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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Archana Venkataraman, Koene R. A. Van Dijk, Randy L. Buckner, Polina Golland
• Keywords: Functional Connectivity Analysis, ICASSP 2009, Seed-Based Analysis, Signal Processing, Spectral Clustering