Yuxin Chen, Siyuan Xie, Libo Zhang, Desheng Li, Hui Su, Rongfei Wang, Ran Ao, Xiaoxue Lin, Yingyuan Liu, Shuhua Zhang, Deqi Zhai, Yin Sun, Shuqing Wang, Li Hu, Zhao Dong, Xuejing Lu
{"title":"偏头痛患者的注意网络缺陷:行为学和电生理学证据。","authors":"Yuxin Chen, Siyuan Xie, Libo Zhang, Desheng Li, Hui Su, Rongfei Wang, Ran Ao, Xiaoxue Lin, Yingyuan Liu, Shuhua Zhang, Deqi Zhai, Yin Sun, Shuqing Wang, Li Hu, Zhao Dong, Xuejing Lu","doi":"10.1186/s10194-024-01905-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Patients with migraine often experience not only headache pain but also cognitive dysfunction, particularly in attention, which is frequently overlooked in both diagnosis and treatment. The influence of these attentional deficits on the pain-related clinical characteristics of migraine remains poorly understood, and clarifying this relationship could improve care strategies.</p><p><strong>Methods: </strong>This study included 52 patients with migraine and 34 healthy controls. We employed the Attentional Network Test for Interactions and Vigilance-Executive and Arousal Components paradigm, combined with electroencephalography, to assess attentional deficits in patients with migraine, with an emphasis on phasic alerting, orienting, executive control, executive vigilance, and arousal vigilance. An extreme gradient boosting binary classifier was trained on features showing group differences to distinguish patients with migraine from healthy controls. Moreover, an extreme gradient boosting regression model was developed to predict clinical characteristics of patients with migraine using their attentional deficit features.</p><p><strong>Results: </strong>For general performance, patients with migraine presented a larger inverse efficiency score, a higher prestimulus beta-band power spectral density and a lower gamma-band event-related synchronization at Cz electrode, and stronger high alpha-band activity at the primary visual cortex, compared to healthy controls. Although no behavior differences in three basic attentional networks were found, patients showed magnified N1 amplitude and prolonged latency of P2 for phasic alerting-trials as well as an increased orienting evoked-P1 amplitude. For vigilance function, improvements in the hit rate of executive vigilance-trials were exhibited in controls but not in patients. Besides, patients with migraine exhibited longer reaction time as well as larger variability in arousal vigilance-trials than controls. The binary classifier developed by such attentional deficit features achieved an F1 score of 0.762 and an accuracy of 0.779 in distinguishing patients with migraine from healthy controls. Crucially, the predicted value available from the regression model involving attentional deficit features significantly correlated with the real value for the frequency of headache.</p><p><strong>Conclusions: </strong>Patients with migraine demonstrated significant attentional deficits, which can be used to differentiate migraine patients from healthy populations and to predict clinical characteristics. These findings highlight the need to address cognitive dysfunction, particularly attentional deficits, in the clinical management of migraine.</p>","PeriodicalId":16013,"journal":{"name":"Journal of Headache and Pain","volume":"25 1","pages":"195"},"PeriodicalIF":7.3000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552239/pdf/","citationCount":"0","resultStr":"{\"title\":\"Attentional network deficits in patients with migraine: behavioral and electrophysiological evidence.\",\"authors\":\"Yuxin Chen, Siyuan Xie, Libo Zhang, Desheng Li, Hui Su, Rongfei Wang, Ran Ao, Xiaoxue Lin, Yingyuan Liu, Shuhua Zhang, Deqi Zhai, Yin Sun, Shuqing Wang, Li Hu, Zhao Dong, Xuejing Lu\",\"doi\":\"10.1186/s10194-024-01905-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Patients with migraine often experience not only headache pain but also cognitive dysfunction, particularly in attention, which is frequently overlooked in both diagnosis and treatment. The influence of these attentional deficits on the pain-related clinical characteristics of migraine remains poorly understood, and clarifying this relationship could improve care strategies.</p><p><strong>Methods: </strong>This study included 52 patients with migraine and 34 healthy controls. We employed the Attentional Network Test for Interactions and Vigilance-Executive and Arousal Components paradigm, combined with electroencephalography, to assess attentional deficits in patients with migraine, with an emphasis on phasic alerting, orienting, executive control, executive vigilance, and arousal vigilance. An extreme gradient boosting binary classifier was trained on features showing group differences to distinguish patients with migraine from healthy controls. Moreover, an extreme gradient boosting regression model was developed to predict clinical characteristics of patients with migraine using their attentional deficit features.</p><p><strong>Results: </strong>For general performance, patients with migraine presented a larger inverse efficiency score, a higher prestimulus beta-band power spectral density and a lower gamma-band event-related synchronization at Cz electrode, and stronger high alpha-band activity at the primary visual cortex, compared to healthy controls. Although no behavior differences in three basic attentional networks were found, patients showed magnified N1 amplitude and prolonged latency of P2 for phasic alerting-trials as well as an increased orienting evoked-P1 amplitude. For vigilance function, improvements in the hit rate of executive vigilance-trials were exhibited in controls but not in patients. Besides, patients with migraine exhibited longer reaction time as well as larger variability in arousal vigilance-trials than controls. The binary classifier developed by such attentional deficit features achieved an F1 score of 0.762 and an accuracy of 0.779 in distinguishing patients with migraine from healthy controls. Crucially, the predicted value available from the regression model involving attentional deficit features significantly correlated with the real value for the frequency of headache.</p><p><strong>Conclusions: </strong>Patients with migraine demonstrated significant attentional deficits, which can be used to differentiate migraine patients from healthy populations and to predict clinical characteristics. These findings highlight the need to address cognitive dysfunction, particularly attentional deficits, in the clinical management of migraine.</p>\",\"PeriodicalId\":16013,\"journal\":{\"name\":\"Journal of Headache and Pain\",\"volume\":\"25 1\",\"pages\":\"195\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552239/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Headache and Pain\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s10194-024-01905-0\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Headache and Pain","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s10194-024-01905-0","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Attentional network deficits in patients with migraine: behavioral and electrophysiological evidence.
Background: Patients with migraine often experience not only headache pain but also cognitive dysfunction, particularly in attention, which is frequently overlooked in both diagnosis and treatment. The influence of these attentional deficits on the pain-related clinical characteristics of migraine remains poorly understood, and clarifying this relationship could improve care strategies.
Methods: This study included 52 patients with migraine and 34 healthy controls. We employed the Attentional Network Test for Interactions and Vigilance-Executive and Arousal Components paradigm, combined with electroencephalography, to assess attentional deficits in patients with migraine, with an emphasis on phasic alerting, orienting, executive control, executive vigilance, and arousal vigilance. An extreme gradient boosting binary classifier was trained on features showing group differences to distinguish patients with migraine from healthy controls. Moreover, an extreme gradient boosting regression model was developed to predict clinical characteristics of patients with migraine using their attentional deficit features.
Results: For general performance, patients with migraine presented a larger inverse efficiency score, a higher prestimulus beta-band power spectral density and a lower gamma-band event-related synchronization at Cz electrode, and stronger high alpha-band activity at the primary visual cortex, compared to healthy controls. Although no behavior differences in three basic attentional networks were found, patients showed magnified N1 amplitude and prolonged latency of P2 for phasic alerting-trials as well as an increased orienting evoked-P1 amplitude. For vigilance function, improvements in the hit rate of executive vigilance-trials were exhibited in controls but not in patients. Besides, patients with migraine exhibited longer reaction time as well as larger variability in arousal vigilance-trials than controls. The binary classifier developed by such attentional deficit features achieved an F1 score of 0.762 and an accuracy of 0.779 in distinguishing patients with migraine from healthy controls. Crucially, the predicted value available from the regression model involving attentional deficit features significantly correlated with the real value for the frequency of headache.
Conclusions: Patients with migraine demonstrated significant attentional deficits, which can be used to differentiate migraine patients from healthy populations and to predict clinical characteristics. These findings highlight the need to address cognitive dysfunction, particularly attentional deficits, in the clinical management of migraine.
期刊介绍:
The Journal of Headache and Pain, a peer-reviewed open-access journal published under the BMC brand, a part of Springer Nature, is dedicated to researchers engaged in all facets of headache and related pain syndromes. It encompasses epidemiology, public health, basic science, translational medicine, clinical trials, and real-world data.
With a multidisciplinary approach, The Journal of Headache and Pain addresses headache medicine and related pain syndromes across all medical disciplines. It particularly encourages submissions in clinical, translational, and basic science fields, focusing on pain management, genetics, neurology, and internal medicine. The journal publishes research articles, reviews, letters to the Editor, as well as consensus articles and guidelines, aimed at promoting best practices in managing patients with headaches and related pain.