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Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning
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 mag...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Milan
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531441/ https://www.ncbi.nlm.nih.gov/pubmed/36192689 http://dx.doi.org/10.1186/s10194-022-01500-1 |
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author | Hsiao, Fu-Jung Chen, Wei-Ta Pan, Li-Ling Hope Liu, Hung-Yu Wang, Yen-Feng Chen, Shih-Pin Lai, Kuan-Lin Coppola, Gianluca Wang, Shuu-Jiun |
author_facet | Hsiao, Fu-Jung Chen, Wei-Ta Pan, Li-Ling Hope Liu, Hung-Yu Wang, Yen-Feng Chen, Shih-Pin Lai, Kuan-Lin Coppola, Gianluca Wang, Shuu-Jiun |
author_sort | Hsiao, Fu-Jung |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-022-01500-1. |
format | Online Article Text |
id | pubmed-9531441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Milan |
record_format | MEDLINE/PubMed |
spelling | pubmed-95314412022-10-05 Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning Hsiao, Fu-Jung Chen, Wei-Ta Pan, Li-Ling Hope Liu, Hung-Yu Wang, Yen-Feng Chen, Shih-Pin Lai, Kuan-Lin Coppola, Gianluca Wang, Shuu-Jiun J Headache Pain Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-022-01500-1. Springer Milan 2022-10-03 /pmc/articles/PMC9531441/ /pubmed/36192689 http://dx.doi.org/10.1186/s10194-022-01500-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hsiao, Fu-Jung Chen, Wei-Ta Pan, Li-Ling Hope Liu, Hung-Yu Wang, Yen-Feng Chen, Shih-Pin Lai, Kuan-Lin Coppola, Gianluca Wang, Shuu-Jiun Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
title | Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
title_full | Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
title_fullStr | Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
title_full_unstemmed | Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
title_short | Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
title_sort | resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531441/ https://www.ncbi.nlm.nih.gov/pubmed/36192689 http://dx.doi.org/10.1186/s10194-022-01500-1 |
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