Computation and regulation of pain dynamics in the human central nervous system

人类中枢神经系统疼痛动力学的计算和调节

• 批准号: MR/T010614/1
• 负责人: Flavia Mancini
• 金额: $ 160.21万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT010614%2F1
• 关键词: Computation; regulation; pain; dynamics; human

Chronic pain affects one in five people and is the leading cause of disability in the world; musculoskeletal pain alone costs the UK healthcare system £10.2 billion per year. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. The brain does not passively receive information from the nerves but rather interprets it based on what it already knows, anticipating and trying to adjust its responses to what will happen next. It remains to be discovered how the brain accomplishes these fundamental functions when we are in pain. This is a critical concern, as our interpretations and expectations of pain determine not only how much pain we feel but also how well we cope with it.I hypothesise that the brain tries to learn how pain fluctuates over time; by doing so, it generates a 'mental' template of the temporal evolution of pain. Even if pain fluctuates randomly, the brain could learn that information. The brain then uses the template it has learned to predict how pain is likely to evolve in the future. These predictions may regulate the response to pain in the brain and spinal cord, modifying how much pain we feel; indeed, previous work has shown that expecting pain to worsen enhances the brain and spinal-cord response to pain, whereas expecting relief reduces the brain and spinal response to pain (as in the nocebo/placebo effects). I will test these hypotheses using a combination of mathematical models, behavioural and neuroimaging experiments in humans. If the brain cannot accurately guess how pain evolves over time, it may fail to regulate its response to pain effectively, resulting in unnecessarily amplified pain (much like a false alarm). Why would the brain fail to learn the temporal evolution of pain? There may be many reasons, but anxiety is likely to be the most relevant to the risk of developing a great variety of chronic pain disorders. Thus, I will test whether the computations that the brain uses in attempting to learn and regulate the temporal evolution of pain are dysfunctional in persons who are anxious about pain. Importantly, I will develop a new, non-pharmacological intervention to improve how the brain computes and tries to control the temporal evolution of pain. In the future, this intervention could be used to ease the anxiety's grip on pain in vulnerable individuals, improving pain management and wellbeing. My research will provide a set of quantitative measures that capture how the brain computes and tries to control the temporal evolution of pain. These measures could be used to examine whether they are dysfunctional in chronic pain disorders, such as back pain. This new research avenue could advance our understanding of chronic pain, help individualise treatments and monitor their outcomes. The research path opened by this project will ultimately contribute to reducing the societal and financial burden of pain.

慢性疼痛影响着五分之一的人，是世界上导致残疾的主要原因；仅肌肉骨骼疼痛一项每年就要花费英国医疗系统102亿英镑。为了有效地治疗疼痛，有许多尚未解决的问题迫切需要答案，其中一个关键问题是疼痛的感觉是如何从大脑活动中产生的。大脑不是被动地接受来自神经的信息，而是根据它已经知道的东西来解释它，预测并试图调整它对接下来会发生的事情的反应。当我们处于疼痛状态时，大脑是如何完成这些基本功能的还有待发现。这是一个至关重要的问题，因为我们对疼痛的解释和预期不仅决定了我们感受到的疼痛程度，还决定了我们应对疼痛的能力。我假设大脑试图学习疼痛是如何随时间波动的；通过这样做，它产生了疼痛的时间演变的“心理”模板。即使疼痛是随机波动的，大脑也可以学习到这些信息。然后，大脑使用它已经学会的模板来预测疼痛在未来可能如何演变。这些预测可能会调节大脑和脊髓对疼痛的反应，改变我们感受到的疼痛程度；事实上，先前的研究表明，预期疼痛恶化会增强大脑和脊髓对疼痛的反应，而预期缓解会降低大脑和脊髓对疼痛的反应（就像反安慰剂/安慰剂效应一样）。我将使用数学模型、行为和人类神经成像实验相结合的方法来检验这些假设。如果大脑不能准确地猜测疼痛是如何随着时间的推移而演变的，它可能无法有效地调节对疼痛的反应，从而导致不必要的放大疼痛（很像虚惊一场）。为什么大脑不能学习疼痛的时间进化？可能有很多原因，但焦虑可能是与发展成各种慢性疼痛障碍的风险最相关的。因此，我将测试大脑在试图学习和调节疼痛的时间进化时使用的计算是否在对疼痛感到焦虑的人身上功能失调。重要的是，我将开发一种新的，非药物干预来改善大脑如何计算并试图控制疼痛的时间演变。在未来，这种干预可以用来缓解焦虑对脆弱个体疼痛的控制，改善疼痛管理和健康。我的研究将提供一套定量测量方法，捕捉大脑如何计算并试图控制疼痛的时间演变。这些措施可以用来检查它们是否在慢性疼痛疾病中功能失调，比如背痛。这一新的研究途径可以促进我们对慢性疼痛的理解，帮助个性化治疗并监测其结果。该项目开辟的研究路径最终将有助于减轻疼痛的社会和经济负担。

项目成果

Confidence of probabilistic predictions modulates the cortical response to pain.

概率预测的置信度调节皮质对疼痛的反应。

• DOI: 10.1073/pnas.2212252120
• 发表时间: 2023-01-24
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Computational and neural mechanisms of statistical pain learning.

• DOI: 10.1038/s41467-022-34283-9
• 发表时间: 2022-11-03
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Mancini, Flavia;Zhang, Suyi;Seymour, Ben
• 通讯作者: Seymour, Ben

Learning the statistics of pain: computational and neural mechanisms

学习疼痛的统计数据：计算和神经机制

• DOI: 10.1101/2021.10.21.465270
• 发表时间: 2021
• 作者: Mancini F

Statistical learning shapes pain perception and prediction independently of external cues

• DOI: 10.7554/elife.90634
• 发表时间: 2024-07-10
• 期刊: ELIFE
• 影响因子: 7.7
• 作者: Onysk，Jakub;Gregory，Nicholas;Mancini，Flavia
• 通讯作者: Mancini，Flavia

Confidence of probabilistic predictions modulates the cortical response to pain

概率预测的置信度调节皮质对疼痛的反应

• DOI: 10.1101/2022.08.11.503296
• 发表时间: 2022
• 作者: Mulders D