title: Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI.
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.
authors.affiliation: College of Computer Science & Information Technology, King Faisal University, Al Ahsa, Kingdom of Saudi Arabia; School of Electrical Engineering and Computer Science, National University of Sciences & Technology, Islamabad, Pakistan.
authors.forename: Kashif
authors.initials: K
authors.lastname: Rajpoot
authors.affiliation: School of Electrical Engineering and Computer Science, National University of Sciences & Technology, Islamabad, Pakistan.
authors.forename: Atif
authors.initials: A
authors.lastname: Riaz
authors.affiliation: School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan.
authors.forename: Waqas
authors.initials: W
authors.lastname: Majeed
authors.affiliation: Department of Computer Science & Engineering, Qatar University, Doha, Qatar; Department of Computer Science, University of Warwick, Coventry, United Kingdom.
authors.forename: Nasir
authors.initials: N
authors.lastname: Rajpoot
mesh_terms: D001921:Brain / Q000503:physiopathology; D016000:Cluster Analysis; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D009415:Nerve Net / Q000503:physiopathology*; D059906:Neuroimaging; D012146:Rest; D055815:Young Adult