Cortical information integration as a model for pain perception and behavior

感觉处理需要外部输入和内部大脑状态之间的相互作用。疼痛是独一无二的 由外部信号触发的感觉体验，但也强烈地受到内部认知和 情绪变数。在电路层面上，不存在单一的初级痛觉皮层；相反，它是一个分布式网络 大脑皮层处理和调节疼痛。例如，已知初级躯体感觉皮质(S1) 处理刺激诱发的信息，如位置和时间。前扣带回皮质(ACC)，在 相反，会引起疼痛的厌恶体验，并显示出高水平的神经元可塑性 慢性疼痛状态。同时，前额叶皮质(PFC)对疼痛行为有很强的调节作用。然而， 这些分布式皮质痛觉回路整合信息的机制在很大程度上仍不清楚。 因此，我们提出了一个新的概念和计算框架的疼痛作为一个汇聚，时间 具体地说，S1、ACC和PFC之间的相互作用。这种相互作用可以用预测性的 将前馈输入与依赖于先验厌恶的自上而下预测相结合的编码框架 经验和调制命令，基于S1、ACC和PFC中的神经活动。为了测试这一点 假设，我们将为疼痛研究创造一套新的工具。我们将设计设备来精确测量 疼痛反应；设计闭环式脑机接口(BCI)，选择性地扰乱皮质电路 在疼痛的精确时间过程中；并定义新的统计方法，如机械平均场 使用局域场势(LFP)和系综分析动态皮层信息整合的模型 斯派克斯。在目标1中，我们将确定伤害性信息对中枢痛觉回路动力学的影响。我们 将描述S1、ACC和PFC之间的定向信息流(更具体地说是初步的 PFC)，伤害性刺激前后。我们将使用实时疼痛检测来创建闭环式BCI 基于统计分析的S1和ACC同步尖峰和LFP对光遗传的影响 调制S1，并显示这样的扰动中断了来自ACC和PFC的信号的整合，以 影响疼痛行为。我们还将分析慢性疼痛如何改变预测性编码方案和响应 到剧烈的疼痛。在目标2中，我们将使用BCI来测试ACC调制对S1和 PFC以及对疼痛行为的影响。更重要的是，我们将展示慢性疼痛会导致适应不良 ACC的可塑性，进而改变了S1和PFC的信息流，从而产生疼痛 预期和紧张性疼痛--由内心厌恶状态驱动的疼痛体验的两个例子。在目标3中，我们 将表明，BCI驱动的PFC输出调制可以提供对伤害性感受器的可扩展调节 来自S1和ACC的信息流改变疼痛行为。更进一步，我们将展示这种大脑皮层 慢性疼痛会损害调制功能。

Sensory processing requires the interaction between external inputs and an internal brain state. Pain is a unique sensory experience that is triggered by external signals, but is also strongly shaped by internal cognitive and emotional variables. At the circuit level, there is not a single primary pain cortex; instead, a distributed network of cortical areas process and regulate pain. For example, the primary somatosensory cortex (S1) is known to process stimulus-evoked information, such as location and timing. The anterior cingulate cortex (ACC), in contrast, gives rise to the aversive experience of pain and displays a high level of neuronal plasticity in the chronic pain state. Meanwhile, the prefrontal cortex (PFC) can strongly modulate pain behaviors. However, the mechanisms whereby these distributed cortical pain circuits integrate information remain largely unknown. Thus, we propose a novel conceptual and computational framework for pain as a converging, temporally specific, interaction among the S1, the ACC, and the PFC. This interaction can be described by a predictive coding framework that combines feedforward inputs with top-down predictions dependent on prior aversive experiences, and modulatory commands, based on neural activities in the S1, ACC and PFC. To test this hypothesis, we will create a new set of tools for pain studies. We will design devices to accurately measure pain responses; engineer closed-loop brain-computer interfaces (BCIs) to selectively perturb cortical circuits during the precise time course of pain; and define novel statistical methods such as mechanistic mean-field models to analyze dynamic cortical information integration, using local field potentials (LFPs) and ensemble spikes. In Aim 1, we will identify the impact of the nociceptive information on central pain circuit dynamics. We will characterize the directed information flow between the S1, ACC, and PFC (more specifically the prelimbic PFC), before and after noxious stimulation. We will create closed-loop BCIs using a real-time pain detection algorithm based on statistical analyses of simultaneous spikes and LFPs in the S1 and ACC to optogenetically modulate the S1, and show that such perturbations disrupt the integration of signals from the ACC and PFC to impact pain behaviors. We will also analyze how chronic pain alters predictive coding schemes and response to acute pain. In Aim 2, we will use BCIs to test the impact of ACC modulation on neural activities in the S1 and PFC, as well as on pain behaviors. More importantly, we will show that chronic pain can induce maladaptive plasticity in the ACC, which in turn alters the information flow from the S1 and PFC to give rise to pain anticipation and tonic pain – two examples of pain experience driven by an internal aversive state. In Aim 3, we will show that BCI-driven modulation of PFC outputs can provide scalable regulation of the nociceptive information flow from the S1 and ACC to alter pain behaviors. Further, we will show how such cortical modulation is impaired by chronic pain.