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文件名称:Analysis of fMRI Data
文件大小:1.51MB
文件格式:PDF
更新时间:2018-03-30 09:26:05
self-healing
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.