Jerry Li

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.

The development of new analgesic drugs has been hampered by the inability to translate preclinical findings to humans. This failure is due in part to the weak connection between commonly used pain outcome measures in rodents and the clinical symptoms of chronic pain. Most rodent studies rely on the use of experimenter-evoked measures of pain and assess behavior under ethologically unnatural conditions, which limits the translational potential of preclinical research. Here, we addressed this problem by conducting an unbiased, prospective study of behavioral changes in mice within a natural homecage environment using conventional preclinical pain assays. Unexpectedly, we observed that cage-lid hanging, a species-specific elective behavior, was the only homecage behavior reliably impacted by pain assays. Noxious stimuli reduced hanging behavior in an intensity-dependent manner, and the reduction in hanging could be restored by analgesics. Finally, we developed an automated approach to assess hanging behavior. Collectively, our results indicate that the depression of hanging behavior is a novel, ethologically valid, and translationally relevant pain outcome measure in mice that could facilitate the study of pain and analgesic development.