CoCA). Brain and Development Research Center, Leiden University: London and Maudsley NHS Foundation Trust and King's College London. European Research Council (ERC-2010-StG-263234 to EAC); Research CAMH: BBRF; Canadian Institutes of Health Research; Natural Sciences Council of Norway (#223273, #288083, #230345); South-Eastern and Engineering Research Council; National Institute of Mental Health; Norway Regional Health Authority (#2017112, #2019069). BRAINSCALE: CAMH Foundation; University of Toronto. Cardiff University: We are Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO grateful to all researchers within Cardiff University who contributed to the 51.02.061 to H.H., NWO 51.02.062 to D.B., NWO- NIHC Programs of MBBrains panel, and Cardiff University Brain Research Imaging Centre 494 WIERENGA ET AL. F I G U R E 6 (a–c) Stronger anatomical correlations for males than females are indicated in blue (larger homogeneity in males than females), while stronger correlations for females are displayed in red (larger homogeneity in females than males). The bottom left half shows the significant variance ratio's only, using two sided permutation testing. Results are displayed for subcortical volumes (a), surface area (b), and cortical thickness (c). Cortical regions are ordered by lobe and hemisphere (left frontal, left occipital, left parietal, left temporal, right frontal, right occipital, right parietal, right temporal) WIERENGA ET AL. 495 FIGURE 6 (Continued) (CUBRIC) and the National Centre for Mental Health (NCMH) for their Institute). ENIGMA-OCD (London Cohort/Mataix-Cols): Wellcome Trust support. CEG (London): UK Medical Research Council Grant G03001896 and a pump priming grant from the South London and Maudsley Trust, to J Kuntsi; NIHR Biomedical Research Centre for Mental Health, NIHR/ London, UK (Project grant no. 064846). ENIGMA-OCD (van den Heuvel MRC (14/23/17); NIHR senior investigator award (NF-SI-0616-10040). 1.5T; van den Heuvel 3T): The Dutch Organization for Scientific Research CIAM: University Research Committee, University of Cape Town; National (NWO-ZonMw) VENI grant 916.86.036; NARSAD Young Investigators Research Foundation; South African Medical Research Council. CODE – Award; Netherlands Brain Foundation grant 2010(1)-50. ENIGMA-OCD- Berlin: Lundbeck; the German Research Foundation (WA 1539/4-1, 3T-CONTROLS: South African Medical Research Council (SA MRC); SCHN 1205/3-1). Conzelmann Study: Deutsche Forschungsgemeinschaft South African National Research Foundation (NRF). FIDMAG: Generalitat (KFO 125, TRR 58/A1 and A5, SFB-TRR 58/B01, B06 and Z02, de Catalunya (2017SGR01271); several grants funded by Instituto de RE1632/5-1); EU H2020 (#667302); German Research Foundation (KFO Salud Carlos III co-funded by the European Regional Development Fund/ 125). ENIGMA Core: NIA T32AG058507; NIH/NIMH 5T32MH073526; European Social Fund “Investing in your future”: Miguel Servet Research NIH grant U54EB020403 from the Big Data to Knowledge (BD2K) Contract (CPII16/00018 to EP-C, CPII19/00009 to JR) and Research Program; Core funding NIH Big Data to Knowledge (BD2K) program Projects (PI18/00810 to EP-C, PI18/00877 to RS, and PI19/00394 to JR); under consortium grant U54 EB020403; ENIGMA World Aging Center AGAUR; CIBERSAM. GSP: R01MH120080, K01MH099232, (R56 AG058854; PI PMT); ENIGMA Sex Differences Initiative (R01 R00MH101367, R01MH119243; R01MH101486; K24 MH094614. We MH116147; PI PMT); ENIGMA Suicidal Thoughts and Behavior Working thank Randy Buckner for access to this dataset. Homburg Multidiagnosis Group (R01 MH117601; PI NJ). ENIGMA Lifespan: National Institute of Study (HMS) - Gottingen, CLING: CLING/HMS: The CliNG study sample Mental Health (R01MH113619, R01MH116147, R01 MH104284); was partially supported by the Deutsche Forschungsgemeinschaft (DFG) National Institute for Health Research (NIHR) Biomedical Research Centre via the Clinical Research Group 241 “Genotype-phenotype relationships at South London and Maudsley NHS Foundation Trust and King's College and neurobiology of the longitudinal course of psychosis,” TP2 London; Psychiatry Research Trust; 2014 NARSAD Young Investigator (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1); data Award; National Institute of Aging (R03AG064001); National Institute of storage service SDS@hd supported by the Ministry of Science, Research General Medical Sciences (P20GM130447); ENIGMA-HIV (NHIV; HIV- and the Arts Baden-Württemberg (MWK) and the German Research R01): NIH grant MH085604. ENIGMA-OCD (IDIBELL): FI17/00294 Foundation (DFG) through grant INST 35/1314-1 FUGG and INST (Carlos III Health Institute). PI16/00889; CPII16/00048 (Carlos III Health 35/1503-1 FUGG. HUBIN: Swedish Research Council (2003-5485, 496 WIERENGA ET AL. 2006-2992, 2006-986, 2008-2167, K2012-61X-15078-09-3, Radboud University Medical Center, University Medical Center Groningen, 521-2011-4622, 521-2014-3487, 2017-00949); regional agreement Accare; VU University Amsterdam; the European Community's Sev- on medical training and clinical research between Stockholm County enth Framework Programme (FP7/2007 – 2013) under grant agree- Council and the Karolinska Institutet; Knut and Alice Wallenberg ments n 602805 (Aggressotype), n 278948 (TACTICS), and n Foundation; HUBIN project. IDIBELL: Carlos III Health Institute 602450 (IMAGEMEND); the European Community's Horizon 2020 (PI13/01958, PI16/00889, CPII16/00048); FEDER funds/European Programme (H2020/2014 – 2020) under grant agreements n Regional Development Fund (ERDF) - a way to build Europe-; the 643051 (MiND) and n 667302 (CoCA); Research Council of Norway Department of Health of the Generalitat de Catalunya (PERIS (#276082). Neuroventure: Canadian Institutes of Health Research SLT006/17/249); AGAUR (2017 SGR 1262). IMpACT-NL: The Neth- (#287378, #FRN114887, #FRN126053). New York University: erlands Organization for Scientific Research (NWO), i.e. the Veni Inno- R01MH083246. Northwestern University: NIH grants P50 vation Program (grant 016-196-115 to MH) and the Vici Innovation MH071616, R01 MH056584, R01 MH084803 (Wang PI), U01 Program (grant 016–130-669 to BF); U54 EB020403 to the ENIGMA MH097435 (Wang, Turner, Ambite, Potkin PIs), R01 EB020062 Consortium from the BD2K Initiative, a cross-NIH partnership, and by (Miller, Paulsen, Mostfosky, Wang PIs), NSF 1636893 (Pestilli, Wang, the European College of Neuropsychopharmacology (ECNP) Network Saykin, Sporns PIs), NSF 1734853 (Pestilli, Garyfallidis, Henschel, “ADHD Across the Lifespan”; The Dutch National Science Agenda Wang, Dinov PIs). NUI Galway: Health Research Board Ireland NeurolabNL project (grant 400-17-602). Indiana 1.5T; Indiana 3T: (HRA-POR-2013-324, HRA-POR-2011-100). Older Australian Twins NIH grants P30 AG010133, R01 AG019771 and R01 CA129769; Sie- Sample (OATS): NHMRC/ARC Strategic Award (ID401162); NHMRC mens Medical Solutions; the members of the Partnership for Pediatric Program Grants (ID568969, ID1093083); NHMRC Project Grants Epilepsy Research, which includes the American Epilepsy Society, the (ID1045325, ID1024224, ID1025243); we also thank Twins Epilepsy Foundation, the Epilepsy Therapy Project, Fight Against Research Australia. Oxford University: MRC G0500092. QTIM - Uni- Childhood Epilepsy and Seizures (F.A.C.E.S.), and Parents Against versity of Queensland: National Institute of Child Health and Human Childhood Epilepsy (P.A.C.E.); the GE/NFL Head Health Challenge I; Development (R01 HD050735); National Health and Medical the Indiana State Department of Health Spinal Cord and Brain Injury Research Council (NHMRC 486682, 1009064) Australia. S~ao Paolo Fund Research Grant Program; a Project Development Team within 1, S~ao Paolo 3: Conselho Nacional de Desenvolvimento Científico e the ICTSI NIH/NCRR Grant Number RR025761. Institute of Mental Tecnológico (CNPq, Brazil); Wellcome Trust, UK. SHIP: SHIP is part Health, Singapore: Singapore Bioimaging Consortium (RP C-009/06) of the Community Medicine Research net of the University of and NMRC CSSSP (Jun17033) awarded to KS. KaSP: Swedish Medical Greifswald, Germany, funded by the Federal Ministry of Education Research Council (SE: 2009-7053; 2013-2838; SC: 523-2014-3467); and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the the Swedish Brain Foundation; Svenska Läkaresällskapet; Torsten Ministry of Cultural Affairs and the Social Ministry of the Federal Söderbergs Stiftelse; Söderbergs Königska Stiftelse; Knut and Alice State of Mecklenburg-West Pomerania. MRI scans in SHIP and Wallenberg Foundation; Stockholm County Council (ALF and PPG); SHIP-TREND have been supported by a joint grant from Siemens KID-funding from the Karolinska Institutet. MCIC: NIH Healthineers, Erlangen, Germany and the Federal State of P20GM103472; NIH R01EB020407; the Department of Energy DE- Mecklenburg-West Pomerania. Stanford University: NIH Grant FG02-99ER62764 through its support of the Mind Research Network R37-MH101495; NIH Grant R01 MH059259 (to IHG). STROKEMRI: (MRN, formerly known as the MIND Institute); National Association South-Eastern Norway Regional Health Authority (#2019107, for Research in Schizophrenia and Affective Disorders (NARSAD) #2015044); Norwegian ExtraFoundation for Health and Rehabilita- Young Investigator Award (to SE); Blowitz-Ridgeway and Essel Foun- tion (#2015/FO5146). Sydney Memory and Aging Study (MAS): dations, NWO ZonMw TOP 91211021; the DFG research fellowship NHMRC Program Grants (ID350833, ID568969, ID1093083). TOP: (to SE); the Mind Research Network, National Institutes of Health Research Council of Norway (#223273, #248778, #249795, through NCRR 5 month-RR001066 (MGH General Clinical Research #300768); South-Eastern Norway Regional Health Authority Center); NIMH K08 MH068540; the Biomedical Informatics Research (#2019-108, #2014097, #2019101); K.G. Jebsen Foundation (SKGJ- Network with NCRR Supplements to P41 RR14075 (MGH), M01 RR MED-008); EU (847776); European Research Council Starting Grant 01066 (MGH), NIBIB R01EB006841 (MRN), R01EB005846 (MRN), (#802998 to LTW); Department of Psychology, University of Oslo. 2R01 EB000840 (MRN), 1RC1MH089257 (MRN); U24 RR021992. UMC Utrecht 1 (CTR): zonmw 60-6360098602. University of Bari METHCT: South African Medical Research Council. NETHERLANDS Aldo Moro (UNIBA): European Union Seventh Framework Pro- TWIN REGISTRY (NTR): Netherlands Organization for Scientific gramme for research, technological development and demonstration Research (NWO) MW904-61-193 (de Geus & Boomsma), MaGW-nr: under grant agreement no. 602450 (IMAGEMEND) awarded to 400-07- 080 (van't Ent), MagW 480-04-004 (Boomsma); NWO/SPI Alessandro Bertolino; “Ricerca Finalizzata” Grant PE-2011-02347951 56-464-14192 (Boomsma); the 646 European Research Council, ERC- awarded to Alessandro Bertolino; European Union's Horizon 2020 230374 (Boomsma); Amsterdam Neuroscience; KNAW Academy Profes- research and innovation program under the Marie Skłodowska-Curie sor Award (PAH/6635) NeuroIMAGE: National Institutes of Health Grant No. 798181 (FLOURISH) awarded to Giulio Pergola. University of (R01MH62873 to SV Faraone); NWO Large Investment (1750102007010 Bonn (Financial Risk Seeking Study): Frankfurt Institute for Risk to JK Buitelaar); NWO Brain & Cognition (433-09-242 to JK Buitelaar); Managemand and Regulation. University of Edinburgh: Wellcome WIERENGA ET AL. 497 Trust (104036/Z/14/Z, 216767/Z/19/Z); UKRI MRC (MC_PC_17209, AUTHOR CONTRIBU TIONS MR/S035818/1); the European Union H2020 (SEP-210574971). LMW developed the theoretical framework and prepared the manu- University of Melbourne: National Health and Medical Research script with support from GED, PMT, EAC, SF, and CKT. LMW Council of Australia (NHMRC) (#1064643, #1024570); NHMRC designed the models and scripts, GED and SF analyzed the data. All Career Development Fellowships (1141738). University of Pennsylva- sites processed the imaging data and conducted quality control. GD, nia: R01MH120482; K23MH085096; R01MH101111; MH117014; DD, and SF brought together and organized the datasets. Cohort MH119219. University of Sydney: NHMRC Research Fellowship. Yale PI/ENIGMA core: DD, IA, OAA, PA, TB, AB, DIB, SB, DB, HB, GFB, University: K23 MH115206; IOCDF Award. Yale University (Olin): DMC, XC, TMCA, CRKC, VPC, PJC, AC, DvE, SEF, BF, ADG, DCG, R01 MH106324; R01 MH096957. IHG, HJG, OG, PG, REG, RCG, LdH, BJH, PJH, OAvdH, FMH, HEHP, CH, NJ, JAJ, AJK, JK, LL, ISL, CL, NGM, DM-C, BM, BCM, CMcD, COLLABOR ATOR S AMM, KLM, JMM, LN, JO, PP, EP-C, MJP, JR, JLR, PGPR, MDS, PSS, Members of the Karolinska Schizophrenia Project (KaSP) consortium: TDS, AJS, KS, AS, JWS, IES, CS-M, AJS, DJS, SIT, JNT, DJV, HW, YW, Farde L1, Flyckt L1, Engberg G2, Erhardt S2, Fatouros-Bergman H1, BW, LTW, HCW, SCRW, MJW, MVZ, GIdZ, YW, PMT, EAC, SF. Image Cervenka S1, Schwieler L2, Piehl F3, Agartz I1,4,5, Collste K1, Sellgren data collection: IA, TNA, AA-E, KIA, PA, SB, RB-S, AB, AB, SB, JB, AdB, 2 1 2 2 2 CM , Victorsson P , Malmqvist A , Hedberg M , Orhan F . 1 Centre AB, VDC, XC, FXC, TMCA, VPC, AC, FC, CGD, DvE, PF-C, EJCdG, for Psychiatry Research, Department of Clinical Neuroscience, ADG, DCG, IHG, HJG, PG, REG, LdH, BH, BJH, SNH, IBH, OAvdH, Karolinska Institutet, & Stockholm Health Care Services, Stockholm IBB, CAH, DJH, SH, AJH, MH, NH, FMH, CH, ACJ, EGJ, AJK, KKK, JL, County Council, Stockholm, Sweden; 2 Department of Physiology and LL, LdH, ISL, CL, MWJM, BM, BCM, YW, CMcD, AMM, GM, JN, YP, Pharmacology, Karolinska Institutet, Stockholm, Sweden; 3 Neu- PP, GP, EP-C, JR, SS, AR, GR, JLR, PSS, RS, SS, TDS, AJS, MHS, KS, roimmunology Unit, Department of Clinical Neuroscience, Karolinska AS, LTS, PRS, AST, JNT, AU, N, HV, LW, YW, BW, WW, JDW, LTW, Institutet, Stockholm, Sweden; 4 NORMENT, Division of Mental SCRW, DHW, YNY, MVZ, GCZ, EAC. Image data processing/quality Health and Addiction, Oslo University Hospital & Institute of Clinical control: GED, MA, TNA, AA-E, DA, KIA, AA, NB, SB, SE, AB, JB, AdB, Medicine, University of Oslo, Oslo, Norway; 5 Department of Psychi- RMB, VDC, EJC-R, XC, FXC, CRKC, AC, CGD, EWD, SE, DvE, JPF, atry, Diakonhjemmet Hospital, Oslo, Norway. PF-C, ADG, DCG, IHG, PG, TPG, BJH, SNH, OAvdH, AJH, MH, CH, ACJ, JJ, LK, BK, JL, ISL, PHL, MWJM, SM, IM-Z, BM, BCM, YW, GM, CONF LICT OF IN TE RE ST DvdM, JN, RS, EJC-R, YP, JR, GR, MDS, RS, TDS, KS, AS, LTS, PRS, The authors declare the following competing interests: OAA: Speaker's SIT, AST, AU, IMV, LW, YW, WW, JDW, SCRW, KW, DHW, YNY, honorarium from Lundbeck, Consultant of HealthLyti; PA: Received pay- CKT. Manuscript revision: GED, IA, MA, AA-E, PA, AB, HB, RMB, JKB, ments for consultancy to Shire/Takeda, Medic, educational/research VDC, EJC-R, XC, AC, CGD, DD, SE, PF-C, EJCdG, ADG, DCG, IHG, awards from Shire/Takeda, GW Pharma, Janssen-Cila, speaker at spon- HJG, REG, RCG, TPG, BH, BJH, CAH, OAvdH, AJH, NH, FMH, ACJ, sored events for Shire, Flynn Pharma, Medic; TB: advisory or consultancy EGJ, JAJ, MK, JL, PHL, CL, DM-C, BM, BCM, AMM, DvdM, YP, GP, role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, EP-C, MJP, JR, GR, PSS, RS, AJS, KS, AS, DJS, HST, AST, JNT, AU, N, Shire, and Infectopharm, conference support or speaker's fee by Lilly, HV, BW, LTW, KW, DHW. Medice, and Shire, received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press - the present work is unrelated to the DATA AVAILABILITY STAT EMEN T above grants and relationship; DB: serves as an unpaid scientific consul- The data that support the findings of this study are available on tant for an EU-funded neurofeedback trial that is unrelated to the request from the corresponding author. The data are not publicly present work; HB: Advisory Board, Nutricia Australi; CRKC: available due to privacy or ethical restrictions. received partial research support from Biogen, Inc. (Boston, USA) for work unrelated to the topic of this manuscript; BF: received OR CID educational speaking fees from Medice; HJG: received travel grants Lara M Wierenga https://orcid.org/0000-0001-9362-563X and speakers honoraria from Fresenius Medical Care, Neuraxpharm, Gaelle E Doucet https://orcid.org/0000-0003-4120-0474 Servier and Janssen Cilag as well as research funding from Rachel M Brouwer https://orcid.org/0000-0002-7466-1544 Fresenius Medical Care; NJ and PMT: MPI of a research related Christopher RK Ching https://orcid.org/0000-0003-2921-3408 grant from Biogen, Inc., for research unrelated to the contents of Simon E Fisher https://orcid.org/0000-0002-3132-1996 this manuscript; JK: given talks at educational events sponsored by Jean-Paul Fouche https://orcid.org/0000-0002-0830-2324 Medic; all funds are received by King's College London and used for David C Glahn https://orcid.org/0000-0002-4749-6977 studies of ADHD; DM-C: receives fees from UpToDate, Inc and Tiril P Gurholt https://orcid.org/0000-0002-1272-7616 Elsevier, all unrelated to the current work; AMM: received research Odile A van den Heuvel https://orcid.org/0000-0002-9804-7653 support from Eli Lilly, Janssen, and the Sackler Foundation, and Martine Hoogman https://orcid.org/0000-0002-1261-7628 speaker fees from Illumina and Janssen; DJS: received research Hilleke E Hulshoff Pol https://orcid.org/0000-0002-2038-5281 grants and/or honoraria from Lundbeck and Sun. 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