National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, United States.
National Institute on Drug Abuse, National Institutes of Health, Baltimore, United States.
Elife. 2022 Nov 1;11:e73353. doi: 10.7554/eLife.73353.
Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during value-based learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n=40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and other regions updated as contingencies changed, and responses in the prefrontal cortex mediated dynamic cue effects on pain, whereas responses in the brainstem's rostroventral medulla (RVM) were shaped by initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, thalamus, and posterior insula, among other regions. These results show how predictions dynamically impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.
最近的数据表明,涉及更高阶知识和联想学习的系统之间的相互作用驱动了基于价值的学习过程中的反应。然而,目前尚不清楚这些系统如何影响主观反应,例如疼痛。我们测试了指令和反转学习如何影响疼痛和疼痛诱发的大脑激活。健康志愿者(n=40)要么接受关于线索和厌恶结果之间的关联的指令,要么通过在一个关联三次反转的范式中学习。我们使用功能磁共振成像测量了预测线索对疼痛和热诱发大脑反应的影响。无论参与者是否收到关联指令,随着关联的变化,预测线索都会动态调节疼痛感知。在岛叶、前扣带回和其他区域的热诱发反应随着关联的变化而更新,前额叶的反应介导了疼痛的动态线索效应,而脑干的 rostral ventromedial medulla(RVM)的反应则受到整个任务中初始关联的塑造。定量建模表明,在指令组中,期望价值完全由指令塑造,而在未指令组中,期望价值随着错误学习的动态更新。这些差异伴随着基于价值学习的大脑中 rostral anterior cingulate、丘脑和后岛叶等区域的神经相关性的分离。这些结果表明,预测如何动态地影响主观疼痛。此外,成像数据描绘了涉及疼痛产生和基于价值的学习的三种类型的网络:那些对初始关联做出反应的网络、那些在关联变化时随反馈驱动学习动态更新的网络,以及那些对指令敏感的网络。这些发现为设计影响疼痛的治疗方法提供了多个切入点。