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

View ORCID ProfileElvisha Dhamala, View ORCID ProfileKeith W. Jamison, View ORCID ProfileMert R. Sabuncu, View ORCID ProfileAmy Kuceyeski
doi: https://doi.org/10.1101/809954
Elvisha Dhamala
1Department of Radiology, Weill Cornell Medicine, New York, NY, USA
2Department of Neuroscience, Weill Cornell Medicine, New York, NY, USA
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  • For correspondence: eld2024@med.cornell.edu
Keith W. Jamison
1Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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Mert R. Sabuncu
3School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
4Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
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Amy Kuceyeski
1Department of Radiology, Weill Cornell Medicine, New York, NY, USA
2Department of Neuroscience, Weill Cornell Medicine, New York, NY, USA
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Abstract

A thorough understanding of sex differences, if any, that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit differences in clinical and behavioural phenotypes between males and females. In this work, we evaluate sex differences in regional temporal dependence of resting-state brain activity using 195 male-female pairs (aged 22-37) from the Human Connectome Project. Male-female pairs are strictly matched for total grey matter volume. We find that males have more persistent long-range temporal dependence than females in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies of up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, frontal cortex, and occipital cortex. Additionally, we find that even after males and females are strictly matched on total grey matter volume, significant regional volumetric sex differences persist in many cortical and subcortical regions. Our results indicate males have larger cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger cingulates, precunei, frontal cortices, and parietal cortices. Sex classification based on regional volume achieves accuracies of up to 85%; cerebellum, cingulate cortex, and temporal cortex are the most important features. These findings highlight the important role of strict volume matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between males and females identifies a potential neurobiological substrate underlying sex differences in functional brain activation patterns and the behaviours with which they correlate.

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Posted October 21, 2019.
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Sex classification using long-range temporal dependence of resting-state functional MRI time series
Elvisha Dhamala, Keith W. Jamison, Mert R. Sabuncu, Amy Kuceyeski
bioRxiv 809954; doi: https://doi.org/10.1101/809954
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Sex classification using long-range temporal dependence of resting-state functional MRI time series
Elvisha Dhamala, Keith W. Jamison, Mert R. Sabuncu, Amy Kuceyeski
bioRxiv 809954; doi: https://doi.org/10.1101/809954

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