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Pan LLH, Chen SP, Ling YH, Wang YF, Lai KL, Liu HY, Chen WT, Huang WJ, Coppola G, Treede RD, Wang SJ. Salivary Testosterone Levels and Pain Perception Exhibit Sex-Specific Association in Healthy Adults But Not in Patients With Migraine. THE JOURNAL OF PAIN 2024:104575. [PMID: 38788888 DOI: 10.1016/j.jpain.2024.104575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/30/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
This study investigated the sex-specific associations between pain perception and testosterone levels in healthy controls (HCs) and patients with migraine. Male and female HCs and migraine patients were recruited. A series of questionnaires were completed by the participants to evaluate their psychosocial profiles, which included data on mood, stress, and sleep quality. Heat pain thresholds and suprathreshold pain ratings at 45 °C (referred to as the pain perception score [PPS]) were assessed using the Thermode system. Salivary testosterone levels were analyzed using a commercial enzyme-linked immunosorbent assay kit. A total of 88 HCs (men/women: 41/47, age: 29.9 ± 7.7 years) and 75 migraine patients (men/women: 30/45, age: 31.1 ± 7.7 years) completed all assessments. No significant differences were observed in either the psychosocial profiles or heat pain thresholds and PPSs between the sexes in the control and migraine groups. A positive correlation between testosterone levels and PPSs was identified in the male controls (r = .341, P = .029), whereas a negative correlation was identified in the female controls (r = -.407, P = .005). No such correlations were identified in the migraine group. This study confirms that a negative association is present between PPSs and testosterone levels in female controls, which is in line with the findings that testosterone is associated with reduced pain perception. Our study is the first to demonstrate a sex-specific association between PPSs and testosterone levels in HCs. Moreover, this study also revealed that the presence of migraine appears to disrupt this association. PERSPECTIVE: This study revealed that testosterone levels demonstrate opposite associations with pain perception in healthy men and women. However, the presence of migraine appears to disrupt this sex-specific association.
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Affiliation(s)
- Li-Ling Hope Pan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Shih-Pin Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Hsiang Ling
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yen-Feng Wang
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Kuan-Lin Lai
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hung-Yu Liu
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Wei-Ta Chen
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College 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
| | - William J Huang
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Urology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Gianluca Coppola
- Department of Medico‑Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Latina, Italy
| | - Rolf-Detlef Treede
- Mannheim Center for Translational Neurosciences, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College 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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2
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van den Hoek TC, van de Ruit M, Terwindt GM, Tolner EA. EEG Changes in Migraine-Can EEG Help to Monitor Attack Susceptibility? Brain Sci 2024; 14:508. [PMID: 38790486 PMCID: PMC11119734 DOI: 10.3390/brainsci14050508] [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: 04/03/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024] Open
Abstract
Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient's brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.
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Affiliation(s)
- Thomas C. van den Hoek
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
| | - Mark van de Ruit
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Gisela M. Terwindt
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
| | - Else A. Tolner
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
- Department of Human Genetics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
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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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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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Rodríguez-González V, Núñez P, Gómez C, Shigihara Y, Hoshi H, Tola-Arribas MÁ, Cano M, Guerrero Á, García-Azorín D, Hornero R, Poza J. Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses. Neuroimage 2023; 280:120332. [PMID: 37619796 DOI: 10.1016/j.neuroimage.2023.120332] [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: 04/02/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
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Affiliation(s)
- Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain
| | | | | | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Mónica Cano
- Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Ángel Guerrero
- Hospital Clínico Universitario, Valladolid, Spain; Department of Medicine, University of Valladolid, Spain
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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6
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Zebhauser PT, Hohn VD, Ploner M. Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review. Pain 2023; 164:1200-1221. [PMID: 36409624 PMCID: PMC10184564 DOI: 10.1097/j.pain.0000000000002825] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/28/2022] [Accepted: 11/04/2022] [Indexed: 11/22/2022]
Abstract
ABSTRACT Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.
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Affiliation(s)
- Paul Theo Zebhauser
- Department of Neurology, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Vanessa D. Hohn
- Department of Neurology, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Markus Ploner
- Department of Neurology, School of Medicine, Technical University of Munich (TUM), Munich, Germany
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7
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Guldiken YC, Petropoulos IN, Malik A, Malik RA, Yüksel R, Budak F, Selekler HM. Corneal confocal microscopy identifies corneal nerve fiber loss in patients with migraine. Cephalalgia 2023; 43:3331024231170810. [PMID: 37177828 DOI: 10.1177/03331024231170810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
BACKGROUND/HYPOTHESIS Migraine affects >1 billion people but its pathophysiology remains poorly understood. Alterations in the trigeminovascular system play an important role. We have compared corneal nerve morphology in patients with migraine to healthy controls. METHODS Sixty patients with episodic (n = 32) or chronic (n = 28) migraine and 20 age-matched healthy control subjects were studied cross-sectionally. Their migraine characteristics and signs and symptoms of dry eyes were assessed. Manual and automated quantification of corneal nerves was undertaken by corneal confocal microscopy. RESULTS In patients with migraine compared to controls, manual corneal nerve fiber density (P < 0.001), branch density (P = 0.015) and length (P < 0.001); and automated corneal nerve fiber density (P < 0.001), branch density (P < 0.001), length (P < 0.001), total branch density (P < 0.001), nerve fiber area (P < 0.001), nerve fiber width (P = 0.045) and fractal dimension (P < 0.001) were lower. Automated corneal nerve fiber density was higher in patients with episodic migraine and aura (P = 0.010); and fractal dimension (P = 0.029) was lower in patients with more headache days in the last three months. Automated corneal nerve fiber density predicted a significant amount of the observed variance in pain intensity (adjusted r2 = 0.14, partial r = -0.37, P = 0.004) in patients with migraine. CONCLUSIONS Corneal confocal microscopy reveals corneal nerve loss in patients with migraine. It may serve as an objective imaging biomarker of neurodegeneration in migraine.
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Affiliation(s)
- Yigit Can Guldiken
- Department of Neurology, Kocaeli University Research and Application Hospital, İzmit/Kocaeli, Turkey
| | | | - Ayesha Malik
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Rayaz A Malik
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Refref Yüksel
- Department of Ophthalmology, Kocaeli University Research and Application Hospital, İzmit/Kocaeli, Turkey
| | - Faik Budak
- Department of Neurology, Kocaeli University Research and Application Hospital, İzmit/Kocaeli, Turkey
| | - Hamit Macit Selekler
- Department of Neurology, Kocaeli University Research and Application Hospital, İzmit/Kocaeli, Turkey
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8
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Hsiao FJ, Chen WT, Pan LLH, Liu HY, Wang YF, Chen SP, Lai KL, Coppola G, Wang SJ. Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning. J Headache Pain 2022; 23:130. [PMID: 36192689 PMCID: PMC9531441 DOI: 10.1186/s10194-022-01500-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
To identify and validate the neural signatures of resting-state oscillatory connectivity for chronic migraine (CM), we used machine learning techniques to classify patients with CM from healthy controls (HC) and patients with other pain disorders. The cross-sectional study obtained resting-state magnetoencephalographic data from 240 participants (70 HC, 100 CM, 35 episodic migraine [EM], and 35 fibromyalgia [FM]). Source-based oscillatory connectivity of relevant cortical regions was calculated to determine intrinsic connectivity at 1–40 Hz. A classification model that employed a support vector machine was developed using the magnetoencephalographic data to assess the reliability and generalizability of CM identification. In the findings, the discriminative features that differentiate CM from HC were principally observed from the functional interactions between salience, sensorimotor, and part of the default mode networks. The classification model with these features exhibited excellent performance in distinguishing patients with CM from HC (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with EM (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (FM in this study). These resting-state magnetoencephalographic electrophysiological features yield oscillatory connectivity to identify patients with CM from those with a different type of migraine and pain disorder, with adequate reliability and generalizability.
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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, Taiwan, 11217. .,Department of Neurology, Keelung Hospital, Ministry of Health and Welfare, Keelung, Taiwan.
| | - Li-Ling Hope Pan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hung-Yu Liu
- 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, Taiwan, 11217
| | - 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, Taiwan, 11217
| | - 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, Taiwan, 11217
| | - 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, Taiwan, 11217
| | - 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, Taiwan, 11217
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9
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Gomez-Pilar J, Martínez-Cagigal V, García-Azorín D, Gómez C, Guerrero Á, Hornero R. Headache-related circuits and high frequencies evaluated by EEG, MRI, PET as potential biomarkers to differentiate chronic and episodic migraine: Evidence from a systematic review. J Headache Pain 2022; 23:95. [PMID: 35927625 PMCID: PMC9354370 DOI: 10.1186/s10194-022-01465-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/21/2022] [Indexed: 11/25/2022] Open
Abstract
Background The diagnosis of migraine is mainly clinical and self-reported, which makes additional examinations unnecessary in most cases. Migraine can be subtyped into chronic (CM) and episodic (EM). Despite the very high prevalence of migraine, there are no evidence-based guidelines for differentiating between these subtypes other than the number of days of migraine headache per month. Thus, we consider it timely to perform a systematic review to search for physiological evidence from functional activity (as opposed to anatomical structure) for the differentiation between CM and EM, as well as potential functional biomarkers. For this purpose, Web of Science (WoS), Scopus, and PubMed databases were screened. Findings Among the 24 studies included in this review, most of them (22) reported statistically significant differences between the groups of CM and EM. This finding is consistent regardless of brain activity acquisition modality, ictal stage, and recording condition for a wide variety of analyses. That speaks for a supramodal and domain-general differences between CM and EM that goes beyond a differentiation based on the days of migraine per month. Together, the reviewed studies demonstrates that electro- and magneto-physiological brain activity (M/EEG), as well as neurovascular and metabolic recordings from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), show characteristic patterns that allow to differentiate between CM and EM groups. Conclusions Although a clear brain activity-based biomarker has not yet been identified to distinguish these subtypes of migraine, research is approaching headache specialists to a migraine diagnosis based not only on symptoms and signs reported by patients. Future studies based on M/EEG should pay special attention to the brain activity in medium and fast frequency bands, mainly the beta band. On the other hand, fMRI and PET studies should focus on neural circuits and regions related to pain and emotional processing.
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Affiliation(s)
- Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales Y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Víctor Martínez-Cagigal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales Y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - David García-Azorín
- Headache Unit, Neurology Department, Hospital Clínico Universitario de Valladolid, Ramón y Cajal 3, 47003, Valladolid, Spain.
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales Y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Ángel Guerrero
- Headache Unit, Neurology Department, Hospital Clínico Universitario de Valladolid, Ramón y Cajal 3, 47003, Valladolid, Spain.,Department of Medicine, University of Valladolid, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales Y Nanomedicina (CIBER-BBN), Valladolid, Spain
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