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Graeme-Drury TJ, Worthen SF, Maden M, Raphael JH, Khan S, Vreugdenhil M, Duarte RV. Contact Heat in Magnetoencephalography: A Systematic Review. Can J Neurol Sci 2024; 51:179-186. [PMID: 36803520 DOI: 10.1017/cjn.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND Contact heat is commonly used in experimental research to evoke brain activity, most frequently acquired with electroencephalography (EEG). Although magnetoencephalography (MEG) improves spatial resolution, using some contact heat stimulators with MEG can present methodological challenges. This systematic review assesses studies that utilise contact heat in MEG, their findings and possible directions for further research. METHODS Eight electronic databases were searched for relevant studies, in addition to the selected papers' reference lists, citations and ConnectedPapers maps. Best practice recommendations for systematic reviews were followed. Papers met inclusion criteria if they used MEG to record brain activity in conjunction with contact heat, regardless of stimulator equipment or paradigm. RESULTS Of 646 search results, seven studies met the inclusion criteria. Studies demonstrated effective electromagnetic artefact removal from MEG data, the ability to elicit affective anticipation and differences in deep brain stimulation responders. We identify contact heat stimulus parameters that should be reported in publications to ensure comparisons between data outcomes are consistent. CONCLUSIONS Contact heat is a viable alternative to laser or electrical stimulation in experimental research, and methods exist to successfully mitigate any electromagnetic noise generated by PATHWAY CHEPS equipment - though there is a dearth of literature exploring the post-stimulus time window.
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Affiliation(s)
| | - Siân F Worthen
- Aston Institute of Health and Neurodevelopment, Birmingham, UK
| | - Michelle Maden
- Liverpool Reviews and Implementation Group; University of Liverpool, Liverpool, UK
| | - Jon H Raphael
- School of Health Sciences, Birmingham City University, Birmingham, UK
| | - Salim Khan
- School of Health Sciences, Birmingham City University, Birmingham, UK
| | | | - Rui V Duarte
- Liverpool Reviews and Implementation Group; University of Liverpool, Liverpool, UK
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Cascella M, Schiavo D, Cuomo A, Ottaiano A, Perri F, Patrone R, Migliarelli S, Bignami EG, Vittori A, Cutugno F. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. Pain Res Manag 2023; 2023:6018736. [PMID: 37416623 PMCID: PMC10322534 DOI: 10.1155/2023/6018736] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/03/2023] [Accepted: 04/20/2023] [Indexed: 07/08/2023]
Abstract
Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.
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Affiliation(s)
- Marco Cascella
- Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy
| | - Daniela Schiavo
- Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy
| | - Arturo Cuomo
- Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy
| | - Alessandro Ottaiano
- SSD-Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori di Napoli IRCCS “G. Pascale”, Via M. Semmola, Naples 80131, Italy
| | - Francesco Perri
- Head and Neck Oncology Unit, Istituto Nazionale Tumori IRCCS-Fondazione “G. Pascale”, Naples 80131, Italy
| | - Renato Patrone
- Dieti Department, University of Naples, Naples, Italy
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS, Fondazione Pascale-IRCCS di Napoli, Naples, Italy
| | - Sara Migliarelli
- Department of Pharmacology, Faculty of Medicine and Psychology, University Sapienza of Rome, Rome, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alessandro Vittori
- Department of Anesthesia and Critical Care, ARCO ROMA, Ospedale Pediatrico Bambino Gesù IRCCS, Rome 00165, Italy
| | - Francesco Cutugno
- Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, Naples 80100, Italy
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Teel EF, Ocay DD, Blain-Moraes S, Ferland CE. Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain. FRONTIERS IN PAIN RESEARCH 2022; 3:991793. [PMID: 36238349 PMCID: PMC9552004 DOI: 10.3389/fpain.2022.991793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/14/2022] [Indexed: 11/23/2022] Open
Abstract
Objective We assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain. Methods Thirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants. Results SVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups. Conclusions Our results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report.
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Affiliation(s)
- Elizabeth F. Teel
- Department of Health, Kinesiology, & Applied Physiology, School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
| | - Don Daniel Ocay
- Department of Experimental Surgery, McGill University, Montreal, QC, Canada
- Shriners Hospitals for Children-Canada, Montreal, QC, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
- Correspondence: Stefanie Blain-Moraes
| | - Catherine E. Ferland
- Shriners Hospitals for Children-Canada, Montreal, QC, Canada
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
- Research Institute-McGill University Health Centre, Montreal, QC, Canada
- Alan Edwards Research Center for Pain, McGill University, Montreal, QC, Canada
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