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 c...Show MoreMetadata
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 Nystrom 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.
Date of Conference: 19-24 April 2009
Date Added to IEEE Xplore: 26 May 2009
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PubMed ID: 26028993