Sex classification using long-range temporal dependence of resting-state functional MRI time series.

ABSTRACT: A thorough understanding of sex differences that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit phenotypic differences between males and females. Here we evaluate sex differences in regional temporal dependence of resting-state brain activity in 195 adult male-female pairs strictly matched for total grey matter volume from the Human Connectome Project. We find that males have more persistent temporal dependence in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, and frontal and occipital cor

SUBMITTER: Dhamala E 

PROVIDER: S-EPMC7416025 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature