Agglomerative clustering for resting state MRI

Methods to interpret data obtained from resting state functional magnetic imaging (rs-fMRI) must be developed to more thoroughly understand how network structure of the brain supports the body and the mind. To this end, we examine the use of agglomerative clustering (AC) as a method for rs-fMRI analysis. AC is a data driven approach for organizing spatially distinct clusters of temporally similar activity. Its application to rs-fMRI data produces spatial parcellation of brain areas that share similar temporal characteristics. The technique is scalable, enabling identification of local to widespread organization. Using a wavelet based filter bank, the technique is made amenable to frequency domain scaling as well. Comparisons drawn between AC and two alternative rs-fMRI analytics - seed-based correlation, and spatial independent component analysis - highlight the ability of the proposed technique to recognize well known functional brain regions.