| Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis. | |
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MedLine Citation:
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PMID: 20338245 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Han Zhang; Xi-Nian Zuo; Shuang-Ye Ma; Yu-Feng Zang; Michael P Milham; Chao-Zhe Zhu |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2010-03-23 |
Journal Detail:
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Title: NeuroImage Volume: 51 ISSN: 1095-9572 ISO Abbreviation: Neuroimage Publication Date: 2010 Jul |
Date Detail:
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Created Date: 2010-05-17 Completed Date: 2010-08-03 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9215515 Medline TA: Neuroimage Country: United States |
Other Details:
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Languages: eng Pagination: 1414-24 Citation Subset: IM |
Copyright Information:
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Copyright 2010 Elsevier Inc. All rights reserved. |
Affiliation:
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State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Adult Algorithms Brain Mapping Data Interpretation, Statistical Executive Function / physiology Female Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging / statistics & numerical data* Male Oxygen / blood Principal Component Analysis Reproducibility of Results Rest / physiology Young Adult |
| Chemical | |
Reg. No./Substance:
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7782-44-7/Oxygen |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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