Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest.

Details

Serval ID
serval:BIB_591BB49FEC73
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest.
Journal
Neuroimage
Author(s)
Leonardi N., Richiardi J., Gschwind M., Simioni S., Annoni J.M., Schluep M., Vuilleumier P., Van De Ville D.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Publication state
Published
Issued date
2013
Peer-reviewed
Oui
Volume
83
Pages
937-950
Language
english
Notes
Publication types: Journal ArticlePublication Status: ppublish
Abstract
Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.
Pubmed
Web of science
Create date
15/12/2013 15:48
Last modification date
11/10/2022 5:38
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