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Hanycz SA, Noorani A, Hung PSP, Walker MR, Zhang AB, Latypov TH, Hodaie M. Hippocampus diffusivity abnormalities in classical trigeminal neuralgia. Pain Rep 2024; 9:e1159. [PMID: 38655236 PMCID: PMC11037743 DOI: 10.1097/pr9.0000000000001159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 02/16/2024] [Accepted: 02/24/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Patients with chronic pain frequently report cognitive symptoms that affect memory and attention, which are functions attributed to the hippocampus. Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder characterized by paroxysmal attacks of unilateral orofacial pain. Given the stereotypical nature of TN pain and lack of negative symptoms including sensory loss, TN provides a unique model to investigate the hippocampal implications of chronic pain. Recent evidence demonstrated that TN is associated with macrostructural hippocampal abnormalities indicated by reduced subfield volumes; however, there is a paucity in our understanding of hippocampal microstructural abnormalities associated with TN. Objectives To explore diffusivity metrics within the hippocampus, along with its functional and structural subfields, in patients with TN. Methods To examine hippocampal microstructure, we utilized diffusion tensor imaging in 31 patients with TN and 21 controls. T1-weighted magnetic resonance images were segmented into hippocampal subfields and registered into diffusion-weighted imaging space. Fractional anisotropy (FA) and mean diffusivity were extracted for hippocampal subfields and longitudinal axis segmentations. Results Patients with TN demonstrated reduced FA in bilateral whole hippocampi and hippocampal body and contralateral subregions CA2/3 and CA4, indicating microstructural hippocampal abnormalities. Notably, patients with TN showed significant correlation between age and hippocampal FA, while controls did not exhibit this correlation. These effects were driven chiefly by female patients with TN. Conclusion This study demonstrates that TN is associated with microstructural hippocampal abnormalities, which may precede and potentially be temporally linked to volumetric hippocampal alterations demonstrated previously. These findings provide further evidence for the role of the hippocampus in chronic pain and suggest the potential for targeted interventions to mitigate cognitive symptoms in patients with chronic pain.
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Affiliation(s)
- Shaun Andrew Hanycz
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Alborz Noorani
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Peter Shih-Ping Hung
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Matthew R. Walker
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Ashley B. Zhang
- MD Program, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Timur H. Latypov
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mojgan Hodaie
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Neurosurgery, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
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Latypov TH, So MC, Hung PSP, Tsai P, Walker MR, Tohyama S, Tawfik M, Rudzicz F, Hodaie M. Brain imaging signatures of neuropathic facial pain derived by artificial intelligence. Sci Rep 2023; 13:10699. [PMID: 37400574 DOI: 10.1038/s41598-023-37034-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
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.
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Affiliation(s)
- Timur H Latypov
- 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
- Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Peter Shih-Ping Hung
- 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
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Matthew R Walker
- Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Sarasa Tohyama
- A.A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, USA
| | - Marina Tawfik
- 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
- 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
- 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.
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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