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


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.

In MICCAI MLMI workshop
Meenakshi Khosla
Meenakshi Khosla
Assistant Professor of Cognitive Science

My research interests include computational neuroscience, artificial intelligence and large-scale data analysis.