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

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

References

[1] Engel J Jr. Mesial temporal lobe epilepsy: what have we learned? Neuroscientist 2001; 7(4): 340–352

[2] Burianová H, Faizo NL, Gray M, Hocking J, Galloway G, Reutens D. Altered functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res 2017; 137: 45–52

[3] Chen S, Chen L, Huang H, Lin W. Relationship between resting state functional magnetic resonance imaging and memory function in mesial temporal lobe epilepsy. J Neurol Sci 2017; 372: 117–125

[4] Memarian N, Thompson PM, Engel J Jr, Staba RJ. Quantitative analysis of structural neuroimaging of mesial temporal lobe epilepsy. Imaging Med 2013; 5(3): 10–14

[5] Liu M, Chen Z, Beaulieu C, Gross DW. Disrupted anatomic white matter network in left mesial temporal lobe epilepsy. Epilepsia 2014; 55(5): 674–682

[6] Kassahun Y, Perrone R, De Momi E, Berghöfer E, Tassi L, Canevini MP, Spreafico R, Ferrigno G, Kirchner F. Automatic classification of epilepsy types using ontology-based and genetics-based machine learning. Artif Intell Med 2014; 61(2): 79–88

[7] Acharya UR, Yanti R, Zheng JW, Krishnan MMR, Tan JH, Martis RJ, Lim CM. Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 2013; 23(3): 1350009

[8] Kerr WT, Cho AY, Anderson A, Douglas PK, Lau EP, Hwang ES, Raman KR, Trefler A, Cohen MS, Nguyen ST, Reddy NM, Silverman DH. Balancing clinical and pathologic relevence in the machine learning diagnosis of epilepsy. Int Workshop Pattern Recognit Neuroimaging 2013; 2013: 86–89

[9] Focke NK, Yogarajah M, Symms MR, Gruber O, Paulus W, Duncan JS. Automated MR image classification in temporal lobe epilepsy. Neuroimage 2012; 59(1): 356–362

[10] Memarian N, Kim S, Dewar S, Engel J Jr, Staba RJ. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput Biol Med 2015; 64: 67–78

[11] Rudie JD, Colby JB, Salamon N. Machine learning classification of mesial temporal sclerosis in epilepsy patients. Epilepsy Res 2015; 117: 63–69

[12] Yang Z, Choupan J, Reutens D, Hocking J. Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Front Neurol 2015; 6: 184

[13] Del Gaizo J, Mofrad N, Jensen JH, Clark D, Glenn R, Helpern J, Bonilha L. Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain Behav 2017; 7(10): e00801

[14] An J, Fang P, Wang W, Liu Z, Hu D, Qiu S. Decreased white matter integrity in mesial temporal lobe epilepsy: a machine learning approach. Neuroreport 2014; 25(10): 788–794

[15] Pereira FR, Alessio A, Sercheli MS, Pedro T, Bilevicius E, Rondina JM, Ozelo HF, Castellano G, Covolan RJ, Damasceno BP, Cendes F. Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: evidence from resting state fMRI. BMC Neurosci 2010; 11(1): 66

[16] Kemmotsu N, Girard HM, Bernhardt BC, Bonilha L, Lin JJ, Tecoma ES, Iragui VJ, Hagler DJ Jr, Halgren E, McDonald CR. MRI analysis in temporal lobe epilepsy: cortical thinning and white matter disruptions are related to side of seizure onset. Epilepsia 2011; 52(12): 2257–2266

[17] Kucukboyaci NE, Girard HM, Hagler DJ Jr, Kuperman J, Tecoma ES, Iragui VJ, Halgren E, McDonald CR. Role of frontotemporal fiber tract integrity in task-switching performance of healthy controls and patients with temporal lobe epilepsy. J Int Neuropsychol Soc 2012; 18(1): 57–67

[18] Coan AC, Campos BM, Beltramini GC, Yasuda CL, Covolan RJ, Cendes F. Distinct functional and structural MRI abnormalities in mesial temporal lobe epilepsy with and without hippocampal sclerosis. Epilepsia 2014; 55(8): 1187–1196

[19] Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, Luo C, Wang Z, Tan Q, Lu G, Chen H. Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp 2011; 32(6): 883–895

[20] Wee CY, Yap PT, Zhang D, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D. Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 2012; 59(3): 2045–2056

[21] Scanlon C, Mueller SG, Cheong I, Hartig M, Weiner MW, Laxer KD. Grey and white matter abnormalities in temporal lobe epilepsy with and without mesial temporal sclerosis. J Neurol 2013; 260(9): 2320–2329

[22] Braga B, Yasuda CL, Cendes F. White matter atrophy in patients with mesial temporal lobe epilepsy: voxel-based morphometry analysis of T1- and T2-Weighted MR Images. Radiol Res Pract 2012; 2012: 481378

[23] Labate A, Cerasa A, Aguglia U, Mumoli L, Quattrone A, Gambardella A. Neocortical thinning in “benign” mesial temporal lobe epilepsy. Epilepsia 2011; 52(4): 712–717

[24] Zhang Z, Lu G, Zhong Y, Tan Q, Chen H, Liao W, Tian L, Li Z, Shi J, Liu Y. fMRI study of mesial temporal lobe epilepsy using amplitude of low-frequency fluctuation analysis. Hum Brain Mapp 2010; 31(12): 1851–1861

[25] Zhong J, Chen S, Ouyang Q, An D, Lu S. Study on the ReHo in treatment-naïve of temporal lobe epilepsy patients with depressive symptoms using resting functional-MRI (fMRI). J Biomedical Eng (Sheng Wu Yi Xue Gong Cheng Xue Za Zhi) 2012; 29(2): 229–232 (in Chinese)

[26] Zeng H, Pizarro R, Nair VA, La C, Prabhakaran V. Alterations in regional homogeneity of resting-state brain activity in mesial temporal lobe epilepsy. Epilepsia 2013; 54(4): 658–666

[27] Commission on Classification and Terminology of the International League Against Epilepsy. Proposal for revised classification of epilepsies and epileptic syndromes. Epilepsia 1989; 30(4): 389–399

[28] Shorvon SD. The etiologic classification of epilepsy. Epilepsia 2011; 52(6): 1052–1057

[29] Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM. Automatically parcellating the human cerebral cortex. Cereb Cortex 2004; 14(1): 11–22

[30] Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006; 31(3): 968–980

[31] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273–297

[32] Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011; 2(3): 115–121

[33] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in Python. J Mach Learn Res 2011; 12: 2825–2830

[34] Pettersson-Yeo W, Benetti S, Marquand AF, Joules R, Catani M, Williams SCR, Allen P, McGuire P, Mechelli A. An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine. Front Neurosci 2014; 8: 189

[35] Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15(1): 273–289

[36] Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 2006; 103(37): 13848–13853

[37] Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 2008; 1124(1): 1–38

[38] Cantor-Rivera D, Khan AR, Goubran M, Mirsattari SM, Peters TM. Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging. Comput Med Imaging Graph 2015; 41: 14–28

[39] Kemmotsu N, Kucukboyaci NE, Cheng CE, Girard HM, Tecoma ES, Iragui VJ, McDonald CR. Alterations in functional connectivity between the hippocampus and prefrontal cortex as a correlate of depressive symptoms in temporal lobe epilepsy. Epilepsy Behav 2013; 29(3): 552–559

[40] McDonald CR, Hagler DJ Jr, Ahmadi ME, Tecoma E, Iragui V, Gharapetian L, Dale AM, Halgren E. Regional neocortical thinning in mesial temporal lobe epilepsy. Epilepsia 2008; 49(5): 794–803

[41] Kucukboyaci NE, Kemmotsu N, Cheng CE, Girard HM, Tecoma ES, Iragui VJ, McDonald CR. Functional connectivity of the hippocampus in temporal lobe epilepsy: feasibility of a task-regressed seed-based approach. Brain Connect 2013; 3(5): 464–474

[42] Ji GJ, Zhang Z, Zhang H, Wang J, Liu DQ, Zang YF, Liao W, Lu G. Disrupted causal connectivity in mesial temporal lobe epilepsy. PLoS One 2013; 8(5): e63183

[43] Pustina D, Doucet G, Sperling M, Sharan A, Tracy J. Increased microstructural white matter correlations in left, but not right, temporal lobe epilepsy. Hum Brain Mapp 2015; 36(1): 85–98

[44] Lu J, Li W, He H, Feng F, Jin Z, Wu L. Altered hemispheric symmetry found in left-sided mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE/HS) but not found in right-sided MTLE/HS. Magn Reson Imaging 2013; 31(1): 53–59

[45] Lishman WA, McMeekan ER. Handedness in relation to direction and degree of cerebral dominance for language. Cortex 1977; 13(1): 30–43

[46] Zhang Q, Wu Q, Zhu H, He L, Huang H, Zhang J, Zhang W. Multimodal MRI-based classification of trauma survivors with and without post-traumatic stress disorder. Front Neurosci 2016; 10: 292

[47] Hinrichs C, Singh V, Xu G, Johnson SC; Alzheimers Disease Neuroimaging Initiative.Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 2011; 55(2): 574–589

[48] Barron DS, Tandon N, Lancaster JL, Fox PT. Thalamic structural connectivity in medial temporal lobe epilepsy. Epilepsia 2014; 55(6): e50–e55

[49] Peng B, Wu L, Zhang L, Chen Y. The relationship between hippocampal volumes and nonverbal memory in patients with medial temporal lobe epilepsy. Epilepsy Res 2014; 108(10): 1839–1844

[50] Sequeira KM, Tabesh A, Sainju RK, DeSantis SM, Naselaris T, Joseph JE, Ahlman MA, Spicer KM, Glazier SS, Edwards JC, Bonilha L. Perfusion network shift during seizures in medial temporal lobe epilepsy. PLoS One 2013; 8(1): e53204

[51] Bertram EH. Extratemporal lobe circuits in temporal lobe epilepsy. Epilepsy Behav 2014; 38: 13–18

[52] Sankar T, Bernasconi N, Kim H, Bernasconi A. Temporal lobe epilepsy: differential pattern of damage in temporopolar cortex and white matter. Hum Brain Mapp 2008; 29(8): 931–944

[53] Curia G, Lucchi C, Vinet J, Gualtieri F, Marinelli C, Torsello A, Costantino L, Biagini G. Pathophysiogenesis of mesial temporal lobe epilepsy: is prevention of damage antiepileptogenic? Curr Med Chem 2014; 21(6): 663–688

[54] Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, Luo C, Lu G, Chen H. Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One 2010; 5(1): e8525

[55] Pittau F, Grova C, Moeller F, Dubeau F, Gotman J. Patterns of altered functional connectivity in mesial temporal lobe epilepsy. Epilepsia 2012; 53(6): 1013–1023

[56] Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci 2011; 15(10): 483–506

[57] Zhang Z, Lu G, Zhong Y, Tan Q, Liao W, Wang Z, Wang Z, Li K, Chen H, Liu Y. Altered spontaneous neuronal activity of the default-mode network in mesial temporal lobe epilepsy. Brain Res 2010; 1323: 152–160

[58] Cataldi M, Avoli M, de Villers-Sidani E. Resting state networks in temporal lobe epilepsy. Epilepsia 2013; 54(12): 2048–2059

[59] Bouilleret V, Semah F, Chassoux F, Mantzaridez M, Biraben A, Trebossen R, Ribeiro MJ. Basal ganglia involvement in temporal lobe epilepsy: a functional and morphologic study. Neurology 2008; 70(3): 177–184

[60] Vieira S, Pinaya WHL, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev 2017; 74(Pt A): 58–75

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Nos. 81501452, 81621003, 81761128023, 81220108031, and 81227002), the Program for Innovative Research Team in University (PCSIRT, No. IRT16R52) of China, the Scholar Professorship Award (No. T2014190) of China, and the CMB Distinguished Professorship Award (No. F510000/G16916411) administered by the Institute of International Education. Du Lei was supported by the Newton International Fellowship Alumni Award from the Royal Society. The authors would like to thank all of the study participants and their families.

Compliance with ethics guidelines

Baiwan Zhou, Dongmei An, Fenglai Xiao, Running Niu, Wenbin Li, Wei Li, Xin Tong, Graham J Kemp, Dong Zhou, Qiyong Gong, and Du Lei declare that they have no conflicts of interest. This study was approved by the West China Hospital Clinical Trials and Biomedical Ethics Committee of Sichuan University, and written informed consent was obtained from all of the participants. The study protocol was performed in accordance with the approved guidelines.

RIGHTS & PERMISSIONS

2019 Higher Education Press