Advertisement

Neuroradiology

, Volume 58, Issue 11, pp 1103–1108 | Cite as

Spinal cord diffusion tensor imaging in patients with sensory neuronopathy

  • Raphael Fernandes Casseb
  • Jean Levi Ribeiro de Paiva
  • Lucas Melo Teixeira Branco
  • Alberto Rolim Muro Martinez
  • Fabiano Reis
  • José Carlos de Lima-Junior
  • Gabriela Castellano
  • Marcondes Cavalcante França Junior
Diagnostic Neuroradiology

Abstract

Introduction

We investigated whether MR diffusion tensor imaging (DTI) analysis of the cervical spinal cord could aid the (differential) diagnosis of sensory neuronopathies, an underdiagnosed group of diseases of the peripheral nervous system.

Methods

We obtained spinal cord DTI and T2WI at 3 T from 28 patients, 14 diabetic subjects with sensory-motor distal polyneuropathy, and 20 healthy controls. We quantified DTI-based parameters and looked at the hyperintense T2W signal at the spinal cord posterior columns. Fractional anisotropy and mean diffusivity values at C2–C3 and C3–C4 levels were compared between groups. We also compared average fractional anisotropy (mean of values at C2–C3 and C3–C4 levels). A receiver operating characteristic (ROC) curve was used to determine diagnostic accuracy of average fractional anisotropy, and we compared its sensitivity against the hyperintense signal in segregating patients from the other subjects.

Results

Mean age and disease duration were 52 ± 10 and 11.4 ± 9.3 years in the patient group. Eighteen subjects had idiopathic disease and 6 dysimmune etiology. Fractional anisotropy at C3–C4 level and average fractional anisotropy were significantly different between patients and healthy controls (p < 0.001 and <0.001) and between patients and diabetic subjects (p = 0.019 and 0.027). Average fractional anisotropy presented an area under the curve of 0.838. Moreover, it had higher sensitivity than visual detection of the hyperintense signal (0.86 vs. 0.54), particularly for patients with short disease duration.

Conclusion

DTI-based analysis enables in vivo detection of posterior column damage in sensory neuronopathy patients and is a useful diagnostic test for this condition. It also helps the differential diagnosis between sensory neuronopathy and distal polyneuropathies.

Keywords

Diffusion tensor imaging (DTI) Sensory neuronopathy MRI Spinal cord Peripheral neuronopathy 

Notes

Acknowledgments

We thank Brunno Machado de Campos, Benílton de Sá Carvalho and biomedical technicians for their help in MRI discussions, statistical analysis and data acquisition, respectively. We thank FAPESP (Sao Paulo Research Foundation - Grants 2013/01766-7, 2013/07559-3 – Brazilian governmental agency) for financial support.

Compliance with ethical standards

We declare that all human studies have been approved by the research ethics committee of the School of Medical Sciences - UNICAMP and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.

Conflict of interest

We declare that we have no conflict of interest.

References

  1. 1.
    Camdessanché J-P, Jousserand G, Ferraud K et al (2009) The pattern and diagnostic criteria of sensory neuronopathy: a case–control study. Brain 132:1723–1733. doi: 10.1093/brain/awp136 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Bao Y-F, Tang W-J, Zhu D-Q et al (2013) Sensory neuronopathy involves the spinal cord and brachial plexus: a quantitative study employing multiple-echo data image combination (MEDIC) and turbo inversion recovery magnitude (TIRM). Neuroradiology 55:41–48. doi: 10.1007/s00234-012-1085-x CrossRefPubMedGoogle Scholar
  3. 3.
    Casseb RF, Martinez ARM, de Paiva JLR, França MC Neuroimaging in sensory neuronopathy. J Neuroimaging 25:704–9. doi: 10.1111/jon.12210
  4. 4.
    Paiva JLR de, Casseb RF, Martinez ARM et al (2015) Diffusion tensor imaging (DTI) of the cervical cord in sensory neuronopathies (P5.070). Neurology 84, P5.070Google Scholar
  5. 5.
    Alexander AL, Hurley SA, Samsonov AA et al (2011) Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connect 1:423–446. doi: 10.1089/brain.2011.0071 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kearney H, Miller DH, Ciccarelli O (2015) Spinal cord MRI in multiple sclerosis—diagnostic, prognostic and clinical value. Nat Rev Neurol 11:327–338. doi: 10.1038/nrneurol.2015.80 CrossRefPubMedGoogle Scholar
  7. 7.
    El Mendili M-M, Cohen-Adad J, Pelegrini-Issac M et al (2014) Multi-parametric spinal cord MRI as potential progression marker in amyotrophic lateral sclerosis. PLoS ONE 9:e95516. doi: 10.1371/journal.pone.0095516 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Garcia RU, Ricardo JAG, Horta CA et al (2013) Ulnar sensory-motor amplitude ratio: a new tool to differentiate ganglionopathy from polyneuropathy. Arq Neuropsiquiatr 71:465–469. doi: 10.1590/0004-282X20130063 CrossRefPubMedGoogle Scholar
  9. 9.
    Merkies IS, Schmitz PI, van der Meché FG, van Doorn PA (2000) Psychometric evaluation of a new sensory scale in immune-mediated polyneuropathies. Inflammatory Neuropathy Cause and Treatment (INCAT) Group. Neurology 54:943–949CrossRefPubMedGoogle Scholar
  10. 10.
    Bennett M (2001) The LANSS pain scale: the Leeds assessment of neuropathic symptoms and signs. Pain 92:147–157. doi: 10.1016/S0304-3959(00)00482-6 CrossRefPubMedGoogle Scholar
  11. 11.
    Schmitz-Hübsch T, du Montcel ST, Baliko L et al (2006) Scale for the assessment and rating of ataxia: development of a new clinical scale. Neurology 66:1717–1720. doi: 10.1212/01.wnl.0000219042.60538.92 CrossRefPubMedGoogle Scholar
  12. 12.
    Okumura R, Asato R, Shimada T et al Degeneration of the posterior columns of the spinal cord: postmortem MRI and histopathology. J Comput Assist Tomogr 16:865–7Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Raphael Fernandes Casseb
    • 1
    • 2
  • Jean Levi Ribeiro de Paiva
    • 1
  • Lucas Melo Teixeira Branco
    • 1
  • Alberto Rolim Muro Martinez
    • 1
  • Fabiano Reis
    • 3
  • José Carlos de Lima-Junior
    • 4
  • Gabriela Castellano
    • 2
  • Marcondes Cavalcante França Junior
    • 1
  1. 1.Department of Neurology, School of MedicineUniversity of Campinas – UNICAMPCampinasBrazil
  2. 2.Neurophysics Group, Department of Cosmic Rays and Chronology, Institute of Physics Gleb WataghinUniversity of Campinas – UNICAMPCampinasBrazil
  3. 3.Department of Radiology, School of MedicineUniversity of Campinas – UNICAMPCampinasBrazil
  4. 4.Laboratory of Cell Signaling, Department of Internal MedicineUniversity of Campinas – UNICAMPCampinasBrazil

Personalised recommendations