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

Autor:	Latypov TH; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada.; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.; Collaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada., Wolfensohn A; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada.; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.; Faculty of Science, McGill University, Montreal, QC, Canada., Yakubov R; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada.; MD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada., Li J; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada.; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.; Collaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada., Srisaikaew P; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada., Jörgens D; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada., Jones A; Division of Neurology, Department of Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada., Colak E; Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada., Mikulis D; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada.; Joint Department of Medical Imaging, Toronto Western Hospital, University Health Network, Toronto, ON, Canada., Rudzicz F; Vector Institute for Artificial Intelligence, Toronto, ON, Canada.; Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada., Oh J; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.; Division of Neurology, Department of Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada., Hodaie M; Division of Brain, Imaging and Behaviour, Krembil Research Institute University Health Network, Toronto, ON, Canada.; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.; Department of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
Jazyk:	angličtina
Zdroj:	Pain [Pain] 2024 Dec 13. Date of Electronic Publication: 2024 Dec 13.
DOI:	10.1097/j.pain.0000000000003497
Abstrakt:	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. (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain.)
Databáze:	MEDLINE
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