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Scientific article
English

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

Contributors

Leonardi, Nora; Richiardi, Jonas; Gschwind, Markus A.; Simioni, Samanta; Annoni, Gian-Maria; Schluep, Myriam; Vuilleumier, Patrik; Van De Ville, Dimitri

Published in

NeuroImage, vol. 83, p. 937-950

Publication date

2013

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.

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

LEONARDI, Nora et al. Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest. In: NeuroImage, 2013, vol. 83, p. 937–950. doi: 10.1016/j.neuroimage.2013.07.019

Main files (1)

Article (Published version)

Identifiers

Journal ISSN

1053-8119
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Technical informations

Creation

22/11/2013 09:49:00

First validation

22/11/2013 09:49:00

Update

14/03/2023 20:57:03

Status update

14/03/2023 20:57:03

Last indexation

30/10/2024 16:12:25
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