Paper Info
Title
Multi-subject fMRI connectivity analysis using sparse dictionary learning and multiset canonical correlation analysis
Abstract
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.
Year
DOI
Venue
2015
10.1109/ISBI.2015.7163965
IEEE International Symposium on Biomedical Imaging
Keywords
Field
DocType
MCCA, fMRI, K-SVD, functional connectivity
Data modeling,Functional magnetic resonance imaging,Pattern recognition,Multiset,Computer science,Canonical correlation,Artificial intelligence,Blind signal separation,Maximization,Principal component analysis,Encoding (memory)
Conference
ISSN
Citations 
PageRank 
1945-7928
3
0.38
References 
Authors
9
2
Name
Order
Citations
PageRank
Muhammad Usman Khalid1313.22
Abd-Krim Seghouane219324.99