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

Abstract:

: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 © 2016 John Wiley & Sons, Ltd.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Ye J,Li Y,Lazar NA,Schaeffer DJ,McDowell JE

doi

10.1002/sim.6906

subject

Has Abstract

pub_date

2016-07-10 00:00:00

pages

2635-51

issue

15

eissn

0277-6715

issn

1097-0258

journal_volume

35

pub_type

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