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![]() | |
2011-11-01 | |
Source Publication | IEEE Transactions on Biomedical Engineering
![]() |
ISSN | 00189294 15582531 |
Volume | 58Issue: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. |
Keyword | Functional Magnetic Resonance Imaging (Fmri) Hierarchical Clustering (Hc) K-centers Clustering Principal Component Analysis (Pca) Supervised Affinity Propagation Clustering Analysis (Sapc) |
DOI | 10.1109/TBME.2011.2165542 |
URL | View the original |
Indexed By | SCI |
Language | 英语 |
WOS Research Area | Engineering |
WOS Subject | Engineering, Biomedical |
WOS ID | WOS:000296019500017 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Health Sciences |
Affiliation | 1.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. |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment