Development of a neural network model to establish a surgical paradigm for trigeminal neuralgia patients difficult to classify

Abstract

Abstract Backgrounds: Trigeminal neuralgia (TN) is a serious, intense and recurring pain in the sensory distribution of the trigeminal nerve in the face that is associated with decreased quality of life and increased risk of emotional disorders and physical health problems. Theoretically, TN can be divided into the classic type if vascular compression is found upon the trigeminus or the idiopathic type if vascular compression is not found upon any part of the trigeminus. Microvascular decompression (MVD) and internal neurolysis (IN) surgery are usually performed for classic or idiopathic TN, respectively, with satisfactory treatment effects. However, in clinical practice, there are patients with slight vascular contact with the trigeminus, and this is a dilemma when planning surgery because pain might be caused by this contact, which is usually insufficient to cause demyelination of the trigeminal nerve. Therefore, MVD is probably not effective and requires a second surgery, while IN is generally chosen blindly because of the high success rate along with some side effects and injury. Achieving a model with a clearer classifying boundary, especially for these patients, offers better opportunities for improved treatment efficacy. Methods: Toward this goal, in the present study, an SVM model was constructed with resting-state fMRI data from 70 definite CTN and ITN patients. Specifically, these 70 data points were randomly assigned to the training dataset and test dataset. The linear kernel function and 2-fold cross-validation modes of SVM and feature selection were used, and the process was repeated 10 times. Features maintained in all 10 random allocations were defined as final features of the SVM model. Results:We found that four ROI-pair connectivities were robustly effective in classification. With this model, another 16 patients with slight vascular contact who had received IN without model guidance were reclassified; 13 of these patients were classified as CTN and were likely to be appropriate for MVD. Conclusions:Taken together, the results of the present study could guide future clinical work, and TN patients who are difficult to classify could be labeled and returned to the model for improved classification accuracy in clinical use.