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2020年 第14卷 第5期

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《医学前沿（英文）》 >> 2020年 第14卷 第5期 doi: 10.1007/s11684-019-0718-4

Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

. 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.. Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London WC1E 6BT, UK.. Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L9 7AL, UK.. Department of Psychology, School of Public Administration, Sichuan University, Chengdu 610041, China.. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5?8AF, UK.. Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA

录用日期： 2020-01-07 发布日期： 2020-01-07
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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.

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