Brain imaging signatures of neuropathic facial pain derived by artificial intelligence.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
03 07 2023
Historique:
received: 31 10 2022
accepted: 13 06 2023
medline: 5 7 2023
pubmed: 4 7 2023
entrez: 3 7 2023
Statut: epublish

Résumé

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.

Identifiants

pubmed: 37400574
doi: 10.1038/s41598-023-37034-y
pii: 10.1038/s41598-023-37034-y
pmc: PMC10318015
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

10699

Informations de copyright

© 2023. The Author(s).

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Auteurs

Timur H Latypov (TH)

Division of Brain, Imaging & 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.

Matthew C So (MC)

Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.

Peter Shih-Ping Hung (PS)

Division of Brain, Imaging & 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.

Pascale Tsai (P)

Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.

Matthew R Walker (MR)

Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada.

Sarasa Tohyama (S)

A.A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, USA.

Marina Tawfik (M)

Collaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada.
Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Vector Institute for Artificial Intelligence, Toronto, ON, Canada.

Frank Rudzicz (F)

Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.

Mojgan Hodaie (M)

Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada. mojgan.hodaie@uhn.ca.
Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. mojgan.hodaie@uhn.ca.
Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada. mojgan.hodaie@uhn.ca.

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