A connectivity difference measure for identification of functional neuroimaging markers for epilepsy

• Published in: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) pp. 1517 - 1520
• Main Authors: Riaz, Atif, Rajpoot, Kashif, Rajpoot, Nasir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2013
• Subjects: Accuracy; Clustering algorithms; Communities; Correlation; Epilepsy; Neuroimaging; Support vector machines
• ISSN: 1948-3546
• DOI: 10.1109/NER.2013.6696234

Identification of functional brain connectivity differences induced by certain neurological disorders from resting state functional MRI (rfMRI) is generally considered a difficult task. This challenging task requires the identification of discriminative neuroimaging markers. In this paper, 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 discriminative neuroimaging markers using a novel affinity propagation clustering method. Next, we propose a difference statistic to select the most discriminative connections between the cortical regions. Using selected connections and a support vector machine classifier, we achieve classification accuracy of 81.33% on unseen dataset. 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.