Finding common task-related regions in fMRI data from multiple subjects by periodogram clustering and clustering ensemble

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• 作者：Jun Ye ; Yehua Li ; Nicole A. Lazar ; David J. Schaeffer and Jennifer E. McDowell
• 刊名：Statistics in Medicine
• 出版年：2016
• 出版时间：10 July 2016
• 年：2016
• 卷：35
• 期：15
• 页码：2635-2651
• 全文大小：1590K
• ISSN：1097-0258

文摘

We propose an innovative and practically relevant clustering method to find common task-related brain regions among different subjects who respond to the same set of stimuli. Using functional magnetic resonance imaging (fMRI) time series data, we first cluster the voxels within each subject on a voxel by voxel basis. To extract signals out of noisy data, we estimate a new periodogram at each voxel using multi-tapering and low-rank spline smoothing and then use the periodogram as the main feature for clustering. We apply a divisive hierarchical clustering algorithm to the estimated periodograms within a single subject and identify the task-related region as the cluster of voxels that have periodograms with a peak frequency matching that of the stimulus sequence. Finally, we apply a machine learning technique called clustering ensemble to find common task-related regions across different subjects. The efficacy of the proposed approach is illustrated via a simulation study and a real fMRI data set. Copyright