Resting state fMRI analysis using seed based and ICA methods

In this paper, we analyze the functional connectivity between the various parts of the brain using resting state fMRI(Functional Magnetic Resonance Imaging). Resting state fMRI scans are obtained when the subjects are relaxed and not involved in any task. During rest, various networks are active in the brain, named as resting state network (RSN) which includes Default Mode Network (DMN), Executives control network, salience, auditory, visual, dorsal attention and sensorimotor networks. We are using two methods to analyze the connectivity i.e. seed based method and Independent Component Analysis (ICA). In seed based method, the correlation of the seed is found with all other voxels. ICA aims at finding the various independent components based on the time series BOLD (Blood Oxygen Level Dependent) signals. Both of these methods are able to successfully identify various RSN. Seed based method is useful for the detailed analysis of a particular Region of Interest(ROI). On the other hand, ICA clearly identifies all the independent networks.