静息态脑电图 delta 和 theta 波段作为慢性神经病理性疼痛的代偿振荡:二次数据分析。

Brain network and modulation Pub Date : 2024-04-01 Epub Date: 2024-06-27 DOI:10.4103/bnm.bnm_17_24
Sara Pinto Barbosa, Ygor Nascimento Junqueira, Milena Apetito Akamatsu, Lucas Murrins Marques, Adriano Teixeira, Matheus Lobo, Mohamed H Mahmoud, Walid E Omer, Kevin Pacheco-Barrios, Felipe Fregni
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引用次数: 0

摘要

慢性神经病理性疼痛(CNP)仍是一项重大的临床挑战,其复杂的神经生理学基础尚未被完全理解。识别与疼痛感知和干扰相关的特定神经振荡模式可以增强我们对慢性神经病理性疼痛的理解和治疗。分析慢性神经病理性疼痛患者的静息脑电图数据,探索与疼痛强度、疼痛干扰和特定神经病理性疼痛特征相关的可能神经特征。我们利用之前一项研究的脑电图数据和 36 名慢性神经病理性疼痛患者的简明疼痛量表对一项横断面研究进行了二次分析。为了进行统计分析,我们对每个模型的因变量进行了线性或逻辑回归建模。作为自变量,我们使用了脑电图数据中的大脑振荡:δ、θ、α和β,以及中央区、额叶区和顶叶区的低α、高α、低β和高β振荡。所有模型都测试了年龄和药物等混杂因素。疼痛对一般活动、行走、工作、人际关系、睡眠和生活享受的干扰没有明显的模型。然而,过去四周的疼痛强度模型显示,α振荡减少、δ和θ振荡增加与疼痛程度减轻有关,尤其是在中央区域。就疼痛对情绪的干扰而言,该模型显示额叶和中央区域的高阿尔法振荡信号与疼痛导致的情绪损害相关。我们的模型证实了最近的研究结果,即较低的振荡频率可能与皮层下疼痛源有关,可能与大脑的补偿机制有关,因此可能与疼痛水平的降低有关。另一方面,包括阿尔法振荡在内的较高频率可能会破坏自上而下的补偿机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resting-state electroencephalography delta and theta bands as compensatory oscillations in chronic neuropathic pain: a secondary data analysis.

Chronic neuropathic pain (CNP) remains a significant clinical challenge, with complex neurophysiological underpinnings that are not fully understood. Identifying specific neural oscillatory patterns related to pain perception and interference can enhance our understanding and management of CNP. To analyze resting electroencephalography data from individuals with chronic neuropathic pain to explore the possible neural signatures associated with pain intensity, pain interference, and specific neuropathic pain characteristics. We conducted a secondary analysis from a cross-sectional study using electroencephalography data from a previous study, and Brief Pain Inventory from 36 patients with chronic neuropathic pain. For statistical analysis, we modeled a linear or logistic regression by dependent variable for each model. As independent variables, we used electroencephalography data with such brain oscillations: as delta, theta, alpha, and beta, as well as the oscillations low alpha, high alpha, low beta, and high beta, for the central, frontal, and parietal regions. All models tested for confounding factors such as age and medication. There were no significant models for Pain interference in general activity, walking, work, relationships, sleep, and enjoyment of life. However, the model for pain intensity during the past four weeks showed decreased alpha oscillations, and increased delta and theta oscillations were associated with decreased levels of pain, especially in the central area. In terms of pain interference in mood, the model showed high oscillatory Alpha signals in the frontal and central regions correlated with mood impairment due to pain. Our models confirm recent findings proposing that lower oscillatory frequencies, likely related to subcortical pain sources, may be associated with brain compensatory mechanisms and thus may be associated with decreased pain levels. On the other hand, higher frequencies, including alpha oscillations, may disrupt top-down compensatory mechanisms.

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