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Frontiers of Medicine

Front. Med.    2020, Vol. 14 Issue (5) : 630-641     https://doi.org/10.1007/s11684-019-0718-4
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 Zhou2(), Qiyong Gong1,5, Du Lei1,6,7()
1. Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
2. Department of Neurology, West China Hospital of Sichuan University, Chengdu 610041, China
3. Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London WC1E 6BT, UK
4. Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L9 7AL, UK
5. Department of Psychology, School of Public Administration, Sichuan University, Chengdu 610041, China
6. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
7. Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA
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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     
Corresponding Author(s): Dong Zhou,Du Lei   
Just Accepted Date: 07 November 2019   Online First Date: 07 January 2020    Issue Date: 12 October 2020
 Cite this article:   
Baiwan Zhou,Dongmei An,Fenglai Xiao, et al. Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging[J]. Front. Med., 2020, 14(5): 630-641.
 URL:  
http://journal.hep.com.cn/fmd/EN/10.1007/s11684-019-0718-4
http://journal.hep.com.cn/fmd/EN/Y2020/V14/I5/630
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Articles by authors
Baiwan Zhou
Dongmei An
Fenglai Xiao
Running Niu
Wenbin Li
Wei Li
Xin Tong
Graham J Kemp
Dong Zhou
Qiyong Gong
Du Lei
Variables Left mTLE Right mTLE HC
Sample size 37 37 74
Ageb 24.4±8.0 25.2±7.3 25.9±7.8
Gender (M/F) 18 (48.6%)/19 (51.4%) 19 (51.4%)/18 (48.6%) 37 (50%)/37 (50%)
Disease duration (years) b 10.4±9.0 12.4±6.9
Onset of epilepsy (years) c 14.6±9.1 14.3±8.3
Underwent surgical resection (ATL) 21 (56.8%) 16 (43.2%)
Histological examination after surgery
HS
Glial cell proliferation
Normal
No results
11 (52.4%)
2 (9.5%)
2 (9.5%)
6 (28.6%)
7 (43.8%)
2 (12.5%)
None
7 (43.8%)
Initial precipitating insults
Febrile seizures
CNS infection
Status epilepticus
11 (30.0%)
6 (16.2%)
None
8 (21.6%)
7 (18.9%)
None
Antiepileptic drugs (AEDs)
Monotherapy
Ditherapy
Multiple AEDs
10 (27.0%)
14 (37.9%)
13 (35.1%)
9 (24.3%)
12 (32.4%)
16 (43.3%)
-
Tab.1  Demographic and clinical dataa
Fig.1  Overview of the classification approach used to assess the diagnostic value of sMRI and rs-fMRI data. Abbreviations: sMRI, structural MRI; rs-fMRI, resting state functional MRI; GM, gray matter; WM, white matter; ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation.
Fig.2  Overview of the classification accuracy based on different modalities. “Functional combinations” means a multimodal SVM classifier via functional measures ReHo and ALFF as input; “structural combinations” means a multimodal SVM classifier based on structural measures GM, WM, and cortical thickness; “ALL combinations” means a multimodal SVM classifier via all of the five measures, namely, ReHo, ALFF, GM, WM, and cortical thickness as inputs.
Accuracy Sensitivity Specificity Recall F1 Score AUC P value
All patients vs. HC
ReHo 62.6% 67.5% 57.7% 61.5% 62.0% 64.1% 0.019
ALFF 62.8% 69.2% 56.4% 61.3% 62.0% 65.7% 0.017
GM 58.2% 56.6% 59.8% 58.5% 58.3% 61.4% 0.023
WM 72.3% 77.8% 66.8% 70.1% 71.2% 75.2% 0.012
Cortical thickness 62.6% 57.5% 67.7% 64.0% 63.3% 65.6% 0.026
Functional combinationsa 62.6% 68.7% 56.5% 61.2% 61.9% 64.3% 0.023
Structural combinationsb 62.8% 65.4% 60.2% 62.2% 62.5% 66.7% 0.019
All combinationsc 58.2% 67.5% 48.9% 57.0% 57.6% 64.3% 0.035
Left mTLE vs. HC
ReHo 74.8% 80.7% 69.0% 72.2% 73.5% 77.8% 0.003
ALFF 75.0% 81.7% 68.3% 72.0% 73.5% 79.5% 0.001
GM 72.9% 72.5% 73.3% 73.1% 73.0% 74.1% 0.002
WM 75.8% 77.8% 73.7% 74.7% 75.2% 81.2% 0.009
Cortical thickness 72.1% 70.5% 73.7% 72.8% 72.4% 73.6% 0.002
Functional combinationsa 77.5% 84.1% 70.8% 74.2% 75.8% 81.4% 0.001
Structural combinationsb 79.2% 81.7% 76.7% 77.8% 78.5% 83.6% 0.001
ALL combinationsc 84.1% 86.5% 81.7% 82.5% 83.3% 87.8% 0.001
Right mTLE vs. HC
ReHo 67.5% 61.7% 73.3% 69.8% 68.6% 71.3% 0.005
ALFF 73.3% 67.5% 79.2% 76.4% 74.8% 75.2% 0.002
GM 66.3% 55.0% 77.5% 71.0% 68.6% 68.2% 0.021
WM 72.9% 72.5% 73.3% 73.1% 73.0% 75.1% 0.002
Cortical thickness 66.1% 57.1% 75.1% 69.6% 67.8% 71.3% 0.017
Functional combinationsa 72.9% 67.5% 78.3% 75.7% 74.3% 73.6% 0.002
Structural combinationsb 69.2% 65.0% 73.3% 70.9% 70.0% 71.4% 0.006
All combinationsc 72.9% 77.5% 68.3% 71.0% 71.9% 74.6% 0.002
Tab.2  SVM classifier performance for the different modalities and combinations
Fig.3  Distribution maps of the regions detected by at least two individual measures in the classification between left mTLE and healthy controls.
Fig.4  Distribution maps of the regions detected by at least two individual measures in the classification between the right mTLE and healthy controls.
Left mTLE VS HC Right mTLE VS HC
Brain regions Significance values ?Brain regions Significance values
ReHo ?ReHo
Inferior temporal gyrus L 0.066 ?Caudate L 0.078
Pallidum L 0.064 ?Inferior temporal gyrus L 0.062
Temporal pole: middle temporal gyrus L 0.058 ?Temporal pole: middle temporal gyrus R 0.055
Lingual gyrus R 0.053 ?Inferior temporal gyrus R 0.053
Inferior occipital gyrus R 0.042 ?Inferior occipital gyrus R 0.042
Superior parietal gyrus L 0.037 ?Paracentral lobule R 0.035
Inferior parietal gyrus L 0.036 ?Putamen R 0.034
Putamen L 0.035 ?Pallidum L 0.031
Cuneus R 0.033 ?Temporal pole: superior temporal gyrus L 0.030
Supramarginal gyrus R 0.032 ?Thalamus R 0.030
ALFF ?ALFF
Superior parietal gyrus L 0.025 ?Precuneus L 0.035
Precuneus L 0.021 ?Superior parietal gyrus L 0.029
Angular gyrus L 0.018 ?Angular gyrus L 0.028
Inferior parietal gyrus L 0.018 ?Inferior parietal gyrus L 0.026
Superior parietal gyrus R 0.017 ?Middle frontal gyrus (orbital part) R 0.025
Inferior occipital gyrus R 0.016 ?Paracentral lobule R 0.021
Supramarginal gyrus L 0.015 ?Supramarginal gyrus L 0.020
Cuneus R 0.015 ?Temporal pole: middle temporal gyrus R 0.020
Paracentral lobule R 0.014 ?Rectus R 0.019
Postcentral gyrus R 0.014 ?Superior parietal gyrus R 0.019
GM   GM
Fusiform gyrus R 0.032 ?Heschl gyrus R 0.028
Angular gyrus L 0.028 ?Putamen L 0.027
Temporal pole: superior temporal gyrus L 0.022 ?Cuneus R 0.027
Thalamus R 0.021 ?Temporal pole: superior temporal gyrus R 0.025
Putamen L 0.021 ?Fusiform gyrus R 0.023
Cuneus L 0.019 ?Putamen R 0.022
Supramarginal gyrus L 0.019 ?Fusiform gyrus L 0.021
Heschl’s gyrus R 0.018 ?Pallidum L 0.021
Cuneus R 0.015 ?Paracentral lobule R 0.020
Fusiform gyrus L 0.015 ?Insula R 0.017
WM ?WM
Temporal pole: middle temporal gyrus L 0.043 ?Inferior temporal gyrus R 0.036
Parahippocampal gyrus L 0.035 ?Parahippocampal gyrus R 0.033
Inferior temporal gyrus L 0.033 ?Temporal pole: middle temporal gyrus L 0.031
Paracentral lobule L 0.028 ?Middle temporal gyrus R 0.030
Superior temporal gyrus L 0.027 ?Temporal pole: superior temporal gyrus R 0.027
Superior occipital gyrus L 0.026 ?Putamen L 0.026
Fusiform gyrus L 0.021 ?Putamen R 0.021
Temporal pole: middle temporal gyrus R 0.021 ?Cuneus R 0.020
Inferior occipital gyrus R 0.019 ?Angular gyrus L 0.020
Parahippocampal gyrus R 0.018 ?Parahippocampal gyrus L 0.020
Tab.3  Ten brain regions with the greatest contribution to single-subject classification across the different measures
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