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

Area of Science:

• Neuroimaging
• Neuroscience
• Brain Connectivity

Background:

• Psychiatric conditions like autism and depression show gender prevalence differences.
• Understanding neurotypical gender differences may reveal insights into these conditions.
• Resting-state functional magnetic resonance imaging (rfMRI) is a key tool for mapping brain networks.

Purpose of the Study:

• To determine if rfMRI functional connectivity (FC) can predict gender.
• To identify specific FC features that are most predictive of gender.
• To assess the robustness of gender differences in brain connectivity.

Main Methods:

• Utilized a large dataset of 820 healthy controls from the Human Connectome Project.
• Applied a predefined functional template and partial least squares regression modeling.
• Performed permutation tests to confirm the reliability of gender prediction.

Main Results:

• Achieved 87% gender prediction accuracy using multi-run rfMRI data.
• Confirmed reliable gender prediction (p<.001).
• Identified default mode (DMN), fronto-parietal, and sensorimotor networks as most predictive, with specific DMN regions (right fusiform gyrus, right ventromedial prefrontal cortex) being key contributors.

Conclusions:

• Gender can be reliably predicted from rfMRI functional connectivity.
• Findings suggest potential gender differences in social cognition mediated by the DMN.
• Emphasizes the importance of controlling for gender in brain imaging studies.