title: Analysis of FMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach.
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  p 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.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. zhangjiang_@hotmail.com
authors.forename: Jiang
authors.initials: J
authors.lastname: Zhang
authors.forename: Xianguo
authors.initials: X
authors.lastname: Tuo
authors.forename: Zhen
authors.initials: Z
authors.lastname: Yuan
authors.forename: Wei
authors.initials: W
authors.lastname: Liao
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010775:Photic Stimulation; D025341:Principal Component Analysis / Q000379:methods*; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013647:Task Performance and Analysis