关键词: 疼痛, 疼痛测量, 神经生理学, 脑电, 功能磁共振

Abstract: As a complex and subjective experience, pain is influenced by physiological, psychological, social, and several other factors. As defined by the International Association for the Study of Pain (IASP), pain was a kind of unpleasant subjective feelings 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 scale, analogue scale, 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 extracted pain-related neurophysiological signatures, and thus establishing an effective, objective, and accurate evaluation system of pain. In physiology, the 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 time domain and in time-frequency domain. In the time domain, the evoked LEPs mainly include N1, 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 (S2), 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, although the accuracy of neurophysiological measuring of pain is not high enough as well as the neurophysiological indexes that are specific for processing of pain remain indeterminate, supplemented to the traditional pain measurement method in both basic research and clinical practice, this neurophysiological system can promote greatly the developments of the researches in the diagnosis and treatment of pain. Therefore, the present paper has important implications in the clinical and basic researches of pain.

Key words: pain, pain measurement, neurophysiology, electroencephalography (EEG), functional magnetic resonance imaging (fMRI)