Xu Xiaohan, Huang Yuguang
Department of Anesthesiology, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, China.
F1000Res. 2020 Jan 23;9. doi: 10.12688/f1000research.20441.1. eCollection 2020.
The individual and social burdens associated with chronic pain have been escalating globally. Accurate pain measurement facilitates early diagnosis, disease progression monitoring and therapeutic efficacy evaluation, thus is a key for the management of chronic pain. Although the "golden standards" of pain measurement are self-reported scales in clinical practice, the reliability of these subjective methods could be easily affected by patients' physiological and psychological status, as well as the assessors' predispositions. Therefore, objective pain assessment has attracted substantial attention recently. Previous studies of functional magnetic resonance imaging (fMRI) revealed that certain cortices and subcortical areas are commonly activated in subjects suffering from pain. Dynamic pain connectome analysis also found various alterations of neural network connectivity that are correlated with the severity of clinical pain symptoms. Electroencephalograph (EEG) demonstrated suppressed spontaneous oscillations during pain experience. Spectral power and coherence analysis of EEG also identified signatures of different types of chronic pain. Furthermore, fMRI and EEG can visualize objective brain activities modulated by analgesics in a mechanism-based way, thus bridge the gaps between animal studies and clinical trials. Using fMRI and EEG, researchers are able to predict therapeutic efficacy and identify personalized optimal first-line regimens. In the future, the emergence of magnetic resonance spectroscopy and cell labelling in MRI would encourage the investigation on metabolic and cellular pain biomarkers. The incorporation of machine learning algorithms with neuroimaging or behavior analysis could further enhance the specificity and accuracy of objective pain assessments.
在全球范围内,与慢性疼痛相关的个人和社会负担一直在不断增加。准确的疼痛测量有助于早期诊断、疾病进展监测和治疗效果评估,因此是慢性疼痛管理的关键。尽管在临床实践中,疼痛测量的“金标准”是自我报告量表,但这些主观方法的可靠性很容易受到患者生理和心理状态以及评估者偏好的影响。因此,客观疼痛评估最近受到了广泛关注。以往的功能磁共振成像(fMRI)研究表明,患有疼痛的受试者中某些皮质和皮质下区域通常会被激活。动态疼痛连接组分析还发现了与临床疼痛症状严重程度相关的神经网络连接的各种变化。脑电图(EEG)显示在疼痛体验期间自发振荡受到抑制。EEG的频谱功率和相干分析也确定了不同类型慢性疼痛的特征。此外,fMRI和EEG可以以基于机制的方式可视化由镇痛药调节的客观脑活动,从而弥合动物研究和临床试验之间的差距。利用fMRI和EEG,研究人员能够预测治疗效果并确定个性化的最佳一线治疗方案。未来,磁共振波谱和MRI中的细胞标记的出现将鼓励对代谢和细胞疼痛生物标志物的研究。将机器学习算法与神经成像或行为分析相结合可以进一步提高客观疼痛评估的特异性和准确性。