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Published on: August 1, 2025
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Whole-brain morphological alterations associated with trigeminal neuralgia
Jiajie Mo1,2, Jianguo Zhang1,2, Wenhan Hu1,2
1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, China.
The Journal of Headache and Pain
|August 14, 2021
Summary
Trigeminal neuralgia (TN) is linked to distinct whole-brain structural changes. Machine learning accurately identifies these neuroimaging patterns for diagnosing TN.
Area of Science:
- Neuroimaging
- Neurology
- Machine Learning
Background:
- Novel neuroimaging strategies offer insights into trigeminal neuralgia (TN) mechanisms.
- Whole-brain morphometry in TN patients can reveal diagnostic biomarkers.
Purpose of the Study:
- Conduct whole-brain morphometry analyses in TN patients.
- Assess group-level neocortical and subcortical structural patterns for diagnostic biomarker exploration.
Main Methods:
- Magnetic resonance imaging (MRI) measured cortical thickness, surface area, and myelin.
- Compared radial distance and Jacobian determinant in subcortex for 43 TN patients and 43 controls.
- Utilized pattern learning algorithms for TN prediction and validated with 40 hemifacial spasm controls.
Main Results:
- TN patients showed reduced cortical indices in ACC, MCC, and PCC.
- Widespread subcortical volume reduction observed in putamen, thalamus, accumbens, pallidum, and hippocampus.
- Automated TN diagnosis achieved high specificity (95.35%) using whole-brain morphological alterations.
Conclusions:
- Trigeminal neuralgia exhibits a distinctive whole-brain structural neuroimaging pattern.
- Machine learning effectively differentiates morphological phenotypes in TN.
- Identified relevant diagnostic biomarkers for the full spectrum of TN.
