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A Meta-Analysis of Sex Differences in Human Brain Structure

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A Meta-Analysis of Sex Differences in Human Brain Structure A meta-analysis of sex differences in human brain structure Article Published Version Creative Commons: Attribution 3.0 (CC-BY) Open Access Ruigrok, A. N. V., Salimi-Khorshidi, G., Lai, M.-C., Baron- Cohen, S., Lombardo, M. V., Tait, R. J. and Suckling, J. (2014) A meta-analysis of sex differences in human brain structure. Neuroscience & Biobehavioral Reviews, 39. pp. 34- 50. ISSN 01497634 doi: https://doi.org/10.1016/j.neubiorev.2013.12.004 Available at http://centaur.reading.ac.uk/86759/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1016/j.neubiorev.2013.12.004 Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online Neuroscience and Biobehavioral Reviews 39 (2014) 34–50 Contents lists available at ScienceDirect Neuroscience and Biobehavioral Reviews journal homepage: www.elsevier.com/locate/neubiorev A meta-analysis of sex differences in human brain structure a,∗ b a,c Amber N.V. Ruigrok , Gholamreza Salimi-Khorshidi , Meng-Chuan Lai , a,d a,e f,g d,f,g Simon Baron-Cohen , Michael V. Lombardo , Roger J. Tait , John Suckling a Autism Research Centre, Department of Psychiatry, University of Cambridge, Douglas House, 18B Trumpington Road, Cambridge CB2 8AH, United Kingdom b Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom c Department of Psychiatry, College of Medicine, National Taiwan University, No. 1 Jen-Ai Road Section 1, Taipei 10051, Taiwan d Cambridgeshire and Peterborough NHS Foundation Trust, Elizabeth House, Fulbourn Hospital, Cambridge CB21 5EF, United Kingdom e Department of Psychology, University of Cyprus, P.O. Box 20537, CY 1678 Nicosia, Cyprus f Brain Mapping Unit, School of Clinical Medicine, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge CB2 0SP, United Kingdom g Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, United Kingdom a r t i c l e i n f o a b s t r a c t Article history: The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between Received 15 June 2013 males and females. To understand the causes and consequences of sex differences it is important to estab- Received in revised form lish where they occur in the human brain. We report the first meta-analysis of typical sex differences on 13 December 2013 global brain volume, a descriptive account of the breakdown of studies of each compartmental volume Accepted 17 December 2013 by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian- process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based Keywords: regional volume and density. On average, males have larger total brain volumes than females. Exami- Brain nation of the breakdown of studies providing total volumes by age categories indicated a bias towards Sex differences Meta-analysis the 18–59 year-old category. Regional sex differences in volume and tissue density include the amyg- Gaussian-process regression (GPR) dala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Voxel-based morphometry Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on Volume the developing brain, and for understanding sex-biased neurological and psychiatric conditions. © 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY license. Contents 1. Introduction . 35 2. Methods. 35 2.1. Systematic literature search . 35 2.2. Selection criteria . 35 2.3. Data analysis . 36 2.3.1. Overall volumes meta-analysis . 36 2.3.2. Regional coordinate-based meta-analysis . 36 3. Results . 37 3.1. Literature search . 37 3.2. Sex differences in overall volumes . 37 3.2.1. Breakdown of studies looking at overall volume . 37 3.3. Regional sex differences in volume and tissue density. 41 3.3.1. Regional volume meta-analysis . 41 3.3.2. Regional tissue density meta-analysis . 43 4. Discussion . 43 4.1. Brain development . 43 ∗ Corresponding author. Tel.: +44 1223 746030/746057; fax: +44 1223 746033. E-mail address: [email protected] (A.N.V. Ruigrok). 0149-7634 © 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY license. http://dx.doi.org/10.1016/j.neubiorev.2013.12.004 A.N.V. Ruigrok et al. / Neuroscience and Biobehavioral Reviews 39 (2014) 34–50 35 4.2. Potential implications for understanding neuropsychiatric conditions . 43 4.3. Limitations . 45 4.4. Conclusion and future research . 46 Acknowledgements . 46 Appendix A. Supplementary data . 46 References . 46 1. Introduction has been conducted on overall or regional voxel-based structural brain differences between human males and females. The prevalence, age of onset, and symptomatology of many neu- In the current study, we carried out two types of meta-analysis. rological and psychiatric conditions differ substantially between First, we examined sex differences in overall brain volumes. As males and females (Bao and Swaab, 2010; Baron-Cohen et al., development and ageing have a large influence on total brain vol- 2011; Central Brain Tumour Registry of the United States, 2012; ume, we also investigated if different age categories were well Paus et al., 2008; Rutter et al., 2003). Examples of male-biased represented in the literature by providing a description of the num- conditions include autism, attention deficit/hyperactivity disorder, ber of articles, number of total participants and weighted mean conduct disorder, specific language impairment, Tourette syn- volume of each compartmental volume for each of the six age cate- drome, and dyslexia, and examples of female-biased conditions gories. Next, we conducted foci-based meta-analyses on regional include depression, anxiety disorder, and anorexia nervosa (Bao differences between males and females, one with voxel-based and Swaab, 2010; Baron-Cohen et al., 2011; Rutter et al., 2003). studies of volume and one with voxel-based studies of tissue den- Factors influencing the asymmetric effect that sex has on brain sity. Gaussian-process regression coordinate-based meta-analysis development may help us understand how and why male and (GPR-CBMA) was used for the voxel-based meta-analyses, as this female brains differ in their predisposition for risk for or resilience new technique allows for relatively more accurate results by incor- to such conditions. Identifying where and in what way male and porating effect-size estimates from source data (Salimi-Khorshidi female brains differ will help illuminate these factors and asso- et al., 2011). Furthermore, GPR-CBMA is also advantageous because ciated mechanisms. Previous whole-brain and region-of-interest its output includes meta-analytic effects in both positive and studies on sex differences in typically developing human brains negative directions as well as an.
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