Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging
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RESEARCH ARTICLE
Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging Baiwan Zhou1, Dongmei An2, Fenglai Xiao2,3, Running Niu1, Wenbin Li1, Wei Li2, Xin Tong2, Graham J Kemp4, Dong Zhou (
✉)2, Qiyong Gong1,5, Du Lei (✉)1,6,7
1
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Department of Neurology, West China Hospital of Sichuan University, Chengdu 610041, China; 3Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London WC1E 6BT, UK; 4Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L9 7AL, UK; 5Department of Psychology, School of Public Administration, Sichuan University, Chengdu 610041, China; 6Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK; 7Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA 2
© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE. Keywords mesial temporal lobe epilepsy; functional magnetic resonance imaging; structural magnetic resonance imaging; machine learning; support vector machine
Introduction Mesial temporal lobe epilepsy (mTLE) is the most common type of focal epilepsy in adults, and its pathophysiological substrate is usually hippocampal sclerosis (HS) [1]. Magnetic resonance imaging (MRI), notably resting state-functional MRI (rs-fMRI) [2,3] and structural MRI (sMRI) [4,5], has a pivotal role in the evaluation of patients with mTLE. However, most of the previous MRI studies measured average group-level dif
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