Multi-subject fMRI connectivity analysis using sparse dictionary learning and multiset canonical correlation analysis

In this paper, we propose an effective technique to analyze task-based functional connectivity across multiple subjects for functional magnetic resonance imaging (fMRI) data. Instead of applying the assumption of group-independence or multiset correlation maximization, an alternative approach is ado...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 683 - 686
Main Authors Khalid, Muhammad Usman, Seghouane, Abd-Krim
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Australia
Blind source separation
Correlation
Data models
Dictionaries
Encoding
fMRI
functional connectivity
K-SVD
MCCA
Principal component analysis
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Summary:In this paper, we propose an effective technique to analyze task-based functional connectivity across multiple subjects for functional magnetic resonance imaging (fMRI) data. Instead of applying the assumption of group-independence or multiset correlation maximization, an alternative approach is adopted based on a combined framework of sparse dictionary learning (SDL) and multi-set canonical correlation analysis (MCCA) to obtain connectivity maps. The proposed technique encapsulates commonality and uniqueness solely based on sparsity of cross dataset corresponding components. It is validated using real fMRI data and its superior performance is illustrated using a simulation study, which shows its better capability in obtaining connectivity maps that are more specific.
ISSN:1945-7928
DOI:10.1109/ISBI.2015.7163965