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ICASSP
2009
IEEE

Exploring functional connectivity in fMRI via clustering

10 years 4 months ago
Exploring functional connectivity in fMRI via clustering
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 Nystr¨om 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.
Archana Venkataraman, Koene R. A. Van Dijk, Randy
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Archana Venkataraman, Koene R. A. Van Dijk, Randy L. Buckner, Polina Golland
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