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慢性疼痛患者静息态脑电图评估的脑功能障碍。

Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography.

机构信息

Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany.

TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany.

出版信息

Pain. 2019 Dec;160(12):2751-2765. doi: 10.1097/j.pain.0000000000001666.

Abstract

Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches.

摘要

慢性疼痛是一种常见且严重致残的疾病,其治疗往往不尽如人意。对慢性疼痛的大脑机制的深入了解有望促进对潜在病理生理学的理解,并可能有助于开发疾病标志物和新的治疗方法。在这里,我们系统地利用脑电图的潜力来确定慢性疼痛患者在静息状态下大脑功能的异常。为此,我们对 101 名慢性疼痛患者(无论性别)进行了最先进的脑振荡活动、脑连接和脑网络分析。结果表明,慢性疼痛患者和健康对照组之间的脑活动的全局和局部指标没有差异。然而,我们观察到在前额脑区的 theta(4-8 Hz)和 gamma(>60 Hz)频率以及慢性疼痛患者的 gamma 频率的全局网络重组中,连接性显著增加。此外,机器学习算法可以以 57%的高于机会准确率区分患者和健康对照组,主要基于额部连接。这些结果表明,前额脑区的 theta 和 gamma 同步增加与慢性疼痛的病理生理学有关。尽管关于发现的可重复性以及潜在脑电图疾病标志物的准确性、特异性和有效性仍然存在重大挑战,但我们的研究表明,theta 和 gamma 频率的异常额部同步可能是无创性脑刺激和/或神经反馈方法的有前途的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710a/7195856/1d66ae715e67/jop-160-2751-g005.jpg

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