# Machine learning in resting-state fMRI analysis

## TL;DR

This paper provides a comprehensive overview of machine learning techniques applied to resting-state fMRI data, categorizing methods and highlighting their roles in understanding brain activity.

## Contribution

It offers a systematic taxonomy of machine learning approaches in rs-fMRI, clarifying their applications in discovering brain variation and improving predictions.

## Key findings

- Classifies unsupervised learning methods by their focus on spatial, temporal, or population variation.
- Reviews algorithms and feature representations used in supervised rs-fMRI analysis.
- Highlights the growth and potential of machine learning in understanding resting-state brain activity.

## Abstract

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.