Traumatic and nontraumatic spinal cord injury: pathological insights from neuroimaging


Pathophysiological changes in the spinal cord white and grey matter resulting from injury can be observed with MRI techniques. These techniques provide sensitive markers of macrostructural and microstructural tissue integrity, which correlate with histological findings. Spinal cord MRI findings in traumatic spinal cord injury (tSCI) and nontraumatic spinal cord injury — the most common form of which is degenerative cervical myelopathy (DCM) — have provided important insights into the pathophysiological processes taking place not just at the focal injury site but also rostral and caudal to the spinal injury. Although tSCI and DCM have different aetiologies, they show similar degrees of spinal cord pathology remote from the injury site, suggesting the involvement of similar secondary degenerative mechanisms. Advanced quantitative MRI protocols that are sensitive to spinal cord pathology have the potential to improve diagnosis and, more importantly, predict outcomes in patients with tSCI or nontraumatic spinal cord injury. This Review describes the insights into tSCI and DCM that have been revealed by neuroimaging and outlines current activities and future directions for the field.

Key points

  • Traumatic spinal cord injury (tSCI) and (nontraumatic) degenerative cervical myelopathy (DCM) have different aetiologies but lead to similar secondary degenerative changes in the spinal cord.

  • Advanced MRI techniques have been used to improve the sensitivity and specificity with which spinal degenerative changes can be detected.

  • Diffusion tensor imaging (DTI) is an advanced MRI technique that characterizes tissue microstructure by measuring the water diffusion profile of the tissue.

  • In tSCI and DCM, DTI has been used to identify changes in microstructure above and below the lesion or stenosis that are not visible with conventional MRI.

  • Spinal cord DTI metrics correlate with neurological and electrophysiological outcomes after tSCI and DCM.

  • Further advances in data acquisition, processing and modelling are needed to improve the reliability and specificity of quantitative MRI metrics as predictors of patient outcome.

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Fig. 1: Primary and secondary injury mechanisms in DCM and tSCI.
Fig. 2: Radiological characteristics of DCM and tSCI.
Fig. 3: Main concepts of DTI.
Fig. 4: Voxel-wise analysis of DTI metrics in chronic tSCI.
Fig. 5: Voxel-wise analysis of DTI metrics in DCM.


  1. 1.

    Ahuja, C. S. et al. Traumatic spinal cord injury. Nat. Rev. Dis. Primers 3, 17018 (2017).

    PubMed  Google Scholar 

  2. 2.

    Kato, S. & Fehlings, M. Degenerative cervical myelopathy. Curr. Rev. Musculoskelet. Med. 9, 263–271 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Akter, F. & Kotter, M. Pathobiology of degenerative cervical myelopathy. Neurosurg. Clin. N. Am. 29, 13–19 (2018).

    PubMed  Google Scholar 

  4. 4.

    Buss, A. et al. Gradual loss of myelin and formation of an astrocytic scar during Wallerian degeneration in the human spinal cord. Brain 127, 34–44 (2004).

    CAS  PubMed  Google Scholar 

  5. 5.

    Fehlings, M. G. et al. A clinical practice guideline for the management of patients with degenerative cervical myelopathy: recommendations for patients with mild, moderate, and severe disease and nonmyelopathic patients with evidence of cord compression. Glob. Spine J. 7, 70S–83S (2017).

    Google Scholar 

  6. 6.

    Fehlings, M. G. et al. A clinical practice guideline for the management of patients with acute spinal cord injury: recommendations on the role of baseline magnetic resonance imaging in clinical decision making and outcome prediction. Glob. Spine J. 7, 221S–230S (2017).

    Google Scholar 

  7. 7.

    Prados, F. et al. Spinal cord grey matter segmentation challenge. Neuroimage 152, 312–329 (2017).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Martin, A. R. et al. Translating state-of-the-art spinal cord MRI techniques to clinical use: a systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI. NeuroImage. Clin. 10, 192–238 (2016).

    PubMed  Google Scholar 

  9. 9.

    Basser, P. J., Mattiello, J. & LeBihan, D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. 103, 247–254 (1994).

    CAS  Google Scholar 

  10. 10.

    Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A. & Di Chiro, G. Diffusion tensor MR imaging of the human brain. Radiology 201, 637–648 (1996).

    CAS  PubMed  Google Scholar 

  11. 11.

    Beaulieu, C. & Allen, P. S. Determinants of anisotropic water diffusion in nerves. Magn. Reson. Med. 31, 394–400 (1994).

    CAS  PubMed  Google Scholar 

  12. 12.

    Wheeler-Kingshott, C. A. et al. The current state-of-the-art of spinal cord imaging: applications. Neuroimage 84, 1082–1093 (2014).

    CAS  PubMed  Google Scholar 

  13. 13.

    Grabher, P., Mohammadi, S., David, G. & Freund, P. Neurodegeneration in the spinal ventral horn prior to motor impairment in cervical spondylotic myelopathy. J. Neurotrauma 34, 2329–2334 (2017).

    PubMed  Google Scholar 

  14. 14.

    Allen, A. R. Remarks on the histopathological changes in the spinal cord due to impact: an experimental study. J. Nerv. Ment. Dis. 41, 141–147 (1914).

    Google Scholar 

  15. 15.

    Dusart, I. & Schwab, M. E. Secondary cell death and the inflammatory reaction after dorsal hemisection of the rat spinal cord. Eur. J. Neurosci. 6, 712–724 (1994).

    CAS  PubMed  Google Scholar 

  16. 16.

    Karadimas, S. K. et al. A novel experimental model of cervical spondylotic myelopathy (CSM) to facilitate translational research. Neurobiol. Dis. 54, 43–58 (2013).

    CAS  PubMed  Google Scholar 

  17. 17.

    Dhillon, R. S. et al. Axonal plasticity underpins the functional recovery following surgical decompression in a rat model of cervical spondylotic myelopathy. Acta Neuropathol. Commun. 4, 89 (2016).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Karadimas, S. K. et al. Riluzole blocks perioperative ischemia-reperfusion injury and enhances postdecompression outcomes in cervical spondylotic myelopathy. Sci. Transl Med. 7, 316ra194 (2015).

    PubMed  Google Scholar 

  19. 19.

    Ohshio, I., Hatayama, A., Kaneda, K., Takahara, M. & Nagashima, K. Correlation between histopathologic features and magnetic resonance images of spinal cord lesions. Spine 18, 1140–1149 (1993).

    CAS  PubMed  Google Scholar 

  20. 20.

    Fukuoka, M., Matsui, N., Otsuka, T., Murakami, M. & Seo, Y. Magnetic resonance imaging of experimental subacute spinal cord compression. Spine 23, 1540–1549 (1998).

    CAS  PubMed  Google Scholar 

  21. 21.

    Hackney, D. B., Finkelstein, S. D., Hand, C. M., Markowitz, R. S. & Black, P. Postmortem magnetic resonance imaging of experimental spinal cord injury. Neurosurgery 35, 1104–1111 (1994).

    CAS  PubMed  Google Scholar 

  22. 22.

    Takahashi, T., Suto, Y., Kato, S. & Ohama, E. Experimental acute dorsal compression of cat spinal cord: correlation of magnetic resonance signal intensity with spinal cord evoked potentials and morphology. Spine 21, 166–173 (1996).

    CAS  PubMed  Google Scholar 

  23. 23.

    FALCI, S. et al. Obliteration of a posttraumatic spinal cord cyst with solid human embryonic spinal cord grafts: first clinical attempt. J. Neurotrauma 14, 875–884 (1997).

    CAS  PubMed  Google Scholar 

  24. 24.

    Ito, D. et al. Prognostic value of magnetic resonance imaging in dogs with paraplegia caused by thoracolumbar intervertebral disk extrusion: 77 cases (2000–2003). J. Am. Vet. Med. Assoc. 227, 1454–1460 (2005).

    PubMed  Google Scholar 

  25. 25.

    Budde, M. D. et al. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn. Reson. Med. 57, 688–695 (2007).

    PubMed  Google Scholar 

  26. 26.

    Kim, J. H. et al. Noninvasive diffusion tensor imaging of evolving white matter pathology in a mouse model of acute spinal cord injury. Magn. Reson. Med. 58, 253–260 (2007).

    PubMed  Google Scholar 

  27. 27.

    Kozlowski, P. et al. Characterizing white matter damage in rat spinal cord with quantitative MRI and histology. J. Neurotrauma 25, 653–676 (2008).

    PubMed  Google Scholar 

  28. 28.

    Xie, M., Wang, Q., Wu, T.-H., Song, S.-K. & Sun, S.-W. Delayed axonal degeneration in slow Wallerian degeneration mutant mice detected using diffusion tensor imaging. Neuroscience 197, 339–347 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Brennan, F. H., Cowin, G. J., Kurniawan, N. D. & Ruitenberg, M. J. Longitudinal assessment of white matter pathology in the injured mouse spinal cord through ultra-high field (16.4T) in vivo diffusion tensor imaging. Neuroimage 82, 574–585 (2013).

    PubMed  Google Scholar 

  30. 30.

    Zhang, J. et al. Diffusion tensor magnetic resonance imaging of Wallerian degeneration in rat spinal cord after dorsal root axotomy. J. Neurosci. 29, 3160–3171 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Song, S.-K. et al. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage 20, 1714–1722 (2003).

    PubMed  Google Scholar 

  32. 32.

    Sun, S.-W., Liang, H.-F., Cross, A. H. & Song, S.-K. Evolving Wallerian degeneration after transient retinal ischemia in mice characterized by diffusion tensor imaging. Neuroimage 40, 1–10 (2008).

    CAS  PubMed  Google Scholar 

  33. 33.

    Huber, E., Lachappelle, P., Sutter, R., Curt, A. & Freund, P. Are midsagittal tissue bridges predictive of outcome after cervical spinal cord injury? Ann. Neurol. 81, 740–748 (2017).

    PubMed  Google Scholar 

  34. 34.

    Vallotton, K. et al. Width and neurophysiologic properties of tissue bridges predict recovery after cervical injury. Neurology 92, e2793–e2802 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Farhadi, H. F. et al. Impact of admission imaging findings on neurological outcomes in acute cervical traumatic spinal cord injury. J. Neurotrauma 35, 1398–1406 (2018).

    PubMed  Google Scholar 

  36. 36.

    Talbott, J. F. et al. The brain and spinal injury center score: a novel, simple, and reproducible method for assessing the severity of acute cervical spinal cord injury with axial T2-weighted MRI findings. J. Neurosurg. Spine 23, 495–504 (2015).

    PubMed  Google Scholar 

  37. 37.

    Pfyffer, D., Huber, E., Sutter, R., Curt, A. & Freund, P. Tissue bridges predict recovery after traumatic and ischemic thoracic spinal cord injury. Neurology 93, e1550 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Freund, P. et al. MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study. Lancet Neurol. 12, 873–881 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Grabher, P. et al. Tracking sensory system atrophy and outcome prediction in spinal cord injury. Ann. Neurol. 78, 751–761 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ziegler, G. et al. Progressive neurodegeneration following spinal cord injury. Neurology 90, e1257–e1266 (2018).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Freund, P. et al. Disability, atrophy and cortical reorganization following spinal cord injury. Brain 134, 1610–1622 (2011).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Huber, E. et al. Dorsal and ventral horn atrophy is associated with clinical outcome after spinal cord injury. Neurology 90, e1510–e1522 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Lundell, H. et al. Independent spinal cord atrophy measures correlate to motor and sensory deficits in individuals with spinal cord injury. Spinal Cord 49, 70–75 (2011).

    CAS  PubMed  Google Scholar 

  44. 44.

    David, G. et al. In vivo evidence of remote neural degeneration in the lumbar enlargement after cervical injury. Neurology 92, e1367–e1377 (2019).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Huber, E., Curt, A. & Freund, P. Tracking trauma-induced structural and functional changes above the level of spinal cord injury. Curr. Opin. Neurol. 28, 365–372 (2015).

    CAS  PubMed  Google Scholar 

  46. 46.

    Freund, P. et al. MRI in traumatic spinal cord injury: from clinical assessment to neuroimaging biomarkers. Lancet Neurol. (2019).

    Article  PubMed  Google Scholar 

  47. 47.

    Kucher, K. et al. First-in-man intrathecal application of neurite growth-promoting anti-nogo-A antibodies in acute spinal cord injury. Neurorehabil. Neural Repair 32, 578–589 (2018).

    PubMed  Google Scholar 

  48. 48.

    Freund, P. et al. Nogo-A-specific antibody treatment enhances sprouting and functional recovery after cervical lesion in adult primates. Nat. Med. 12, 790–792 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Jungmann, P. M., Agten, C. A., Pfirrmann, C. W. & Sutter, R. Advances in MRI around metal. J. Magn. Reson. Imaging 46, 972–991 (2017).

    PubMed  Google Scholar 

  50. 50.

    Jungmann, P. M. et al. View-angle tilting and slice-encoding metal artifact correction for artifact reduction in MRI: experimental sequence optimization for orthopaedic tumor endoprostheses and clinical application. PLOS ONE 10, e0124922 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Lu, W., Pauly, K. B., Gold, G. E., Pauly, J. M. & Hargreaves, B. A. SEMAC: slice encoding for metal artifact correction in MRI. Magn. Reson. Med. 62, 66–76 (2009).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Song, K. D., Yoon, Y. C. & Park, J. Reducing metallic artefacts in post-operative spinal imaging: slice encoding for metal artefact correction with dual-source parallel radiofrequency excitation MRI at 3.0T. Br. J. Radiol. 86, 20120524 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Worters, P. W., Sung, K., Stevens, K. J., Koch, K. M. & Hargreaves, B. A. Compressed-sensing multispectral imaging of the postoperative spine. J. Magn. Reson. Imaging 37, 243–248 (2013).

    PubMed  Google Scholar 

  54. 54.

    Shanmuganathan, K., Gullapalli, R. P., Zhuo, J. & Mirvis, S. E. Diffusion tensor MR imaging in cervical spine trauma. AJNR Am. J. Neuroradiol. 29, 655–659 (2008).

    CAS  PubMed  Google Scholar 

  55. 55.

    Cheran, S. et al. Correlation of MR diffusion tensor imaging parameters with ASIA motor scores in hemorrhagic and nonhemorrhagic acute spinal cord injury. J. Neurotrauma 28, 1881–1892 (2011).

    PubMed  Google Scholar 

  56. 56.

    Chang, Y., Jung, T.-D., Yoo, D. S. & Hyun, J. K. Diffusion tensor imaging and fiber tractography of patients with cervical spinal cord injury. J. Neurotrauma 27, 2033–2040 (2010).

    PubMed  Google Scholar 

  57. 57.

    Schwartz, E. D. et al. Apparent diffusion coefficients in spinal cord transplants and surrounding white matter correlate with degree of axonal dieback after injury in rats. AJNR. Am. J. Neuroradiol. 26, 7–18 (2005).

    PubMed  Google Scholar 

  58. 58.

    Deo, A. A., Grill, R. J., Hasan, K. M. & Narayana, P. A. In vivo serial diffusion tensor imaging of experimental spinal cord injury. J. Neurosci. Res. 83, 801–810 (2006).

    CAS  PubMed  Google Scholar 

  59. 59.

    Ellingson, B. M., Ulmer, J. L., Kurpad, S. N. & Schmit, B. D. Diffusion tensor MR imaging in chronic spinal cord injury. AJNR Am. J. Neuroradiol. 29, 1976–1982 (2008).

    CAS  PubMed  Google Scholar 

  60. 60.

    Koskinen, E. et al. Assessing the state of chronic spinal cord injury using diffusion tensor imaging. J. Neurotrauma 30, 1587–1595 (2013).

    PubMed  Google Scholar 

  61. 61.

    Vedantam, A., Eckardt, G., Wang, M. C., Schmit, B. D. & Kurpad, S. N. Clinical correlates of high cervical fractional anisotropy in acute cervical spinal cord injury. World Neurosurg. 83, 824–828 (2015).

    PubMed  Google Scholar 

  62. 62.

    Petersen, J. A. et al. Chronic cervical spinal cord injury: DTI correlates with clinical and electrophysiological measures. J. Neurotrauma 29, 1556–1566 (2012).

    PubMed  Google Scholar 

  63. 63.

    Loy, D. N. et al. Diffusion tensor imaging predicts hyperacute spinal cord injury severity. J. Neurotrauma 24, 979–990 (2007).

    PubMed  Google Scholar 

  64. 64.

    Li, X.-H. et al. Timing of diffusion tensor imaging in the acute spinal cord injury of rats. Sci. Rep. 5, 12639 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Freund, P. et al. Degeneration of the injured cervical cord is associated with remote changes in corticospinal tract integrity and upper limb impairment. PLOS ONE 7, e51729 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Cohen-Adad, J. et al. Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI. Neuroimage 55, 1024–1033 (2011).

    CAS  PubMed  Google Scholar 

  67. 67.

    Pearse, D. D. et al. Histopathological and behavioral characterization of a novel cervical spinal cord displacement contusion injury in the rat. J. Neurotrauma 22, 680–702 (2005).

    CAS  PubMed  Google Scholar 

  68. 68.

    Anderson, K. D., Borisoff, J. F., Johnson, R. D., Stiens, S. A. & Elliott, S. L. The impact of spinal cord injury on sexual function: concerns of the general population. Spinal Cord 45, 328–337 (2007).

    CAS  PubMed  Google Scholar 

  69. 69.

    Yiannakas, M. C. et al. Reduced field-of-view diffusion-weighted imaging of the lumbosacral enlargement: a pilot in vivo study of the healthy spinal cord at 3T. PLOS ONE 11, e0164890 (2016).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Schwab, M. E. & Bartholdi, D. Degeneration and regeneration of axons in the lesioned spinal cord. Physiol. Rev. 76, 319–370 (1996).

    CAS  PubMed  Google Scholar 

  71. 71.

    O’Dell, D. R. et al. Midsagittal tissue bridges are associated with walking ability in incomplete spinal cord injury: a magnetic resonance imaging case series. J. Spinal Cord Med. 22, 1–4 (2018).

    Google Scholar 

  72. 72.

    Koskinen, E. A. et al. Clinical correlates of cerebral diffusion tensor imaging findings in chronic traumatic spinal cord injury. Spinal Cord 52, 202–208 (2014).

    CAS  PubMed  Google Scholar 

  73. 73.

    Shanmuganathan, K. et al. Diffusion tensor imaging parameter obtained during acute blunt cervical spinal cord injury in predicting long-term outcome. J. Neurotrauma 34, 2964–2971 (2017).

    PubMed  Google Scholar 

  74. 74.

    Kim, J. H. et al. Diffusion tensor imaging at 3 hours after traumatic spinal cord injury predicts long-term locomotor recovery. J. Neurotrauma 27, 587–598 (2010).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Martin, A. R. et al. Monitoring for myelopathic progression with multiparametric quantitative MRI. PLOS ONE 13, e0195733 (2018).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Nouri, A. et al. MRI analysis of the combined prospectively collected AOSpine North America and international data: the prevalence and spectrum of pathologies in a global cohort of patients with degenerative cervical myelopathy. Spine 42, 1058–1067 (2017).

    PubMed  Google Scholar 

  77. 77.

    Harrop, J. S. et al. Cervical myelopathy. Spine 35, 620–624 (2010).

    PubMed  Google Scholar 

  78. 78.

    Martin, A. R. et al. A novel MRI biomarker of spinal cord white matter injury: t2*-weighted white matter to gray matter signal intensity ratio. AJNR Am. J. Neuroradiol. 38, 1266–1273 (2017).

    CAS  PubMed  Google Scholar 

  79. 79.

    Grabher, P. et al. Voxel-based analysis of grey and white matter degeneration in cervical spondylotic myelopathy. Sci. Rep. 6, 24636 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Martin, A. R. et al. Can microstructural MRI detect subclinical tissue injury in subjects with asymptomatic cervical spinal cord compression? A prospective cohort study. BMJ Open 8, e019809 (2018).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Martin, A. R. et al. Imaging evaluation of degenerative cervical myelopathy. Neurosurg. Clin. N. Am. 29, 33–45 (2018).

    PubMed  Google Scholar 

  82. 82.

    Facon, D. et al. MR diffusion tensor imaging and fiber tracking in spinal cord compression. AJNR Am. J .Neuroradiol. 26, 1587–1594 (2005).

    PubMed  Google Scholar 

  83. 83.

    Ford, J. C., Hackney, D. B., Lavi, E., Phillips, M. & Patel, U. Dependence of apparent diffusion coefficients on axonal spacing, membrane permeability, and diffusion time in spinal cord white matter. J. Magn. Reson. Imaging 8, 775–782 (1998).

    CAS  PubMed  Google Scholar 

  84. 84.

    Budzik, J.-F. et al. Diffusion tensor imaging and fibre tracking in cervical spondylotic myelopathy. Eur. Radiol. 21, 426–433 (2011).

    PubMed  Google Scholar 

  85. 85.

    Uda, T. et al. Assessment of cervical spondylotic myelopathy using diffusion tensor magnetic resonance imaging parameter at 3.0 tesla. Spine 38, 407–414 (2013).

    PubMed  Google Scholar 

  86. 86.

    Rajasekaran, S. et al. The assessment of neuronal status in normal and cervical spondylotic myelopathy using diffusion tensor imaging. Spine 39, 1183–1189 (2014).

    CAS  PubMed  Google Scholar 

  87. 87.

    Chen, X. et al. Magnetic resonance diffusion tensor imaging of cervical spinal cord and lumbosacral enlargement in patients with cervical spondylotic myelopathy. J. Magn. Reson. Imaging 43, 1484–1491 (2016).

    PubMed  Google Scholar 

  88. 88.

    Vedantam, A. et al. Diffusion tensor imaging correlates with short-term myelopathy outcome in patients with cervical spondylotic myelopathy. World Neurosurg. 97, 489–494 (2017).

    PubMed  Google Scholar 

  89. 89.

    Wen, C. Y. et al. Is diffusion anisotropy a biomarker for disease severity and surgical prognosis of cervical spondylotic myelopathy? Radiology 270, 197–204 (2014).

    PubMed  Google Scholar 

  90. 90.

    Cui, J.-L. et al. Quantitative assessment of column-specific degeneration in cervical spondylotic myelopathy based on diffusion tensor tractography. Eur. Spine J. 24, 41–47 (2015).

    PubMed  Google Scholar 

  91. 91.

    Yu, W.-R. et al. Molecular mechanisms of spinal cord dysfunction and cell death in the spinal hyperostotic mouse: implications for the pathophysiology of human cervical spondylotic myelopathy. Neurobiol. Dis. 33, 149–163 (2009).

    CAS  PubMed  Google Scholar 

  92. 92.

    Martin, A. R. et al. Clinically feasible microstructural MRI to quantify cervical spinal cord tissue injury using DTI, MT, and t2*-weighted imaging: assessment of normative data and reliability. Am. J. Neuroradiol. 38, 1257–1265 (2017).

    CAS  PubMed  Google Scholar 

  93. 93.

    Jones, J. G. A., Cen, S. Y., Lebel, R. M., Hsieh, P. C. & Law, M. Diffusion tensor imaging correlates with the clinical assessment of disease severity in cervical spondylotic myelopathy and predicts outcome following surgery. Am. J. Neuroradiol. 34, 471–478 (2013).

    CAS  PubMed  Google Scholar 

  94. 94.

    Kerkovský, M. et al. Magnetic resonance diffusion tensor imaging in patients with cervical spondylotic spinal cord compression. Spine 37, 48–56 (2012).

    PubMed  Google Scholar 

  95. 95.

    Sato, T. et al. Evaluation of cervical myelopathy using apparent diffusion coefficient measured by diffusion-weighted imaging. Am. J. Neuroradiol. 33, 388–392 (2012).

    CAS  PubMed  Google Scholar 

  96. 96.

    Lemon, R. N. & Griffiths, J. Comparing the function of the corticospinal system in different species: organizational differences for motor specialization? Muscle Nerve 32, 261–279 (2005).

    PubMed  Google Scholar 

  97. 97.

    Starkey, M. L. & Schwab, M. E. Anti-nogo-A and training: can one plus one equal three? Exp. Neurol. 235, 53–61 (2012).

    CAS  PubMed  Google Scholar 

  98. 98.

    Karadimas, S. K., Gatzounis, G. & Fehlings, M. G. Pathobiology of cervical spondylotic myelopathy. Eur. Spine J. 2, 132–138 (2015).

    Google Scholar 

  99. 99.

    Yu, W. R., Liu, T., Kiehl, T. R. & Fehlings, M. G. Human neuropathological and animal model evidence supporting a role for Fas-mediated apoptosis and inflammation in cervical spondylotic myelopathy. Brain 134, 1277–1292 (2011).

    PubMed  Google Scholar 

  100. 100.

    Seif, M. et al. Cervical cord neurodegeneration in traumatic and non-traumatic spinal cord injury. J. Neurotrauma (2019).

    Article  PubMed  Google Scholar 

  101. 101.

    Cohen-Adad, J. & Wheeler-Kingshott, C. A. M. Quantitative MRI of the Spinal Cord (Elsevier, 2014).

  102. 102.

    Setsompop, K. et al. High-resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: simultaneous multislice (gSlider-SMS). Magn. Reson. Med. 79, 141–151 (2018).

    CAS  PubMed  Google Scholar 

  103. 103.

    Barry, R. L., Vannesjo, S. J., By, S., Gore, J. C. & Smith, S. A. Spinal cord MRI at 7T. Neuroimage 168, 437–451 (2018).

    PubMed  Google Scholar 

  104. 104.

    Wilm, B. J. et al. Diffusion-weighted imaging of the entire spinal cord. NMR Biomed. 22, 174–181 (2009).

    CAS  PubMed  Google Scholar 

  105. 105.

    Finsterbusch, J., Eippert, F. & Büchel, C. Single, slice-specific z-shim gradient pulses improve T2*-weighted imaging of the spinal cord. Neuroimage 59, 2307–2315 (2012).

    PubMed  Google Scholar 

  106. 106.

    Topfer, R., Foias, A., Stikov, N. & Cohen-Adad, J. Real-time correction of respiration-induced distortions in the human spinal cord using a 24-channel shim array. Magn. Reson. Med. 80, 935–946 (2018).

    PubMed  Google Scholar 

  107. 107.

    Vannesjo, S. J., Clare, S., Kasper, L., Tracey, I. & Miller, K. L. A method for correcting breathing-induced field fluctuations in T2*-weighted spinal cord imaging using a respiratory trace. Magn. Reson. Med. 81, 3745–3753 (2019).

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Stejskal, E. O. & Tanner, J. E. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42, 288–292 (1965).

    CAS  Google Scholar 

  109. 109.

    Shemesh, N. et al. Conventions and nomenclature for double diffusion encoding NMR and MRI. Magn. Reson. Med. 75, 82–87 (2016).

    CAS  PubMed  Google Scholar 

  110. 110.

    Coelho, S., Pozo, J. M., Jespersen, S. N., Jones, D. K. & Frangi, A. F. Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding. Magn. Reson. Med. 82, 395–410 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Reisert, M., Kiselev, V. G. & Dhital, B. A unique analytical solution of the white matter standard model using linear and planar encodings. Magn. Reson. Med. 81, 3819–3825 (2019).

    PubMed  Google Scholar 

  112. 112.

    Yang, G., Tian, Q., Leuze, C., Wintermark, M. & McNab, J. A. Double diffusion encoding MRI for the clinic. Magn. Reson. Med. 80, 507–520 (2018).

    PubMed  Google Scholar 

  113. 113.

    Jones, D. K., Knösche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73, 239–254 (2013).

    PubMed  Google Scholar 

  114. 114.

    Wheeler-Kingshott, C. A. & Cercignani, M. About ‘axial’ and ‘radial’ diffusivities. Magn Reson. 61, 1255–1260 (2009).

    Google Scholar 

  115. 115.

    Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H. & Kaczynski, K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 53, 1432–1440 (2005).

    PubMed  Google Scholar 

  116. 116.

    Assaf, Y. & Basser, P. J. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 27, 48–58 (2005).

    PubMed  Google Scholar 

  117. 117.

    Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012).

    PubMed  Google Scholar 

  118. 118.

    Hori, M. et al. Application of quantitative microstructural MR imaging with atlas-based analysis for the spinal cord in cervical spondylotic myelopathy. Sci. Rep. 8, 5213 (2018).

    PubMed  PubMed Central  Google Scholar 

  119. 119.

    Okita, G. et al. Application of neurite orientation dispersion and density imaging or diffusion tensor imaging to quantify the severity of cervical spondylotic myelopathy and to assess postoperative neurologic recovery. Spine J. 18, 268–275 (2018).

    PubMed  Google Scholar 

  120. 120.

    Novikov, D. S., Kiselev, V. G. & Jespersen, S. N. On modeling. Magn. Reson. Med. 79, 3172–3193 (2018).

    PubMed  PubMed Central  Google Scholar 

  121. 121.

    Morawski, M. et al. Developing 3D microscopy with CLARITY on human brain tissue: towards a tool for informing and validating MRI-based histology. Neuroimage 182, 417–428 (2018).

    PubMed  PubMed Central  Google Scholar 

  122. 122.

    Stikov, N. et al. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage 54, 1112–1121 (2011).

    PubMed  Google Scholar 

  123. 123.

    Callaghan, M. F., Helms, G., Lutti, A., Mohammadi, S. & Weiskopf, N. A general linear relaxometry model of R1 using imaging data. Magn. Reson. Med. 73, 1309–1314 (2015).

    PubMed  Google Scholar 

  124. 124.

    Helms, G., Dathe, H., Kallenberg, K. & Dechent, P. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magn. Reson. Med. 60, 1396–1407 (2008).

    PubMed  Google Scholar 

  125. 125.

    MacKay, A. et al. In vivo visualization of myelin water in brain by magnetic resonance. Magn. Reson. Med. 31, 673–677 (1994).

    CAS  PubMed  Google Scholar 

  126. 126.

    Wolff, S. D. & Balaban, R. S. Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magn. Reson. Med. 10, 135–144 (1989).

    CAS  PubMed  Google Scholar 

  127. 127.

    Thiessen, J. D. et al. Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination. NMR Biomed. 26, 1562–1581 (2013).

    CAS  PubMed  Google Scholar 

  128. 128.

    Levesque, I. et al. The role of edema and demyelination in chronic T1 black holes: a quantitative magnetization transfer study. J. Magn. Reson. Imaging 21, 103–110 (2005).

    PubMed  Google Scholar 

  129. 129.

    Wyss, P. O. et al. MR Spectroscopy of the cervical spinal cord in chronic spinal cord injury. Radiology 291, 131–138 (2019).

    PubMed  Google Scholar 

  130. 130.

    Holly, L. T., Freitas, B., McArthur, D. L. & Salamon, N. Proton magnetic resonance spectroscopy to evaluate spinal cord axonal injury in cervical spondylotic myelopathy. J. Neurosurg. Spine 10, 194–200 (2009).

    PubMed  Google Scholar 

  131. 131.

    Powers, J. et al. Ten key insights into the use of spinal cord fMRI. Brain Sci. 8, 173 (2018).

    PubMed Central  Google Scholar 

  132. 132.

    Stroman, P. W. et al. Noninvasive assessment of the injured human spinal cord by means of functional magnetic resonance imaging. Spinal Cord 42, 59–66 (2004).

    CAS  PubMed  Google Scholar 

  133. 133.

    Cadotte, D. W. et al. Plasticity of the injured human spinal cord: insights revealed by spinal cord functional MRI. PLOS ONE 7, e45560 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Ellingson, B. M., Woodworth, D. C., Leu, K., Salamon, N. & Holly, L. T. Spinal cord perfusion MR imaging implicates both ischemia and hypoxia in the pathogenesis of cervical spondylosis. World Neurosurg. 128, e773–e781 (2019).

    PubMed  Google Scholar 

  135. 135.

    Tabelow, K. et al. hMRI — a toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194, 191–210 (2019).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Edwards, L. J., Kirilina, E., Mohammadi, S. & Weiskopf, N. Microstructural imaging of human neocortex in vivo. Neuroimage 182, 184–206 (2018).

    PubMed  Google Scholar 

  137. 137.

    Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Curr. Opin. Neurol. 28, 313–322 (2015).

    CAS  PubMed  Google Scholar 

  138. 138.

    West, K. L. et al. Myelin volume fraction imaging with MRI. Neuroimage 182, 511–521 (2018).

    PubMed  Google Scholar 

  139. 139.

    Edwards, L. J., Pine, K. J., Ellerbrock, I., Weiskopf, N. & Mohammadi, S. NODDI-DTI: estimating neurite orientation and dispersion parameters from a diffusion tensor in healthy white matter. Front. Neurosci. 11, 720 (2017).

    PubMed  PubMed Central  Google Scholar 

  140. 140.

    Jespersen, S. N., Olesen, J. L., Hansen, B. & Shemesh, N. Diffusion time dependence of microstructural parameters in fixed spinal cord. Neuroimage 182, 329–342 (2018).

    PubMed  Google Scholar 

  141. 141.

    Lampinen, B. et al. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: a model comparison using spherical tensor encoding. Neuroimage 147, 517–531 (2017).

    PubMed  Google Scholar 

  142. 142.

    Stikov, N. et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage 118, 397–405 (2015).

    PubMed  Google Scholar 

  143. 143.

    Mohammadi, S. et al. Whole-brain in-vivo measurements of the axonal g-ratio in a group of 37 healthy volunteers. Front. Neurosci. 9, 441 (2015).

    PubMed  PubMed Central  Google Scholar 

  144. 144.

    De Leener, B., Kadoury, S. & Cohen-Adad, J. Robust, accurate and fast automatic segmentation of the spinal cord. Neuroimage 98, 528–536 (2014).

    PubMed  Google Scholar 

  145. 145.

    Mohammadi, S., Freund, P., Feiweier, T., Curt, A. & Weiskopf, N. The impact of post-processing on spinal cord diffusion tensor imaging. Neuroimage 70, 377–385 (2013).

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    David, G., Freund, P. & Mohammadi, S. The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI. Neuroimage 158, 296–307 (2017).

    PubMed  PubMed Central  Google Scholar 

  147. 147.

    De Leener, B. et al. PAM50: unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. Neuroimage 165, 170–179 (2018).

    PubMed  Google Scholar 

  148. 148.

    Lévy, S. et al. White matter atlas of the human spinal cord with estimation of partial volume effect. Neuroimage 119, 262–271 (2015).

    PubMed  Google Scholar 

  149. 149.

    Eden, D. et al. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord. Brain 142, 633–646 (2019).

    PubMed  Google Scholar 

  150. 150.

    Hopkins, B. S. et al. Tract-specific volume loss on 3T MRI in patients with cervical spondylotic myelopathy. Spine 43, 1 (2018).

    Google Scholar 

  151. 151.

    Sprenger, C., Stenmans, P., Tinnermann, A. & Büchel, C. Evidence for a spinal involvement in temporal pain contrast enhancement. Neuroimage 183, 788–799 (2018).

    PubMed  Google Scholar 

  152. 152.

    Cadotte, D. W. & Fehlings, M. G. Will imaging biomarkers transform spinal cord injury trials? Lancet Neurol. 12, 843–844 (2013).

    PubMed  Google Scholar 

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S.M. has received funding from the ERA-NET NEURON (hMRIofSCI) and the Bundesministerium für Bildung und Forschung (BMBF; 01EW1711A and B) and is supported by the Deutsche Forschungsgemeinschaft (grant MO 2397/4-1) and the Forschungszentrums Medizintechnik Hamburg (fmthh; grant 01fmthh2017). A.R.M. has received support from the Canadian Institutes of Health Research (CHIR; 359116). J.C.-A. holds funding from the Canada Research Chair in Quantitative Magnetic Resonance Imaging (950–230815), the CIHR (FDN-143263), the Canada Foundation for Innovation (32454, 34824), the Fonds de Recherche du Québec – Santé (28826), the Natural Sciences and Engineering Research Council of Canada (435897-2013), the Canada First Research Excellence Fund (IVADO and TransMedTech) and the Quebec BioImaging Network (5886), Spinal Research and Wings for Life (INSPIRED project). N.W. reports grants from the European Research Council/ERC grant agreement no. 616905, grants from the BMBF (01EW1711A B) in the framework of ERA-NET NEURON, grants from the BRAINTRAIN European research network (Collaborative Project) supported by the European Commission (grant agreement no. 602186), grants from NISCI supported by the European Union’s Horizon 2020 research and innovation program under the grant agreement no. 681094, and supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0137, and grants from UCL Impact Awards; The Wellcome Centre for Human Neuroimaging and Max Planck Institute for Human Cognitive and Brain Sciences have institutional research agreements with Siemens Healthcare. P.F. reports grants from ERA-NET NEURON (hMRIofSCI no. 32NE30_173678), grants from NISCI supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 681094, and supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0137, grants from the Wings for life charity (no. WFL-CH-007/14) and grants from the International Foundation for Research (IRP-158). P.F. is funded by a SNF Eccellenza Professorial Fellowship grant (PCEFP3_181362/1). A.T. acknowledges support from the UCL/UCLH NIHR Biomedical Research Centre.

Review criteria

PubMed and MEDLINE were searched for articles published in English from 1 January 2010 to 31 March 2019. The search terms “traumatic spinal cord injury”, “cervical spondylotic myelopathy” and “degenerative spondylotic myelopathy” were used in combination with each of the terms “diffusion MRI”, “diffusion tensor imaging”, “DTI”, “conventional MRI”, and “atrophy”. The reference lists of the identified papers were also searched for further articles. The final reference list for inclusion in this Review was generated on the basis of originality and relevance to the topics covered.

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All authors provided substantial contributions to discussion of the content of the article, wrote the article and undertook the review and/or editing of the manuscript before submission. G.D and P.F also researched data for the article.

Corresponding author

Correspondence to Patrick Freund.

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Competing interests

A.T. reports personal fees paid to his institution from Eisai Ltd. and fees and support for travel from Hoffmann-La Roche outside the submitted work. A.T. is an Editorial Board member of The Lancet Neurology and receives a free subscription. A.T. is the Editor-in-Chief of the Multiple Sclerosis Journal and receives an honorarium from SAGE Publications. A.T. receives support for travel as Chair, Scientific Advisory Committee, International Progressive MS Alliance, and member, National MS Society (USA), Research Programs Advisory Committee. A.T. has received honoraria and support for travel for lecturing from Almirall and EXCEMED. N.W. is supported by Siemens Healthcare. The other authors declare no competing interests.

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Nature Reviews Neurology thanks L. Holly, S. Kurpad and P. Narayana for their contribution to the peer review of this work.

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T1-weighted MRI pulse sequences are optimized to create an image contrast that primarily depends on the T1 (longitudinal or spin-lattice) relaxation time of the tissue. In T1-weighted images, white matter appears bright and grey matter appears dark.


T2-weighted MRI pulse sequences are optimized to create an image contrast that primarily depends on the T2 (transversal or spin-spin) relaxation time of the tissue. In T2-weighted images, areas of water accumulation (for example, oedema or lesions) appear bright in contrast with other tissue.


A type of diffusion with direction dependency; that is, the diffusion is not the same in all spatial directions (for example, in white matter).


Mathematical object used to describe diffusion in homogeneous anisotropic media, having the form of a 3 × 3 symmetrical matrix for 3D diffusion.


A scalar is a quantity that is completely described by its magnitude.


A type of diffusion with no directional dependency; that is, the diffusion is the same in all spatial directions (for example, in a glass of water).

Wallerian degeneration

A degenerative process that involves the breakdown of the distal part of a severed axon.

Pulse sequences

In MRI, a pulse sequence refers to a particular setting of measurement parameters. Each pulse sequence results in a particular image appearance, with T1-weighted, T2-weighted, and diffusion-weighted pulse sequences resulting in T1, T2, and diffusion image contrasts, respectively.

Gradient echo-based T2*-weighted sequences

MRI pulse sequences that are optimized to create an image contrast that depends primarily on T2* (effective transversal) relaxation time. T2*-weighted images offer good distinction between grey and white matter in the spinal cord and form the basis of grey matter segmentation.

Partial volume effects

Artefacts that arise at tissue interfaces where multiple tissues are contained in a single voxel. These artefacts are more severe at lower resolution.

Susceptibility artefacts

Artefacts resulting from the different magnetic properties of tissues. They appear at and near tissue interfaces and lead to image distortions and signal dropouts.

Coil coverage

Part of the body that falls into the receptive field of the receiver coils.

Receive coil

Part of the MRI hardware that is responsible for receiving the signal and is crucial for acquiring images with high signal-to-noise ratios.


A procedure that aims to make the magnetic field homogeneous within the imaging volume, which is crucial for good-quality, distortion-free images.

Field fluctuations

Variations in the magnetic field within the body caused by susceptibility artefacts and imperfect shimming.

Pulsed field gradient design

A pair of spatially varying magnetic fields (gradients) applied in succession to encode the water diffusion along a particular direction in diffusion-weighted MRI scans.

Double diffusion encoding

In contrast to the pulsed field gradient design, this technique encodes diffusion along two directions in the images.

Nonlinear diffusion encoding

A modification of the pulsed field gradient design, where the variation of the applied spatially varying magnetic field is not linear.


A scalar measure that describes the amount of diffusion weighting in the images; a higher b-value means more diffusion weighting but also less signal.

Restricted diffusion component

Usually refers to the intracellular space of white matter, which is assumed to show a restricted diffusion profile, meaning that the myelinated axons are impermeable to water molecules.

Multicontrast biophysical models

Models that incorporate information from multiple types of MRI acquisitions (contrasts) to estimate a meaningful tissue parameter such as axonal or myelin volume fraction.

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David, G., Mohammadi, S., Martin, A.R. et al. Traumatic and nontraumatic spinal cord injury: pathological insights from neuroimaging. Nat Rev Neurol 15, 718–731 (2019).

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