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

Sjir J. C. Schielen (), Jesper Pilmeyer, Albert P. Aldenkamp, Danny Ruijters and Svitlana Zinger
Additional contact information
Sjir J. C. Schielen: Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
Jesper Pilmeyer: Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
Albert P. Aldenkamp: Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
Danny Ruijters: Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
Svitlana Zinger: Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands

Data, 2025, vol. 10, issue 7, 1-16

Abstract: Functional magnetic resonance imaging (fMRI) has become instrumental in researching the functioning of the brain. One application of fMRI is investigating the brains of people with autism spectrum disorder (ASD). The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers raw data and data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. ICA is a data-driven way to reduce dimensionality without prior assumptions on delineations. Additionally, ICA separates the noise from the signal, and the signal components correspond well to functional brain networks called resting-state networks (RSNs). Currently, no large, readily available dataset preprocessed with ICA exists. Here, we address this gap by presenting ABIDE’s data preprocessed to extract ICA-based resting-state networks, which are publicly available. 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 resource for further research into ASD, benchmarking between methodologies, and the development of new analytical approaches.

Keywords: resting-state functional MRI; independent component analysis; autism spectrum disorder; ABIDE; preprocessing (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/10/7/109/pdf (application/pdf)
https://www.mdpi.com/2306-5729/10/7/109/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:7:p:109-:d:1694227

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().