Resting state functional Magnetic Resonance Imaging (rs-fMRI) is used to obtain spontaneous activation within the human brain in the absence of specific tasks. Analysis of the rs-fMRI data required spatially and functionally homogenous parcellation of the whole brain based on underlying temporal fluctuations. Commonly used parcellation schemes have a tradeoff between intra-cluster functional similarity and alignment with anatomical regions. In this article, we present a clustering scheme for rs-fMRI data that obtains spatially and functionally homogenous clusters. Results show that the proposed multistage approach can identify various brain networks. Moreover, the functional homogeneity of the clusters is shown to be better than those found with functional atlas and simple k-means clusters. The spatial homogeneity is shown to be better than Independent Component Analysis (ICA), and simple k-means clusters.
Keywords: clustering; k-means; resting state fMRI.