title: Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning.
abstract: Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia ; Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia.
authors.forename: Zhengyi
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia ; Queensland Brain Institute, The University of Queensland , Brisbane, QLD , Australia.
authors.forename: Jeiran
authors.initials: J
authors.lastname: Choupan
authors.affiliation: Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia.
authors.forename: David
authors.initials: D
authors.lastname: Reutens
authors.affiliation: School of Psychology and Counselling, Queensland University of Technology , Brisbane, QLD , Australia.
authors.forename: Julia
authors.initials: J
authors.lastname: Hocking
keywords: feature selection; laterality of TLE; machine learning; resting-state functional connectivity; temporal lobe epilepsy