Document Detail

Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.
MedLine Citation:
PMID:  20338245     Owner:  NLM     Status:  MEDLINE    
Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets.
Han Zhang; Xi-Nian Zuo; Shuang-Ye Ma; Yu-Feng Zang; Michael P Milham; Chao-Zhe Zhu
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-03-23
Journal Detail:
Title:  NeuroImage     Volume:  51     ISSN:  1095-9572     ISO Abbreviation:  Neuroimage     Publication Date:  2010 Jul 
Date Detail:
Created Date:  2010-05-17     Completed Date:  2010-08-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9215515     Medline TA:  Neuroimage     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1414-24     Citation Subset:  IM    
Copyright Information:
Copyright 2010 Elsevier Inc. All rights reserved.
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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MeSH Terms
Brain Mapping
Data Interpretation, Statistical
Executive Function / physiology
Image Processing, Computer-Assisted
Magnetic Resonance Imaging / statistics & numerical data*
Oxygen / blood
Principal Component Analysis
Reproducibility of Results
Rest / physiology
Young Adult
Reg. No./Substance:

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