Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.
Elife. 2018 Feb 27;7:e31949. doi: 10.7554/eLife.31949.
Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief.
损伤后的紧张性疼痛表现出一种优先考虑恢复的行为状态。尽管通常会抑制认知和注意力,但紧张性疼痛需要允许有效的缓解学习来减少疼痛的原因。在这里,我们描述了一个支持缓解学习的中枢学习回路,同时抑制了持续疼痛的程度。我们使用行为、生理和神经影像学数据的计算模型,在两个实验中,被试学习在静态和动态逃避学习范式中终止紧张性疼痛。在两项研究中,我们都表明,主动寻求缓解涉及到一个强化学习过程,表现在背侧纹状体中观察到的错误信号。关键的是,这个系统使用了在扣带前回检测到的不确定性(“可联想性”)信号,它不仅控制着缓解学习的速度,而且内源性地和参数性地调节着紧张性疼痛的程度。研究结果定义了一个自我组织的学习回路,当学习潜在的缓解方法时,它可以减轻持续的疼痛。