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Qiu D, Ge Z, Mei Y, Wang W, Xiong Z, Li X, Yuan Z, Zhang P, Zhang M, Liu X, Zhang Y, Yu X, Tang H, Wang Y. Mapping brain functional networks topological characteristics in new daily persistent headache: a magnetoencephalography study. J Headache Pain 2023; 24:161. [PMID: 38053071 DOI: 10.1186/s10194-023-01695-x] [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: 10/29/2023] [Accepted: 11/20/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND The brain functional network topology in new daily persistent headache (NDPH) is not well understood. In this study, we aim to assess the cortical functional network topological characteristics of NDPH using non-invasive neural signal recordings. METHODS Resting-state magnetoencephalography (MEG) was used to measure power fluctuations in neuronal oscillations from distributed cortical parcels in 35 patients with NDPH and 40 healthy controls (HCs). Their structural data were collected by 3T MRI. Functional connectivity (FC) of neural networks from 1 to 80 Hz frequency ranges was analyzed with topographic patterns and calculated network topological parameters with graph theory. RESULTS In the delta (1-4 Hz) and beta (13-30 Hz) bands, the lateral occipital cortex and superior frontal gyrus FC were increased in NDPH groups compared to HCs. Graph theory analysis revealed that the NDPH had significantly increased global efficiency in the delta band and decreased nodal clustering coefficient (left medial orbitofrontal cortex) in the theta (4-8 Hz) band. The clinical characteristics had a significant correlation with network topological parameters. Age at onset of patients showed a positive correlation with global efficiency in the delta band. The degree of depression of patients showed a negative correlation with the nodal clustering coefficient (left medial orbitofrontal cortex) in the theta band. CONCLUSION The FC and topology of NDPH in brain networks may be altered, potentially leading to cortical hyperexcitability. Moreover, medial orbitofrontal cortex is involved in the pathophysiological mechanism of depression in patients with NDPH. Increased FC observed in the lateral occipital cortex and superior frontal gyrus during resting-state MEG could serve as one of the imaging characteristics associated with NDPH.
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Affiliation(s)
- Dong Qiu
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Zhaoli Ge
- Department of Neurology, Shenzhen Second People's Hospital, Shenzhen, 518000, Guangdong, China
| | - Yanliang Mei
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Wei Wang
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Zhonghua Xiong
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xiaoshuang Li
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Ziyu Yuan
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Peng Zhang
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Mantian Zhang
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xin Liu
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Yaqing Zhang
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xueying Yu
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Hefei Tang
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China
| | - Yonggang Wang
- Department of Neurology, Headache Center, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
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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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