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.

Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning / Hsiao, F. -J.; Chen, W. -T.; Pan, L. -L. H.; Liu, H. -Y.; Wang, Y. -F.; Chen, S. -P.; Lai, K. -L.; Coppola, G.; Wang, S. -J.. - In: THE JOURNAL OF HEADACHE AND PAIN. - ISSN 1129-2369. - 23:1(2022). [10.1186/s10194-022-01500-1]

Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning

Coppola G.
Membro del Collaboration Group
;
2022

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.
2022
chronic migraine; machine learning; magnetoencephalography; pain disorders; resting-state oscillatory connectivity
01 Pubblicazione su rivista::01a Articolo in rivista
Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning / Hsiao, F. -J.; Chen, W. -T.; Pan, L. -L. H.; Liu, H. -Y.; Wang, Y. -F.; Chen, S. -P.; Lai, K. -L.; Coppola, G.; Wang, S. -J.. - In: THE JOURNAL OF HEADACHE AND PAIN. - ISSN 1129-2369. - 23:1(2022). [10.1186/s10194-022-01500-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1686439
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