title: EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.
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 anya prioriconstraints, 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.
authors.affiliation: MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA.
authors.forename: Archana
authors.initials: A
authors.lastname: Venkataraman
authors.affiliation: Department of Psychology, Harvard University, Cambridge, MA.
authors.forename: Koene R A
authors.initials: KR
authors.lastname: Van Dijk
authors.affiliation: Department of Psychology, Harvard University, Cambridge, MA.
authors.forename: Randy L
authors.initials: RL
authors.lastname: Buckner
authors.affiliation: MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA.
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
keywords: Biomedical Imaging; Brain Modeling; Clustering Methods; Magnetic Resonance Imaging