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Mathew J, Adhia DB, Hall M, De Ridder D, Mani R. EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation. THE JOURNAL OF PAIN 2024; 25:104429. [PMID: 37989404 DOI: 10.1016/j.jpain.2023.11.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 11/05/2023] [Accepted: 11/10/2023] [Indexed: 11/23/2023]
Abstract
Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PERSPECTIVE: This study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.
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Affiliation(s)
- Jerin Mathew
- Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, New Zealand; Department of Anatomy, School of Biomedical Sciences, University of Otago, New Zealand; Pain@Otago Research Theme, University of Otago, New Zealand
| | - Divya B Adhia
- Pain@Otago Research Theme, University of Otago, New Zealand; Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Matthew Hall
- Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Dirk De Ridder
- Pain@Otago Research Theme, University of Otago, New Zealand; Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Ramakrishnan Mani
- Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, New Zealand; Pain@Otago Research Theme, University of Otago, New Zealand
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Karabey Aksalli I, Baygin N, Hagiwara Y, Paul JK, Iype T, Barua PD, Koh JEW, Baygin M, Dogan S, Tuncer T, Acharya UR. Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D'hondt pooling technique. Cogn Neurodyn 2024; 18:383-404. [PMID: 38699621 PMCID: PMC11061097 DOI: 10.1007/s11571-023-10005-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/08/2023] [Accepted: 08/24/2023] [Indexed: 05/05/2024] Open
Abstract
Fibromyalgia is a soft tissue rheumatism with significant qualitative and quantitative impact on sleep macro and micro architecture. The primary objective of this study is to analyze and identify automatically healthy individuals and those with fibromyalgia using sleep electroencephalography (EEG) signals. The study focused on the automatic detection and interpretation of EEG signals obtained from fibromyalgia patients. In this work, the sleep EEG signals are divided into 15-s and a total of 5358 (3411 healthy control and 1947 fibromyalgia) EEG segments are obtained from 16 fibromyalgia and 16 normal subjects. Our developed model has advanced multilevel feature extraction architecture and hence, we used a new feature extractor called GluPat, inspired by the glucose chemical, with a new pooling approach inspired by the D'hondt selection system. Furthermore, our proposed method incorporated feature selection techniques using iterative neighborhood component analysis and iterative Chi2 methods. These selection mechanisms enabled the identification of discriminative features for accurate classification. In the classification phase, we employed a support vector machine and k-nearest neighbor algorithms to classify the EEG signals with leave-one-record-out (LORO) and tenfold cross-validation (CV) techniques. All results are calculated channel-wise and iterative majority voting is used to obtain generalized results. The best results were determined using the greedy algorithm. The developed model achieved a detection accuracy of 100% and 91.83% with a tenfold and LORO CV strategies, respectively using sleep stage (2 + 3) EEG signals. Our generated model is simple and has linear time complexity.
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Affiliation(s)
- Isil Karabey Aksalli
- Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey
| | - Nursena Baygin
- Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey
| | - Yuki Hagiwara
- Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany
| | - Jose Kunnel Paul
- Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala India
| | - Thomas Iype
- Department of Neurology, Government Medical College, Thiruvananthapuram, Kerala India
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Springfield, Australia
| | - Joel E. W. Koh
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - U. Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Mathew J, Perez TM, Adhia DB, De Ridder D, Mani R. Is There a Difference in EEG Characteristics in Acute, Chronic, and Experimentally Induced Musculoskeletal Pain States? a Systematic Review. Clin EEG Neurosci 2024; 55:101-120. [PMID: 36377346 DOI: 10.1177/15500594221138292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Electroencephalographic (EEG) alterations have been demonstrated in acute, chronic, and experimentally induced musculoskeletal (MSK) pain conditions. However, there is no cumulative evidence on the associated EEG characteristics differentiating acute, chronic, and experimentally induced musculoskeletal pain states, especially compared to healthy controls. The present systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PRISMA) to review and summarize available evidence for cortical brain activity and connectivity alterations in acute, chronic, and experimentally induced MSK pain states. Five electronic databases were systematically searched from their inception to 2022. A total of 3471 articles were screened, and 26 full articles (five studies on chronic pain and 21 studies on experimentally induced pain) were included for the final synthesis. Using the Downs and Black risk of assessment tool, 92% of the studies were assessed as low to moderate quality. The review identified a 'very low' level of evidence for the changes in EEG and subjective outcome measures for both chronic and experimentally induced MSK pain based on the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria. Overall, the findings of this review indicate a trend toward decreased alpha and beta EEG power in evoked chronic clinical pain conditions and increased theta and alpha power in resting-state EEG recorded from chronic MSK pain conditions. EEG characteristics are unclear under experimentally induced pain conditions.
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Affiliation(s)
- Jerin Mathew
- Centre for Health, Activity, and Rehabilitation Research (CHARR), School of Physiotherapy, University of Otago, Dunedin, New Zealand
| | - Tyson Michael Perez
- Department of Surgical Sciences, Section of Neurosurgery, Otago Medical School-Dunedin campus, University of Otago, Dunedin, New Zealand
| | - Divya Bharatkumar Adhia
- Department of Surgical Sciences, Section of Neurosurgery, Otago Medical School-Dunedin campus, University of Otago, Dunedin, New Zealand
| | - Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Otago Medical School-Dunedin campus, University of Otago, Dunedin, New Zealand
| | - Ramakrishnan Mani
- Centre for Health, Activity, and Rehabilitation Research (CHARR), School of Physiotherapy, University of Otago, Dunedin, New Zealand
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