Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging_Journal of Biomedical Engineering

Authors：

XIE Chong ^1,2 , GE Manling ^1,2 , FU Xiaoxuan ^1,2 ,  CHEN Shenghua ^1,2 , ZHANG Fuyi ^1,2 , GUO Zhitong ^1,2 ,  ZHANG Zhiqiang ³

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, P.R.China;
3. Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine/General Hospital of Eastern Theater, Nanjing 210002, P.R.China;

Corresponding author：

CHEN Shenghua, Email: chenshenghua@hebut.edu.cn; ZHANG Zhiqiang, Email: zhangzq2001@126.com

Keywords：

multi-scale entropy; resting-state functional magnetic resonance imaging; temporal lobe epilepsy; support vector machine; epileptogenic hemisphere

DOI：

10.7507/1001-5515.202011048

Video：

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Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.

Citation： XIE Chong, GE Manling, FU Xiaoxuan, CHEN Shenghua, ZHANG Fuyi, GUO Zhitong, ZHANG Zhiqiang. Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging. Journal of Biomedical Engineering, 2021, 38(6): 1163-1172. doi: 10.7507/1001-5515.202011048 Copy

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2.	Warren B. Surgical considerations of intractable mesial temporal lobe epilepsy. Brain Sci, 2018, 8(2): 35.
3.	Kumar A, Valentín A, Humayon D, et al. Preoperative estimation of seizure control after resective surgery for the treatment of epilepsy. Seizure-Eur J Epilep, 2013, 22(10): 818-826.
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5.	Wang Cong, Sun Wanbing, Zhang Jun, et al. An electric-field-responsive paramagnetic contrast agent enhances the visualization of epileptic foci in mouse models of drug-resistant epilepsy. Nat Biomed Eng, 2020, 5(3): 278-289.
6.	杨宏宇, 陈楠, 李坤成. 伴认知功能障碍的颞叶癫痫MRI研究进展. 中国医学影像技术, 2017, 33(9): 1421-1424.
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13.	Tong Xin, An D M, Xiao Fenglai, et al. Real-time effects of interictal spikes on hippocampus and amygdala functional connectivity in unilateral temporal lobe epilepsy: An EEG-fMRI study. Epilepsia, 2019, 60(2): 246-254.
14.	Reyes A, Thesen T, Wang X Y, et al. Resting-state functional MRI distinguishes temporal lobe epilepsy subtypes. Epilepsia, 2016, 57(9): 1475-1484.
15.	Jones A L, Cascino G D. Evidence on use of neuroimaging for surgical treatment of temporal lobe epilepsy. JAMA Neurol, 2016, 73(4): 464-470.
16.	Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett, 2002, 89(6): 068102.
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20.	Li Xuanyu, Zhu Zhaojun, Zhao Weina, et al. Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer's disease: A multi-scale entropy analysis. Biomed Opt Express, 2018, 9(4): 1916-1929.
21.	Wang D, Jann K, Fan Chang, et al. Neurophysiological basis of multi-scale entropy of brain complexity and its relationship with functional connectivity. Front Neurosci, 2018, 12: 352.
22.	覃国萍, 李双燕. 多尺度熵算法研究进展及其在神经信号分析中的应用. 生物医学工程学杂志, 2020, 37(3): 541-548.
23.	Roldan E, Calero S, Hidalgo V M, et al. Multi-scale entropy evaluates the proarrhythmic condition of persistent atrial fibrillation patients predicting early failure of electrical cardioversion. Entropy, 2020, 22(7): 748.
24.	Yang A C, Huang C C, Yeh H L, et al. Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: A multiscale entropy analysis. Neurobiol Aging, 2013, 34(2): 428-438.
25.	Morgan V L, Gore J C, Abou-Khalil B. Functional epileptic network in left mesial temporal lobe epilepsy detected using resting fMRI. Epilepsy Res, 2010, 88(2-3): 168-178.
26.	Wu Rina, Zang Yufeng, Zhao Shigang. Resting-state fMRI studies in epilepsy. Neurosci Bull, 2012, 28(4): 449-455.
27.	吴寒, 张志强, 许强, 等. 间期痫样发放对内侧颞叶癫痫脑网络的影响. 磁共振成像, 2015, 6(11): 801-806.
28.	Woolrich M W, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage, 2009, 45(1): S173-S186.
29.	Jenkinson M, Bannister P, Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 2002, 2(17): 825-841.
30.	Wang Danhong, Li Meiling, Wang Meiyun, et al. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatr, 2020, 25(9): 2119-2129.
31.	Yeo B T, Krienen F M, Sepulcre J, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 2011, 3(106): 1125-1165.
32.	Smith S M. Fast robust automated brain extraction. Hum Brain Mapp, 2002, 17(3): 143-155.
33.	Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
34.	Xia Mingrui, Wang Jinhui, He Yong, et al. BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS One, 2013, 8(7): e68910.
35.	Cortes C, Vapnik V. Support vector networks. Mach Learn, 1995, 20(3): 273-297.
36.	Peter A, Lino R, Nelson D, et al. A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis. IEEE Trans Inf Technol Biomed, 2005, 9(2): 276-282.
37.	Jafari-Khouzani K, Elisevich K, Karvelis K C, et al. Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy. Epilepsy Res, 2011, 95(1): 35-30.
38.	Jin S H, Chung C K. Electrophysiological resting-state biomarker for diagnosing mesial temporal lobe epilepsy with hippocampal sclerosis. Epilepsy Res, 2017, 129: 138-145.
39.	Beheshti I, Sone D, Maikusa N, et al. FLAIR-Wise machine-learning classification and lateralization of MRI-negative 18F-FDG PET-positive temporal lobe epilepsy. Front Neurol, 2020, 11: 580713.
40.	Morgan V L, Englot D J, Rogers B P, et al. Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy. Epilepsia, 2017, 58(7): 1251-1260.
41.	Laufs H. Functional imaging of seizures and epilepsy: Evolution from zones to networks. Curr Opin Neurol, 2012, 25(2): 194-200.
42.	Fahoum F, Lopes R, Pittau F, et al. Widespread epileptic networks in focal epilepsies: EEG-fMRI study. Epilepsia, 2012, 53(9): 1618-1627.
43.	Farid N, Girard H M, Kemmotsu N, et al. Temporal lobe epilepsy: Quantitative MR volumetry in detection of hippocampal atrophy. Radiology, 2012, 264(2): 542-550.
44.	Moran N F, Lemieux L, Kitchen N D, et al. Extrahippocampal temporal lobe atrophy in temporal lobe epilepsy and mesial temporal sclerosis. Brain, 2001, 124(Pt1): 167-175.
45.	Bonilha L, Rorden C, Castellano G, et al. Voxel-based morphometry of the thalamus in patients with refractory medial temporal lobe epilepsy. Neuroimage, 2005, 25(3): 1016-1021.

1. Duncan J S, Winston G P, Koepp M J, et al. Brain imaging in the assessment for epilepsy surgery. Lancet Neurol, 2016, 15(4): 420-433.
2. Warren B. Surgical considerations of intractable mesial temporal lobe epilepsy. Brain Sci, 2018, 8(2): 35.
3. Kumar A, Valentín A, Humayon D, et al. Preoperative estimation of seizure control after resective surgery for the treatment of epilepsy. Seizure-Eur J Epilep, 2013, 22(10): 818-826.
4. 李欣, 王正阁, 张冰, 等. fMRI在颞叶癫痫术前定位和预后评估中的研究进展. 磁共振成像, 2020, 11(8): 691-694.
5. Wang Cong, Sun Wanbing, Zhang Jun, et al. An electric-field-responsive paramagnetic contrast agent enhances the visualization of epileptic foci in mouse models of drug-resistant epilepsy. Nat Biomed Eng, 2020, 5(3): 278-289.
6. 杨宏宇, 陈楠, 李坤成. 伴认知功能障碍的颞叶癫痫MRI研究进展. 中国医学影像技术, 2017, 33(9): 1421-1424.
7. Lee R W, Hoogs M M, Burkholder D B, et al. Outcome of intracranial electroencephalography monitoring and surgery in magnetic resonance imaging-negative temporal lobe epilepsy. Epilepsy Res, 2014, 108(5): 937-944.
8. 韩静, 刘亚洲. 磁共振成像对颞叶癫痫患者病灶定侧定位的诊断评估. 实用医学影像杂志, 2018, 19(6): 531-533.
9. Kerr W T, Nguyen S T, Cho A Y, et al. Computer-aided diagnosis and localization of lateralized temporal lobe epilepsy using interictal FDG-PET. Front Neurol, 2013, 4: 31-45.
10. Pereira F R S, Alessio A, Sercheli M S, et al. Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: Evidence from resting state fMRI. BMC Neurosci, 2010, 11(1): 66.
11. Barron D S, Fox P T, Pardoe H, et al. Thalamic functional connectivity predicts seizure laterality in individual TLE patients: Application of a biomarker development strategy. Neuroimage-Clin, 2015, 7: 273-280.
12. Yang Zhengyi, Jeiran C P, David R, et al. Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Front Neurol, 2015, 6: 184.
13. Tong Xin, An D M, Xiao Fenglai, et al. Real-time effects of interictal spikes on hippocampus and amygdala functional connectivity in unilateral temporal lobe epilepsy: An EEG-fMRI study. Epilepsia, 2019, 60(2): 246-254.
14. Reyes A, Thesen T, Wang X Y, et al. Resting-state functional MRI distinguishes temporal lobe epilepsy subtypes. Epilepsia, 2016, 57(9): 1475-1484.
15. Jones A L, Cascino G D. Evidence on use of neuroimaging for surgical treatment of temporal lobe epilepsy. JAMA Neurol, 2016, 73(4): 464-470.
16. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett, 2002, 89(6): 068102.
17. Hadoush H, Alafeef M, Abdulhay E. Brain complexity in children with mild and severe autism spectrum disorders: Analysis of multiscale entropy in EEG. Brain Topogr, 2019, 32(5): 914-921.
18. Ives-Deliperi V, Butler J T, Jokeit H. Left or right? Lateralizing temporal lobe epilepsy by dynamic amygdala fMRI. Epilepsy Behav, 2017, 70(Pt A): 118-124.
19. 张夫一, 葛曼玲, 郭志彤, 等. 静息态功能磁共振成像评估健康老年人认知行为的多尺度熵模型研究. 物理学报, 2020, 69(10): 108703.
20. Li Xuanyu, Zhu Zhaojun, Zhao Weina, et al. Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer's disease: A multi-scale entropy analysis. Biomed Opt Express, 2018, 9(4): 1916-1929.
21. Wang D, Jann K, Fan Chang, et al. Neurophysiological basis of multi-scale entropy of brain complexity and its relationship with functional connectivity. Front Neurosci, 2018, 12: 352.
22. 覃国萍, 李双燕. 多尺度熵算法研究进展及其在神经信号分析中的应用. 生物医学工程学杂志, 2020, 37(3): 541-548.
23. Roldan E, Calero S, Hidalgo V M, et al. Multi-scale entropy evaluates the proarrhythmic condition of persistent atrial fibrillation patients predicting early failure of electrical cardioversion. Entropy, 2020, 22(7): 748.
24. Yang A C, Huang C C, Yeh H L, et al. Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: A multiscale entropy analysis. Neurobiol Aging, 2013, 34(2): 428-438.
25. Morgan V L, Gore J C, Abou-Khalil B. Functional epileptic network in left mesial temporal lobe epilepsy detected using resting fMRI. Epilepsy Res, 2010, 88(2-3): 168-178.
26. Wu Rina, Zang Yufeng, Zhao Shigang. Resting-state fMRI studies in epilepsy. Neurosci Bull, 2012, 28(4): 449-455.
27. 吴寒, 张志强, 许强, 等. 间期痫样发放对内侧颞叶癫痫脑网络的影响. 磁共振成像, 2015, 6(11): 801-806.
28. Woolrich M W, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage, 2009, 45(1): S173-S186.
29. Jenkinson M, Bannister P, Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 2002, 2(17): 825-841.
30. Wang Danhong, Li Meiling, Wang Meiyun, et al. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatr, 2020, 25(9): 2119-2129.
31. Yeo B T, Krienen F M, Sepulcre J, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 2011, 3(106): 1125-1165.
32. Smith S M. Fast robust automated brain extraction. Hum Brain Mapp, 2002, 17(3): 143-155.
33. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
34. Xia Mingrui, Wang Jinhui, He Yong, et al. BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS One, 2013, 8(7): e68910.
35. Cortes C, Vapnik V. Support vector networks. Mach Learn, 1995, 20(3): 273-297.
36. Peter A, Lino R, Nelson D, et al. A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis. IEEE Trans Inf Technol Biomed, 2005, 9(2): 276-282.
37. Jafari-Khouzani K, Elisevich K, Karvelis K C, et al. Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy. Epilepsy Res, 2011, 95(1): 35-30.
38. Jin S H, Chung C K. Electrophysiological resting-state biomarker for diagnosing mesial temporal lobe epilepsy with hippocampal sclerosis. Epilepsy Res, 2017, 129: 138-145.
39. Beheshti I, Sone D, Maikusa N, et al. FLAIR-Wise machine-learning classification and lateralization of MRI-negative 18F-FDG PET-positive temporal lobe epilepsy. Front Neurol, 2020, 11: 580713.
40. Morgan V L, Englot D J, Rogers B P, et al. Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy. Epilepsia, 2017, 58(7): 1251-1260.
41. Laufs H. Functional imaging of seizures and epilepsy: Evolution from zones to networks. Curr Opin Neurol, 2012, 25(2): 194-200.
42. Fahoum F, Lopes R, Pittau F, et al. Widespread epileptic networks in focal epilepsies: EEG-fMRI study. Epilepsia, 2012, 53(9): 1618-1627.
43. Farid N, Girard H M, Kemmotsu N, et al. Temporal lobe epilepsy: Quantitative MR volumetry in detection of hippocampal atrophy. Radiology, 2012, 264(2): 542-550.
44. Moran N F, Lemieux L, Kitchen N D, et al. Extrahippocampal temporal lobe atrophy in temporal lobe epilepsy and mesial temporal sclerosis. Brain, 2001, 124(Pt1): 167-175.
45. Bonilha L, Rorden C, Castellano G, et al. Voxel-based morphometry of the thalamus in patients with refractory medial temporal lobe epilepsy. Neuroimage, 2005, 25(3): 1016-1021.

Journal of Biomedical Engineering

Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging

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