RS-fMRI data analysis for identification of changes in functional connectivity networks of bi-polar patients

Abstract

One third of the world’s population suffers from some kind of neurological disorder. The development of technology allows us to analyze, model and visualize these disorders in order to help MDs in further treatments. Resting state fMRI is one of the most common ways for investigating the functional connectivity of the brain, which produces time series data of activation of the brain’s regions when subjects are in resting state. In this paper we show that changes occur in the Default Mode Network of bipolar patients by statistically analyzing time series data from their resting state fMRI. We discover several differences in the functional connectivity of these subjects compared to a control group. We then use clustering algorithm in order to find the clusters of active regions during the rs-fMRI, i.e. the groups of regions with similar time series data.