Journal Article

FZJ-2019-03635

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Sex Classification by Resting State Brain Connectivity

Weis, S. (Corresponding author)FZJ* ; Patil, K. R.FZJ* ; Hoffstaedter, F.FZJ* ; Nostro, A. ; Yeo, B. T. T. ; Eickhoff, S. B.FZJ*

2020
Oxford Univ. Press Oxford

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Please use a persistent id in citations: http://hdl.handle.net/2128/24591  doi:10.1093/cercor/bhz129

Abstract: A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

Note: The Deutsche Forschungsgemeinschaft (EI 816/11-1), TheNational Institute of Mental Health (R01-MH074457); TheHelmholtz Portfolio Theme “Supercomputing and Modeling forthe Human Brain”; The European Union [Horizon 2020 Researchand Innovation Programme under grant agreement no. 720270(HBP SGA1) 785907 (HBP SGA2)]; Singapore National ResearchFoundation [fellowship (class of 2017) to B.T.T.Y.].APC & Rechnung ergänzt 10.07.19

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 571 - Connectivity and Activity (POF3-571) (POF3-571)
2. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)
3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)

Appears in the scientific report 2020

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