Exploring Functional Connectivity in fMRI via Clustering

Citation:

Venkataraman A, Van Dijk KRA, Buckner RL, Golland P. Exploring Functional Connectivity in fMRI via Clustering. Proc IEEE Int Conf Acoust Speech Signal Process. 2009;2009 :441-4.

Date Published:

2009 Apr

Abstract:

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Last updated on 11/21/2017