Cortical thickness across the lifespan: Data from 17,075healthy individuals aged 3–90 years

RESEARCH ARTICLE Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years Sophia Frangou 1,2 †| Amirhossein Modabbernia 1 †| Steven C. R. Williams 3 | Efstathios Papachristou 4 | Gaelle E. Doucet 5 | Ingrid Agartz 6,7,8 | Moji Aghajani 9,10 | Theophilus N. Akudjedu 11,12 | Anton Albajes-Eizagirre 13,14 | Dag Alnæs 6,15 | Kathryn I. Alpert 16 | Micael Andersson 17 | Nancy C. Andreasen 18 | Ole A. Andreassen 6 | Philip Asherson 19 | Tobias Banaschewski 20 | Nuria Bargallo 21,22 | Sarah Baumeister 20 | Ramona Baur-Streubel 23 | Alessandro Bertolino 24 | Aurora Bonvino 25 | Dorret I. Boomsma 25 | Stefan Borgwardt 26 | Josiane Bourque 27 | Daniel Brandeis 20 | Alan Breier 28 | Henry Brodaty 29 | Rachel M. Brouwer 30 | Jan K. Buitelaar 31,32,33 | Geraldo F. Busatto 34 | Randy L. Buckner 35,36 | Vincent Calhoun 37 | Erick J. Canales-Rodríguez 13,14 | Dara M. Cannon 12 | Xavier Caseras 38 | Francisco X. Castellanos 39 | Simon Cervenka 8,40 | Tiffany M. Chaim-Avancini 34 | Christopher R. K. Ching 41 | Victoria Chubar 42 | Vincent P. Clark 43,44 | Patricia Conrod 45 | Annette Conzelmann 46 | Benedicto Crespo-Facorro 14,47 | Fabrice Crivello 48 | Eveline A. Crone 49,50 | Anders M. Dale 51,52 | Udo Dannlowski 53 | Christopher Davey 54 | Eco J. C. de Geus 25 | Lieuwe de Haan 55 | Greig I. de Zubicaray 56 | Anouk den Braber 25 | Erin W. Dickie 57,58 | Annabella Di Giorgio 59 | Nhat Trung Doan 6 | Erlend S. Dørum 6,60,61 | Stefan Ehrlich 62,63 | Susanne Erk 64 | Thomas Espeseth 59,65 | Helena Fatouros-Bergman 8,40 | Simon E. Fisher 33,66 | Jean-Paul Fouche 67 | Barbara Franke 33,68,69 | Thomas Frodl 70 | Paola Fuentes-Claramonte 13,14 | David C. Glahn 71 | Ian H. Gotlib 72 | Hans-Jörgen Grabe 73,74 | Oliver Grimm 75 | Nynke A. Groenewold 67,76 | Dominik Grotegerd 76 | Oliver Gruber 77 | Patricia Gruner 78,79 | Rachel E. Gur 27,80,81 | Ruben C. Gur 27,80,81 | Tim Hahn 53 | Ben J. Harrison 82 | Catharine A. Hartman 83 | Sean N. Hatton 84 | Andreas Heinz 64 | Dirk J. Heslenfeld 85 | Derrek P. Hibar 86 | Ian B. Hickie 84 | Beng-Choon Ho 18 | † Sophia Frangou and Amirhossein Modabbernia contributed equally to this manuscript # Members of Karolinska Schizophrenia Project (KaSP) are given in Appendix. Received: 10 November 2020 Revised: 2 January 2021 Accepted: 21 January 2021 DOI: 10.1002/hbm.25364 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. © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. Hum Brain Mapp. 2022;43:431–451. wileyonlinelibrary.com/journal/hbm 431 Pieter J. Hoekstra 87 | Sarah Hohmann 20 | Avram J. Holmes 88 | Martine Hoogman 33,68 | Norbert Hosten 89 | Fleur M. Howells 67,76 | Hilleke E. Hulshoff Pol 30 | Chaim Huyser 90 | Neda Jahanshad 42 | Anthony James 91 | Terry L. Jernigan 92 | Jiyang Jiang 29 | Erik G. Jönsson 6 | John A. Joska 67 | Rene Kahn 1 | Andrew Kalnin 93 | Ryota Kanai 94 | Marieke Klein 33,68,95 | Tatyana P. Klyushnik 96 | Laura Koenders 55 | Sanne Koops 30 | Bernd Krämer 77 | Jonna Kuntsi 19 | Jim Lagopoulos 97 | Luisa Lázaro 14,98 | Irina Lebedeva 96 | Won Hee Lee 1 | Klaus-Peter Lesch 99 | Christine Lochner 100 | Marise W. J. Machielsen 55 | Sophie Maingault 48 | Nicholas G. Martin 101 | Ignacio Martínez-Zalacaín 14,102 | David Mataix-Cols 8,40 | Bernard Mazoyer 48 | Colm McDonald 12 | Brenna C. McDonald 28 | Andrew M. McIntosh 103 | Katie L. McMahon 104 | Genevieve McPhilemy 12 | Susanne Meinert 53 | José M. Menchón 14,102 | Sarah E. Medland 101 | Andreas Meyer-Lindenberg 105 | Jilly Naaijen 32,33 | Pablo Najt 12 | Tomohiro Nakao 106 | Jan E. Nordvik 107 | Lars Nyberg 17,108 | Jaap Oosterlaan 109 | Víctor Ortiz-García de la Foz 14,110,111 | Yannis Paloyelis 3 | Paul Pauli 23,112 | Giulio Pergola 24 | Edith Pomarol-Clotet 13,14 | Maria J. Portella 13,113 | Steven G. Potkin 114 | Joaquim Radua 8,22,115 | Andreas Reif 75 | Daniel A. Rinker 6 | Joshua L. Roffman 36 | Pedro G. P. Rosa 34 | Matthew D. Sacchet 116 | Perminder S. Sachdev 29 | Raymond Salvador 13 | Pascual Sánchez-Juan 110,117 | Salvador Sarró 13 | Theodore D. Satterthwaite 27 | Andrew J. Saykin 28 | Mauricio H. Serpa 34 | Lianne Schmaal 118,119 | Knut Schnell 120 | Gunter Schumann 19,121 | Kang Sim 122 | Jordan W. Smoller 123 | Iris Sommer 124 | Carles Soriano-Mas 14,102 | Dan J. Stein 100 | Lachlan T. Strike 125 | Suzanne C. Swagerman 25 | Christian K. Tamnes 6,7,126 | Henk S. Temmingh 67 | Sophia I. Thomopoulos 41 | Alexander S. Tomyshev 96 | Diana Tordesillas-Gutiérrez 13,127 | Julian N. Trollor 29 | Jessica A. Turner 128 | Anne Uhlmann 67 | Odile A. van den Heuvel 9 | Dennis van den Meer 6,15,129 | Nic J. A. van der Wee 130,131 | Neeltje E. M. van Haren 132 | Dennis van 't Ent 25 | Theo G. M. van Erp 114,133,134 | Ilya M. Veer 64 | Dick J. Veltman 9 | Aristotle Voineskos 57,58 | Henry Völzke 134,135,136 | Henrik Walter 64 | Esther Walton 137 | Lei Wang 138 | Yang Wang 139 | Thomas H. Wassink 18 | Bernd Weber 140 | Wei Wen 29 | John D. West 28 | Lars T. Westlye 60 | Heather Whalley 103 | Lara M. Wierenga 141 | Katharina Wittfeld 73,74 | Daniel H. Wolf 27 | Amanda Worker 2 | Margaret J. Wright 125 | Kun Yang 142 | Yulyia Yoncheva 143 | Marcus V. Zanetti 34,144 | Georg C. Ziegler 145 | Karolinska Schizophrenia Project (KaSP) | Paul M. Thompson 41 | Danai Dima 3,146 1 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York 432 FRANGOU ET AL. 2 Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada 3 Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom 4 Psychology and Human Development, Institute of Education, University College London, London, United Kingdom 5 Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, Nebraska 6 Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway 7 Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway 8 Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden 9 Department of Psychiatry, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, Netherlands 10 Section Forensic Family & Youth Care, Institute of Education & Child Studies, Leiden University, Netherlands 11 Institute of Medical Imaging and Visualisation, Department of Medical Science and Public Health, Faculty of Health and Social Sciences, Bournemouth University, Poole, United Kingdom 12 Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience Centre, National University of Ireland, Galway, Ireland 13 FIDMAG Germanes Hospitalàries, Barcelona, Spain 14 Mental Health Research Networking Center (CIBERSAM), Madrid, Spain 15 Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway 16 Radiologics, Inc, Saint Louis, Missouri 17 Department of Integrative Medical Biology, Umeå University, Umeå, Sweden 18 Department of Psychiatry, Carver College of Medicine, The University of Iowa, Iowa City, Iowa 19 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom 20 Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany 21 Imaging Diagnostic Centre, Hospital Clinic, Barcelona University Clinic, Barcelona, Spain 22 August Pi i Sunyer Biomedical Research Institut (IDIBAPS), Barcelona, Spain 23 Department of Psychology, Biological Psychology, Clinical Psychology and Psychotherapy, University of Würzburg, Würzburg, Germany 24 Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy 25 Department of Biological Psychology, Vrije Universiteit, Amsterdam, Netherlands 26 Department of Psychiatry & Psychotherapy, University of Lübeck, Lübeck, Germany 27 Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania 28 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana 29 Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Kensington, New South Wales, Australia 30 Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, Netherlands 31 Donders Center of Medical Neurosciences, Radboud University, Nijmegen, Netherlands 32 Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands 33 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands 34 Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de S~ ao Paulo, S~ ao Paulo, Brazil 35 Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts 36 Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 37 Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, USA Neurology, Radiology, Psychiatry and Biomedical Engineering, Emory University, Atlanta, Georgia 38 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom 39 Department of Child and Adolescent Psychiatry, New York University, New York, New York 40 Stockholm Health Care Services, Stockholm, Sweden 41 Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California 42 Mind-Body Research Group, Department of Neuroscience, KU Leuven, Leuven, Belgium 43 Department of Psychology, University of New Mexico, Albuquerque, New Mexico 44 Mind Research Network, Albuquerque, New Mexico 45 Department of Psychiatry, Université de Montréal, Montreal, Canada 46 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Tübingen, Tübingen, Germany 47 HU Virgen del Rocio, IBiS, University of Sevilla, Sevilla, Spain FRANGOU ET AL.433 48 Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France 49 Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands 50 Faculteit der Sociale Wetenschappen, Instituut Psychologie, Universiteit Leiden, Leiden, Netherlands 51 Center for Multimodal Imaging and Genetics, Department of Neuroscience, University of California-San Diego, San Diego, California 52 Department of Radiology, University of California-San Diego, San Diego, California 53 Department of Psychiatry and Psychotherapy, University of Münster, Germany 54 Department of Psychiatry, University of Melbourne, Melbourne, Australia 55 Academisch Medisch Centrum, Universiteit van Amsterdam, Amsterdam, Netherlands 56 Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia 57 Kimel Family Translational Imaging Genetics Laboratory, Campbell Family Mental Health Research Institute, CAMH, Campbell, Canada 58 Department of Psychiatry, University of Toronto, Toronto, Canada 59 Biological Psychiatry Lab, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy 60 Department of Psychology, University of Oslo, Oslo, Norway 61 Sunnaas Rehabilitation Hospital HT, Nesodden, Norway 62 Division of Psychological and Social Medicine and Developmental Neurosciences, Technische Universität Dresden, Dresden, Germany 63 Faculty of Medicine, Universitätsklinikum Carl Gustav Carus an der TU Dresden, Dresden, Germany 64 Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany 65 Bjørknes College, Oslo, Norway 66 Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands 67 Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa 68 Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands 69 Department of Psychiatry, Radboud University Medical Center, Nijmegen, Netherlands 70 Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany 71 Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts 72 Department of Psychology, Stanford University, Stanford, California 73 Department of Psychiatry and Psychotherapy, University Medicine Greifswald, University of Greifswald, Greifswald, Germany 74 German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany 75 Department for Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Frankfurt, Goethe Universitat, Frankfurt, Germany 76 Neuroscience Institute, University of Cape Town, Cape Town, South Africa 77 Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany 78 Department of Psychiatry, Yale University, New Haven, Connecticut 79 Learning Based Recovery Center, VA Connecticut Health System, West Haven, Connecticut 80 Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 81 Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania 82 Melbourne Neuropsychiatry Center, University of Melbourne, Melbourne, Australia 83 Interdisciplinary Center Psychopathology and Emotion regulation, University Medical Center Groningen, University of Groningen, Groningen, Netherlands 84 Brain and Mind Centre, University of Sydney, Sydney, Australia 85 Departments of Experimental and Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands 86 Personalized Healthcare, Genentech, Inc., South San Francisco, California 87 Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, Netherlands 88 Department of Psychology, Yale University, New Haven, Connecticut 89 Norbert Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, University of Greifswald, Greifswald, Germany 90 De Bascule, Academic Centre for Children and Adolescent Psychiatry, Amsterdam, Netherlands 91 Department of Psychiatry, Oxford University, Oxford, United Kingdom 92 Center for Human Development, Departments of Cognitive Science, Psychiatry, and Radiology, University of California, San Diego, California 93 Department of Radiology, Ohio State University College of Medicine, Columbus, Ohio 94 Department of Neuroinformatics, Araya, Inc., Tokyo, Japan 95 Department of Psychiatry, University of California San Diego, San Diego, California 96 Mental Health Research Center, Russian Academy of Medical Sciences, Moscow, Russia 434 FRANGOU ET AL. 97 Sunshine Coast Mind and Neuroscience, Thompson Institute, University of the Sunshine Coast, Queensland, Australia 98 Department of Child and Adolescent Psychiatry and Psychology, Hospital Clinic, University of Barcelona, Barcelona, Spain 99 Department of Psychiatry, Psychosomatics and Psychotherapy, Julius-Maximilians Universität Würzburg, Würzburg, Germany 100 SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa 101 Queensland Institute of Medical Research, Berghofer Medical Research Institute, Queensland, Australia 102 Department of Psychiatry, Bellvitge University Hospital-IDIBELL, University of Barcelona, Barcelona, Spain 103 Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom 104 School of Clinical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia 105 Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany 106 Department of Clinical Medicine, Kyushu University, Fukuoka, Japan 107 CatoSenteret Rehabilitation Hospital, Son, Norway 108 Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden 109 Department of Clinical Neuropsychology, Amsterdam University Medical Centre, Vrije Universiteit Amsterdam, Amsterdam, Netherlands 110 Department of Psychiatry, University Hospital “Marques de Valdecilla”, Instituto de Investigación Valdecilla (IDIVAL), Santander, Spain 111 Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain 112 Centre of Mental Health, University of Würzburg, Würzburg, Germany 113 Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain 114 Department of Psychiatry, University of California at Irvine, Irvine, California 115 Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom 116 Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, Boston, Massachusetts 117 Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED), Valderrebollo, Spain 118 Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia 119 Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia 120 Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany 121 Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom 122 Department of General Psychiatry, Institute of Mental Health, Singapore, Singapore 123 Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 124 Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit Groningen, University Medical Center Groningen, Groningen, Netherlands 125 Queensland Brain Institute, University of Queensland, Queensland, Australia 126 PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway 127 Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Cantabria, Spain 128 College of Arts and Sciences, Georgia State University, Atlanta, Georgia 129 School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands 130 Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands 131 Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, Netherlands 132 Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, The Netherlands 133 Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, California 134 Institute of Community Medicine, University Medicine, Greifswald, University of Greifswald, Greifswald, Germany 135 German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany 136 German Center for Diabetes Research (DZD), partner site Greifswald, Greifswald, Germany 137 Department of Psychology, University of Bath, Bath, United Kingdom 138 Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, Illinois 139 Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin 140 Institute for Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany 141 Developmental and Educational Psychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands 142 National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 143 Department of Child and Adolescent Psychiatry, Child Study Center, NYU Langone Health, New York City, New York 144 Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, S~ ao Paulo, Brazil 145 Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany FRANGOU ET AL.435 146 Department of Psychology, School of Arts and Social Sciences, City University of London, London, United Kingdom Correspondence Sophia Frangou, Icahn School of Medicine at Mount Sinai, Department of Psychiatry, 1425 Madison Avenue, New York, NY 10029, USA. Email: [email protected] Funding information European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/ Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO- MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: Abstract Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes. KEYWORDS aging, cortical thickness, development, trajectories 436 FRANGOU ET AL. PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/ Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR) 1|INTRODUCTION In the last two decades, there has been a steady increase in the number of studies of age-related changes in cerebral morphometry (Ducharme, et al., 2015; Good et al., 2001; Mutlu et al., 2013; Salat et al., 2004; Shaw et al., 2008; Storsve et al., 2014; Thambisetty et al., 2010; Wierenga, Langen, Oranje, & Durston, 2014) as a means to understand genetic and environmental influences on the human brain (Grasby, 2020; Modabbernia et al., 2020). Here we focus specifically on cortical thickness, as assessed using magnetic resonance imaging (MRI), as this measure has established associations with behavior and cognition in healthy populations (Goh et al., 2011; Schmitt et al., 2019; Shaw et al., 2006) and with disease mechanisms implicated in neuropsychiatric disorders (Boedhoe, et al., 2018; Hibar et al., 2018; Hoogman et al., 2019; Schmaal et al., 2017; Thompson et al., 2007; van Erp et al., 2018; van Rooij et al., 2018; Whelan et al., 2018). Structural MRI is the most widely used neuroimaging method in research and clinical settings because of its excellent safety profile, ease of data acquisition and high patient acceptability. Thus, establishing the typical patterns of age-related changes in cortical thickness as reference data could be a significant first step in the translational application of neuroimaging. The value of reference data is firmly established in medicine where deviations from an expected range are used to trigger further investigations or interventions. A classic example is the body mass index (BMI) which has been instrumental in informing about risk for relating to cardio-metabolic outcomes (Aune et al., 2016). There is significant uncertainty about the shape and interindividual variability of the association between age and cortical thickness. Prior studies have reported linear and nonlinear associations (e.g., Hedman, van Haren, Schnack, Kahn, & Hulshoff Pol, 2012; Mills et al., 2016) that may be influenced by sex (Paus, 2010; Raz, Ghisletta, Rodrigue, Kennedy, & Lindenberger, 2010; Wierenga et al., 2020). The present study harnessed the power of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium, a multinational collaborative network of researchers organized into working groups, which conducts large-scale analyses integrating data from over 250 institutions (Thompson et al., 2017; Thompson et al., 2020). Within ENIGMA, the focus of the Lifespan Working group is to delineate age-associations in brain morphometric measures extracted from MRI images using standardized protocols and unified quality control procedures harmonized and validated across all participating sites. The ENIGMA Lifespan data set is the largest sample of healthy individuals available worldwide that offers the most comprehensive coverage of the human lifespan. This distinguishes the ENIGMA Lifespan data set from other imaging samples, such as the UK Biobank (http:// www.ukbiobank.ac.uk) which includes individuals over 40 years of age. In the present study, we used MRI data from 17,075 healthy participants aged 3–90 years to infer age-associated trajectories of cortical thickness. We also estimated regional interindividual variability in cortical thickness across the lifespan because it represents a major source of inter-study variation (Raz et al., 2010; Wierenga et al., 2020). Based on prior literature, our initial hypotheses were that in most regions the relationship between age and thickness will follow an inverse U-shape and will be influenced by sex. 2|MATERIALS AND METHODS 2.1 |Study samples De-identified demographic and cortical thickness data from 83 worldwide samples (Figure 1) were pooled to create the data set analyzed in this study. For samples from longitudinal studies, only baseline MRI scans were considered. The pooled sample comprised 17,075 participants (52% female) aged 3–90 years; only participants with complete data were included (Table 1). All participants had been screened to exclude psychiatric disorders, medical and neurological morbidity and cognitive impairment. Information on the screening protocols and eligibility criteria is provided in Table S1. 2.2 |Image acquisition and processing Prior to pooling the data used in this study, researchers at each participating institution (a) used the ENIGMA MRI analysis protocols, which FRANGOU ET AL.437 are based on FreeSurfer (http://surfer.nmr.mgh.harvard.edu), to compute the cortical thickness of 68 regions from high-resolution T1-weighted MRI brain scans collected at their site; (b) inspected all images by overlaying the cortical parcellations on the participants' anatomical scans and excluded improperly segmented scans; (c) identified outliers using five median absolute deviations (MAD) of the median value (additional details in the supplement). Information on scanner vendor, magnetic field strength, FreeSurfer version and FIGURE 1 ENIGMA Lifespan samples. Abbreviations are explained in Table 1; further details of each sample are provided in the supplemental material 438 FRANGOU ET AL. TABLE 1 Characteristics of the included samples Sample Age, mean, years Age, SD, years Age range Sample NMale NFemale N ADHD NF 14 0.7 13 14 3 1 2 AMC 23 3.4 17 32 99 65 34 Barcelona 1.5 T 15 1.9 11 17 24 10 14 Barcelona 3 T 15 2.2 11 17 31 13 18 Betula 62 12.4 26 81 231 105 126 BIG 1.5 T 28 14.3 13 82 1,319 657 662 BIG 3 T 24 8.1 18 71 1,291 553 738 BIL&GIN 27 7.7 18 57 452 220 232 Bonn 39 6.5 29 50 175 175 0 BRAINSCALE 10 1.4 9 15 172 102 70 BRCATLAS 40 17.2 18 84 163 84 79 CAMH 44 19.3 18 86 141 72 69 Cardiff 26 7.8 18 58 265 78 187 CEG 16 1.8 13 19 31 31 0 CIAM 27 4.2 19 34 24 13 11 CLING 25 5.3 18 58 323 132 191 CODE 40 13.3 20 64 72 31 41 COMPULS/TS Eurotrain 11 1 9 13 42 29 13 Edinburgh 24 2.9 19 31 55 20 35 ENIGMA-HIV 25 4.3 19 33 30 16 14 ENIGMA-OCD (AMC/Huyser) 14 2.8 9 17 6 2 4 ENIGMA-OCD (IDIBELL) 33 10.4 20 50 20 8 12 ENIGMA-OCD (Kyushu/Nakao) 45 14.1 24 64 16 6 10 ENIGMA-OCD (London Cohort/Mataix-Cols) 38 11.6 26 63 10 2 8 ENIGMA-OCD (van den Heuvel 1.5 T) 41 12.9 26 50 3 0 3 ENIGMA-OCD (van den Heuvel 3 T) 36 10.9 22 55 8 4 4 ENIGMA-OCD-3 T-CONTROLS 32 11 20 56 17 4 13 FBIRN 37 11.4 19 60 164 117 47 FIDMAG 38 10.1 19 64 123 54 69 GSP 27 16.5 18 90 2008 893 1,115 HMS 40 12.2 19 64 55 21 34 HUBIN 42 8.8 19 56 102 69 33 IDIVAL (1) 65 9.8 49 87 34 13 21 IDIVAL (3) 30 7.8 19 50 104 63 41 IDIVAL(2) 28 7.6 15 52 80 50 30 IMAGEN 14 0.4 13 16 1722 854 868 IMH 32 9.8 20 58 73 48 25 IMpACT-NL 36 12.1 19 62 91 27 64 Indiana 1.5 T 62 11.7 37 84 49 9 40 Indiana 3 T 27 19.7 6 87 199 95 104 Johns Hopkins 44 12.5 20 65 85 42 43 KaSP 27 5.7 20 43 32 15 17 Leiden 17 4.8 8 29 572 279 293 MAS 79 4.7 70 90 385 176 209 MCIC 32 12.1 18 60 91 61 30 Melbourne 20 2.9 15 25 70 39 31 METHCT 27 6.5 19 53 39 29 10 MHRC 22 3.1 16 27 27 27 0 Muenster 35 12.1 17 65 744 323 421 NCNG 51 16.9 19 80 345 110 235 (Continues) FRANGOU ET AL.439 cortex is perialiocortical (Insausti, Muñoz-López, Insausti, & Artacho- Pérula, 2017), the cingulate and parahippocampal cortices are proisocortical and the cortex of the temporopolar region is mixed (Blaizot et al., 2010; Petrides, Tomaiuolo, Yeterian, & Pandya, 2012). Considered together, these regions are core nodes of the default mode network (DMN; Raichle et al., 2001). At present, it is unclear whether this higher interindividual variation in the cortical thickness of the DMN nodes is associated with functional variation, but this is an important question for future studies. The results presented here are based on the largest available brain MRI data set worldwide covering the human lifespan. However, none of the pooled samples in the current study was longitudinal. We fully appreciate that longitudinal studies are considered preferable to cross-sectional designs when aiming to define age-related brain morphometric trajectories. However, a longitudinal study of this size over nine decades of life is not feasible. In addition to problems with participant recruitment and retention, such a lengthy study would have involved changes in scanner types, magnetic field strengths, and acquisition protocols in line with necessary upgrades and technological advances. Nevertheless, it is possible to test the alignment between the results presented here and data from longitudinal cohorts, many of which are also available through the ENIGMA consortium. We consider this an important direction for follow-up studies. We took several steps to mitigate against site effects. First, we ensured that we used age-overlapping data sets throughout. Second, standardized analyses and quality control protocols were used to extract cortical thickness measures at all participating institutions. Third, we estimated and controlled for the contribution of site and scanner using ComBat prior to conducting our analysis. The validity of the findings reported here is reinforced by their alignment with the results from short-term longitudinal studies of cortical thickness (Shaw et al., 2008; Storsve et al., 2014; Tamnes et al., 2010; Thambisetty et al., 2010; Wierenga et al., 2014). The generalizability of our findings for the older age-group is qualified by our selection of individuals who appear to be aging successfully in terms of cognitive function and absence of significant medical morbidity. Nevertheless, despite the efforts to include only healthy older individuals, the observed pattern of brain aging may still be influenced by subclinical mental or medical conditions. For example, vascular risk factors (e.g., hypertension) are prevalent in older individuals and have been associated with decline in the age-sensitive regions identified here (Raz et al., 2005). Thus, we cannot conclusively exclude the possibility that such factors may have contributed to our results. In addition, a wide range of factors have been associated with cortical morphology throughout the lifespan. Key among them are genetic factors (Grasby, 2020; Teeuw et al., 2019) and indices of socioeconomic status (Chan et al., 2018; Modabbernia et al., 2020; Ziegler et al., 2020) and possibly race (Zahodne et al., 2015). These factors were not modeled here as the relevant information was not collected in a systematic and harmonized fashion across contributing cohorts. It is therefore unclear to what extent they might have influenced the general pattern of age-related associations with cortical thickness reported in the current study; qualifying their possible effects is a priority for future investigations. Cellular studies show that the number of neurons, the extent of dendritic arborization, and amount of glial support explain most of the variability in cortical thickness (la Fougère et al., 2011; Pelvig, Pakkenberg, Stark, & Pakkenberg, 2008; Terry, DeTeresa, & Hansen, 1987). MRI lacks the resolution to assess microstructural tissue properties but provides an estimate of cortical thickness based on the MR signal. Nevertheless, there is remarkable similarity between MRI-derived thickness maps and postmortem data (Fischl & Dale, 2000). Finally, we present the centile curves to stimulate further research in developing normative reference values for neuroimaging phenotypes which should include investigation of measurement errors and reproducibility. In this context, the centile curves should not be used clinically or to make inferences about single individuals. The findings of the current study suggest several avenues of further research. MRI-derived measures of cortical thickness do not provide information on the mechanisms that underlie the observed agerelated associations. However, the results provided here could be used to study further factors that may lead to deviations in cortical thickness way from the expected age-appropriate range. Additionally, the results of the current study provide a new avenue for investigating the functional correlates, either cognitive or behavioral, of agerelated changes and interindividual variation in regional cortical thickness. In summary, using existing cross-sectional data from 17,075 individuals we performed a large-scale analysis to investigate the agerelated changes in cortical thickness. The size and age-coverage of the analysis sample has the potential to inform about developmental and aging changes in cortical morphology and provide a foundation the study of factors that may lead to deviations from normative patterns. ACKNOWLEDGMENTS This study presents independent research funded by multiple agencies. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data. The views expressed in the manuscript are those of the authors and do not necessarily represent those of any of the funding agencies. Dr. Dima received funding from the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Psychiatry Research Trust and 2014 NARSAD Young Investigator Award. Dr. Frangou received support from the National Institutes of Health (R01 MH104284, R01 MH113619, R01 MH116147), the European Community's Seventh Framework Programme (FP7/2007-2013) (grant agreement no 602450). This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai, USA. Dr. Agartz was supported by the Swedish Research Council (grant numbers: 521-2014-3487 and 2017-00949). Dr. Alnæs was supported by the South Eastern Norway Regional Health Authority (grant number: 2019107). Dr. O Andreasen was supported by the Research Council of Norway (grant number: 223273) and South-Eastern Norway Health Authority (grant number: 2017-112). Dr. Cervenka was supported by 446 FRANGOU ET AL. the Swedish Research Council (grant number 523-2014-3467). Dr. Crespo-Facorro was supported by the IDIVAL Neuroimaging Unit for imaging acquisition; Instituto de Salud Carlos III (grant numbers: PI020499, PI050427, PI060507, PI14/00639, and PI14/00918) and the Fundación Instituto de Investigación Marqués de Valdecilla (grant numbers: NCT0235832, NCT02534363, and API07/011). Dr. Gur was supported by the National Institute of Mental Health (grant numbers: R01MH042191 and R01MH117014). Dr. James was supported by the Medical Research Council (grant no G0500092). Dr. Saykin received support from U.S. National Institutes of Health grants R01 AG19771, P30 AG10133, and R01 CA101318. Dr. Thompson, Dr. Jahanshad, Dr. Wright, Dr. Medland, Dr. O Andreasen, Dr. Rinker, Dr. Schmaal, Dr. Veltam, Dr. van Erp, and D. P. H. were supported in part by a Consortium grant (U54EB020403 to P. M. T.) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative. FBIRN sample: Data collection and analysis was supported by the National Center for Research Resources at the National Institutes of Health (grant numbers: NIH 1U24 RR021992 (Function Biomedical Informatics Research Network) and NIH 1U24 RR025736-01 (Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org). FBIRN data were processed by the UCI High Performance Computing cluster supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000153. Brainscale: This work was supported by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 to H.H., NWO 51.02.062 to D. B., NWO–NIHC Programs of excellence 433-09-220 to H.H., NWO-MagW 480-04-004 to D. B., and NWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European Research Council (ERC-230374 to D. B.); and Universiteit Utrecht (High Potential Grant to H. H.). UMCU-1.5T: This study is partially funded through the Geestkracht programme of the Dutch Health Research Council (Zon-Mw, grant No 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical center The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal and Delta.). UMCU-3T: This study was supported by NIMH grant number: R01 MH090553 (to R. A. O.). The NIMH had no further role in study design, in the collection, analysis and interpretation of the data, in the writing of the report, and in the decision to submit the paper for publication. Netherlands Twin Register: Funding was obtained from the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organization for Health Research and Development (ZonMW) grants 904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004, 463-06-001, 451-04-034, 400-05-717, 400-07-080, 31160008, 016-115-035, 481-08-011, 056-32-010, 911-09-032, 024.001.003, 480-15-001/674, Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI–NL, 184.021.007 and 184.033.111); Spinozapremie (NWO-56-464-14192), and the Neuroscience Amsterdam research institute (former NCA). The BIG database, established in Nijmegen in 2007, is now part of Cognomics, a joint initiative by researchers of the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud university medical centre, and the Max Planck Institute for Psycholinguistics. The Cognomics Initiative is supported by the participating departments and centres and by external grants, including grants from the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL) and the Hersenstichting Nederland. The authors also acknowledge grants supporting their work from the Netherlands Organization for Scientific Research (NWO), that is, the NWO Brain & Cognition Excellence Program (grant 433-09-229), the Vici Innovation Program (grant 016-130-669 to BF) and #91619115. Additional support is received from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements no 602805 (Aggressotype), no 603016 (MATRICS), no 602450 (IMAGEMEND), and no 278948 (TACTICS), and from the European Community's Horizon 2020 Programme (H2020/2014-2020) under grant agreements no 643051 (MiND) and no 667302 (CoCA). Betula sample: Data collection for the BETULA sample was supported by a grant from Knut and Alice Wallenberg Foundation (KAW); the Freesurfer segmentations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N in Umeå, Sweden. Indiana sample: This sample was supported in part by grants to BCM from Siemens Medical Solutions, from the members of the Partnership for Pediatric Epilepsy Research, which includes the American Epilepsy Society, the Epilepsy Foundation, the Epilepsy Therapy Project, Fight Against Childhood Epilepsy and Seizures (F.A.C.E.S.), and Parents Against Childhood Epilepsy (P.A.C.E.), from the Indiana State Department of Health Spinal Cord and Brain Injury Fund Research Grant Program, and by a Project Development Team within the ICTSI NIH/NCRR Grant Number RR025761. MHRC study: It was supported in part by RFBR grant 20-013-00748. PING study: Data collection and sharing for the Pediatric Imaging, Neurocognition and Genetics (PING) Study (National Institutes of Health Grant RC2DA029475) were funded by the National Institute on Drug Abuse and the Eunice Kennedy Shriver National Institute of Child Health & Human Development. A full list of PING investigators is at http://pingstudy.ucsd.edu/investigators.html. QTIM sample: The authors are grateful to the twins for their generosity of time and willingness to participate in our study and thank the many research assistants, radiographers, and other staff at QIMR FRANGOU ET AL.447 Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland. QTIM was funded by the Australian National Health and Medical Research Council (Project Grants No. 496682 and 1009064) and US National Institute of Child Health and Human Development (RO1HD050735). Lachlan Strike was supported by a University of Queensland PhD scholarship. Study of Health in Pomerania (SHIP): this is part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald. de/icm) of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg- West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. This study was further supported by the DZHK (German Centre for Cardiovascular Research), the German Centre of Neurodegenerative Diseases (DZNE) and the EU-JPND Funding for BRIDGET (FKZ:01ED1615). TOP study: this was supported by the European Community's Seventh Framework Programme (FP7/2007-2013), grant agreement no 602450. The Southern and Eastern Norway Regional Health Authority supported Lars T. Westlye (grant no. 2014-097) and STROKEMRI (grant no. 2013-054). HUBIN sample: HUBIN was supported by the Swedish Research Council (K2007-62X-15077-04-1, K2008-62P- 20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3), the regional agreement on medical training and clinical research between Stockholm County Council, and the Karolinska Institutet, and the Knut and Alice Wallenberg Foundation. The BIG database: this was established in Nijmegen in 2007, is now part of Cognomics, a joint initiative by researchers of the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud university medical centre, and the Max Planck Institute for Psycholinguistics. The Cognomics Initiative is supported by the participating departments and centers and by external grants, including grants from the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL) and the Hersenstichting Nederland. The authors also acknowledge grants supporting their work from the Netherlands Organization for Scientific Research (NWO), that is, the NWO Brain & Cognition Excellence Program (grant 433-09-229), the Vici Innovation Program (grant 016-130-669 to BF) and #91619115. Additional support is received from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements no 602805 (Aggressotype), no 603016 (MATRICS), no 602450 (IMAGEMEND), and no 278948 (TACTICS), and from the European Community's Horizon 2020 Programme (H2020/2014-2020) under grant agreements no 643051 (MiND) and no 667302 (CoCA). CONFLICT OF INTERESTS Hans Jörgen Grabe: Travel grants and speaker honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag; reseach funding from Fresenius Medical Care. Ole A Andreasen: Consultant to HealthLytix, speaker honorarium from Lundbeck. Anders M Dale: Founder and member of the Scientific Advisory Board CorTechs Labs, Inc where he holds equity; member of the Scientific Advisory of Human Longevity Inc; research grants with General Electric Healthcare. DATA AVAILABILITY STATEMENT The ENIGMA Lifespan Working Group welcomes expression of interest from researchers in the field who wish to use the ENIGMA samples. Data sharing is possible subsequent to consent for the principal investigators of the contributing datasets. Requests should be directed to the corresponding authors. ORCID Sophia Frangou https://orcid.org/0000-0002-3210-6470 Moji Aghajani https://orcid.org/0000-0003-2040-4881 Rachel M. Brouwer https://orcid.org/0000-0002-7466-1544 Christopher R. K. Ching https://orcid.org/0000-0003-2921-3408 Simon E. Fisher https://orcid.org/0000-0002-3132-1996 Thomas Frodl https://orcid.org/0000-0002-8113-6959 David C. Glahn https://orcid.org/0000-0002-4749-6977 Ian H. 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J. (2020). Childhood socio-economic disadvantage predicts reduced myelin growth across adolescence and young adulthood. Human Brain Mapping,41(12), 3392–3402. https:// doi.org/10.1002/hbm.25024 SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section at the end of this article. How to cite this article: Frangou S, Modabbernia A, Williams SCR, et al. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years. Hum Brain Mapp. 2022;43:431–451. https://doi.org/10.1002/hbm. 25364 APPENDIX A. Göran Engberg, Department of Physiology and Pharmacology, Karolinska Institute, Sweden. Sophie Erhardt, Department of Physiology and Pharmacology, Karolinska Institute, Sweden. Lilly Schwieler, Department of Physiology and Pharmacology, Karolinska Institute, Sweden. Funda Orhan, Department of Physiology and Pharmacology, Karolinska Institute, Sweden. Anna Malmqvist, Department of Physiology and Pharmacology, Karolinska Institute, Sweden. Mikael Hedberg, Department of Physiology and Pharmacology, Karolinska Institute, Sweden. Lars Farde, Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Sweden. Simon Cervenka, Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Sweden. Lena Flyckt, Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Sweden. Karin Collste, Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Sweden. Pauliina Ikonen, Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Sweden. Fredrik Piehl, Neuroimmunology Unit, Department of Clinical Neuroscience, Karolinska Institutet, Sweden. Ingrid Agartz, NORMENT, Division of Mental Health and Addiction, KG Jebsen Centre for Psychosis Research, University of Oslo and Department of Psychiatric Research, Diakonhjemmet Hospital, Norway; Center for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet, Sweden. FRANGOU ET AL.451