基于神经生理学的疼痛测量

Measuring of Pain Based on Neurophysiology

疼痛是一种受多重因素影响的复杂主观感受。临床上,疼痛测量主要依赖于患者的主观评价。然而,这种传统的疼痛测量方法具有多方面的局限。近年来,研究者借助生理记录、脑电和功能磁共振等技术,揭示疼痛的神经生理、心理机制,挖掘与疼痛相关的神经生理指标,进而构建起有效、客观和精确的疼痛评价体系。在基础研究和临床实践中,这些技术有望弥补传统疼痛测量方法的不足,从而极大地推动疼痛测量及其治疗等相关领域研究的发展。

As a complex and subjective experience, pain is influenced by physiological, psychological, social, and several other factors. As defined by the Intemational Association for the Study of Pain （IASP）, pain is a kind of unpleasant subjective feeling and emotional experience, which was associated with tissue damage or potential tissue damage. Clinically, the measurement of pain dominantly relies on the patients＇ subjective evaluation, which mainly uses a psychophysical method, that is, all kinds of scales. For example, verbal and numerical rating scales, McGill pain questionnaire （MPQ）, ratio scales, analogue scales, and some behavioral measurements. Although this traditional method to measure pain and its components is to some extent considered as to be a golden rule, it is not objective, accurate, and universally applicable due to the complexity of pain. Thus, to optimize the assessment and treatment of pain, developing some objective and effective methods to measure pain is an important and urgent scientific problem. Recently, using novel sampling techniques, like eye-movement tracking, electromyography （EMG）, electroencephalography （EEG）, and functional magnetic resonance imaging （fMRI）, researchers have revealed both neurophysiological and neuropsychological mechanism of pain processing, and have extracted pain-related neurophysiological signatures, and thus establishing an effective, objective, and accurate evaluation system of pain. In physiology, skin conductance （SC）, skin temperature （ST）, heart rate （HR）, and pupil diameter （PD） are usually used to investigate the response characteristics from autonomic nervous system, and EMG is used to measure the neuromuscular activity. All these measurements are associated with pain. In EEG studies, laser-evoked potentials （LEPs） have been widely used to investigate the peripheral and central processing of nociceptive sensory input. LEPs can be elicited by intense laser heat pulses that selectively excite nociceptive free nerve endings in the epidermis, which include many components both in the time domain and in the time-frequency domain. In the time domain, the evoked LEPs mainly include N 1, N2, P2, and P4 waves. In the time-frequency domain, gamma （more than 30 Hz） oscillation activity originating from the primary somatosensory cortex （S1） can be elicited by nociceptive stimuli, and has been validated to be associated with pain intensity. In the aspect of fMRI studies, by combining fMRI technology with machine learning theory, an effective and precise assessment of pain may be achieved. Meanwhile, some studies find that using fMRI technology combined with the support vector machine （SVM） and other machine learning algorithm may more precisely assess pain. For example, some pain-related brain areas including the S1, secondary somatosensory cortex （$2）, insula, primary motor cortex, and anterior cingulate cortex （ACC） have been identified. More importantly, the pain and non-pain stimuli can be identified. Taken together, the accuracy of neurophysiological measuring of pain is not high enough and the neurophysiological indexes that are specific for processing of pain remain indeterminate. To add to the traditional pain measurement method in basic research and clinical practice, this neurophysiological system can greatly promote the development of the researches in the diagnosis and treatment of pain. Therefore, the present paper has important implications for the clinical and basic research of pain.