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Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects

Abdelrahman, Hiba Abuelgasim Fadlelmoula Ubukata, Shiho Ueda, Keita Fujimoto, Gaku Oishi, Naoya Aso, Toshihiko Murai, Toshiya 京都大学 DOI:10.2147/ndt.s354265

2022.08

概要

Aim: Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls. Methods: Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers. Results: Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09. Conclusion: These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.

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参考文献

1. Hyder AA, Wunderlich CA, Puvanachandra P, Gururaj G, Kobusingye OC. The impact of traumatic brain injuries: a global perspective. NeuroRehabilitation. 2007;22(5):341–353. doi:10.3233/nre-2007-22502

2. Koponen S, Taiminen T, Portin R, et al. Axis I and II psychiatric disorders after traumatic brain injury: a 30-year follow-up study. Am J Psychiatry. 2002;159(8):1315–1321. doi:10.1176/appi.ajp.159.8.1315

3. Holsinger T, Steffens DC, Phillips C, et al. Head injury in early adulthood and the lifetime risk of depression. Arch Gen Psychiatry. 2002;59 (1):17–22. doi:10.1001/archpsyc.59.1.17

4. Smith DH, Meaney DF. Axonal damage in traumatic brain injury. Neuroscientist. 2000;6(6):483–495. doi:10.1177/107385840000600611

5. Browne KD, Chen X-H, Meaney DF, Smith DH. Mild traumatic brain injury and diffuse axonal injury in swine. J Neurotrauma. 2011;28 (9):1747–1755. doi:10.1089/neu.2011.1913

6. Gennarelli TA, Thibault LE, Adams JH, Graham DI, Thompson CJ, Marcincin RP. Diffuse axonal injury and traumatic coma in the primate. Ann Neurol. 1982;12(6):564–574. doi:10.1002/ana.410120611

7. Werner C, Engelhard K. Pathophysiology of traumatic brain injury. Br J Anaesth. 2007;99(1):4–9. doi:10.1093/bja/aem131

8. Hulkower M, Poliak D, Rosenbaum S, Zimmerman M, Lipton ML. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. 2013;34(11):2064–2074. doi:10.3174/ajnr.A3395

9. Klimova A, Korgaonkar MS, Whitford T, Bryant RA. Diffusion tensor imaging analysis of mild traumatic brain injury and posttraumatic stress disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(1):81–90. doi:10.1016/j.bpsc.2018.10.004

10. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci. 2008;34(1):51–61. doi:10.1007/s12031-007-0029-0

11. Kinnunen KM, Greenwood R, Powell JH, et al. White matter damage and cognitive impairment after traumatic brain injury. Brain. 2010;134 (2):449–463. doi:10.1093/brain/awq347

12. Niogi SN, Mukherjee P. Diffusion tensor imaging of mild traumatic brain injury. J Head Trauma Rehabil. 2010;25(4):241–255. doi:10.1097/ HTR.0b013e3181e52c2a

13. Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics. 2007;4(3):316–329. doi:10.1016/j. nurt.2007.05.011

14. Douglas DB, Iv M, Douglas PK, et al. Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging. 2015;24(5):241. doi:10.3390/medsci7010002

15. Liu Y, Duan Y, He Y, et al. Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: a TBSS study. Eur J Radiol. 2012;81(10):2826–2832. doi:10.1016/j.ejrad.2011.11.022

16. Porter EJ, Counsell SJ, Edwards AD, Allsop J, Azzopardi D. Tract-based spatial statistics of magnetic resonance images to assess disease and treatment effects in perinatal asphyxial encephalopathy. Pediatr Res. 2010;68(3):205–209. doi:10.1203/PDR.0b013e3181e9f1ba

17. Chen, H.J., Gao, Y.Q., Che, C.H., Lin, H. and Ruan, X.L., Diffusion tensor imaging with tract-based spatial statistics reveals white matter abnormalities in patients with vascular cognitive impairment. Front Neuroanat. 2018;12:53.

18. Aung WY, Mar S, Benzinger TL. Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging Med. 2013;5(5):427.

19. Shenton ME, Hamoda H, Schneiderman J, et al. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav. 2012;6(2):137–192. doi:10.1007/s11682-012-9156-5

20. Inglese M, Makani S, Johnson G, et al. Diffuse axonal injury in mild traumatic brain injury: a diffusion tensor imaging study. J Neurosurg. 2005;103(2):298–303. doi:10.3171/jns.2005.103.2.0298

21. Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain. 2007;130(10):2508–2519. doi:10.1093/brain/awm216

22. Chang MC, Kim SH, Kim OL, Bai DS, Jang SH. The relation between fornix injury and memory impairment in patients with diffuse axonal injury: a diffusion tensor imaging study. NeuroRehabilitation. 2010;26(4):347–353. doi:10.3233/NRE-2010-0572

23. Kasahara K, Hashimoto K, Abo M, Senoo A. Voxel-and atlas-based analysis of diffusion tensor imaging may reveal focal axonal injuries in mild traumatic brain injury—comparison with diffuse axonal injury. Magn Reson Imaging. 2012;30(4):496–505. doi:10.1016/j.mri.2011.12.018

24. Kumar R, Gupta RK, Husain M, et al. Comparative evaluation of corpus callosum DTI metrics in acute mild and moderate traumatic brain injury: its correlation with neuropsychometric tests. Brain Inj. 2009;23(7–8):675–685. doi:10.1080/02699050903014915

25. Aoki Y, Inokuchi R, Gunshin M, Yahagi N, Suwa H. Diffusion tensor imaging studies of mild traumatic brain injury: a meta-analysis. J Neurol Neurosurg Psychiatry. 2012;83(9):870–876. doi:10.1136/jnnp-2012-302742

26. Dodd AB, Epstein K, Ling JM, Mayer AR. Diffusion tensor imaging findings in semi-acute mild traumatic brain injury. J Neurotrauma. 2014;31 (14):1235–1248.

27. Gardner A, Kay-Lambkin F, Stanwell P, et al. A systematic review of diffusion tensor imaging findings in sports-related concussion. J Neurotrauma. 2012;29:2521–2538.

28. Arfanakis K, Haughton VM, Carew JD, Rogers BP, Dempsey RJ, Meyerand ME. Diffusion tensor MR imaging in diffuse axonal injury. AJNR Am J Neuroradiol. 2002;23(5):794–802.

29. Kennedy MR, Wozniak JR, Muetzel RL, et al. White matter and neurocognitive changes in adults with chronic traumatic brain injury. J Int Neuropsychol Soc. 2009;15(1):130–136. doi:10.1017/S1355617708090024

30. Mohamed AZ, Cumming P, Nasrallah FA. White matter alterations are associated with cognitive dysfunction decades after moderate-to-severe traumatic brain injury and/or posttraumatic stress disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6(11):1100–1109. doi:10.1016/j.bpsc.2021.04.014

31. Perez AM, Adler J, Kulkarni N, et al. Longitudinal white matter changes after traumatic axonal injury. J Neurotrauma. 2014;31(17):1478–1485. doi:10.1089/neu.2013.3216

32. Cubon VA, Putukian M, Boyer C, Dettwiler A. A diffusion tensor imaging study on the white matter skeleton in individuals with sports-related concussion. J Neurotrauma. 2011;28(2):189–201. doi:10.1089/neu.2010.1430

33. Jorge RE, Acion L, White T, et al. White matter abnormalities in veterans with mild traumatic brain injury. AJP. 2012;169(12):1284–1291. doi:10.1176/appi.ajp.2012.12050600

34. Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36(4):1140–1152. doi:10.1016/j.neubiorev.2012.01.004

35. Linden DE. The challenges and promise of neuroimaging in psychiatry. Neuron. 2012;73(1):8–22. doi:10.1016/j.neuron.2011.12.014

36. Zarogianni E, Moorhead TW, Lawrie SM. Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. Neuroimage Clin. 2013;3:279–289. doi:10.1016/j.nicl.2013.09.003

37. Saha A, Fadaiefard P, Rabski JE, Sadeghian A, Cusimano MD. Machine learning applications using diffusion tensor imaging of human brain: a PubMed literature review. arXiv preprint arXiv:2012.10517; 2020.

38. Vergara Victor M, MayerAndrew R, KiehlKent A. Detection of mild traumatic brain injury by machine learning classification using resting state functional network connectivity and fractional anisotropy. J Neurotrauma. 2017. doi:10.1089/neu.2016.4526

39. Zheng ZS, Reggente N, Lutkenhoff E, Owen AM, Monti MM. Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning. Hum Brain Mapp. 2017;38(1):431–443. doi:10.1002/hbm.23370

40. Hellyer PJ, Leech R, Ham TE, Bonnelle V, Sharp DJ. Individual prediction of white matter injury following traumatic brain injury. Ann Neurol. 2013;73(4):489–499. doi:10.1002/ana.23824

41. Fagerholm ED, Hellyer PJ, Scott G, Leech R, Sharp DJ. Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain. 2015;138(6):1696–1709. doi:10.1093/brain/awv075

42. Goswami R, Dufort P, Tartaglia MC, et al. Frontotemporal correlates of impulsivity and machine learning in retired professional athletes with a history of multiple concussions. Brain Struct Funct. 2016;221(4):1911–1925. doi:10.1007/s00429-015-1012-0

43. Mitra J, Shen K-K, Ghose S, et al. Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. Neuroimage. 2016;129:247–259. doi:10.1016/j.neuroimage.2016.01.056

44. Lui YW, Xue Y, Kenul D, Ge Y, Grossman RI, Wang Y. Classification algorithms using multiple MRI features in mild traumatic brain injury. Neurology. 2014;83(14):1235–1240. doi:10.1212/WNL.0000000000000834

45. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2014;23:S208–S19. doi:10.1016/j.neuroimage.2004.07.051.

46. Andersson JL, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–1078.

47. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM. Bayesian analysis of neuroimaging data in FSL. Neuroimage. 2009;45:S173–S86.

48. Ennis DB, Kindlmann G. Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn Reson Med. 2006;55 (1):136–146.

49. Behrens TE, Woolrich MW, Jenkinson M, Johansen–Berg H, Nunes RG, Clare S, Matthews PM, Brady JM, Smith SM. Characterization and propagation of uncertainty in diffusion–weighted MR imaging. Magn Reson Med. 2003;50:1077–88. doi:10.1002/mrm.10609.

50. Mori S, Wakana S, Van Zijl PC, Nagae-Poetscher L. MRI Atlas of Human White Matter. Elsevier; 2005.

51. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ Matthews PM. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:1487 505. doi:10.1016/j.neuroimage.2006.02.024

52. Andersson JL, Jenkinson M, Smith S. Non-linear registration aka spatial normalisation FMRIB technial report TR07JA2. FMRIB Analysis Group of the University of Oxford; 2007.

53. Mwangi B, Tian TS, Soares JC A review of feature reduction techniques in neuroimaging. Neuroinformatics. 2014;12:229–44. doi:10.1007/s12021-013- 9204-3

54. Passos IC, Mwangi B, Kapczinski F Big data analytics and machine learning: 2015 and beyond. Lancet Psychiatry. 2016;3:13–5. doi:10.1016/ S2215-0366(15)00549-0

55. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82. doi:10.1162/153244303322753616

56. Joliffe I. Principal Component Analysis. NY: Springer; 2002.

57. O’Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B, O’Neill D, Bartley M, Collins DR. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PloS one. 2012;7:e32441. doi:10.1371/journal.pone.0032441

58. Namiki J, Yamazaki M, Funabiki T, et al. Difficulty and inaccuracy of assessment of the consciousness level by the Glasgow Coma Scale: comparison with the Japan Coma Scale. J Jpn Soc Emerg Med. 2007;10:20–25.

59. Mayer AR, Ling JM, Yang Z, Pena A, Yeo RA, Klimaj S. Diffusion abnormalities in pediatric mild traumatic brain injury. J Neurosci. 2012;32 (50):17961–17969. doi:10.1523/JNEUROSCI.3379-12.2012

60. Mannell MALJ, Gasparovic C, Phillips JP, Doezema D, Reichard R, Yeo RA. A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology. 2010;74(8):643–650. doi:10.1212/WNL.0b013e3181d0ccdd

61. Wallace EJ, Mathias JL, Ward L. Diffusion tensor imaging changes following mild, moderate and severe adult traumatic brain injury: a meta-analysis. Brain Imaging Behav. 2018;12(6):1607–1621. doi:10.1007/s11682-018-9823-2

62. Nakayama N, Okumura A, Shinoda J, et al. Evidence for white matter disruption in traumatic brain injury without macroscopic lesions. J Neurol Neurosurg Psychiatry. 2006;77(7):850–855. doi:10.1136/jnnp.2005.077875

63. Xu J, Rasmussen I-A, Lagopoulos J, Håberg A. Diffuse axonal injury in severe traumatic brain injury visualized using high-resolution diffusion tensor imaging. J Neurotrauma. 2007;24(5):753–765. doi:10.1089/neu.2006.0208

64. Main KL, Soman S, Pestilli F, et al. DTI measures identify mild and moderate TBI cases among patients with complex health problems: a receiver operating characteristic analysis of US veterans. NeuroImage Clin. 2017;16:1–6. doi:10.1016/j.nicl.2017.06.031

65. Hasan KM, Narayana PA. Retrospective measurement of the diffusion tensor eigenvalues from diffusion anisotropy and mean diffusivity in DTI. Magn Reson Med. 2006;56(1):130–137. doi:10.1002/mrm.20935

66. Martinez BI, Stabenfeldt SE. Current trends in biomarker discovery and analysis tools for traumatic brain injury. J Biol Eng. 2019;13(1):16. doi:10.1186/s13036-019-0145-8

67. Mateos-Perez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: a review of machine learning applications. Neuroimage Clin. 2018;20:506–522. doi:10.1016/j.nicl.2018.08.019

68. Harrington DL, Hsu PY, Theilmann RJ, et al. Detection of chronic blast-related mild traumatic brain injury with diffusion tensor imaging and support vector machines. Diagnostics. 2022;12(4):987. doi:10.3390/diagnostics12040987

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Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects

概要

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