Tract-based spatial statistics analysis on the white matter of patients with temporal lobe epilepsy and automatic recognition_Journal of Biomedical Engineering

Authors：

ZHAO Di ¹ ,  GUO Shengwen ¹ , WU Congling ¹ , LAI Chunren ¹ , CHENG Lina ² , WANG Wensheng ²

1. School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, P.R.China;
2. Medical Imaging Center, Guangdong 999 Brain Hospital, Guangzhou 510510, P.R.China;

Corresponding author：

GUO Shengwen, Email: shwguo@scut.edu.cn

Keywords：

temporal lobe epilepsy; diffusion tensor imaging; tract-based spatial statistics; support vector machine; recursive feature elimination

DOI：

10.7507/1001-5515.201610038

Video：

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Abstract Full text Figures/Tables Video References Cited by

This study aims to determine the salient brain regions with abnormal changes in white matter structures from diffusion tensor imaging (DTI) images of the patients with temporal lobe epilepsy (TLE), and to discriminate the patients with TLE from normal controls (NCs). Firstly, the DTI images from 50 subjects (28 NCs and 22 TLE) were acquired. Secondly, the four measures including the fractional anisotropy (FA), the mean diffusivity (MD), the axial diffusivity (AD) and the radial diffusivity (RD) were calculated. Thirdly, the tract-based spatial statistics (TBSS) was adopted to extract the measures in brain regions with significant differences between the two compared groups. Fourthly, the obtained measures were used as input features of the support vector machine (SVM) for classification, and the support vector machine-recursive feature elimination (SVM-RFE) was compared with the support vector machine-tract-based spatial statistics (SVM-TBSS) method. Finally, the essential brain regions and their spatial distribution were analyzed and discussed. The experimental results showed that the FA measures of the TLE group decreased significantly in the corpus callosum, superior longitudinal fasciculus, corona radiata, external capsule, internal capsule, inferior fronto-occipital fasciculus, fasciculus uncinatus and sagittal stratum, which were nearly bilaterally distributed, while the MD and RD increased significantly in most of these brain regions of the TLE group. Although the AD also increased, the differences were not statistically significant. The SVM-TBSS classifier obtained accuracies of 82%, 76% and 76% using the FA, MD and RD for classification, respectively, and 80% using combined measures. The SVM-RFE classifier obtained accuracies of 90%, 90% and 92% using the FA, MD and RD respectively, while the highest accuracy was 100% using combined measures. These results demonstrated that the SVM-RFE outperformed the SVM-TBSS, and the dominant characteristic influencing classification in brain regions were in associative and commissural fibers. These results illustrated that the measures of DTI images could reveal the abnormal changes in white matter structure of patients with TLE, providing effective information to clarify its pathological mechanism, localize the focus and diagnose automatically.

Citation： ZHAO Di, GUO Shengwen, WU Congling, LAI Chunren, CHENG Lina, WANG Wensheng. Tract-based spatial statistics analysis on the white matter of patients with temporal lobe epilepsy and automatic recognition. Journal of Biomedical Engineering, 2017, 34(4): 500-509. doi: 10.7507/1001-5515.201610038 Copy

1.	Ngugi A K, Bottomley C, Kleinschmidt I, et al. Estimation of the burden of active and life-time epilepsy: a meta-analytic approach. Epilepsia, 2010, 51(5): 883-890.
2.	Kwan P, Brodie M J. Drug treatment of epilepsy: when does it fail and how to optimize its use? CNS Spectr, 2004, 9(2): 110-119.
3.	Duncan J. The current status of neuroimaging for epilepsy. Curr Opin Neurol, 2009, 22(2): 179-184.
4.	Kemmotsu N, Girard H M, Bernhardt B C, et al. 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.
5.	Mueller S G, Laxer K D, Cashdollar N, et al. Identification of abnormal neuronal metabolism outside the seizure focus in temporal lobe epilepsy. Epilepsia, 2004, 45(4): 355-366.
6.	Govindan R M, Makki M I, Sundaram S K, et al. Diffusion tensor analysis of temporal and extra-temporal lobe tracts in temporal lobe epilepsy. Epilepsy Res, 2008, 80(1): 30-41.
7.	Chiang S, Levin H S, Wilde E, et al. White matter structural connectivity changes correlate with epilepsy duration in temporal lobe epilepsy. Epilepsy Res, 2016, 120(2016): 37-46.
8.	Song S K, Sun Shuwei, Ramsbottom M J, et al. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage, 2002, 17(3): 1429-1436.
9.	Campos B M, Coan A C, Beltramini G C, et al. White matter abnormalities associate with type and localization of focal epileptogenic lesions. Epilepsia, 2015, 56(1): 125-132.
10.	Meng Lu, Xiang Jing, Kotecha R, et al. White matter abnormalities in children and adolescents with temporal lobe epilepsy. Magn Reson Imaging, 2010, 28(9): 1290-1298.
11.	Liu Zhenyin, Xu Yong, An Jie, et al. Altered brain white matter integrity in temporal lobe epilepsy: a TBSS study. J Neuroimaging, 2015, 25(3): 460-464.
12.	Mcmillan A B, Hermann B P, Johnson S C, et al. Voxel-based morphometry of unilateral temporal lobe epilepsy reveals abnormalities in cerebral white matter. Neuroimage, 2004, 23(1): 167-174.
13.	Smith S M, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 2006, 31(4): 1487-1505.
14.	Focke N K, Yogarajah M, Bonelli S B, et al. Voxel-based diffusion tensor imaging in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. Neuroimage, 2008, 40(2): 728-737.
15.	Işik H, Sezer E. Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform. J Med Syst, 2012, 36(1): 1-13.
16.	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): 31-44.
17.	Laufs H, Daunizeau J, Carmichael D W, et al. Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging. Neuroimage, 2008, 40(2): 515-528.
18.	Bernhardt B C, Hong S J, Bernasconi A, et al. Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics. Ann Neurol, 2015, 77(3): 436-446.
19.	Memarian N, Kim S, Dewar S, et al. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput Biol Med, 2015, 64(2015): 67-78.
20.	Cantor-Rivera D, Khan A R, Goubran M, et al. Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative Mr imaging. Comput Med Imaging Graph, 2015, 41(2015): 14-28.
21.	Focke N K, Yogarajah M, Symms M R, et al. Automated Mr image classification in temporal lobe epilepsy. Neuroimage, 2012, 59(1): 356-362.
22.	An Jie, Fang Peng, Wang Wensheng, et al. Decreased white matter integrity in mesial temporal lobe epilepsy: a machine learning approach. Neuroreport, 2014, 25(10): 788-794.
23.	Cui Zaixu, Zhong Suyu, Xu Pengfei, et al. PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci, 2013, 7(42): 42.
24.	Le Bihan D, Mangin J F, Poupon C, et al. Diffusion tensor imaging: concepts and applications. Magn Reson Imaging, 2001, 13(4): 534-546.
25.	Smith S M, Jenkinson M, Woolrich M W, et al. Advances in functional and structural Mr image analysis and implementation as FSL. Neuroimage, 2004, 23(Suppl 1): S208-S219.
26.	Mori Susumu, Oishi K, Jiang Hangyi, et al. Stereotaxic white matter Atlas based on diffusion tensor imaging in an ICBM template. Neuroimage, 2008, 40(2): 570-582.
27.	Arbabshirani M R, Plis S, Sui Jing, et al. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage, 2017, 145(Pt B): 137-165.

1. Ngugi A K, Bottomley C, Kleinschmidt I, et al. Estimation of the burden of active and life-time epilepsy: a meta-analytic approach. Epilepsia, 2010, 51(5): 883-890.
2. Kwan P, Brodie M J. Drug treatment of epilepsy: when does it fail and how to optimize its use? CNS Spectr, 2004, 9(2): 110-119.
3. Duncan J. The current status of neuroimaging for epilepsy. Curr Opin Neurol, 2009, 22(2): 179-184.
4. Kemmotsu N, Girard H M, Bernhardt B C, et al. 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.
5. Mueller S G, Laxer K D, Cashdollar N, et al. Identification of abnormal neuronal metabolism outside the seizure focus in temporal lobe epilepsy. Epilepsia, 2004, 45(4): 355-366.
6. Govindan R M, Makki M I, Sundaram S K, et al. Diffusion tensor analysis of temporal and extra-temporal lobe tracts in temporal lobe epilepsy. Epilepsy Res, 2008, 80(1): 30-41.
7. Chiang S, Levin H S, Wilde E, et al. White matter structural connectivity changes correlate with epilepsy duration in temporal lobe epilepsy. Epilepsy Res, 2016, 120(2016): 37-46.
8. Song S K, Sun Shuwei, Ramsbottom M J, et al. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage, 2002, 17(3): 1429-1436.
9. Campos B M, Coan A C, Beltramini G C, et al. White matter abnormalities associate with type and localization of focal epileptogenic lesions. Epilepsia, 2015, 56(1): 125-132.
10. Meng Lu, Xiang Jing, Kotecha R, et al. White matter abnormalities in children and adolescents with temporal lobe epilepsy. Magn Reson Imaging, 2010, 28(9): 1290-1298.
11. Liu Zhenyin, Xu Yong, An Jie, et al. Altered brain white matter integrity in temporal lobe epilepsy: a TBSS study. J Neuroimaging, 2015, 25(3): 460-464.
12. Mcmillan A B, Hermann B P, Johnson S C, et al. Voxel-based morphometry of unilateral temporal lobe epilepsy reveals abnormalities in cerebral white matter. Neuroimage, 2004, 23(1): 167-174.
13. Smith S M, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 2006, 31(4): 1487-1505.
14. Focke N K, Yogarajah M, Bonelli S B, et al. Voxel-based diffusion tensor imaging in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. Neuroimage, 2008, 40(2): 728-737.
15. Işik H, Sezer E. Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform. J Med Syst, 2012, 36(1): 1-13.
16. 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): 31-44.
17. Laufs H, Daunizeau J, Carmichael D W, et al. Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging. Neuroimage, 2008, 40(2): 515-528.
18. Bernhardt B C, Hong S J, Bernasconi A, et al. Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics. Ann Neurol, 2015, 77(3): 436-446.
19. Memarian N, Kim S, Dewar S, et al. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput Biol Med, 2015, 64(2015): 67-78.
20. Cantor-Rivera D, Khan A R, Goubran M, et al. Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative Mr imaging. Comput Med Imaging Graph, 2015, 41(2015): 14-28.
21. Focke N K, Yogarajah M, Symms M R, et al. Automated Mr image classification in temporal lobe epilepsy. Neuroimage, 2012, 59(1): 356-362.
22. An Jie, Fang Peng, Wang Wensheng, et al. Decreased white matter integrity in mesial temporal lobe epilepsy: a machine learning approach. Neuroreport, 2014, 25(10): 788-794.
23. Cui Zaixu, Zhong Suyu, Xu Pengfei, et al. PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci, 2013, 7(42): 42.
24. Le Bihan D, Mangin J F, Poupon C, et al. Diffusion tensor imaging: concepts and applications. Magn Reson Imaging, 2001, 13(4): 534-546.
25. Smith S M, Jenkinson M, Woolrich M W, et al. Advances in functional and structural Mr image analysis and implementation as FSL. Neuroimage, 2004, 23(Suppl 1): S208-S219.
26. Mori Susumu, Oishi K, Jiang Hangyi, et al. Stereotaxic white matter Atlas based on diffusion tensor imaging in an ICBM template. Neuroimage, 2008, 40(2): 570-582.
27. Arbabshirani M R, Plis S, Sui Jing, et al. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage, 2017, 145(Pt B): 137-165.

Journal of Biomedical Engineering

Tract-based spatial statistics analysis on the white matter of patients with temporal lobe epilepsy and automatic recognition

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