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Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach
Zhang J.2; Tuo X.3; Yuan Z.1; Liao W.2; Chen H.2
2011-11-01
Source PublicationIEEE Transactions on Biomedical Engineering
ISSN00189294 15582531
Volume58Issue:11Pages:3184-3196
Abstract

Clustering analysis is a promising data-driven method for analyzing functional magnetic resonance imaging (fMRI) time series data. The huge computational load, however, creates practical difficulties for this technique. We present a novel approach, integrating principal component analysis (PCA) and supervised affinity propagation clustering (SAPC). In this method, fMRI data are initially processed by PCA to obtain a preliminary image of brain activation. SAPC is then used to detect different brain functional activation patterns. We used a supervised Silhouette index to optimize clustering quality and automatically search for the optimal parameter in SAPC, so that the basic affinity propagation clustering is improved by applying SAPC. Four simulation studies and tests with three in vivo fMRI datasets containing data from both block-design and event-related experiments revealed that functional brain activation was effectively detected and different response patterns were distinguished using our integrated method. In addition, the improved SAPC method was superior to the k -centers clustering and hierarchical clustering methods in both block-design and event-related fMRI data, as measured by the average squared error. These results suggest that our proposed novel integrated approach will be useful for detecting brain functional activation in both block-design and event-related experimental fMRI data. © 2011 IEEE.

KeywordFunctional Magnetic Resonance Imaging (Fmri) Hierarchical Clustering (Hc) K-centers Clustering Principal Component Analysis (Pca) Supervised Affinity Propagation Clustering Analysis (Sapc)
DOI10.1109/TBME.2011.2165542
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000296019500017
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Cited Times [WOS]:23   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Health Sciences
Affiliation1.University of Florida
2.University of Electronic Science and Technology of China
3.Chengdu University of Technology
4.Southwest Jiaotong University
Recommended Citation
GB/T 7714
Zhang J.,Tuo X.,Yuan Z.,et al. Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach[J]. IEEE Transactions on Biomedical Engineering,2011,58(11):3184-3196.
APA Zhang J.,Tuo X.,Yuan Z.,Liao W.,&Chen H..(2011).Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach.IEEE Transactions on Biomedical Engineering,58(11),3184-3196.
MLA Zhang J.,et al."Analysis of fMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach".IEEE Transactions on Biomedical Engineering 58.11(2011):3184-3196.
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