Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients’ DTI data – Theory, simulations and example cases

Autoři: Gyula Gyebnár aff001;  Zoltán Klimaj aff001;  László Entz aff002;  Dániel Fabó aff002;  Gábor Rudas aff001;  Péter Barsi aff001;  Lajos R. Kozák aff001
Působiště autorů: Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary aff001;  National Institute of Clinical Neurosciences, Budapest, Hungary aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0222720


Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.

Klíčová slova:

Data processing – Diffusion tensor imaging – Eigenvalues – Lesions – Magnetic resonance imaging – Microstructure – Neuroimaging – Simulation and modeling


1. Kwan P, Schachter SC, Brodie MJ. Drug-resistant epilepsy. The New England journal of medicine. 2011;365(10):919–26. doi: 10.1056/NEJMra1004418 21899452

2. McCagh J, Fisk JE, Baker GA. Epilepsy, psychosocial and cognitive functioning. Epilepsy Res. 2009;86(1):1–14. doi: 10.1016/j.eplepsyres.2009.04.007 19616921

3. Sisodiya SM. Malformations of cortical development: burdens and insights from important causes of human epilepsy. Lancet Neurol. 2004;3(1):29–38. 14693109

4. Blumcke I, Spreafico R, Haaker G, Coras R, Kobow K, Bien CG, et al. Histopathological Findings in Brain Tissue Obtained during Epilepsy Surgery. New England Journal of Medicine. 2017;377(17):1648–56. doi: 10.1056/NEJMoa1703784 29069555

5. Colombo N, Bargalló N, Redaelli D. Neuroimaging Evaluation in Neocortical Epilepsies: The ESNR Textbook. 2018. p. 1–35.

6. Urbach H. Long-Term Epilepsy Associated Tumors. In: Barkhof F, Jager R, Thurnher M, Rovira Cañellas A, editors. Clinical Neuroradiology: The ESNR Textbook. Cham: Springer International Publishing; 2018. p. 1–13.

7. Barkovich AJ, Guerrini R, Kuzniecky RI, Jackson GD, Dobyns WB. A developmental and genetic classification for malformations of cortical development: update 2012. Brain: a journal of neurology. 2012;135(5):1348–69.

8. Bien CG, Szinay M, Wagner J, Clusmann H, Becker AJ, Urbach H. Characteristics and surgical outcomes of patients with refractory magnetic resonance imaging-negative epilepsies. Archives of neurology. 2009;66(12):1491–9. doi: 10.1001/archneurol.2009.283 20008653

9. Hong SJ, Bernhardt BC, Caldairou B, Hall JA, Guiot MC, Schrader D, et al. Multimodal MRI profiling of focal cortical dysplasia type II. Neurology. 2017;88(8):734–42. doi: 10.1212/WNL.0000000000003632 28130467

10. Hong S-J, Kim H, Schrader D, Bernasconi N, Bernhardt BC, Bernasconi A. Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology. 2014;83(1):48–55. doi: 10.1212/WNL.0000000000000543 24898923

11. Urbach H, Mast H, Egger K, Mader I. Presurgical MR Imaging in Epilepsy. Clinical Neuroradiology. 2015;25(2):151–5. doi: 10.1007/s00062-014-0292-8

12. El Azami M, Hammers A, Jung J, Costes N, Bouet R, Lartizien C. Detection of Lesions Underlying Intractable Epilepsy on T1-Weighted MRI as an Outlier Detection Problem. PLOS ONE. 2016;11(9):e0161498. doi: 10.1371/journal.pone.0161498 27603778

13. Huppertz HJ, Grimm C, Fauser S, Kassubek J, Mader I, Hochmuth A, et al. Enhanced visualization of blurred gray-white matter junctions in focal cortical dysplasia by voxel-based 3D MRI analysis. Epilepsy Res. 2005;67(1–2):35–50. doi: 10.1016/j.eplepsyres.2005.07.009 16171974

14. Huppertz HJ, Wellmer J, Staack AM, Altenmuller DM, Urbach H, Kroll J. Voxel-based 3D MRI analysis helps to detect subtle forms of subcortical band heterotopia. Epilepsia. 2008;49(5):772–85. doi: 10.1111/j.1528-1167.2007.01436.x 18047585

15. Wagner J, Weber B, Urbach H, Elger CE, Huppertz HJ. Morphometric MRI analysis improves detection of focal cortical dysplasia type II. Brain: a journal of neurology. 2011;134(Pt 10):2844–54.

16. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophysical journal. 1994;66(1):259–67. doi: 10.1016/S0006-3495(94)80775-1 8130344

17. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of magnetic resonance Series B. 1996;111(3):209–19. 8661285

18. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensor MR imaging of the human brain. Radiology. 1996;201(3):637–48. doi: 10.1148/radiology.201.3.8939209 8939209

19. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magnetic resonance in medicine. 1996;36(6):893–906. doi: 10.1002/mrm.1910360612 8946355

20. Wu Y-C, Field AS, Duncan ID, Samsonov AA, Kondo Y, Tudorascu D, et al. High b-value and diffusion tensor imaging in a canine model of dysmyelination and brain maturation. NeuroImage. 2011;58(3):829–37. doi: 10.1016/j.neuroimage.2011.06.067 21777681

21. Sun SW, Liang HF, Le TQ, Armstrong RC, Cross AH, Song SK. Differential sensitivity of in vivo and ex vivo diffusion tensor imaging to evolving optic nerve injury in mice with retinal ischemia. Neuroimage. 2006;32(3):1195–204. doi: 10.1016/j.neuroimage.2006.04.212 16797189

22. Bava S, Thayer R, Jacobus J, Ward M, Jernigan TL, Tapert SF. Longitudinal characterization of white matter maturation during adolescence. Brain research. 2010;1327:38–46. doi: 10.1016/j.brainres.2010.02.066 20206151

23. Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage. 2008;40(2):570–82. doi: 10.1016/j.neuroimage.2007.12.035 18255316

24. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):1487–505. doi: 10.1016/j.neuroimage.2006.02.024 16624579

25. Sage CA, Van Hecke W, Peeters R, Sijbers J, Robberecht W, Parizel P, et al. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited. Human brain mapping. 2009;30(11):3657–75. doi: 10.1002/hbm.20794 19404990

26. Whelan CD, Alhusaini S, O'Hanlon E, Cheung M, Iyer PM, Meaney JF, et al. White matter alterations in patients with MRI-negative temporal lobe epilepsy and their asymptomatic siblings. Epilepsia. 2015;56(10):1551–61. doi: 10.1111/epi.13103 26249101

27. Alexander AL, Hasan K, Kindlmann G, Parker DL, Tsuruda JS. A geometric analysis of diffusion tensor measurements of the human brain. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2000;44(2):283–91.

28. Koay CG, Yeh PH, Ollinger JM, Irfanoglu MO, Pierpaoli C, Basser PJ, et al. Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging. Neuroimage. 2016;126:151–63. doi: 10.1016/j.neuroimage.2015.11.046 26638985

29. Chung S, Pelletier D, Sdika M, Lu Y, Berman JI, Henry RG. Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing. Neuroimage. 2008;39(4):1693–705. doi: 10.1016/j.neuroimage.2007.10.039 18082426

30. Filippi CG, Maxwell AW, Watts R. Magnetic resonance diffusion tensor imaging metrics in perilesional white matter among children with periventricular nodular gray matter heterotopia. Pediatric radiology. 2013;43(9):1196–203. doi: 10.1007/s00247-013-2677-2 23529629

31. Fonseca Vde C, Yasuda CL, Tedeschi GG, Betting LE, Cendes F. White matter abnormalities in patients with focal cortical dysplasia revealed by diffusion tensor imaging analysis in a voxelwise approach. Frontiers in neurology. 2012;3:121. doi: 10.3389/fneur.2012.00121 22855684

32. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine. 2005;53(6):1432–40. doi: 10.1002/mrm.20508 15906300

33. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage. 2012;61(4):1000–16. doi: 10.1016/j.neuroimage.2012.03.072 22484410

34. Bonilha L, Lee CY, Jensen JH, Tabesh A, Spampinato MV, Edwards JC, et al. Altered microstructure in temporal lobe epilepsy: a diffusional kurtosis imaging study. AJNR Am J Neuroradiol. 2015;36(4):719–24. doi: 10.3174/ajnr.A4185 25500311

35. Winston GP, Micallef C, Symms MR, Alexander DC, Duncan JS, Zhang H. Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy. Epilepsy Research. 2014;108(2):336–9. doi: 10.1016/j.eplepsyres.2013.11.004 24315018

36. Umesh Rudrapatna S, Wieloch T, Beirup K, Ruscher K, Mol W, Yanev P, et al. Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue chronically after experimental stroke? Comparisons with diffusion tensor imaging and histology. Neuroimage. 2014;97:363–73. doi: 10.1016/j.neuroimage.2014.04.013 24742916

37. Heller R, Golland Y, Malach R, Benjamini Y. Conjunction group analysis: An alternative to mixed/random effect analysis. NeuroImage. 2007;37(4):1178–85. doi: 10.1016/j.neuroimage.2007.05.051 17689266

38. Lazar NA, Luna B, Sweeney JA, Eddy WF. Combining Brains: A Survey of Methods for Statistical Pooling of Information. NeuroImage. 2002;16(2):538–50. doi: 10.1006/nimg.2002.1107 12030836

39. Hayasaka S, Du A-T, Duarte A, Kornak J, Jahng G-H, Weiner MW, et al. A non-parametric approach for co-analysis of multi-modal brain imaging data: Application to Alzheimer’s disease. NeuroImage. 2006;30(3):768–79. doi: 10.1016/j.neuroimage.2005.10.052 16412666

40. Naylor MG, Cardenas VA, Tosun D, Schuff N, Weiner M, Schwartzman A. Voxelwise multivariate analysis of multimodality magnetic resonance imaging. Human brain mapping. 2014;35(3):831–46. doi: 10.1002/hbm.22217 23408378

41. Young K, Govind V, Sharma K, Studholme C, Maudsley AA, Schuff N. Multivariate Statistical Mapping of Spectroscopic Imaging Data. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2010;63(1):20–4.

42. Gyebnar G, Szabo A, Siraly E, Fodor Z, Sakovics A, Salacz P, et al. What can DTI tell about early cognitive impairment?—Differentiation between MCI subtypes and healthy controls by diffusion tensor imaging. Psychiatry Res. 2017.

43. Avants B, Duda JT, Kim J, Zhang H, Pluta J, Gee JC, et al. Multivariate Analysis of Structural and Diffusion Imaging in Traumatic Brain Injury. Acad Radiol. 2008;15(11):1360–75. doi: 10.1016/j.acra.2008.07.007 18995188

44. Ahmed B, Brodley CE, Blackmon KE, Kuzniecky R, Barash G, Carlson C, et al. Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia. Epilepsy & behavior: E&B. 2015;48:21–8.

45. De Maesschalck R, Jouan-Rimbaud D, Massart DL. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems. 2000;50(1):1–18.

46. Gnanadesikan R, Kettenring JR. Robust Estimates, Residuals, and Outlier Detection with Multiresponse Data. Biometrics. 1972;28(1):81–124.

47. Xiang S, Nie F, Zhang C. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition. 2008;41(12):3600–12.

48. Mahalanobis PC. On the generalised distance in statistics. Proceedings of the National Institute of Science of India 1936;2(1):49–55.

49. Taxt T, Lundervold A. Multispectral analysis of the brain using magnetic resonance imaging. IEEE transactions on medical imaging. 1994;13(3):470–81. doi: 10.1109/42.310878 18218522

50. Caprihan A, Pearlson GD, Calhoun VD. Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements. NeuroImage. 2008;42(2):675–82. doi: 10.1016/j.neuroimage.2008.04.255 18571937

51. Kulikova S, Hertz-Pannier L, Dehaene-Lambertz G, Buzmakov A, Poupon C, Dubois J. Multi-parametric evaluation of the white matter maturation. Brain Struct Funct. 2015;220(6):3657–72. doi: 10.1007/s00429-014-0881-y 25183543

52. Lindemer ER, Salat DH, Smith EE, Nguyen K, Fischl B, Greve DN, et al. White matter signal abnormality quality differentiates mild cognitive impairment that converts to Alzheimer's disease from nonconverters. Neurobiol Aging. 2015;36(9):2447–57. doi: 10.1016/j.neurobiolaging.2015.05.011 26095760

53. Dean DC, Lange N 3rd, Travers BG, Prigge MB, Matsunami N, Kellett KA, et al. Multivariate characterization of white matter heterogeneity in autism spectrum disorder. NeuroImage Clinical. 2017;14:54–66. doi: 10.1016/j.nicl.2017.01.002 28138427

54. Penny KI. Appropriate Critical Values When Testing for a Single Multivariate Outlier by Using the Mahalanobis Distance. Journal of the Royal Statistical Society Series C (Applied Statistics). 1996;45(1):73–81.

55. Wilks SS. Multivariate Statistical Outliers. Sankhyā: The Indian Journal of Statistics, Series A (1961–2002). 1963;25(4):407–26.

56. Takeshita T, Nozawa S, Kimura F, editors. On the bias of Mahalanobis distance due to limited sample size effect. Document Analysis and Recognition, 1993, Proceedings of the Second International Conference on; 1993 20–22 Oct 1993.

57. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95–113. doi: 10.1016/j.neuroimage.2007.07.007 17761438

58. Friston KJ, Holmes A. P., Worsley K. J., Poline J. B., Frith C. D., and Frackowiak R. S. J. Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapp. 1995;2:189–210.

59. Simon L, Kozak LR, Simon V, Czobor P, Unoka Z, Szabo A, et al. Regional grey matter structure differences between transsexuals and healthy controls—a voxel based morphometry study. PloS one. 2013;8(12):e83947. doi: 10.1371/journal.pone.0083947 24391851

60. Fushimi Y, Okada T, Takagi Y, Funaki T, Takahashi JC, Miyamoto S, et al. Voxel Based Analysis of Surgical Revascularization for Moyamoya Disease: Pre- and Postoperative SPECT Studies. PloS one. 2016;11(2):e0148925. doi: 10.1371/journal.pone.0148925 26867219

61. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical transactions of the Royal Society of London Series B, Biological sciences. 2001;356(1412):1293–322. doi: 10.1098/rstb.2001.0915 11545704

62. Leemans A, Jeurissen B, Sijbers J, Jones D. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. 17th Annual Meeting of Intl Soc Mag Reson Med. 2009:3537.

63. Leemans A, Jones DK. The B-matrix must be rotated when correcting for subject motion in DTI data. Magnetic resonance in medicine. 2009;61(6):1336–49. doi: 10.1002/mrm.21890 19319973

64. Jezzard P, Barnett AS, Pierpaoli C. Characterization of and correction for eddy current artifacts in echo planar diffusion imaging. Magnetic resonance in medicine. 1998;39(5):801–12. doi: 10.1002/mrm.1910390518 9581612

65. Chang L-C, Jones DK, Pierpaoli C. RESTORE: Robust estimation of tensors by outlier rejection. Magnetic resonance in medicine. 2005;53(5):1088–95. doi: 10.1002/mrm.20426 15844157

66. Dyrby TB, Lundell H, Burke MW, Reislev NL, Paulson OB, Ptito M, et al. Interpolation of diffusion weighted imaging datasets. Neuroimage. 2014;103:202–13. doi: 10.1016/j.neuroimage.2014.09.005 25219332

67. Gaser C. Manual Computational Anatomy Toolbox—CAT12 2016 [Available from:

68. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44(1):83–98. doi: 10.1016/j.neuroimage.2008.03.061 18501637

69. Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 2004;23(3):1176–85. doi: 10.1016/j.neuroimage.2004.07.037 15528117

70. Tournier JD, Yeh CH, Calamante F, Cho KH, Connelly A, Lin CP. Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. Neuroimage. 2008;42(2):617–25. doi: 10.1016/j.neuroimage.2008.05.002 18583153

71. Beyer KS, Goldstein J, Ramakrishnan R, Shaft U. When Is ''Nearest Neighbor'' Meaningful? Proceedings of the 7th International Conference on Database Theory. 656271: Springer-Verlag; 1999. p. 217–35.

72. Schnitzer D. FA. Choosing the Metric in High-Dimensional Spaces Based on Hub Analysis. Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium. 2014.

73. Adler S, Lorio S, Jacques TS, Benova B, Gunny R, Cross JH, et al. Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI. NeuroImage Clinical. 2017;15:95–105. doi: 10.1016/j.nicl.2017.04.017 28491496

74. Van Hecke W, Leemans A, De Backer S, Jeurissen B, Parizel PM, Sijbers J. Comparing isotropic and anisotropic smoothing for voxel-based DTI analyses: A simulation study. Human brain mapping. 2010;31(1):98–114. doi: 10.1002/hbm.20848 19593775

75. Bhaganagarapu K, Jackson G, Abbott D. An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI. Frontiers in Human Neuroscience. 2013;7(343).

76. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage. 2014;90:449–68. doi: 10.1016/j.neuroimage.2013.11.046 24389422

77. van Diessen E, Diederen SJ, Braun KP, Jansen FE, Stam CJ. Functional and structural brain networks in epilepsy: what have we learned? Epilepsia. 2013;54(11):1855–65. doi: 10.1111/epi.12350 24032627

78. Jin B, Krishnan B, Adler S, Wagstyl K, Hu W, Jones S, et al. Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning. Epilepsia. 2018;59(5):982–92. doi: 10.1111/epi.14064 29637549

79. Jeong J-W, Asano E, Juhász C, Chugani HT. Quantification of Primary Motor Pathways Using Diffusion MRI Tractography and Its Application to Predict Postoperative Motor Deficits in Children With Focal Epilepsy. Human brain mapping. 2014;35(7):3216–26. doi: 10.1002/hbm.22396 24142581

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