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Petrušić I, Ha WS, Labastida-Ramirez A, Messina R, Onan D, Tana C, Wang W. Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1. J Headache Pain 2024; 25:151. [PMID: 39272003 PMCID: PMC11401391 DOI: 10.1186/s10194-024-01847-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 08/18/2024] [Indexed: 09/15/2024] Open
Abstract
Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload. AI-powered tools, such as large language models, could facilitate automated clinical notes and faster identification of effective drug combinations in headache patients, reducing cognitive burdens and physician burnout. AI diagnostic models also could enhance diagnostic accuracy for non-headache specialists, making headache management more accessible in general medical practice. Furthermore, virtual health assistants, digital applications, and wearable devices are pivotal in migraine management, enabling symptom tracking, trigger identification, and preventive measures. AI tools also could offer stress management and pain relief solutions to headache patients through digital applications. However, considerations such as technology literacy, compatibility, privacy, and regulatory standards must be adequately addressed. Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations.
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Affiliation(s)
- Igor Petrušić
- Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, 12-16 Studentski Trg Street, Belgrade, 11000, Serbia.
| | - Woo-Seok Ha
- Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Alejandro Labastida-Ramirez
- Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Roberta Messina
- Neuroimaging research unit and Neurology unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Dilara Onan
- Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Yozgat Bozok University, Yozgat, Turkey
| | - Claudio Tana
- Center of Excellence on Headache, Geriatrics Unit, SS. University Hospital of Chieti, Chieti, Italy
| | - Wei Wang
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Headache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Stubberud A, Langseth H, Nachev P, Matharu MS, Tronvik E. Artificial intelligence and headache. Cephalalgia 2024; 44:3331024241268290. [PMID: 39099427 DOI: 10.1177/03331024241268290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
BACKGROUND AND METHODS In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. RESULTS We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. CONCLUSIONS The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.
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Affiliation(s)
- Anker Stubberud
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Helge Langseth
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Manjit S Matharu
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, Neuroclinic, StOlav University Hospital, Trondheim, Norway
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Chen WT, Hsiao FJ, Coppola G, Wang SJ. Decoding pain through facial expressions: a study of patients with migraine. J Headache Pain 2024; 25:33. [PMID: 38462615 PMCID: PMC10926654 DOI: 10.1186/s10194-024-01742-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/01/2024] [Indexed: 03/12/2024] Open
Abstract
BACKGROUND The present study used the Facial Action Coding System (FACS) to analyse changes in facial activities in individuals with migraine during resting conditions to determine the potential of facial expressions to convey information about pain during headache episodes. METHODS Facial activity was recorded in calm and resting conditions by using a camera for both healthy controls (HC) and patients with episodic migraine (EM) and chronic migraine (CM). The FACS was employed to analyse the collected facial images, and intensity scores for each of the 20 action units (AUs) representing expressions were generated. The groups and headache pain conditions were then examined for each AU. RESULTS The study involved 304 participants, that is, 46 HCs, 174 patients with EM, and 84 patients with CM. Elevated headache pain levels were associated with increased lid tightener activity and reduced mouth stretch. In the CM group, moderate to severe headache attacks exhibited decreased activation in the mouth stretch, alongside increased activation in the lid tightener, nose wrinkle, and cheek raiser, compared to mild headache attacks (all corrected p < 0.05). Notably, lid tightener activation was positively correlated with the Numeric Rating Scale (NRS) level of headache (p = 0.012). Moreover, the lip corner depressor was identified to be indicative of emotional depression severity (p < 0.001). CONCLUSION Facial expressions, particularly lid tightener actions, served as inherent indicators of headache intensity in individuals with migraine, even during resting conditions. This indicates that the proposed approach holds promise for providing a subjective evaluation of headaches, offering the benefits of real-time assessment and convenience for patients with migraine.
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Affiliation(s)
- Wei-Ta Chen
- Brain Research Center, National Yang Ming Chiao Tung University, 155, Linong Street Sec 2, Taipei, 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Neurology, Keelung Hospital, Ministry of Health and Welfare, Keelung, Taiwan
| | - Fu-Jung Hsiao
- Brain Research Center, National Yang Ming Chiao Tung University, 155, Linong Street Sec 2, Taipei, 112, Taiwan.
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Latina, Italy
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang Ming Chiao Tung University, 155, Linong Street Sec 2, Taipei, 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
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Torrente A, Maccora S, Prinzi F, Alonge P, Pilati L, Lupica A, Di Stefano V, Camarda C, Vitabile S, Brighina F. The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci 2024; 14:85. [PMID: 38248300 PMCID: PMC10813497 DOI: 10.3390/brainsci14010085] [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: 12/22/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs' management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.
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Affiliation(s)
- Angelo Torrente
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Simona Maccora
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology Unit, ARNAS Civico di Cristina and Benfratelli Hospitals, 90127 Palermo, Italy
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Paolo Alonge
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Laura Pilati
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology and Stroke Unit, P.O. “S. Antonio Abate”, 91016 Trapani, Italy
| | - Antonino Lupica
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Vincenzo Di Stefano
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Cecilia Camarda
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Filippo Brighina
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
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Hsiao FJ, Chen WT, Wu YT, Pan LLH, Wang YF, Chen SP, Lai KL, Coppola G, Wang SJ. Characteristic oscillatory brain networks for predicting patients with chronic migraine. J Headache Pain 2023; 24:139. [PMID: 37848845 PMCID: PMC10583316 DOI: 10.1186/s10194-023-01677-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023] Open
Abstract
To determine specific resting-state network patterns underlying alterations in chronic migraine, we employed oscillatory connectivity and machine learning techniques to distinguish patients with chronic migraine from healthy controls and patients with other pain disorders. This cross-sectional study included 350 participants (70 healthy controls, 100 patients with chronic migraine, 40 patients with chronic migraine with comorbid fibromyalgia, 35 patients with fibromyalgia, 30 patients with chronic tension-type headache, and 75 patients with episodic migraine). We collected resting-state magnetoencephalographic data for analysis. Source-based oscillatory connectivity within each network, including the pain-related network, default mode network, sensorimotor network, visual network, and insula to default mode network, was examined to determine intrinsic connectivity across a frequency range of 1-40 Hz. Features were extracted to establish and validate classification models constructed using machine learning algorithms. The findings indicated that oscillatory connectivity revealed brain network abnormalities in patients with chronic migraine compared with healthy controls, and that oscillatory connectivity exhibited distinct patterns between various pain disorders. After the incorporation of network features, the best classification model demonstrated excellent performance in distinguishing patients with chronic migraine from healthy controls, achieving high accuracy on both training and testing datasets (accuracy > 92.6% and area under the curve > 0.93). Moreover, in validation tests, classification models exhibited high accuracy in discriminating patients with chronic migraine from all other groups of patients (accuracy > 75.7% and area under the curve > 0.8). In conclusion, oscillatory synchrony within the pain-related network and default mode network corresponded to altered neurophysiological processes in patients with chronic migraine. Thus, these networks can serve as pivotal signatures in the model for identifying patients with chronic migraine, providing reliable and generalisable results. This approach may facilitate the objective and individualised diagnosis of migraine.
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Affiliation(s)
- Fu-Jung Hsiao
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Ta Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
- Department of Neurology, Keelung Hospital, Ministry of Health and Welfare, Keelung, Taiwan
| | - Yu-Te Wu
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Li-Ling Hope Pan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Feng Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
| | - Shih-Pin Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
| | - Kuan-Lin Lai
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Polo Pontino, Latina, Italy
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 201, Shih Pai Rd Sec 2, Taipei, 11217, Taiwan.
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Marino S, Jassar H, Kim DJ, Lim M, Nascimento TD, Dinov ID, Koeppe RA, DaSilva AF. Classifying migraine using PET compressive big data analytics of brain's μ-opioid and D2/D3 dopamine neurotransmission. Front Pharmacol 2023; 14:1173596. [PMID: 37383727 PMCID: PMC10294712 DOI: 10.3389/fphar.2023.1173596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 05/26/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood in vivo. This study examined migraine patients with a novel machine learning (ML) method based on their central μ-opioid and dopamine D2/D3 profiles, the most critical neurotransmitters in the brain for pain perception and its cognitive-motivational interface. Methods: We employed compressive Big Data Analytics (CBDA) to identify migraineurs and healthy controls (HC) in a large positron emission tomography (PET) dataset. 198 PET volumes were obtained from 38 migraineurs and 23 HC during rest and thermal pain challenge. 61 subjects were scanned with the selective μ-opioid receptor (μOR) radiotracer [11C]Carfentanil, and 22 with the selective dopamine D2/D3 receptor (DOR) radiotracer [11C]Raclopride. PET scans were recast into a 1D array of 510,340 voxels with spatial and intensity filtering of non-displaceable binding potential (BPND), representing the receptor availability level. We then performed data reduction and CBDA to power rank the predictive brain voxels. Results: CBDA classified migraineurs from HC with accuracy, sensitivity, and specificity above 90% for whole-brain and region-of-interest (ROI) analyses. The most predictive ROIs for μOR were the insula (anterior), thalamus (pulvinar, medial-dorsal, and ventral lateral/posterior nuclei), and the putamen. The latter, putamen (anterior), was also the most predictive for migraine regarding DOR D2/D3 BPND levels. Discussion: CBDA of endogenous μ-opioid and D2/D3 dopamine dysfunctions in the brain can accurately identify a migraine patient based on their receptor availability across key sensory, motor, and motivational processing regions. Our ML-based findings in the migraineur's brain neurotransmission partly explain the severe impact of migraine suffering and associated neuropsychiatric comorbidities.
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Affiliation(s)
- Simeone Marino
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United States
| | - Hassan Jassar
- The Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United States
- Headache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - Dajung J. Kim
- The Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United States
- Headache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - Manyoel Lim
- The Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United States
- Headache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - Thiago D. Nascimento
- The Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United States
- Headache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - Ivo D. Dinov
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States
| | - Robert A. Koeppe
- Department of Radiology, Division of Nuclear Medicine, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Alexandre F. DaSilva
- The Michigan Neuroscience Institute (MNI), University of Michigan, Ann Arbor, MI, United States
- Headache and Orofacial Pain Effort (H.O.P.E.) Laboratory, Department of Biologic and Materials Sciences and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, United States
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Zhang N, Pan Y, Chen Q, Zhai Q, Liu N, Huang Y, Sun T, Lin Y, He L, Hou Y, Yu Q, Li H, Chen S. Application of EEG in migraine. Front Hum Neurosci 2023; 17:1082317. [PMID: 36875229 PMCID: PMC9982126 DOI: 10.3389/fnhum.2023.1082317] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/03/2023] [Indexed: 02/19/2023] Open
Abstract
Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.
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Affiliation(s)
- Ning Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Yonghui Pan
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Qihui Chen
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Qingling Zhai
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Ni Liu
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanan Huang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Tingting Sun
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yake Lin
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Linyuan He
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yue Hou
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Qijun Yu
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyan Li
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Shijiao Chen
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
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