Univ Paris Est Creteil, EA4391, ENT, Créteil, France; Clinical Neurophysiology Unit, Henri Mondor Hospital, AP-HP, Créteil, France.
Univ Paris Est Creteil, EA4391, ENT, Créteil, France; Clinical Neurophysiology Unit, Henri Mondor Hospital, AP-HP, Créteil, France.
Neuroimage. 2022 Sep;258:119351. doi: 10.1016/j.neuroimage.2022.119351. Epub 2022 Jun 2.
Diagnosis and management of chronic neuropathic pain are challenging, leading to current efforts to characterize 'objective' biomarkers of pain using imaging or neurophysiological techniques, such as electroencephalography (EEG). A systematic literature review was conducted in PubMed-Medline and Web-of-Science until October 2021 to identify EEG biomarkers of chronic neuropathic pain in humans. The risk of bias was assessed by the Newcastle-Ottawa-Scale. Experimental, provoked, or chronic non-neuropathic pain studies were excluded. We identified 14 studies, in which resting-state EEG spectral analysis was compared between patients with pain related to a neurological disease and patients with the same disease but without pain or healthy controls. From these heterogeneous exploratory studies, some conclusions can be drawn, even if they must be weighted by the fact that confounding factors, such as medication and association with anxio-depressive disorders, are generally not taken into account. Overall, EEG signal power was increased in the θ band (4-7Hz) and possibly in the high-β band (20-30Hz), but decreased in the high-α-low-β band (10-20Hz) in the presence of ongoing neuropathic pain, while increased γ band oscillations were not evidenced, unlike in experimental pain. Consequently, the dominant peak frequency was decreased in the θ-α band and increased in the whole-β band in neuropathic pain patients. Disappointingly, pain intensity correlated with various EEG changes across studies, with no consistent trend. This review also discusses the location of regional pain-related EEG changes in the pain connectome, as the perspectives offered by advanced techniques of EEG signal analysis (source location, connectivity, or classification methods based on artificial intelligence). The biomarkers provided by resting-state EEG are of particular interest for optimizing the treatment of chronic neuropathic pain by neuromodulation techniques, such as transcranial alternating current stimulation or neurofeedback procedures.
慢性神经性疼痛的诊断和管理具有挑战性，因此目前正在努力使用成像或神经生理技术（如脑电图（EEG））来描述疼痛的“客观”生物标志物。在 PubMed-Medline 和 Web-of-Science 中进行了系统的文献综述，以确定人类慢性神经性疼痛的 EEG 生物标志物。使用纽卡斯尔-渥太华量表评估偏倚风险。排除了实验性、诱发性或慢性非神经性疼痛研究。我们确定了 14 项研究，这些研究比较了患有与神经疾病相关的疼痛的患者与患有相同疾病但无疼痛或健康对照者之间的静息状态 EEG 频谱分析。从这些异质的探索性研究中，可以得出一些结论，即使必须考虑到混杂因素（例如药物治疗和与焦虑抑郁障碍的关联）通常未被考虑在内。总的来说，在持续存在神经性疼痛的情况下，EEG 信号功率在θ频段（4-7Hz）增加，并且在高-β频段（20-30Hz）可能增加，但在高-α-低-β频段（10-20Hz）减少，而没有证据表明γ带振荡增加，这与实验性疼痛不同。因此，在神经性疼痛患者中，主导峰频率在θ-α频段降低，在整个β频段增加。令人失望的是，疼痛强度与各项研究中的各种 EEG 变化相关，但没有一致的趋势。本综述还讨论了疼痛连接组中与区域疼痛相关的 EEG 变化的位置，因为 EEG 信号分析的先进技术（源位置、连通性或基于人工智能的分类方法）提供了新的视角。静息状态 EEG 提供的生物标志物对于通过神经调节技术（如经颅交流电刺激或神经反馈程序）优化慢性神经性疼痛的治疗特别有意义。
