Stratifying trigeminal neuralgia and characterizing an abnormal property of brain functional organization: a resting-state fMRI and machine learning study.
Authors
Affiliations (2)
Affiliations (2)
- 1Departments of Neurosurgery and.
- 2Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Abstract
Increasing evidence suggests that primary trigeminal neuralgia (TN), including classical TN (CTN) and idiopathic TN (ITN), share biological, neuropsychological, and clinical features, despite differing diagnostic criteria. Neuroimaging studies have shown neurovascular compression (NVC) differences in these disorders. However, changes in brain dynamics across these two TN subtypes remain unknown. The authors aimed to examine the functional connectivity differences in CTN, ITN, and pain-free controls. A total of 93 subjects, 50 TN patients and 43 pain-free controls, underwent resting-state functional magnetic resonance imaging (rs-fMRI). All TN patients underwent surgery, and the NVC type was verified. Functional connectivity and spontaneous brain activity were analyzed, and the significant alterations in rs-fMRI indices were selected to train classification models. The patients with TN showed increased connectivity between several brain regions, such as the medial prefrontal cortex (mPFC) and left planum temporale and decreased connectivity between the mPFC and left superior frontal gyrus. CTN patients exhibited a further reduction in connectivity between the left insular lobe and left occipital pole. Compared to controls, TN patients had heightened neural activity in the frontal regions. The CTN patients showed reduced activity in the right temporal pole compared to that in the ITN patients. These patterns effectively distinguished TN patients from controls, with an accuracy of 74.19% and an area under the receiver operating characteristic curve of 0.80. This study revealed alterations in rs-fMRI metrics in TN patients compared to those in controls and is the first to show differences between CTN and ITN. The support vector machine model of rs-fMRI indices exhibited moderate performance on discriminating TN patients from controls. These findings have unveiled potential biomarkers for TN and its subtypes, which can be used for additional investigation of the pathophysiology of the disease.