Exploring electroencephalographic chronic pain biomarkers: a mega-analysis.

IF 10.8 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

EBioMedicine Pub Date : 2025-09-30 DOI:10.1016/j.ebiom.2025.105955

Felix S Bott, Paul Theo Zebhauser, Vanessa D Hohn, Özgün Turgut, Elisabeth S May, Laura Tiemann, Cristina Gil Ávila, Henrik Heitmann, Moritz M Nickel, Melissa A Day, Divya B Adhia, Yoni K Ashar, Tor D Wager, Yelena Granovsky, David Yarnitsky, Mark P Jensen, Joachim Gross, Markus Ploner

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引用次数: 0

Abstract

Background: Chronic pain is associated with alterations in brain function, offering promising avenues for advancing diagnostic and therapeutic strategies. In particular, these alterations may serve as brain-based biomarkers to support diagnosis, guide treatment decisions and monitor clinical courses of chronic pain.

Methods: Motivated by this potential, this study analysed associations between chronic pain and changes of large-scale brain network function using resting-state electroencephalography (EEG) from 614 individuals with chronic pain, collected by research groups from Australia, Germany, Israel, New Zealand, and the US.

Findings: Employing a discovery-replication approach, we found limited replicability of associations between pain intensity and brain network connectivity. However, a mega-analysis combining all datasets revealed robust associations between pain intensity and large-scale brain network connectivity at theta frequencies and including the limbic network. Additionally, multivariate analyses identified connectivity patterns spanning theta, alpha, and beta frequencies with strong evidence for associations with pain intensity. Variations and ablations of model features yielded deeper insights into the relative importance of distinct electrophysiological brain features in assessing chronic pain.

Interpretation: Our findings highlight challenges and provide guidance for developing EEG-based, scalable, and affordable biomarkers of chronic pain.

Funding: This project was funded by the Deutsche Forschungsgemeinschaft and the Technical University of Munich.

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探索脑电图慢性疼痛生物标志物：大型分析。

背景：慢性疼痛与脑功能改变有关，为推进诊断和治疗策略提供了有希望的途径。特别是，这些改变可以作为基于大脑的生物标志物来支持诊断，指导治疗决策和监测慢性疼痛的临床过程。方法：基于这一潜力，本研究使用静息状态脑电图（EEG）分析了慢性疼痛与大尺度脑网络功能变化之间的关系，这些数据来自澳大利亚、德国、以色列、新西兰和美国的研究小组收集的614名慢性疼痛患者。研究结果：采用发现-复制方法，我们发现疼痛强度和大脑网络连接之间的关联可复制性有限。然而，一项综合所有数据集的大型分析显示，疼痛强度与theta频率（包括边缘网络）的大规模大脑网络连接之间存在强大的关联。此外，多变量分析确定了跨越θ、α和β频率的连接模式，有力地证明了与疼痛强度的关联。模型特征的变化和消融对不同脑电生理特征在评估慢性疼痛中的相对重要性产生了更深入的见解。解释：我们的研究结果突出了挑战，并为开发基于脑电图的、可扩展的、可负担的慢性疼痛生物标志物提供了指导。资助：该项目由德国研究与发展协会和慕尼黑工业大学资助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology

CiteScore

17.70

自引率

0.90%

发文量

579

审稿时长

5 weeks

期刊介绍： eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.