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J Neurosurg. 2019 Jul 19:1-7. doi: 10.3171/2019.4.JNS19299. [Epub ahead of print]

Diffusion tensor imaging reveals microstructural differences between subtypes of trigeminal neuralgia.

Author information

1
1Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan.
2
2Department of Neurosurgery, University of Washington, Seattle, Washington.
3
3Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; and.
4
4Neuroscience and Sensory CTSU, Michigan Medicine, University of Michigan, Ann Arbor, Michigan.

Abstract

OBJECTIVE:

Trigeminal neuralgia (TN) is an uncommon idiopathic facial pain syndrome. To assist in diagnosis, treatment, and research, TN is often classified as type 1 (TN1) when pain is primarily paroxysmal and episodic or type 2 (TN2) when pain is primarily constant in character. Recently, diffusion tensor imaging (DTI) has revealed microstructural changes in the symptomatic trigeminal root and root entry zone of patients with unilateral TN. In this study, the authors explored the differences in DTI parameters between subcategories of TN, specifically TN1 and TN2, in the pontine segment of the trigeminal tract.

METHODS:

The authors enrolled 8 patients with unilateral TN1, 7 patients with unilateral TN2, and 23 asymptomatic controls. Patients underwent DTI with parameter measurements in a region of interest within the pontine segment of the trigeminal tract. DTI parameters were compared between groups.

RESULTS:

In the pontine segment, the radial diffusivity (p = 0.0049) and apparent diffusion coefficient (p = 0.023) values in TN1 patients were increased compared to the values in TN2 patients and controls. The DTI measures in TN2 were not statistically significant from those in controls. When comparing the symptomatic to asymptomatic sides in TN1 patients, radial diffusivity was increased (p = 0.025) and fractional anisotropy was decreased (p = 0.044) in the symptomatic sides. The apparent diffusion coefficient was increased, with a trend toward statistical significance (p = 0.066).

CONCLUSIONS:

Noninvasive DTI analysis of patients with TN may lead to improved diagnosis of TN subtypes (e.g., TN1 and TN2) and improve patient selection for surgical intervention. DTI measurements may also provide insights into prognosis after intervention, as TN1 patients are known to have better surgical outcomes than TN2 patients.

KEYWORDS:

AD = axial diffusivity; ADC = apparent diffusion coefficient; AUC = area under the curve; DTI = diffusion tensor imaging; FA = fractional anisotropy; MANOVA = multivariate ANOVA; RD = radial diffusivity; REZ = root entry zone; ROC = receiver operating characteristic; ROI = region of interest; TN = trigeminal neuralgia; TN1 = TN type 1; TN2 = TN type 2; diffusion tensor imaging; facial pain; trigeminal neuralgia

PMID:
31323635
DOI:
10.3171/2019.4.JNS19299

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