The Neural Network Representation of Pain in Humans
文献类型：期刊论文
| 作者 | Yi, Yang-Yang1,2; Tu, Yi-Heng1,2
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| 刊名 | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS
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| 出版日期 | 2024-10-01 |
| 卷号 | 51期号:10页码:2357-2368 |
| 关键词 | pain fMRI neural network representation |
| ISSN号 | 1000-3282 |
| DOI | 10.16476/j.pibb.2024.0263 |
| 通讯作者 | Tu, Yi-Heng(tuyh@psych.ac.cn) |
| 英文摘要 | 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 frontoparietal 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. |
| 收录类别 | SCI |
| WOS关键词 | MOTOR CORTEX STIMULATION ; FUNCTIONAL CONNECTIVITY ; SPINAL-CORD ; NEUROPATHIC PAIN ; SOMATOSENSORY CORTEX ; PLACEBO ANALGESIA ; DESCENDING PAIN ; BRAIN ; EXPERIENCE ; NEUROMODULATION |
| 资助项目 | STI2030-Major Projects by the Ministry of Science and Technology of China[2022ZD0206400] ; National Natural Science Foundation of China[32322035] ; National Natural Science Foundation of China[32171078] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001346664800009 |
| 出版者 | CHINESE ACAD SCIENCES, INST BIOPHYSICS |
| 资助机构 | STI2030-Major Projects by the Ministry of Science and Technology of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.psych.ac.cn/handle/311026/49248]  |
| 专题 | 心理研究所_中国科学院心理健康重点实验室 |
| 通讯作者 | Tu, Yi-Heng |
| 作者单位 | 1.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yi, Yang-Yang,Tu, Yi-Heng. The Neural Network Representation of Pain in Humans[J]. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS,2024,51(10):2357-2368. |
| APA | Yi, Yang-Yang,&Tu, Yi-Heng.(2024).The Neural Network Representation of Pain in Humans.PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS,51(10),2357-2368. |
| MLA | Yi, Yang-Yang,et al."The Neural Network Representation of Pain in Humans".PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 51.10(2024):2357-2368. |
入库方式： OAI收割
来源：心理研究所
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