Signatures of chronic pain in multiple sclerosis: a machine learning approach to investigate trigeminal neuralgia

Abstract

Chronic pain is a pervasive, disabling, and understudied feature of multiple sclerosis (MS), a progressive demyelinating and neurodegenerative disease. Current focus on motor components of MS disability combined with difficulties assessing pain symptoms present a challenge for the evaluation and management of pain in MS, highlighting the need for novel methods of assessment of neural signatures of chronic pain in MS. We investigate chronic pain in MS using MS-related trigeminal neuralgia (MS-TN) as a model condition focusing on gray matter structures as predictors of chronic pain. T1 imaging data from people with MS (n = 75) and MS-TN (n = 77) using machine learning (ML) was analyzed to derive imaging predictors at the level of cortex and subcortical gray matter. The ML classifier compared imaging metrics of patients with MS and MS-TN and distinguished between these conditions with 93.4% individual average testing accuracy. Structures within default-mode, somatomotor, salience, and visual networks (including hippocampus, primary somatosensory cortex, occipital cortex, and thalamic subnuclei) were identified as significant imaging predictors of trigeminal neuralgia pain. Our results emphasize the multifaceted nature of chronic pain and demonstrate the utility of imaging and ML in assessing and understanding MS-TN with greater objectivity.

Keywords: Brain imaging; Chronic pain; Machine learning; Multiple sclerosis; Trigeminal neuralgia.

Figure 1.

Data processing and analysis pipeline. Magnetic resonance imaging (MRI) data were processed using FreeSurfer (A), gray matter metrics were corrected for the difference in head size and used for the ML-driven analysis. Machine learning pipeline (B) includes unsupervised (t-SNE) and supervised (SVM) ML components and illustrates nested cross-validation scheme for training, optimising, and testing model. ML, machine learning; SVM, support vector machine; t-SNE, t-distributed stochastic neighbor embedding.

Figure 2.

T-distributed stochastic neighbor embedding (t-SNE) clustering of the data (perplexity = 5), where each point represents a different patient from the dataset and the hue in each of the 4 subplots, corresponds to different variables that were tested because of their potential to be confounders. (A) Diagnosis, (B) sex, (C) scanner, (D) duration of MS in years, and (E) age (decades). The axes on each subplot are arbitrary. MS, multiple sclerosis.

Figure 3.

Average receiver operating characteristic (ROC) curve of individual point-wise predictions (A and B) confusion matrix visualizing the total performance of the model. The average area under the ROC curve is 0.98, indicating that the model is correctly categorizing the data significantly higher than random chance; the dotted line illustrates a random sorting curve, and the gray curves show performance of individual folds. (C) Top features according to the weight attributed by the SVM classifier. Y-axis represents unitless feature importance (coefficient), assigned to it by the SVM model. Features were included if selected by the model during at least 5 of the 10 cross-validation folds of training; the number in the circle above each feature represents the number of times out of 10 it was selected. Features are coloured according to the specific brain network—default mode, somatomotor, salience, control, visual networks, and gyral structures (not classified). A, area; CUN, cuneus; FUG, fusiform gyrus; HG, Heschl gyrus; INFCRINS, inferior circular sulci of the insula; L, left hemisphere; L-SG, limitans suprageniculate thalamic nucleus; MDm, medial mediodorsal thalamic nucleus; PuI, pulvinar inferior thalamic nucleus; PERCAS, pericallosal sulcus; RG, gyrus rectus; R, right hemisphere; S1, postcentral gyrus; SVM; support vector machine; SUPFS, superior frontal sulcus; T, thickness; TOS, superior occipital sulcus and transverse occipital sulcus; V, volume; VM, ventral medial thalamic nucleus; VPL, ventral posterolateral thalamic nucleus.

Figure 4.

Visualization of the univariate statistics of important predictive features, mapped according to the corrected size of the region in MS-TN (blue) vs MS (orange) patients to assess directionality. Features are organized in terms of the dimension they were selected for, with (A) visualizing cortical surface area, (B) visualizing cortical thickness, and (C) visualizing subcortical volume. The corrected P-values are listed above each feature in accordance with the legend on the right, only significant features are displayed (***P < 0.001, **P < 0.01, *P < 0.05). Log scale is used for visualization. MS, multiple sclerosis; TN, trigeminal neuralgia.