Abstract:
Identification of functional brain connectivity differences induced by certain neurological disorders from resting state functional magnetic resonance imaging (rfMRI) is generally considered a difficult task. This challenging task requires the identification of discriminant neuroimaging markers. In this work, we propose a two-stage algorithm to identify functional connectivity differences that can discriminate epileptic patients and healthy subjects. In the first stage, we determine the functional connectivity matrix between brain cortical regions for identification of potentially discriminant neuroimaging markers using a novel affinity propagation clustering method. Next, we propose a difference statistic to select the most discriminant connections between the cortical regions. Using selected connections and a support vector machine classifier, we achieve classification accuracy of 93.08% (specificity: 91.1%; sensitivity: 95.4%) on unseen dataset. We find that default mode network is impaired the most in epileptic subjects as compared to other resting state networks. The results demonstrate that the proposed algorithm is capable of determining functional connections between brain regions which aid in discrimination of epileptic patients versus healthy subjects. The methodology is expected to have broad applications for classification of other neurological diseases also.
