Journal
CEREBRAL CORTEX
Volume 30, Issue 2, Pages 824-835Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhz129
Keywords
classification; functional magnetic resonance imaging; machine learning; resting state brain connectivity; sex differences
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Funding
- Deutsche Forschungsgemeinschaft [EI 816/11-1]
- National Institute of Mental Health [R01-MH074457]
- Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain
- European Union [720270, 785907]
- Singapore National Research Foundation
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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.
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