Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80303, USA.
Neurosci Bull. 2018 Feb;34(1):208-215. doi: 10.1007/s12264-017-0150-1. Epub 2017 Jun 23.
Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity: multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or 'signatures' of pain, by (1) integrating activity across multiple systems, and (2) using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the 'Stimulus Intensity-Independent Pain Signature', explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, including medial and lateral prefrontal cortex, nucleus accumbens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual's pain experience.
疼痛是一种主观而复杂的现象。其复杂性与其异质性有关：多个组成部分的过程，包括感觉、情感和认知，有助于疼痛体验和报告。这些组成部分可能被编码在分布式大脑网络中，这些网络相互作用，产生疼痛体验和与疼痛相关的决策。因此，要了解疼痛，我们必须识别这些网络，并建立这些相互作用的模型，这些模型可以对与疼痛相关的结果做出可测试的预测。我们已经开发了几种这样的疼痛模型或“特征”，方法是（1）整合多个系统的活动，（2）使用模式识别来识别与疼痛体验相关的过程。一种模型，即“神经疼痛特征”，对个体的疼痛具有敏感性和特异性，涉及接收伤害性传入的大脑区域，并且在迄今为止的测试中，期望或自我调节的影响很小。另一种是“刺激强度无关的疼痛特征”，它解释了试验间疼痛报告的大量额外变化。它涉及许多大脑区域，这些区域的活动并没有随着有害刺激强度的增加而增加，包括内侧和外侧前额叶皮层、伏隔核和海马体。在几项研究中，该系统的反应介导了期望和感知控制效应。总的来说，这种方法通过识别跟踪疼痛不同方面的多个系统，为理解疼痛提供了一条途径。这种组成模型可以在个体基础上以独特的方式组合，以解释个体的疼痛体验。
