中国科学院心理健康重点实验室知识库

疼痛是一种不愉快的感觉和情感体验，其涉及到多级神经加工过程，神经活动模式十分复杂。非侵入性脑功能成像技术可以实现在全脑水平上解析人类疼痛的神经机制。其中，功能磁共振成像（functional magnetic resonance imaging，fMRI）技术因具有高空间分辨率的优势，使其在探索人类疼痛的神经机制研究中得到了广泛的应用。本文聚焦于人类疼痛的fMRI研究，首先概述了疼痛相关的脑响应研究发现，梳理了与疼痛加工相关的多个脑区功能活动变化。然而，调节单一脑区的功能难以影响疼痛体验，提示疼痛加工涉及多脑区之间的协同作用。由此，本文综述了参与疼痛加工的脑区之间交互现象，这些研究揭示了多条神经通路以串行或并行的方式构成了复杂的疼痛神经网络，进而处理与疼痛相关的感觉、情绪和认知信息。基于上述研究，近年来不断更迭发展的超高场强fMRI及脑脊同步成像技术，助力人类疼痛研究深入到核团和脊髓层面，拓展了疼痛神经网络的精细度和全面性。综上，本文提出了人类疼痛的神经网络表征，并以此为基础指导神经调控技术调节异常的神经网络表征，进而实现缓解疼痛症状的目标。最后，本文讨论了当前疼痛神经表征研究的局限性，并提出了探索疼痛特异性表征，对比实验诱发性疼痛和临床自发性疼痛，以及疼痛个体化表征的研究展望。

Abstract Pain is an unpleasant sensory and emotional experience involving multi-level neural processing, with a highly complex neural activity pattern. Recent advancements in non-invasive brain functional imaging techniques have enhanced our understanding of the neural mechanisms underlying pain processing in humans at the whole-brain level. Functional magnetic resonance imaging (fMRI), in particular, plays an important role due to its high spatial resolution and has driven significant advancements in this field. This review focused on fMRI studies of pain in humans. We first summarized research that explored brain responses to pain and showing that pain processing involves neural activities across multiple brain regions, constituting the pain matrix, which includes the somatosensory cortex, thalamus, insula, anterior cingulate cortex, and other areas. However, modulating the activity of a single brain region has limited effects on pain experiences, suggesting that pain processing entails communications among multiple brain regions. Thus, we reviewed research investigating interactions between brain regions, finding that multiple neural pathways spanning the whole brain are involved in pain processing. Beyond interactions between pairs of regions, understanding how these interactions construct a pain-related network is crucial for fully comprehending the neural representation of pain. Two main approaches are used to describe neural networks across the whole brain. The first one is theory-driven, such as graph theory. Using this method, researchers explored how network properties evolve during pain processing and identified a tightly connected network that emerges during pain, encompassing the somatosensory, salience, and fronto-parietal networks, forming a pain-related super-system. As pain is modulated or diminishes, this system becomes less connected. The second approach relies on data-driven methods, such as methods based on independent component analysis or principal component analysis, and machine learning. These methods are not constrained by pre-defined brain networks. Advancements in machine learning have provided valuable insights, enabling researchers to develop pain biomarkers with promising clinical potential. Theory-driven and data-driven approaches provide complementary insights into our understanding of the neural mechanisms of pain. In recent years, two rapidly advancing and promising techniques have further enhanced the precision and comprehensiveness of pain neural network. One is ultra-high-field magnetic resonance imaging, and the other is simultaneous brain-spinal imaging. Ultra-high-field magnetic resonance imaging has overcome previous spatial resolution limitations in fMRI. In subcortical regions, it helps distinguish neural activities of different nuclei. In cortical regions, high resolution enables the differentiation of neural activities across cortical layers. Thereby providing a more in-depth and detailed understanding of the neural mechanisms of pain. Simultaneous brain-spinal imaging technology enables the exploration of brain-spinal networks involved in pain processing, making it possible to construct a comprehensive neural network representation of pain throughout the entire central nervous system. Based on current findings, we suggested that in the clinical treatment of pain using neuromodulation techniques, the selection of stimulation targets could be guided by the pain neural network. Targeting hubs within the pain network could significantly impact the network and may efficiently influence pain experiences. Finally, we discussed the limitations of current research on the neural representation of pain and proposed future directions, including exploring pain-specific representation, systematically comparing experimental and clinical pain, and examining individualized neural representations.

科技创新2030-“脑科学与类脑研究”重大项目（2022ZD0206400）;; 国家自然科学基金（32322035，32171078）资助