Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity

Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI)...

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Bibliographic Details
Published in:Human brain mapping 2018-04, Vol.39 (4), p.1765-1776
Main Authors: Zhang, Chao, Dougherty, Chase C., Baum, Stefi A., White, Tonya, Michael, Andrew M.
Format: Article
Language:English
Subjects:
Adult
Autism
Blood pressure
Brain
Brain - diagnostic imaging
Brain - physiology
Brain mapping
Cognition
Connectome
Female
Functional magnetic resonance imaging
Gender aspects
Gender differences
Handedness
Humans
Magnetic Resonance Imaging
Male
Mental depression
Neural networks
Neural Pathways - diagnostic imaging
Neural Pathways - physiology
Neuroimaging
Permutations
Predictive control
Prefrontal cortex
Psychopathology
Rest
Risk factors
Sensorimotor system
Sex Characteristics
Sex differences
Social interactions
Young Adult
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Summary:Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI) is widely used to map the inherent functional networks of the brain. Although previous studies have reported gender differences in rfMRI, the robustness of gender differences is not well characterized. In this study, we use a large data set to test whether rfMRI functional connectivity (FC) can be used to predict gender and identify FC features that are most predictive of gender. We utilized rfMRI data from 820 healthy controls from the Human Connectome Project. By applying a predefined functional template and partial least squares regression modeling, we achieved a gender prediction accuracy of 87% when multi‐run rfMRI was used. Permutation tests confirmed that gender prediction was reliable ( p
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.23950