J Neurosurg 143:74–82, 2025
Resting-state fMRI and machine learning revealed distinct brain connectivity and activity differences between classical and idiopathic trigeminal neuralgia (TN) and controls. These findings identify potential neuroimaging biomarkers for TN subtypes, aiding diagnosis and understanding of TN pathophysiology.
Primary trigeminal neuralgia (TN) includes classical (CTN) and idiopathic (ITN) types, sharing clinical features but differing in neurovascular compression (NVC) presence.
• Resting-state fMRI and machine learning were used to analyze brain functional connectivity and spontaneous activity in 50 TN patients (28 CTN, 22 ITN) and 43 controls.
• TN patients showed increased connectivity between the medial prefrontal cortex (mPFC) and left planum temporale, and decreased connectivity between mPFC and left superior frontal gyrus.
• CTN patients had further reduced connectivity between the left insula and left occipital pole, and decreased activity in the right temporal pole compared to ITN.
• TN patients exhibited heightened neural activity in frontal regions compared to controls.
• Machine learning (support vector machine) distinguished TN patients from controls with moderate accuracy (AUC 0.80).
• Findings suggest potential fMRI biomarkers for TN subtypes, aiding understanding of pathophysiology and improving diagnosis.
• Study limitations include small sample size and exclusion of bilateral/secondary TN, warranting further research.
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