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arXiv · 1908.06168

Detecting abnormalities in resting-state dynamics: An unsupervised learning approach

Meenakshi Khosla·Keith Jamison·Amy Kuceyeski·Mert R. Sabuncu
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Abstract

Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining the temporal patterns in these data. In this paper, we explore two strategies for capturing the normal variability in resting-state activity across a healthy population: (a) an autoencoder approach on the rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both approaches can learn useful representations of rs-fMRI data and demonstrate their novel application for abnormality detection in the context of discriminating autism patients from healthy controls.

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Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu. 2019-08-16. Detecting abnormalities in resting-state dynamics: An unsupervised learning approach. https://arxiv.org/abs/1908.06168

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