Abstract
The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.
Highlights
Human brain can be envisaged as a complex functional network with brain cortical regions as nodes and functional connectivity between these regions as edges between the nodes
We study the brain functional connectivity alterations caused by epilepsy using the resting-state fMRI data
With 450 selected connections, average accuracy of 81.33% was achieved. These results demonstrate that the proposed method is able to identify sensitive neuroimaging markers which have the capability to accurately discriminate between normal and epileptic subjects
Summary
Human brain can be envisaged as a complex functional network with brain cortical regions as nodes and functional connectivity between these regions as edges between the nodes. Each brain region is continuously sharing information with other regions resulting in a largescale functional network [1]. Recent studies on the analysis of rfMRI data have focused on large-scale brain connectivity [1, 3,4,5]. It has been shown [5,6,7,8] that brain disorders can cause disruption of the connectivity patterns amongst brain regions.
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