Paper Info
Title
Blind source separation of functional MRI scans of the human brain based on canonical correlation analysis.
Abstract
A novel approach for multi-subject blind source separation (BSS) of brain functional magnetic resonance imaging (fMRI) data is proposed. Group-level comparison analysis is common in the human brain fMRI analysis. Canonical correlation analysis (CCA) for BSS (BSS-CCA) relies on the basis that all meaningful real signals are auto-correlated compared with white noise, which should generally not be considered. By merely requiring that the second-order statistic be zero, BSS-CCA is more relaxed than independent component analysis (ICA), which demands mutual statistics of all orders to be zero. Based on spatial BSS-CCA, we propose an approach termed group BSS-CCA for the analysis of multi-subject fMRI data. In terms of the simulated situation, in which “sources” were partially overlapping in space, we determined that identification using group BSS-CCA was more efficient than that using group ICA. The results from a real data experiment revealed that the proposed group BSS-CCA approach was effective for extracting functional brain networks that were functionally distinct and spatially overlapping from the fMRI data of the human brain.
Year
DOI
Venue
2017
10.1016/j.neucom.2017.01.106
Neurocomputing
Keywords
Field
DocType
fMRI,Brain network,Canonical correlation analysis,Blind source separation
Brain network,Canonical correlation,White noise,Artificial intelligence,Blind signal separation,Pattern recognition,Functional magnetic resonance imaging,Statistic,Speech recognition,Human brain,Independent component analysis,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
269
C
0925-2312
Citations 
PageRank 
References 
0
0.34
8
Authors
6
Name
Order
Citations
PageRank
Xingjie Wu100.34
Ling-li Zeng2256.79
Hui Shen313415.32
Ming Li4134.67
YunAn Hu5176.91
Dewen Hu61290101.20