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

## TL;DR

This paper introduces unsupervised learning methods, specifically autoencoders and next frame prediction, to detect abnormalities in resting-state fMRI data, aiding in distinguishing autism patients from healthy controls.

## Contribution

It presents novel unsupervised approaches for modeling normal brain activity variability in rs-fMRI data for abnormality detection.

## Key findings

- Both methods effectively learn representations of rs-fMRI data.
- The approaches successfully discriminate autism patients from healthy controls.
- Unsupervised techniques outperform traditional static measures.

## 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.