European Radiology

, Volume 30, Issue 1, pp 357–369 | Cite as

MRI evidence of brain atrophy, white matter damage, and functional adaptive changes in patients with cervical spondylosis and prolonged spinal cord compression

  • Ángela Bernabéu-SanzEmail author
  • José Vicente Mollá-Torró
  • Susana López-Celada
  • Pedro Moreno López
  • Eduardo Fernández-Jover
Magnetic Resonance



To investigate the effect of cervical spondylosis (CS) in the brain with a combination of advanced neuroimaging techniques.


Twenty-seven patients with CS and 24 age- and gender-matched healthy controls were studied. Disease severity was quantified using the Modified Japanese Orthopaedic Association Scoring System (mJOHA). Magnetic resonance (MR) imaging of the brain and spinal cord, functional MR imaging (fMRI) with a bilateral rest/finger-tapping paradigm, brain diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and MR spectroscopy of the sensorimotor cortex were performed.


A total of 92.3% of patients had more than one herniated disc. In the MRI, 33.33% presented signs of myelopathy. The mJOHA score was 13.03 ± 2.83. Compared with controls, DTI results showed significant lower FA values in Corpus callosum, both corticospinal tracts and middle cerebellar peduncles (p < 0.05 corrected). Only in CS patients fMRI results showed activation in both globus pallidi, caudate nucleus, and left thalamus (p < 0.001). Subject-specific activation of the BOLD signal showed in CS patients lower activation in the sensorimotor cortex and increased activation in both cerebellum hemispheres (p < 0.05 corrected). VBM showed bilateral clusters of gray matter loss in the sensorimotor cortex and pulvinar nucleus (p < 0.05 corrected) of CS patients. NAA/Cr was reduced in the sensorimotor cortex of CS patients (p < 0.05). Linear discriminant and support vector machine analyses were able to classify > 97% of CS patients with parameters obtained from the fMRI, DTI, and MRS results.


CS may lead to distal brain damage affecting the white and gray matter of the sensorimotor cortex causing brain atrophy and functional adaptive changes.

Key Points

• This study suggests that patients with cervical spondylosis may present anatomical and functional adaptive changes in the brain.

• Cervical spondylosis may lead to white matter damage, gray matter volume loss, and functional adaptive changes in the sensorimotor cortex.

• The results reported in this work may be of value to better understand the effect of prolonged cervical spine compression in the brain.


Spondylosis Spine Brain Neural plasticity 



Corpus callosum








Cervical spondylosis


Cervical spondylosis myelopathy


Corticospinal tract


Diffusion tensor imaging




Fractional anisotropy


Functional magnetic resonance imaging


Glutamate and glutamine


Middle cerebellar peduncle


Mean diffusivity




Modified Japanese Orthopaedic Association Scoring System.


Magnetic resonance


N-Acetyl aspartate


Radial diffusivity


Supplementary motor area


Tract-based spatial statistics


Voxel-based morphometry



The authors would like to thank the following people: our subjects for their time and help. Our MR technologists Mr. David González García, Miss. Patricia Lucena Ibáñez, Ms. Evelyn Teruel Sanchez, and Ms. Maria Vicenta Picazo Panadero for their outstanding technical support during the acquisition of the studies. Mr. Mikel Val for his invaluable help and advice in the SVM analysis. We would also like to show our gratitude to the anonymous reviewers who provided insight and expertise that greatly improved the manuscript.


This work was supported in part by Grant No. RTI2018-098969-B-100 from the Spanish Government and by the Bidons Egara Research Chair.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Ángela Bernabeu-Sanz.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• case-control study

• performed at one institution

Supplementary material

330_2019_6352_MOESM1_ESM.docx (650 kb)
ESM 1 (DOCX 650 kb)


  1. 1.
    Boden SD, McCowin PR, Davis DO, Dina TS, Mark AS, Wiesel S (1990) Abnormal magnetic-resonance scans of the cervical spine in asymptomatic subjects. A prospective investigation. J Bone Joint Surg Am 72:1178–1184CrossRefGoogle Scholar
  2. 2.
    Bakhsheshian J, Mehta VA, Liu JC (2017) Current diagnosis and management of cervical spondylotic myelopathy. Global Spine J 7:572–586. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Tracy JA, Bartleson JD (2010) Cervical spondylotic myelopathy. Neurologist 16:176–187CrossRefGoogle Scholar
  4. 4.
    Lebl DR, Bono CM (2015) Update on the diagnosis and management of cervical spondylotic myelopathy. J Am Acad Orthop Surg 23:648–660CrossRefGoogle Scholar
  5. 5.
    Clarke E, Robinson PK (1956) Cervical myelopathy: a complication of cervical spondylosis. Brain 79:483–510CrossRefGoogle Scholar
  6. 6.
    Morishita Y, Naito M, Hymanson H, Miyazaki M, Wu G, Wang JC (2009) The relationship between the cervical spinal canal diameter and the pathological changes in the cervical spine. Eur Spine J 18:877–883CrossRefGoogle Scholar
  7. 7.
    Wada E, Yonenobu K, Suzuki S, Kanazawa A, Ochi T (1999) Can intramedullary signal change on magnetic resonance imaging predict surgical outcome in cervical spondylotic myelopathy? Spine (Phila PA 1976) 24:455–461 discussion 462Google Scholar
  8. 8.
    Stroman PW, Nance PW, Ryner LN (1999) BOLD MRI of the human cervical spinal cord at 3 tesla. Magn Reson Med 42:571–576CrossRefGoogle Scholar
  9. 9.
    Okada E, Matsumoto M, Fujiwara H, Toyama Y (2011) Disc degeneration of cervical spine on MRI in patients with lumbar disc herniation: comparison study with asymptomatic volunteers. Eur Spine J 20:585–591CrossRefGoogle Scholar
  10. 10.
    Xiangshui M, Xiangjun C, Xiaoming Z et al (2010) 3 T magnetic resonance diffusion tensor imaging and fiber tracking in cervical myelopathy. Clin Radiol 65:465–473CrossRefGoogle Scholar
  11. 11.
    Duggal N, Rabin D, Bartha R et al (2010) Brain reorganization in patients with spinal cord compression evaluated using fMRI. Neurology 74:1048–1054CrossRefGoogle Scholar
  12. 12.
    Holly LT (2009) Management of cervical spondylotic myelopathy with insights from metabolic imaging of the spinal cord and brain. Curr Opin Neurol 22:575–581CrossRefGoogle Scholar
  13. 13.
    Kowalczyk I, Duggal N, Bartha R (2012) Proton magnetic resonance spectroscopy of the motor cortex in cervical myelopathy. Brain. CrossRefGoogle Scholar
  14. 14.
    Zhou FQ, Tan YM, Wu L, Zhuang Y, He LC, Gong HH (2015) Intrinsic functional plasticity of the sensory-motor network in patients with cervical spondylotic myelopathy. Sci Rep 5:1–8.
  15. 15.
    Dong Y, Holly LT, Albistegui-Dubois R et al (2008) Compensatory cerebral adaptations before evolving changes after surgical decompression in cervical spondylotic myelopathy. J Neurosurg Spine 9:538–551.
  16. 16.
    Yukawa Y, Kato F, Yoshihara H, Yanase M, Ito K (2007) MR T2 image classification in cervical compression myelopathy. Spine (Phila PA 1976) 32:1675–1678. CrossRefGoogle Scholar
  17. 17.
    Oishi K, Zilles K, Amunts K et al (2008) Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage 43:447–457. CrossRefGoogle Scholar
  18. 18.
    Leemans A, Jeurissen B, Sijbers J, Jones D (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc Intl Soc Mag Reson Med 17:3537Google Scholar
  19. 19.
    Jones DK, Basser PJ (2004) “Squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted MR data. Magn Reson Med 52:979–993. CrossRefGoogle Scholar
  20. 20.
    Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44:625–632.<625::AID-MRM17>3.0.CO;2-O
  21. 21.
    Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S (2004) Fiber tract-based atlas of human white matter anatomy. Radiology 230:77–87. CrossRefGoogle Scholar
  22. 22.
    Papke K, Reimer P, Renger B et al (2000) Optimized activation of the primary sensorimotor cortex for clinical functional MR imaging. AJNR Am J Neuroradiol 21:395–401Google Scholar
  23. 23.
    Brett M, Anton J-L, Valabregue R, Poline J-B (2002) Region of interest analysis using an SPM toolbox [Internet]. Vol. 16, Presented at the 8th International Conference on Functional Mapping of the Human Brain. [cited 2019 Feb 18]. Available from:
  24. 24.
    Naressi A, Couturier C, Devos JM et al (2001) Java-based graphical user interface for the MRUI quantitation package. MAGMA 12:141–152CrossRefGoogle Scholar
  25. 25.
    Bernabeu A, Alfaro A, García M, Fernández E (2009) Proton magnetic resonance spectroscopy (1H-MRS) reveals the presence of elevated myo-inositol in the occipital cortex of blind subjects. Neuroimage 47:1172–1176. CrossRefGoogle Scholar
  26. 26.
    Cano M, Martínez-Zalacaín I, Bernabéu-Sanz Á et al (2017) Brain volumetric and metabolic correlates of electroconvulsive therapy for treatment-resistant depression: a longitudinal neuroimaging study. Transl Psychiatry 7:e1023. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Poveda MJ, Bernabeu Á, Concepción L et al (2010) Brain edema dynamics in patients with overt hepatic encephalopathy. A magnetic resonance imaging study. Neuroimage 52:481–487. CrossRefGoogle Scholar
  28. 28.
    Morales S, Bernabeu-Sanz A, López-Mir F, González P, Luna L, Naranjo V (2017) BRAIM: a computer-aided diagnosis system for neurodegenerative diseases and brain lesion monitoring from volumetric analyses. Comput Methods Programs Biomed 145:167–179. CrossRefGoogle Scholar
  29. 29.
    Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12. Available from:
  30. 30.
    Schott GD (1993) Penfield’s homunculus: a note on cerebral cartography. J Neurol Neurosurg Psychiatry 56:329–333CrossRefGoogle Scholar
  31. 31.
    Ijima Y, Furuya T, Koda M et al (2017) Experimental rat model for cervical compressive myelopathy. Neuroreport 28:1239–1245. CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Lee JW, Kim JH, Bin PJ et al (2011) Diffusion tensor imaging and fiber tractography in cervical compressive myelopathy: preliminary results. Skeletal Radiol 40:1543–1551. CrossRefGoogle Scholar
  33. 33.
    Jang SH (2014) The corticospinal tract from the viewpoint of brain rehabilitation. J Rehabil Med 46:193–199. CrossRefGoogle Scholar
  34. 34.
    Fabri M, Pierpaoli C, Barbaresi P, Polonara G (2014) Functional topography of the corpus callosum investigated by DTI and fMRI. World J Radiol 6:895–906. CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Zhou F, Gong H, Liu X, Wu L, Luk KD, Hu Y (2014) Increased low-frequency oscillation amplitude of sensorimotor cortex associated with the severity of structural impairment in cervical myelopathy. PLoS One. CrossRefGoogle Scholar
  36. 36.
    Zhou F, Wu L, Liu X, Gong H, Luk KD, Hu Y (2015) Characterizing thalamocortical disturbances in cervical spondylotic myelopathy : revealed by functional connectivity under two slow frequency bands. PLoS One CrossRefGoogle Scholar
  37. 37.
    Tan Y, Zhou F, Wu L et al (2015) Alteration of Regional homogeneity within the sensorimotor network after spinal cord decompression in cervical spondylotic myelopathy: a resting-state fMRI study. Biomed Res Int 647958. Google Scholar
  38. 38.
    Bruehlmeier M, Dietz V, Leenders KL, Roelcke U, Missimer J, Curt A (1998) How does the human brain deal with a spinal cord injury? Eur J Neurosci 10:3918–3922. CrossRefGoogle Scholar
  39. 39.
    Cramer SC, Lastra L, Lacourse MG, Cohen MJ (2005) Brain motor system function after chronic, complete spinal cord injury. Brain 128:2941–2950. CrossRefGoogle Scholar
  40. 40.
    Bunday KL, Tazoe T, Rothwell JC, Perez MA (2014) Subcortical control of precision grip after human spinal cord injury. J Neurosci 34:7341–7350. CrossRefGoogle Scholar
  41. 41.
    Dobkin BH (2000) Spinal and supraspinal plasticity after incomplete spinal cord injury: correlations between functional magnetic resonance imaging and engaged locomotor networks. Prog Brain Res 128:99–111. CrossRefGoogle Scholar
  42. 42.
    Curt A, Alkadhi H, Crelier GR, Boendermaker SH, Hepp-Reymond MC, Kollias SS (2002) Changes of non-affected upper limb cortical representation in paraplegic patients as assessed by fMRI. Brain 125:2567–2578. CrossRefGoogle Scholar
  43. 43.
    Holly LT, Dong Y, Albistegui-DuBois R, Marehbian J, Dobkin B (2014) Cortical reorganization in patients with cervical spondylotic myelopathy. J Neurosurg Spine 6:544–551.
  44. 44.
    Henderson LA, Gustin SM, Macey PM, Wrigley PJ, Siddall PJ (2011) Functional reorganization of the brain in humans following spinal cord injury: evidence for underlying changes in cortical anatomy. J Neurosci 31:2630–2637. CrossRefGoogle Scholar
  45. 45.
    Wurster CD, Graf H, Ackermann H, Groth K, Kassubek J, Riecker A (2015) Neural correlates of rate-dependent finger-tapping in Parkinson’s disease. Brain Struct Funct 220:1637–1648. CrossRefGoogle Scholar
  46. 46.
    Nikolaidis I, Fouyas IP, Sandercock PA, Statham PF (2010) Surgery for cervical radiculopathy or myelopathy. Cochrane Database Syst Rev CD001466.
  47. 47.
    Dhillon RS, Parker J, Syed YA et al (2016) Axonal plasticity underpins the functional recovery following surgical decompression in a rat model of cervical spondylotic myelopathy. Acta Neuropathol Commun 4:89. CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Martin AR, Aleksanderek I, Cohen-Adad J et al (2016) 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. CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Magnetic Resonance DepartmentInscanner SLAlicanteSpain
  2. 2.Department of NeurosurgeryHospital General Universitario de AlicanteAlicanteSpain
  3. 3.Instituto de BioingenieríaUniversidad Miguel HernándezElcheSpain
  4. 4.Centro de Investigación Biomédica en Red (CIBER BBN)MadridSpain

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