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

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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.
Funding Information
  • CIFAR-Temerty Innovation Catalyst Grant
  • University of Toronto Centre for the Study of Pain
  • Temerty Faculty of Medicine, University of Toronto

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