Advertisement

What is the role of diffusion tensor imaging (DTI) in detecting subclinical pyramidal tract dysfunction in Behçet’s and neuro-Behçet’s cases?

  • Seyma Ciftci AykacEmail author
  • Figen Gökcay
  • Cem Calli
Original Article
  • 39 Downloads

Abstract

The aim of this study is to investigate the pyramidal tract integrity with DTI in Behçet’s and neuro-Behçet’s cases. We performed this technique in two subgroups of neuro-Behçet’s patients (parenchymal and vascular), and Behçet’s cases without neurological involvement and control group. Totally, 28 patients were investigated. The control group was composed of 14 healthy people. Cranial MR and DTI were performed in three patient groups and the control group. At DTI, circular regions of interest (ROI) were symmetrically drawn on axial slices on the left and right sides along the pyramidal tract pathway at two levels: middle one third of the cerebral peduncle and posterior limb of the internal capsule. Fractional anisotropy (FA) values for each ROI were obtained by averaging all voxels within the ROI. Calculated FA values on both sides (left and right) of the posterior limb of the internal capsule and cerebral peduncle are significantly lower in all three patient groups when compared to the control group. But there is no any difference of FA values in the selected brain regions of three patient groups. FA values on the posterior limb of the internal capsule and cerebral peduncle do not show a statistically significant difference in parenchymal neuro-Behçet’s cases. Our study demonstrates that DTI can detect subclinical pyramidal tract dysfunction in neuro-Behçet’s and Behçet’s patients. Detection of subclinical nervous system involvement is crucial for morbidity in Behçet’s disease. For this reason, studies based on DTI, which include a large number of patients and explore different brain regions, are needed to guide clinicians.

Keywords

Neuro-Behçet’s disease DTI Pyramidal tract 

Notes

Funding information

This study was supported financially by Scientific Research Committee of Ege University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Araji et al (2009) Neuro-Behçet’s disease: epidemiology, clinical characteristics and management. Lancet Neurol 8:192–204CrossRefGoogle Scholar
  2. 2.
    Serdaroğlu P (1998) Behçet disease and nervous system. J Neurol 245:197–205CrossRefGoogle Scholar
  3. 3.
    Taber KH, Pierpaoli C, Rose SE, Rugg-Gunn FJ, Chalk JB, Jones DK, Hurley RA (2002) The future for diffusion tensor imaging in neuropsychiatry. J Neuropsychiatry Clin Neurosci 14:1–5CrossRefGoogle Scholar
  4. 4.
    Salat DH, Tuch DS, Greve DN, van der Kouwe AJW, Hevelone ND, Zaleta AK, Rosen BR, Fischl B, Corkin S, Rosas HD, Dale AM (2005) Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 26:1215–1227CrossRefGoogle Scholar
  5. 5.
    Wechsler B, Dell'Isola B, Vidailhet M et al (1993) MRI in 31 patients with Behçet’s disease and neurological involvement: prospective study with clinical correlation. J Neurol Neurosurg Psychiatry 56:793–798CrossRefGoogle Scholar
  6. 6.
    Tali ET, Atilla S, Keskin T, Simonson T, Işik S, Yuh WT (1997) MRI in neuro-Behcets disease. Neuroradiology 39:2–6CrossRefGoogle Scholar
  7. 7.
    Baysal et al (2005) Diffusion-weighted imaging in chronic Behçet patients with and without neurological findings. Neuroradiology 47:431–437CrossRefGoogle Scholar
  8. 8.
    Baysal et al (2003) Proton MRS in Behçet’s disease with and without neurological findings. Neuroradiology 45:860–864CrossRefGoogle Scholar

Copyright information

© Fondazione Società Italiana di Neurologia 2019

Authors and Affiliations

  1. 1.Neurology DepartmentEge University Faculty of MedicineIzmirTurkey
  2. 2.Radiology DepartmentEge University Faculty of MedicineIzmirTurkey

Personalised recommendations