ICA-based Resting-State Networks Obtained on Large Autism fMRI Dataset ABIDE

TL;DR

This paper presents ICA-derived resting-state networks from the large ABIDE autism dataset, providing a new resource that reveals neural activation patterns without relying on predefined brain atlases, aiding autism research.

Contribution

It introduces ICA-based resting-state networks from ABIDE data, filling a gap in the use of ICA for ASD research and offering publicly available neural activation maps.

Findings

ICA-based RSNs reveal neural activation clusters in ASD.

Provides a new resource for autism brain imaging analysis.

Complement existing atlas-based approaches.

Abstract

Functional magnetic resonance imaging (fMRI) has become instrumental in researching brain function. One application of fMRI is investigating potential neural features that distinguish people with autism spectrum disorder (ASD) from healthy controls. The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. We address this gap by presenting ICA-based resting-state networks (RSNs) from preprocessed scans from ABIDE, now publicly available: https://github.com/SjirSchielen/groupICAonABIDE. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a valuable resource for further research into ASD, potentially aiding in developing new analytical approaches.