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Du H, Ke S, Zhang W, Qi D, Sun T. Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm. ANAL SCI 2024:10.1007/s44211-024-00610-x. [PMID: 38836970 DOI: 10.1007/s44211-024-00610-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/23/2024] [Indexed: 06/06/2024]
Abstract
Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064 nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with Random Forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-Random Forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450; coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.
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Affiliation(s)
- Hongkun Du
- Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China
| | - Shaoying Ke
- Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, Fujian, China
| | - Wei Zhang
- Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China
| | - Dongfeng Qi
- Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China
| | - Tengfei Sun
- Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
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2
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Fu Z, Zhu H, Zhang Y, Huan R, Chen S, Pan Y. A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children. IEEE Trans Biomed Eng 2024; 71:1889-1900. [PMID: 38231823 DOI: 10.1109/tbme.2024.3355215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
OBJECTIVE Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. METHODS To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. RESULTS STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. CONCLUSION AND SIGNIFICANCE This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.
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Han Y, Valentini E, Halder S. Validation of Cross-Individual Pain Assessment with Individual Recognition Model from Electroencephalogram. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083495 DOI: 10.1109/embc40787.2023.10340607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Cross-individual pain assessment models based on electroencephalography (EEG) allow pain assessment in individuals who cannot report pain (e.g., unresponsive patients). The main obstacle to the generalisation of pain assessment models is the individual variation of brain responses to pain. Hence, we took the individual variation into account in cross-individual model development. We developed two convolutional neural networks (CNN) sharing an encoder architecture. One CNN predicts pain, while the other predicts the identity of an individual. We performed a leave-one-out (LOO) test with the exclusion of each subject and applied evidence accumulation to it for validating the pain prediction model's performance, where the binary classifier involves the states of pain (Hot) and resting state (Eyes-open). The mean accuracy produced by the LOO tests was 57.81% (max: 73.33%), and the mean accuracy of evidence accumulation achieved 69.75% (max: 100.00%). The individual recognition model achieved an accuracy of 99.63%. Nevertheless, when we acquired the most similar subject to a novel subject using the individual recognition model, where the most similar subject was used to train a subject-wise pain prediction model. The accuracy of predicting the pain-related conditions of the novel subject by the subject-wise model was only 53.73% (max: 79.50%). Therefore, the approach to utilising the features related to individual variation extracted by the CNN model needs more investigation for improving cross-individual pain assessment.Clinical relevance- This model can be applied to assess pain from EEG signals at the bedside with future improvement, which can help caretakers of unresponsive patients.
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Baek J, Won K, Kim H, Lee S, An J, Jun SC. Pain Classification using Evoked EEG Induced by Thermal Grill Illusion - Deep Neural Network Approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083440 DOI: 10.1109/embc40787.2023.10340391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
As the quantification of pain has emerged in biomedical engineering today, studies have been developing biomarkers associated with pain actively by measuring bio-signals such as electroencephalogram (EEG). Recently, some EEG studies of cold and hot pain have been reported. However, they used one type of stimulus condition for each trial and a relatively long stimulation time to collect EEG features. In this study, EEG signals during Cool (20 °C), Warm (40 °C), and Thermal Grill Illusion (TGI, 20-40 °C) stimuli were collected from 43 subjects, and were classified by a deep convolutional neural network referred to as EEGNet. Three binary classifications for the three conditions (TGI, Cool, Warm) were conducted for each subject individually. Classification accuracies for TGI-Cool, TGI-Warm, and Warm-Cool were 0.74±0.01, 0.71±0.01, and 0.74±0.01, respectively. For subjects who rated the TGI significantly hotter than the Warm stimulus, the classification accuracy for TGI-Cool (0.74±0.01) was significantly higher than for TGI-Warm (0.71±0.01). In contrast, the classification accuracy for TGI-Cool (0.72±0.03) did not differ statistically from TGI-Warm (0.73±0.01) in subjects without illusion. We found that the TGI and Cool stimuli were classified better than the TGI and Warm stimuli, implying that objective EEG features are consistent with subjective behavioral results. Further, we observed that most discriminative features between the TGI and the Cool or Warm conditions appeared in the parietal area for subjects who perceived the illusion. We postulate that the somato-sensory cortex may be activated when TGI is perceived to be hot pain.
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5
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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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6
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Fernandez Rojas R, Brown N, Waddington G, Goecke R. A systematic review of neurophysiological sensing for the assessment of acute pain. NPJ Digit Med 2023; 6:76. [PMID: 37100924 PMCID: PMC10133304 DOI: 10.1038/s41746-023-00810-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 03/30/2023] [Indexed: 04/28/2023] Open
Abstract
Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.
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Affiliation(s)
- Raul Fernandez Rojas
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia.
| | - Nicholas Brown
- Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Gordon Waddington
- Australian Institute of Sport, Canberra, ACT, Australia
- University of Canberra Research Institute for Sport and Exercise (UCRISE), University of Canberra, Canberra, ACT, Australia
| | - Roland Goecke
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
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7
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Gamma-band oscillations of pain and nociception: A systematic review and meta-analysis of human and rodent studies. Neurosci Biobehav Rev 2023; 146:105062. [PMID: 36682424 DOI: 10.1016/j.neubiorev.2023.105062] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 01/08/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Pain-induced gamma-band oscillations (GBOs) are one of the most promising biomarkers of the pain experience. Although GBOs reliably encode pain perception across different individuals and species, considerable heterogeneity could be observed in the characteristics and functions of GBOs. However, such heterogeneity of GBOs and its underlying sources have rarely been detailed previously. Here, we conducted a systematic review and meta-analysis to characterize the temporal, frequential, and spatial characteristics of GBOs and summarize the functional significance of distinct GBOs. We found that GBO heterogeneity was mainly related to pain types, with a higher frequency (∼66 Hz) GBOs at the sensorimotor cortex elicited by phasic pain and a lower frequency (∼55 Hz) GBOs at the prefrontal cortex associated with tonic and chronic pains. Positive correlations between GBO magnitudes and pain intensity were observed in healthy participants. Notably, the characteristics and functions of GBOs seemed to be phylogenetically conserved across humans and rodents. Altogether, we provided a comprehensive description of heterogeneous GBOs in pain and nociception, laying the foundation for clinical applications of GBOs.
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8
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Ren Z, Tan Y, Huang L, Yu H, Xiao S. Irregular characteristic analysis of 3D particles—A novel virtual sieving technique. POWDER TECHNOL 2023. [DOI: 10.1016/j.powtec.2023.118383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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9
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Mari T, Asgard O, Henderson J, Hewitt D, Brown C, Stancak A, Fallon N. External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals. Sci Rep 2023; 13:242. [PMID: 36604453 PMCID: PMC9816165 DOI: 10.1038/s41598-022-27298-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 12/29/2022] [Indexed: 01/07/2023] Open
Abstract
Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.
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Affiliation(s)
- Tyler Mari
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Oda Asgard
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Jessica Henderson
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Danielle Hewitt
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Christopher Brown
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Andrej Stancak
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Nicholas Fallon
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
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10
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Topaz LS, Frid A, Granovsky Y, Zubidat R, Crystal S, Buxbaum C, Bosak N, Hadad R, Domany E, Alon T, Meir Yalon L, Shor M, Khamaisi M, Hochberg I, Yarovinsky N, Volkovich Z, Bennett DL, Yarnitsky D. Electroencephalography functional connectivity-A biomarker for painful polyneuropathy. Eur J Neurol 2023; 30:204-214. [PMID: 36148823 PMCID: PMC10092565 DOI: 10.1111/ene.15575] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients. METHODS Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. RESULTS Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non-painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra-band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands. CONCLUSIONS Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.
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Affiliation(s)
- Leah Shafran Topaz
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Alex Frid
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Yelena Granovsky
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel.,Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Rabab Zubidat
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Shoshana Crystal
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Chen Buxbaum
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Noam Bosak
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Rafi Hadad
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Erel Domany
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Tayir Alon
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Lian Meir Yalon
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Merav Shor
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Mogher Khamaisi
- Department of Internal Medicine D, Rambam Health Care Campus, Haifa, Israel.,Endocrinology, Diabetes, and Metabolism Institute, Rambam Health Care Campus, Haifa, Israel
| | - Irit Hochberg
- Endocrinology, Diabetes, and Metabolism Institute, Rambam Health Care Campus, Haifa, Israel
| | | | - Zeev Volkovich
- Department of Software Engineering, ORT Braude College, Karmiel, Israel
| | - David L Bennett
- Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David Yarnitsky
- Laboratory of Clinical Neurophysiology, Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel.,Department of Neurology, Rambam Health Care Campus, Haifa, Israel
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11
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Savignac C, Ocay DD, Mahdid Y, Blain-Moraes S, Ferland CE. Clinical use of Electroencephalography in the Assessment of Acute Thermal Pain: A Narrative Review Based on Articles From 2009 to 2019. Clin EEG Neurosci 2022; 53:124-132. [PMID: 34133245 DOI: 10.1177/15500594211026280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Nowadays, no practical system has successfully been able to decode and predict pain in clinical settings. The inability of some patients to verbally express their pain creates the need for a tool that could objectively assess pain in these individuals. Neuroimaging techniques combined with machine learning are seen as possible candidates for the identification of pain biomarkers. This review aimed to address the potential use of electroencephalographic features as predictors of acute experimental pain. Twenty-six studies using only thermal stimulations were identified using a PubMed and Scopus search. Combinations of the following terms were used: "EEG," "Electroencephalography," "Acute," "Pain," "Tonic," "Noxious," "Thermal," "Stimulation," "Brain," "Activity," "Cold," "Subjective," and "Perception." Results revealed that contact-heat-evoked potentials have been widely recorded over central areas during noxious heat stimulations. Furthermore, a decrease in alpha power over central regions was revealed, as well as increased theta and gamma powers over frontal areas. Gamma and theta rhythms were associated with connectivity between sensory and affective regions involved in pain processing. A machine learning analysis revealed that the gamma band is a predominant predictor of acute thermal pain. This review also addressed the need of supplementing current spectral features with techniques that allow the investigation of network dynamics.
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Affiliation(s)
- Chloé Savignac
- 5620McGill University, Montreal, Quebec, Canada.,70357Shriners Hospitals for Children-Canada, Montreal, Quebec, Canada
| | - Don Daniel Ocay
- 5620McGill University, Montreal, Quebec, Canada.,70357Shriners Hospitals for Children-Canada, Montreal, Quebec, Canada
| | | | | | - Catherine E Ferland
- 5620McGill University, Montreal, Quebec, Canada.,70357Shriners Hospitals for Children-Canada, Montreal, Quebec, Canada.,Research Institute-McGill University Health Centre, Montreal, Quebec, Canada
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12
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Chen D, Zhang H, Kavitha PT, Loy FL, Ng SH, Wang C, Phua KS, Tjan SY, Yang SY, Guan C. Scalp EEG-based Pain Detection using Convolutional Neural Network. IEEE Trans Neural Syst Rehabil Eng 2022; 30:274-285. [PMID: 35089860 DOI: 10.1109/tnsre.2022.3147673] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83±0.09 and 0.81±0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.
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13
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Wu F, Mai W, Tang Y, Liu Q, Chen J, Guo Z. Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment. Neuroscience 2022; 481:144-155. [PMID: 34843893 DOI: 10.1016/j.neuroscience.2021.11.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022]
Abstract
Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key challenge in modeling pain events from EEG is to find invariant representations for intra- and inter-subject variations, where current methods based on hand-crafted features cannot provide satisfactory results. Hence, we propose a novel method based on deep learning to learn such invariant representations from multi-channel EEG signals and demonstrate its great advantages in EEG-based pain classification tasks. To begin, instead of using typical EEG analysis techniques that ignore spatial information of EEG, we convert raw EEG signals into a sequence of multi-spectral topography maps (topology-preserving EEG images). Next, inspired by various deep learning techniques applied in neuroimaging domain, a deep Attentive-Recurrent-Convolutional Neural Network (ARCNN) is proposed here to learn spatial-spectral-temporal representations from EEG images. The proposed method aims to jointly preserve the spatial-spectral-temporal structures of EEG, for learning representations with high robustness against intra-subject and inter-subject variations, making it more conducive to multi-class and subject-independent scenarios. Empirical evaluation on 4-level pain intensity assessment within the subject-independent scenario demonstrated significant improvement over baseline and state-of-the-art methods in this field. Our approach applies deep neural networks (DNNs) to pain intensity assessment for the first time and demonstrates its potential advantages in modeling pain events from EEG.
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Affiliation(s)
- Fengjie Wu
- Laboratory Center of Guangzhou University, Guangzhou University, Guangzhou 510006, China
| | - Weijian Mai
- School of Electronics and Communications Engineering, Guangzhou University, Guangzhou 510006, China.
| | - Yisheng Tang
- School of Electronics and Communications Engineering, Guangzhou University, Guangzhou 510006, China
| | - Qingkun Liu
- School of Electronics and Communications Engineering, Guangzhou University, Guangzhou 510006, China
| | - Jiangtao Chen
- School of Electronics and Communications Engineering, Guangzhou University, Guangzhou 510006, China
| | - Ziqian Guo
- School of Electronics and Communications Engineering, Guangzhou University, Guangzhou 510006, China
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14
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Kayabekir M, Yağanoğlu M. The relationship between snoring sounds and EEG signals on polysomnography. Sleep Breath 2021; 26:1219-1226. [PMID: 34697670 DOI: 10.1007/s11325-021-02516-8] [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: 07/30/2020] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE The aim of this study was to analyze the relationship of snoring sound signals obtained by polysomnography (PSG) in the sleep laboratory with cortical EEG (6 channel) signals to find answers to two important questions that have been covered to a limited extent in the literature: (1) Would the sounds generated by a snoring individual have an effect on the cerebral electrical waves occurring during sleep (specifically deep restorative sleep)? (2) Would the snoring sounds of an individual being examined by PSG have more of an effect on any one of the EEG electrodes? METHODS PSG recordings were obtained from volunteers with primary snoring and those with obstructive sleep apnea syndrome (OSAS) on six different EEG channels (F4-M1, C4-M1, and O2-M1, F3-M2, C3-M2, and O1-M2). The relationship of each of these recordings and snoring sound signals was analyzed by using a computer-based electrophysiological signal analysis method. A three-tier approach was used in this relationship: "Feature extraction, Feature selection, and Classification". RESULTS Data were obtained from a total of 40 volunteers (32 men, mean age (± SD) 47.5 ± 3.2 years), 20 with primary snoring and 20 with OSAS. The discrete wavelet transform (DWT) feature extraction method was the most successful method, and by utilizing this method for analyzing EEG channels, snoring sound signals were found to affect the C3-M2 channel the most (Duncan test, p < 0.05). Delta wave frequency levels during snoring were decreased compared to both before snoring (p = 0.160) and after snoring (p = 0.04) periods (paired sample test). DISCUSSION When snoring sounds and EEG signals were analyzed for frequency, time, and wave conversion with feature extraction methods, the C3-M2 channel was to be found the most affected channel. The sleep physiologist who made the PSG analyses reported that, among the 6 EEG channels analyzed for periods where there was no apnea or hypopnea events but only snoring, C3-M2 was the channel showing changes in delta wave activity. CONCLUSION Our study showed that the monotonous and repetitive snoring sounds of the snorer do not wake the individual, but do affect deep restorative sleep (N3). PSG signal analysis revealed that the most significant changes were in the C3-M2 channel (N3 delta wave amplitude increase and frequency decrease during snoring). Thus, clinicians may be able to monitor the characteristic changes occuring in large cortical delta waves in snoring individuals with innovative single-channel EEG devices without microphones.
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Affiliation(s)
- Murat Kayabekir
- Department of Physiology, Medical School, Atatürk University, Erzurum, Turkey.
| | - Mete Yağanoğlu
- Department of Computer Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey
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15
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Chen ZS. Decoding pain from brain activity. J Neural Eng 2021; 18. [PMID: 34608868 DOI: 10.1088/1741-2552/ac28d4] [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: 06/30/2021] [Accepted: 09/21/2021] [Indexed: 11/12/2022]
Abstract
Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, Neuroscience Institute, Interdisciplinary Pain Research Program, New York University Grossman School of Medicine, New York, NY 10016, United States of America
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16
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Salekin MS, Mouton PR, Zamzmi G, Patel R, Goldgof D, Kneusel M, Elkins SL, Murray E, Coughlin ME, Maguire D, Ho T, Sun Y. Future roles of artificial intelligence in early pain management of newborns. PAEDIATRIC AND NEONATAL PAIN 2021; 3:134-145. [PMID: 35547946 PMCID: PMC8975206 DOI: 10.1002/pne2.12060] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 07/07/2021] [Accepted: 07/19/2021] [Indexed: 12/14/2022]
Affiliation(s)
- Md Sirajus Salekin
- Computer Science and Engineering Department University of South Florida Tampa FL USA
| | | | - Ghada Zamzmi
- Computer Science and Engineering Department University of South Florida Tampa FL USA
| | - Raj Patel
- Muma College of Business University of South Florida Tampa FL USA
| | - Dmitry Goldgof
- Computer Science and Engineering Department University of South Florida Tampa FL USA
| | - Marcia Kneusel
- College of Medicine Pediatrics USF Health University of South Florida Tampa FL USA
| | | | | | | | - Denise Maguire
- College of Nursing USF Health University of South Florida Tampa FL USA
| | - Thao Ho
- College of Medicine Pediatrics USF Health University of South Florida Tampa FL USA
| | - Yu Sun
- Computer Science and Engineering Department University of South Florida Tampa FL USA
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17
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Mari T, Henderson J, Maden M, Nevitt S, Duarte R, Fallon N. Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data. THE JOURNAL OF PAIN 2021; 23:349-369. [PMID: 34425248 DOI: 10.1016/j.jpain.2021.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/25/2021] [Accepted: 07/27/2021] [Indexed: 11/17/2022]
Abstract
Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.
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Affiliation(s)
- Tyler Mari
- Department of Psychology, University of Liverpool, Liverpool, UK.
| | | | - Michelle Maden
- Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK
| | - Sarah Nevitt
- Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK
| | - Rui Duarte
- Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK
| | - Nicholas Fallon
- Department of Psychology, University of Liverpool, Liverpool, UK
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18
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SEN KAUSIK, PAL SAURABH. ALTERNATIVE METHOD FOR PAIN ASSESSMENT USING EMG AND GSR. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Most of the existing pain estimation techniques depend on the response of the subject through verbal or nonverbal communication which does not suit infants, senseless and injured persons, and subjects with cognitive impairment. To bridge this gap, researchers have explored the potential of facial video- and image-based pain recognition methods. However, it provides limited classification performance with complex computation and costly frameworks including a large storage capacity. The dependence of autonomic nervous system (ANS) activities on stimulus like pain provides an alternative pathway to assess pain subjected to external stimuli through ANS-related biosignals. In this article, processing and analysis technique of electromyogram and galvanic skin response signals for assessment of pain for noncooperative subjects are presented and validated against BioVid heat pain database. Different intensities of pain are considered and characterized with the statistical features extracted from the said biosignals. It is noticed that the accuracy level of pain estimation increases with the rise in pain intensity. For highest pain level, 80% detection accuracy is achieved which outperforms the performances of facial expression-based pain assessment techniques.
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Affiliation(s)
- KAUSIK SEN
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - SAURABH PAL
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
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19
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Entropy-based analysis and classification of acute tonic pain from microwave transcranial signals obtained via the microwave-scattering approach. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Hosseini MP, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng 2021; 14:204-218. [PMID: 32011262 DOI: 10.1109/rbme.2020.2969915] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to use, the analysis methods are also equally numerous. In this review, we will be examining specifically machine learning methods that have been developed for EEG analysis with bioengineering applications. We reviewed literature from 1988 to 2018 to capture previous and current classification methods for EEG in multiple applications. From this information, we are able to determine the overall effectiveness of each machine learning method as well as the key characteristics. We have found that all the primary methods used in machine learning have been applied in some form in EEG classification. This ranges from Naive-Bayes to Decision Tree/Random Forest, to Support Vector Machine (SVM). Supervised learning methods are on average of higher accuracy than their unsupervised counterparts. This includes SVM and KNN. While each of the methods individually is limited in their accuracy in their respective applications, there is hope that the combination of methods when implemented properly has a higher overall classification accuracy. This paper provides a comprehensive overview of Machine Learning applications used in EEG analysis. It also gives an overview of each of the methods and general applications that each is best suited to.
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21
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Afrasiabi S, Boostani R, Masnadi-Shirazi MA. Differentiation of pain levels by deploying various EEG synchronization features and dynamic ensemble selection mechanism. Physiol Meas 2020; 41. [PMID: 33108779 DOI: 10.1088/1361-6579/abc4f4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/27/2020] [Indexed: 12/29/2022]
Abstract
OBJECTIVE The target of this study is measuring the pain intensity in an objective manner by analysing the electroencephalogram (EEG) signals. Although this problem has attracted researchers' attention, increasing the resolution of this measurement, by increasing the number of pain states, significantly decreases the accuracy of pain level classification problem. APPROACH To overcome this drawback, we adopt state-of-the-art synchronization schemes to measure the linear, nonlinear and generalized synchronization between different EEG channels. 32 subjects executed the Cold Pressor Task (CPT) and experienced five defined levels of pain while recording their EEGs. Due to high number of synchronization features from 34 channels, the most discriminative features were selected using greedy overall relevancy (GOR) method. The selected features are applied to a dynamic ensemble selection system. MAIN RESULTS Our experiment provides 85.6% accuracy over the five classes, which significantly outperforms the results of past research. Moreover, we observe that the selected features belong to the channels placed over the ridge of cortex, the area responsible for processing somatic sensation arisen from nociceptive temperature. As expected, we noted that continuing the painful stimulus for minutes engaged regions beyond the sensorimotor cortex, e.g., the prefrontal cortex. SIGNIFICANCE We conclude that the amount of synchronization between scalp EEG channels is an informative tool in revealing the pain sensation.
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Affiliation(s)
- Somayeh Afrasiabi
- CSE& IT Department Faculty of Electrical and Computer Engineering, Shiraz University, Biomedical Group, Shiraz, IRAN, Shiraz, 71968-44656, Iran (the Islamic Republic of)
| | - Reza Boostani
- CSE&IT Dept., School of electrical and computer engineering, Shiraz University, Shiraz, Fars, Iran (the Islamic Republic of)
| | - Mohammad-Ali Masnadi-Shirazi
- School of Electrical & Computer Engineering, Shiraz University, Shiraz University, Shiraz, Fars, Iran (the Islamic Republic of)
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22
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Lin Q, Huang G, Li L, Zhang L, Liang Z, Anter AM, Zhang Z. Designing individual-specific and trial-specific models to accurately predict the intensity of nociceptive pain from single-trial fMRI responses. Neuroimage 2020; 225:117506. [PMID: 33127478 DOI: 10.1016/j.neuroimage.2020.117506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 10/06/2020] [Accepted: 10/21/2020] [Indexed: 11/19/2022] Open
Abstract
Using machine learning to predict the intensity of pain from fMRI has attracted rapidly increasing interests. However, due to remarkable inter- and intra-individual variabilities in pain responses, the performance of existing fMRI-based pain prediction models is far from satisfactory. The present study proposed a new approach which can design a prediction model specific to each individual or each experimental trial so that the specific model can achieve more accurate prediction of the intensity of nociceptive pain from single-trial fMRI responses. More precisely, the new approach uses a supervised k-means method on nociceptive-evoked fMRI responses to cluster individuals or trials into a set of subgroups, each of which has similar and consistent fMRI activation patterns. Then, for a new test individual/trial, the proposed approach chooses one subgroup of individuals/trials, which has the closest fMRI patterns to the test individual/trial, as training samples to train an individual-specific or a trial-specific pain prediction model. The new approach was tested on a nociceptive-evoked fMRI dataset and achieved significantly higher prediction accuracy than conventional non-specific models, which used all available training samples to train a model. The generalizability of the proposed approach is further validated by training specific models on one dataset and testing these models on an independent new dataset. This proposed individual-specific and trial-specific pain prediction approach has the potential to be used for the development of individualized and precise pain assessment tools in clinical practice.
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Affiliation(s)
- Qianqian Lin
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China; Department of Brain Functioning Research, The Seventh Hospital of Hangzhou, 305 Tianmushan Road, Hangzhou, Zhejiang, China
| | - Gan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Li Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Zhen Liang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Ahmed M Anter
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China; Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China.
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23
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Shirvalkar P, Sellers KK, Schmitgen A, Prosky J, Joseph I, Starr PA, Chang EF. A Deep Brain Stimulation Trial Period for Treating Chronic Pain. J Clin Med 2020; 9:jcm9103155. [PMID: 33003443 PMCID: PMC7600449 DOI: 10.3390/jcm9103155] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 12/17/2022] Open
Abstract
Early studies of deep brain stimulation (DBS) for various neurological disorders involved a temporary trial period where implanted electrodes were externalized, in which the electrical contacts exiting the patient's brain are connected to external stimulation equipment, so that stimulation efficacy could be determined before permanent implant. As the optimal brain target sites for various diseases (i.e., Parkinson's disease, essential tremor) became better established, such trial periods have fallen out of favor. However, deep brain stimulation trial periods are experiencing a modern resurgence for at least two reasons: (1) studies of newer indications such as depression or chronic pain aim to identify new targets and (2) a growing interest in adaptive DBS tools necessitates neurophysiological recordings, which are often done in the peri-surgical period. In this review, we consider the possible approaches, benefits, and risks of such inpatient trial periods with a specific focus on developing new DBS therapies for chronic pain.
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Affiliation(s)
- Prasad Shirvalkar
- Department of Anesthesiology (Pain Management), University of California San Francisco, San Francisco, CA 94143, USA;
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (K.K.S.); (A.S.); (I.J.); (P.A.S.); (E.F.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
- Correspondence:
| | - Kristin K. Sellers
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (K.K.S.); (A.S.); (I.J.); (P.A.S.); (E.F.C.)
| | - Ashlyn Schmitgen
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (K.K.S.); (A.S.); (I.J.); (P.A.S.); (E.F.C.)
| | - Jordan Prosky
- Department of Anesthesiology (Pain Management), University of California San Francisco, San Francisco, CA 94143, USA;
| | - Isabella Joseph
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (K.K.S.); (A.S.); (I.J.); (P.A.S.); (E.F.C.)
| | - Philip A. Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (K.K.S.); (A.S.); (I.J.); (P.A.S.); (E.F.C.)
| | - Edward F. Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (K.K.S.); (A.S.); (I.J.); (P.A.S.); (E.F.C.)
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Abstract
Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.
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Affiliation(s)
- Junseok A. Kim
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen D. Davis
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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25
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Yu K, Niu X, He B. Neuromodulation Management of Chronic Neuropathic Pain in The Central Nervous system. ADVANCED FUNCTIONAL MATERIALS 2020; 30:1908999. [PMID: 34335132 PMCID: PMC8323399 DOI: 10.1002/adfm.201908999] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Indexed: 05/05/2023]
Abstract
Neuromodulation is becoming one of the clinical tools for treating chronic neuropathic pain by transmitting controlled physical energy to the pre-identified neural targets in the central nervous system. Its nature of drug-free, non-addictive and improved targeting have attracted increasing attention among neuroscience research and clinical practices. This article provides a brief overview of the neuropathic pain and pharmacological routines for treatment, summarizes both the invasive and non-invasive neuromodulation modalities for pain management, and highlights an emerging brain stimulation technology, transcranial focused ultrasound (tFUS) with a focus on ultrasound transducer devices and the achieved neuromodulation effects and applications on pain management. Practical considerations of spatial guidance for tFUS are discussed for clinical applications. The safety of transcranial ultrasound neuromodulation and its future prospectives on pain management are also discussed.
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Affiliation(s)
| | | | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University
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26
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Levitt J, Edhi MM, Thorpe RV, Leung JW, Michishita M, Koyama S, Yoshikawa S, Scarfo KA, Carayannopoulos AG, Gu W, Srivastava KH, Clark BA, Esteller R, Borton DA, Jones SR, Saab CY. Pain phenotypes classified by machine learning using electroencephalography features. Neuroimage 2020; 223:117256. [PMID: 32871260 PMCID: PMC9084327 DOI: 10.1016/j.neuroimage.2020.117256] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 07/24/2020] [Accepted: 08/07/2020] [Indexed: 12/26/2022] Open
Abstract
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.
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Affiliation(s)
- Joshua Levitt
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Muhammad M Edhi
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Ryan V Thorpe
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Jason W Leung
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Mai Michishita
- Laboratory for Pharmacology, Asahi Kasei Pharma Corporation, Mifuku, Shizuoka, Japan
| | - Suguru Koyama
- Laboratory for Pharmacology, Asahi Kasei Pharma Corporation, Mifuku, Shizuoka, Japan
| | - Satoru Yoshikawa
- Laboratory for Pharmacology, Asahi Kasei Pharma Corporation, Mifuku, Shizuoka, Japan
| | - Keith A Scarfo
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | | | - Wendy Gu
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | | | - Bryan A Clark
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | - Rosana Esteller
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | - David A Borton
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Carl Y Saab
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States; Department of Neuroscience, Brown University, Providence, RI, United States.
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Lavanga M, Smets L, Bollen B, Jansen K, Ortibus E, Huffel SV, Naulaers G, Caicedo A. A perinatal stress calculator for the neonatal intensive care unit: an unobtrusive approach. Physiol Meas 2020; 41:075012. [PMID: 32521528 DOI: 10.1088/1361-6579/ab9b66] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Early experience of pain and stress in the neonatal intensive care unit is known to have an effect on the neurodevelopment of the infant. However, an automated method to quantify the procedural pain or perinatal stress in premature patients does not exist. APPROACH In the current study, EEG and ECG data were collected for more than 3 hours from 136 patients in order to quantify stress exposure. Specifically, features extracted from the EEG and heart-rate variability in both quiet and non-quiet sleep segments were used to develop a subspace linear-discriminant analysis stress classifier. MAIN RESULTS The main novelty of the study lies in the absence of intrusive methods or pain elicitation protocols to develop the stress classifier. Three main findings can be reported. First, we developed different stress classifiers for the different age groups and stress intensities, obtaining an area under the curve in the range [0.78-0.93] for non-quiet sleep and [0.77-0.96] for quiet sleep. Second, a dysmature EEG was found in patients under stress. Third, an enhanced cortical connectivity and increased brain-heart communication was correlated with a higher stress load, while the autonomic activity did not seem to be associated to stress exposure. SIGNIFICANCE The results shed a light on the pain and stress processing in preterm neonates, suggesting that software tools to investigate dysmature EEG might be helpful to assess stress load in premature patients. These results could be the foundation to assess the impact of stress on infants' development and to tune preventive care.
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Affiliation(s)
- M Lavanga
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, box 2446, 3001, Leuven, Belgium. Authors contributed equally to this work
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Shamsi F, Haddad A, Zadeh LN. Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2869-2872. [PMID: 33018605 DOI: 10.1109/embc44109.2020.9175627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86-80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating pain/no-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.
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Tripanpitak K, Viriyavit W, Huang SY, Yu W. Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1491. [PMID: 32182766 PMCID: PMC7085779 DOI: 10.3390/s20051491] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 12/30/2019] [Accepted: 01/04/2020] [Indexed: 12/11/2022]
Abstract
Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.
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Affiliation(s)
- Kornkanok Tripanpitak
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan; (K.T.); (W.V.)
| | - Waranrach Viriyavit
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan; (K.T.); (W.V.)
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
| | - Shao Ying Huang
- Engineering Product Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore;
| | - Wenwei Yu
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan; (K.T.); (W.V.)
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
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30
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Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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31
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Naranjo-Hernández D, Reina-Tosina J, Roa LM. Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E365. [PMID: 31936420 PMCID: PMC7014460 DOI: 10.3390/s20020365] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/03/2020] [Accepted: 01/05/2020] [Indexed: 12/15/2022]
Abstract
Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.
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Affiliation(s)
- David Naranjo-Hernández
- Biomedical Engineering Group, University of Seville, 41092 Seville, Spain; (J.R.-T.); (L.M.R.)
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32
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van der Miesen MM, Lindquist MA, Wager TD. Neuroimaging-based biomarkers for pain: state of the field and current directions. Pain Rep 2019; 4:e751. [PMID: 31579847 PMCID: PMC6727991 DOI: 10.1097/pr9.0000000000000751] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/20/2019] [Accepted: 04/07/2019] [Indexed: 12/15/2022] Open
Abstract
Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.
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Affiliation(s)
- Maite M. van der Miesen
- Institute for Interdisciplinary Studies, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Tor D. Wager
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA
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33
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Xiao Z, Martinez E, Kulkarni PM, Zhang Q, Hou Q, Rosenberg D, Talay R, Shalot L, Zhou H, Wang J, Chen ZS. Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex. Front Cell Neurosci 2019; 13:165. [PMID: 31105532 PMCID: PMC6492531 DOI: 10.3389/fncel.2019.00165] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 04/08/2019] [Indexed: 01/08/2023] Open
Abstract
Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.
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Affiliation(s)
- Zhengdong Xiao
- Department of Instrument Science and Technology, Zhejiang University, Hangzhou, China.,Department of Psychiatry, New York University School of Medicine, New York, NY, United States
| | - Erik Martinez
- Department of Psychiatry, New York University School of Medicine, New York, NY, United States.,Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, United States
| | - Prathamesh M Kulkarni
- Department of Psychiatry, New York University School of Medicine, New York, NY, United States
| | - Qiaosheng Zhang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, United States
| | - Qianning Hou
- Department of Psychiatry, New York University School of Medicine, New York, NY, United States.,Department of Biophysics, University of Science and Technology of China, Hefei, China
| | - David Rosenberg
- New York University School of Medicine, New York, NY, United States
| | - Robert Talay
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, United States
| | - Leor Shalot
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, United States
| | - Haocheng Zhou
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, United States
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, United States.,Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, United States
| | - Zhe Sage Chen
- Department of Psychiatry, New York University School of Medicine, New York, NY, United States.,Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, United States.,Neuroscience Institute, New York University School of Medicine, New York, NY, United States
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Abstract
While several features of brain activity can be used to predict the variability of painful percepts within a given individual, it is much more difficult to predict pain variability across individuals. Here, we used electrophysiology to sample brain activity of humans and rodents, and demonstrated that laser-induced gamma oscillations sampled by central electrodes predict pain sensitivity across individuals both reliably and selectively: reliably, because they consistently predict between-subject pain intensity in both humans and rodents; selectively, because they do not track the between-subject reported intensity of nonpainful but equally salient auditory, visual, and nonnociceptive somatosensory stimuli. This discovery indicates that variability in an individual’s pain sensitivity is, at least partly, explained by variability in the amplitude of gamma oscillations of that individual. Individuals exhibit considerable and unpredictable variability in painful percepts in response to the same nociceptive stimulus. Previous work has found neural responses that, while not necessarily responsible for the painful percepts themselves, can still correlate well with intensity of pain perception within a given individual. However, there is no reliable neural response reflecting the variability in pain perception across individuals. Here, we use an electrophysiological approach in humans and rodents to demonstrate that brain oscillations in the gamma band [gamma-band event-related synchronization (γ-ERS)] sampled by central electrodes reliably predict pain sensitivity across individuals. We observed a clear dissociation between the large number of neural measures that reflected subjective pain ratings at within-subject level but not across individuals, and γ-ERS, which reliably distinguished subjective ratings within the same individual but also coded pain sensitivity across different individuals. Importantly, the ability of γ-ERS to track pain sensitivity across individuals was selective because it did not track the between-subject reported intensity of nonpainful but equally salient auditory, visual, and nonnociceptive somatosensory stimuli. These results also demonstrate that graded neural activity related to within-subject variability should be minimized to accurately investigate the relationship between nociceptive-evoked neural activities and pain sensitivity across individuals.
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35
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Lin Q, Li L, Liu J, Liu W, Huang G, Zhang Z. Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance. Front Neurosci 2018; 12:569. [PMID: 30158851 PMCID: PMC6104174 DOI: 10.3389/fnins.2018.00569] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 07/27/2018] [Indexed: 12/20/2022] Open
Abstract
Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models.
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Affiliation(s)
- Qianqian Lin
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Jia Liu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Weixiang Liu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Gan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
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36
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Copot D, Ionescu C. Models for Nociception Stimulation and Memory Effects in Awake and Aware Healthy Individuals. IEEE Trans Biomed Eng 2018; 66:718-726. [PMID: 30010543 DOI: 10.1109/tbme.2018.2854917] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE This paper introduces a primer in the health care practice, namely a mathematical model and methodology for detecting and analysing nociceptor stimulation followed by related tissue memory effects. METHODS Noninvasive nociceptor stimulus protocol and prototype device for measuring bioimpedance is provided. Various time instants, sensor location, and stimulus train have been analysed. RESULTS The method and model indicate that nociceptor stimulation perceived as pain in awake healthy volunteers is noninvasively detected. The existence of a memory effect is proven from data. Sensor location had minimal effect on detection level, while day-to-day variability was observed without being significant. CONCLUSION Following the experimental study, the model enables a comprehensive management of chronic pain patients, and possibly other analgesia, or pain related regulatory loops. SIGNIFICANCE A device and methodology for noninvasive for detecting nociception stimulation have been developed. The proposed method and models have been validated on healthy volunteers.
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