Analysis of activity in fMRI data using affinity propagation clustering

Jiang Zhang, Dahuan Li, Huafu Chen and Fang Fang

Computer Methods in Biomechanics and Biomedical Engineering, 2011, vol. 14, issue 03, 271-281

Abstract: Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) data. The huge computation load, however, makes it difficult for the practical use. We use affinity propagation clustering (APC), a new clustering algorithm especially for large data sets to detect brain functional activation from fMRI. It considers all data points as possible exemplars through the minimisation of an energy function and message-passing architecture, and obtains the optimal set of exemplars and their corresponding clusters. Four simulation studies and three in vivo fMRI studies reveal that brain functional activation can be effectively detected and that different response patterns can be distinguished using this method. Our results demonstrate that APC is superior to the k-centres clustering, as revealed by their performance measures in the weighted Jaccard coefficient and average squared error. These results suggest that the proposed APC will be useful in detecting brain functional activation from fMRI data.

Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/10255841003766829 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gcmbxx:v:14:y:2011:i:03:p:271-281

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gcmb20

DOI: 10.1080/10255841003766829

Access Statistics for this article

Computer Methods in Biomechanics and Biomedical Engineering is currently edited by Director of Biomaterials John Middleton

More articles in Computer Methods in Biomechanics and Biomedical Engineering from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().