SVM Classification of Brain Gray Matter Volume Predicts Classic Trigeminal Neuralgia

Abstract

Background: Previous studies have shown gray matter(GM) abnormalities in the central nervous system at the group level, but this method is limited because it is based on single or cluster voxels. In contrast, machine learning makes full use of all available empirical information, including differences in brain images or behavioral data, to classify or predict data and ensure good generalization ability. This approach has potential use as a prediction tool at the individual level. We thus hypothesized that a multivariate pattern classification method may distinguish classic trigeminal neuralgia(CTN) patients from healthy controls(HC) based on gray matter volume(GMV).Methods: Resting-state fMRI scans in 24 CTN and 22 HC were processed to extract whole-brain GMV. Based on this feature dataset, take the method of Spearman, T-test, F-score, principal component analysis(PCA) respectively to reduce the feature dimension. Applying a linear support vector machine(SVM) algorithm to differentiate CTN and HC. And extract the features that survive each iteration. Pearson correlation analysis was then used to assess the correlations between the deci_value(the distance from the sample to the optimal hyperplane) and VAS scores and pain duration respectively in CTN patients.Results: Compared with other methods of feature dimension reduction, PCA has a higher ability to correctly classify an individual with an accuracy of 85%(AUC 0.9223; 91.67% sensitivity; 86.36% specificity, p<0.001). And the features that survive each iteration were concentrated in the region of the left anterior cingulate cortex(ACC_L), right superior frontal gyrus(SFG_R), and bilateral cerebellum inferior(CI). In the CTN group, deci_value was positively correlated with the VAS scores in PCA(r=0.42, P=0.041, two-tailed). While there was no difference between deci_value and VAS scores in F-score, T-test, Spearman(r=0.39,p=0.06/r=0.36,p=0.09/r=0.37,p=0.08 respectively). Also, we did not find significant correlations between the pain duration for CTN patients and deci_value among the four methods of feature dimension reduction(P > 0.05).Conclusions: This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN, and not only highlights the high accuracy of the PCA method, but also the role of ACC, SFG, and CI for classification.