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

Publication Type	Academic Article
Authors	Dhamala E, Jamison K, Sabuncu M, Kuceyeski A
Journal	Hum Brain Mapp
Volume	41
Issue	13
Pagination	3567-3579
Date Published	07/06/2020
ISSN	1097-0193
Keywords	Cerebral Cortex, Connectome, Gray Matter, Magnetic Resonance Imaging, Sex Characteristics
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 cortices. Secondarily, we show that even after strict matching of total gray matter volume, significant volumetric sex differences persist; males have larger absolute cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger absolute cingulates, precunei, and frontal and parietal cortices. Sex classification based on regional volume achieves accuracies up to 85%, highlighting the importance of strict volume-matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between the sexes identifies a potential neurobiological substrate or environmental effect underlying sex differences in functional brain activation patterns.
DOI	10.1002/hbm.25030
PubMed ID	32627300
PubMed Central ID	PMC7416025