Eigenconnectivities of dynamic functional networks: Consistency across subjects

Functional connectivity (FC) measured using fMRI has provided significant insights into brain function. However, increasing evidence points towards continuously fluctuating FC across the duration of a scan. Using unsupervised learning techniques, reproducible patterns of dynamic FC (dFC) have been r...

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Bibliographic Details
Published inConference record - Asilomar Conference on Signals, Systems, & Computers pp. 620 - 623
Main Authors Leonardi, Nora, Van De Ville, Dimitri
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
canonical correlation analysis
Correlation
dynamic functional connectivity
Eigenvalues and eigenfunctions
functional MRI
Head
Magnetic resonance imaging
Matrix decomposition
Principal component analysis
Time series analysis
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ISSN1058-6393
DOI10.1109/ACSSC.2014.7094520

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Summary:Functional connectivity (FC) measured using fMRI has provided significant insights into brain function. However, increasing evidence points towards continuously fluctuating FC across the duration of a scan. Using unsupervised learning techniques, reproducible patterns of dynamic FC (dFC) have been revealed. In particular, based on principal component analysis, it has recently been proposed to represent dFC as a linear combination of multiple "eigenconnectivities". These group-level results were obtained by concatenating all subjects' timecourses of dFC. Here we investigate the consistency of these results by introducing a subject-level and group-level PCA and comparing the results with those obtained by concatenation.
ISSN:1058-6393
DOI:10.1109/ACSSC.2014.7094520