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Multicenter Study
. 2017 Oct;11(5):1497-1514.
doi: 10.1007/s11682-016-9629-z.

Human Subcortical Brain Asymmetries in 15,847 People Worldwide Reveal Effects of Age and Sex

Tulio Guadalupe  1   2 Samuel R Mathias  3 Theo G M vanErp  4 Christopher D Whelan  5   6 Marcel P Zwiers  7 Yoshinari Abe  8 Lucija Abramovic  9 Ingrid Agartz  10   11   12 Ole A Andreassen  10   13 Alejandro Arias-Vásquez  14   15   16 Benjamin S Aribisala  17   18 Nicola J Armstrong  19   20 Volker Arolt  21 Eric Artiges  22 Rosa Ayesa-Arriola  23   24 Vatche G Baboyan  25 Tobias Banaschewski  26 Gareth Barker  27 Mark E Bastin  18   28   29   30 Bernhard T Baune  31 John Blangero  32   33 Arun L W Bokde  34 Premika S W Boedhoe  35   36   37 Anushree Bose  38 Silvia Brem  39   40 Henry Brodaty  41 Uli Bromberg  42 Samantha Brooks  43 Christian Büchel  42 Jan Buitelaar  16   44   45 Vince D Calhoun  46   47 Dara M Cannon  48 Anna Cattrell  49 Yuqi Cheng  50 Patricia J Conrod  51   52 Annette Conzelmann  53   54 Aiden Corvin  55 Benedicto Crespo-Facorro  23   24 Fabrice Crivello  56 Udo Dannlowski  21   57 Greig I de Zubicaray  58 Sonja M C de Zwarte  9 Ian J Deary  28 Sylvane Desrivières  49 Nhat Trung Doan  10   13 Gary Donohoe  59   60 Erlend S Dørum  13   61   62 Stefan Ehrlich  63   64   65 Thomas Espeseth  13   66 Guillén Fernández  16   44 Herta Flor  67 Jean-Paul Fouche  68 Vincent Frouin  69 Masaki Fukunaga  70 Jürgen Gallinat  71 Hugh Garavan  72 Michael Gill  55   73 Andrea Gonzalez Suarez  74   75 Penny Gowland  76 Hans J Grabe  77   78 Dominik Grotegerd  21 Oliver Gruber  79 Saskia Hagenaars  80 Ryota Hashimoto  81   82 Tobias U Hauser  83   84   85 Andreas Heinz  86 Derrek P Hibar  5 Pieter J Hoekstra  87 Martine Hoogman  14 Fleur M Howells  43 Hao Hu  88 Hilleke E Hulshoff Pol  9 Chaim Huyser  89   90 Bernd Ittermann  91 Neda Jahanshad  25 Erik G Jönsson  12   92 Sarah Jurk  93 Rene S Kahn  9 Sinead Kelly  94 Bernd Kraemer  79 Harald Kugel  95 Jun Soo Kwon  96   97   98 Herve Lemaitre  22 Klaus-Peter Lesch  99   100 Christine Lochner  101 Michelle Luciano  28 Andre F Marquand  7   102 Nicholas G Martin  103 Ignacio Martínez-Zalacaín  104 Jean-Luc Martinot  105   106 David Mataix-Cols  107 Karen Mather  19 Colm McDonald  48 Katie L McMahon  108 Sarah E Medland  103 José M Menchón  104   109   110 Derek W Morris  59 Omar Mothersill  55   59 Susana Munoz Maniega  18   29   30   80 Benson Mwangi  111 Takashi Nakamae  8   112 Tomohiro Nakao  113 Janardhanan C Narayanaswaamy  38 Frauke Nees  67 Jan E Nordvik  61 A Marten H Onnink  14 Nils Opel  21 Roel Ophoff  9   114 Marie-Laure Paillère Martinot  22   115 Dimitri Papadopoulos Orfanos  69 Paul Pauli  116 Tomáš Paus  117 Luise Poustka  26   118 Janardhan Yc Reddy  38 Miguel E Renteria  103 Roberto Roiz-Santiáñez  23   24 Annerine Roos  101 Natalie A Royle  18   29   30   80 Perminder Sachdev  19 Pascual Sánchez-Juan  74   75 Lianne Schmaal  119 Gunter Schumann  49 Elena Shumskaya  7   14 Michael N Smolka  93 Jair C Soares  120 Carles Soriano-Mas  104   109   121 Dan J Stein  122 Lachlan T Strike  123 Roberto Toro  124 Jessica A Turner  47   125   126 Nathalie Tzourio-Mazoyer  56 Anne Uhlmann  127 Maria Valdés Hernández  18   29   30   80 Odile A van den Heuvel  36   37   35 Dennis van der Meer  87 Neeltje E M van Haren  9 Dick J Veltman  119 Ganesan Venkatasubramanian  38 Nora C Vetter  93 Daniella Vuletic  43 Susanne Walitza  83   40   128 Henrik Walter  86 Esther Walton  63   125 Zhen Wang  88 Joanna Wardlaw  18   29   30   80 Wei Wen  19 Lars T Westlye  13   62 Robert Whelan  129 Katharina Wittfeld  130 Thomas Wolfers  14   44 Margaret J Wright  103   131 Jian Xu  50 Xiufeng Xu  50 Je-Yeon Yun  132 JingJing Zhao  133   134 Barbara Franke  14   15 Paul M Thompson  5 David C Glahn  135   136 Bernard Mazoyer  56 Simon E Fisher  1   44 Clyde Francks  137   138
Affiliations
Free PMC article
Multicenter Study

Human Subcortical Brain Asymmetries in 15,847 People Worldwide Reveal Effects of Age and Sex

Tulio Guadalupe et al. Brain Imaging Behav. .
Free PMC article

Abstract

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

Keywords: Age; Enigma; Handedness; Heritability; Meta-analysis; Sex; Subcortical brain asymmetry.

Conflict of interest statement

ENIGMA protocol development is supported by the NIH BD2K “Big Data to Knowledge” initiative (U54 020403; PI: Paul Thompson) which is funded by a cross-NIH partnership.

The AMC OCD dataset was supported by grants from ZonMW (grant numbers: 3160007, 91676084, 31160003, 31180002, 31000056, 2812412, 100001002, 100002034), NWO (grant numbers: 90461193, 40007080, 48004004, 40003330), and grants from the Amsterdam Brain Imaging Platform, Neuroscience Campus Amsterdam and the Dutch Brain foundation. The processing with FreeSurfer was performed on the Dutch e-Science Grid through BiG Grid project and COMMIT project “e-Biobanking with imaging for healthcare”, which are funded by the Netherlands Organization for Scientific Research (NWO).

The Barcelona OCD study was supported by project grants no. PI09/01331, PI10/01753, PI10/01003, CP10/00604, PI13/01958 and CIBER-CB06/03/0034 from the Carlos III Health Institute, grant no. 2014SGR1672 from the Agency for Administration of University and Research (AGAUR), and a ‘Miguel Servet’ contract (CP10/00604) from the Carlos III Health Institute to Dr. Soriano-Mas.

The Brain Imaging Genetics (BIG) database was established in Nijmegen in 2007. This resource 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 Center, and the Max Planck Institute for Psycholinguistics. The Cognomics Initiative is supported by the participating departments and centres and by external grants, i.e. the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL), the Hersenstichting Nederland, and the Netherlands Organisation for Scientific Research (NWO). The research on BIG also receives funding from the European Community‘s Seventh Framework Programme (FP7/2007–2013) under grant agreements #602450 (IMAGEMEND) and #602805 (Aggressotype) and from the National Institutes of Health (NIH) Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centers of Excellence. We would also like to thank Hans van Bokhoven for his contributions to the Cognomics initiative and to all persons who kindly participated in this research. In addition, AF Marquand gratefully acknowledges support from the Language in Interaction project, funded by the NWO under the Gravitation Programme (grant 024.001.006).

The CIAM and OCD-SU datasets were supported by the Medical Research Council of South Africa .

CLiNG and HMS studies were partially supported by a research grant from the Competence Network Schizophrenia to Oliver Gruber.

The FBIRN study was supported by the National Center for Research Resources at the National Institutes of Health (grant numbers: NIH 1 U24 RR021992 (Function Biomedical Informatics Research Network) and NIH 1 U24 RR025736–01 (Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org). FBIRN data was processed by the UCI High Performance Computing cluster supported by Joseph Farran, Harry Mangalam, and Adam Brenner and the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000153. FBIRN thanks Mrs. Liv McMillan for overall study coordination.

The HUBIN study was supported by the Swedish Research Council (grant numbers K2009-62X-15077-06-3 and K2012-61X-15077-09-3), the Karolinska Institutet and the Knut and Alice Wallenberg Foundation.

The IDIVAL-PAFIP study was supported by Instituto de Salud Carlos III, FIS 00/3095, 01/3129, PI020499, PI060507, PI10/00183, PI14/00639, the SENY Fundació Research Grant CI 2005–0308007, and the Fundación Marqués de Valdecilla API07/011. We thank IDIVAL Neuroimaging Unit for its help in the technical execution of this work.

The IMpACT study was supported by a grant from the Brain & Cognition Excellence Program and a Vici grant (to Barbara Franke) of the Netherlands Organization for Scientific Research (NWO, grant numbers 433–09-229 and 016–130-669) and in part by the Netherlands Brain Foundation (grant number, 15F07[2]27)and the and BBMRI-NL (grant CP2010–33). The research leading to these results also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 602805 (Aggressotype), no. 278948 (TACTICS), and no. 602450 (IMAGEMEND). In addition, the project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 643051 (MiND), the NIH BD2K U54 020403 grant for the ENIGMA Consortium, and from the ECNP Network ADHD across the lifespan.

IMAGEN received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the FP7 projects IMAGEMEND(602450; IMAging GEnetics for MENtal Disorders), AGGRESSOTYPE (602805) and MATRICS (603016), the Innovative Medicine Initiative Project EU-AIMS (115300–2), a Medical Research Council Programme Grant “Developmental pathways into adolescent substance abuse” (93558), the Swedish funding agency FORMAS, the Medical Research Council and the Wellcome Trust (Behavioural and Clinical Neuroscience Institute, University of Cambridge), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7–1, SM 80/7–2, SFB 940/1), the National Institutes of Health, U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01 A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence.

LBC1936: Data collection was supported by the Disconnected Mind project, funded by Age UK. J.M.W. is partly funded by the Scottish Funding Council as part of the SINAPSE Collaboration. The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and MRC is gratefully acknowledged. We thank the study participants. We also thank Catherine Murray for recruitment of the participants and the radiographers and other staff at the Brain Research Imaging Centre.

Sydney MAS and OATS were supported by a National Health and Medical Research Council (NHMRC)/Australian Research Council Strategic Award (Grant 401162); NHMRC Program Grants (350833, 568969) and a Project Grant (1045325). OATS was facilitated through access to the Australian Twin Registry, which is funded by the NHMRC Enabling Grant 310667. Karen Mather is supported by the NHMRC Capacity Building Grant 568940 and an Alzheimer’s Australia Dementia Research Foundation Postdoctoral Fellowship. We would like to thank the Sydney MAS and OATS participants and their respective research teams.

The MCIC study was supported by the National Institutes of Health (NIH/NCRR P41RR14075 and R01EB005846 to Vince D. Calhoun), the Department of Energy (DE-FG02-99ER62764), the Mind Research Network, the Morphometry BIRN (1 U24, RR021382A), the Function BIRN (U24RR021992–01, NIH.NCRR MO1 RR025758–01,1RC1MH089257 and 5P20RR021938/P20GM103472 to Vince D. Calhoun), the Deutsche Forschungsgemeinschaft (research fellowship to Stefan Ehrlich and Esther Walton), and a NARSAD Young Investigator Award (to Stefan Ehrlich).

The Münster Neuroimaging Cohort (MüNC) was supported by grants from the German Research Foundation (DFG; grant FOR 2107; DA1151/5–1 to UD) and Innovative Medizinische Forschung (IMF) of the Medical Faculty of Münster (DA120903 to UD, DA111107 to UD, and DA211012 to UD).

The NCNG study was supported by the Research Council of Norway (#154313, #177458, and #231286).

The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10–000-1002) and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care organizations, see www.nesda.nl. Lianne Schmaal is supported by The Netherlands Brain Foundation Grant number F2014(1)-24.

The NeuroIMAGE study was supported by NIH Grant R01MH62873, NWO Large Investment Grant 1750102007010, and grants from Radboud university medical center, University Medical Center Groningen and Accare, and VU University Amsterdam. This work was also supported by a grant from NWO Brain & Cognition (433–09-242). Further support was received from the European Union FP7 programmes TACTICS (278948) and IMAGEMEND (602450).

The OCD-London dataset was supported by project grant no. 064846 from the Wellcome Trust and a pilot R&D grant from the South London & Maudsley Trust, UK.

The Osaka study was partially supported by JSPS KAKENHI Grant Number 25293250 and 23659565, MEXT Grant-in-Aid for Scientific Research on Innovative Areas (Comprehensive Brain Science Network) Grant Number 221S0003, and Brain/MINDS, AMED. Part of computations were performed using Research Center for Computational Science, Okazaki, Japan.

QTIM: Australian National Health and Medical Research Council (Project Grants No. 496682 and 1009064 to MJ Wright and Fellowship No. 464914 to IB Hickie), US National Institute of Child Health and Human Development (RO1HD050735 to MJ Wright), and US National Institute on Drug Abuse (R00DA023549 to NA Gillespie). Baptiste Couvy-Duchesne is supported by a University of Queensland International PhD scholarship. We are grateful to the twins for their generosity of time and willingness to participate in our studies. We thank research assistants Marlene Grace, Ann Eldridge, Richard Parker, Lenore Sullivan, Lorelle Nunn, Kerrie Mcaloney, Kori Johnson, Aaron Quiggle, and Natalie Garden, radiographers Matthew Meredith, Peter Hobden, Kate Borg, Aiman Al Najjar, and Anita Burns for acquisition of the scans, and David Smyth, Anthony Conciotrorre, Daniel Park, and David Butler for IT support.

The Study of Health in Pomerania (SHIP) is supported by the German Federal Ministry of Education and Research (grants 01ZZ9603, 01ZZ0103 and 01ZZ0403) the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania. MRI scans were supported by Siemens Healthcare, Erlangen, Germany. SHIP-LEGEND was supported by the German Research Foundation (GR1912/5–1).

The TCD|NUIG sample was supported by grant funding from the Health Research Board (grant number HRA_POR/2011/100; HRA_POR/2012/54), Science Foundation ireland (12/IP/1359; 08/IN.1/B1916), the Wellcome Trust (grant number 072894/2/03/Z) and the Brain and Behavior Research Foundation (grant number 17026), USA.

The TOP study was supported by the Research Council of Norway (#213837, #217776, #223273), the South-East Norway Health Authority (2013–123), and the KG Jebsen Foundation.

The Zürich OCD study was supported by the Swiss National Science Foundation (No. 320030_130237) and the Hartmann Müller Foundation (No. 1460). We thank Reto Iannaccone for supporting the data collection. In the last 10 years, Prof. Dr. Susanne Walitza has received lecture honoraria from Janssen Cilag, AstraZeneca, Opopharma, and Eli Lilly.

All participating studies were approved by their local ethical committee. Informed consent was obtained from all participants involved. All authors have no conflict of interests to declare for this study.

Figures

Fig. 1
Fig. 1
Visual representation of the 7 bilaterally paired structures, colored on the side of the relatively larger volume
Fig. 2
Fig. 2
Forest plots of the mean sex differences in AIs per dataset, for the structures that showed significant sex effects in meta-analysis. For each structure, the datasets are ordered top-to-bottom by their estimated sex difference. The identities of the datasets are given by the numbers in the left-hand columns, with reference to Table 1. The size of a square is proportional to the weights assigned in meta-analysis. The confidence intervals are shown, as well as dashed vertical lines to indicate the point of no mean sex difference
Fig. 3
Fig. 3
Results from meta-analysis of age effects. a Forest plot of the age coefficients for each dataset on putamen AI. The datasets are ordered top-to-bottom by their estimated age coefficient. The identities of the datasets are given by the numbers in the left-hand columns, with reference to Table 1. The size of a square is proportional to the weights assigned in meta-analysis. The confidence intervals are also depicted, as well as dashed vertical lines to indicate the point of an age coefficient with value zero. b Plot of the weighted regression of the age coefficients on each sample’s median age. The dotted line represents the best linear fit (P = 0.03). The size of a point is proportional to the square-root of a dataset’s sample size

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