Barbosa Sara Pinto, Junqueira Ygor Nascimento, Akamatsu Milena Apetito, Marques Lucas Murrins, Teixeira Adriano, Lobo Matheus, Mahmoud Mohamed H, Omer Walid E, Pacheco-Barrios Kevin, Fregni Felipe
Instituto de Medicina Física e Reabilitação, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brasil.
Principles and Practice of Clinical Research Program, Harvard T.H. Chan School of Public Health, Boston.
Brain Netw Modul. 2024 Apr-Jun;3(2):52-60. doi: 10.4103/bnm.bnm_17_24. Epub 2024 Jun 27.
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. ur 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.
慢性神经性疼痛（CNP）仍然是一个重大的临床挑战，其复杂的神经生理学基础尚未完全明确。识别与疼痛感知和干扰相关的特定神经振荡模式，能够增进我们对CNP的理解和管理。分析慢性神经性疼痛患者的静息脑电图数据，以探索与疼痛强度、疼痛干扰及特定神经性疼痛特征相关的潜在神经特征。我们对一项横断面研究进行了二次分析，该研究使用了先前研究中的脑电图数据以及36例慢性神经性疼痛患者的简明疼痛量表。对于统计分析，我们针对每个模型以因变量构建线性或逻辑回归模型。作为自变量，我们使用了具有如下脑振荡的脑电图数据：δ波、θ波、α波和β波，以及低α波、高α波、低β波和高β波，涉及中央、额叶和顶叶区域。所有模型均对年龄和药物等混杂因素进行了检验。在一般活动、行走、工作、人际关系、睡眠和生活乐趣方面，未发现与疼痛干扰相关的显著模型。然而，过去四周疼痛强度的模型显示，α波振荡减少，而δ波和θ波振荡增加与疼痛程度降低相关，尤其是在中央区域。在疼痛对情绪的干扰方面，该模型显示额叶和中央区域的高振荡α信号与疼痛导致的情绪损害相关。我们的模型证实了近期的研究发现，即较低的振荡频率可能与皮层下疼痛源有关，可能与大脑的代偿机制相关，进而可能与疼痛程度降低有关。另一方面，较高频率，包括α波振荡，可能会破坏自上而下的代偿机制。
