Rajagopal Shreya K, Polk Thad A
University of Michigan, Ann Arbor, MI, USA.
Commun Biol. 2025 Apr 26;8(1):668. doi: 10.1038/s42003-025-08107-7.
Exposure therapy, a standard treatment for anxiety disorders, relies on fear extinction. However, extinction recall is often limited to the spatial and temporal context in which extinction is learned, leading to fear relapse in new settings or after delays. Animal studies offer insights into fear extinction in humans. Computational models that integrate these findings into a neurally grounded framework, while generating testable hypotheses for humans, can bridge this gap. Current models either focus on neuron-level activity, limiting their scope, or abstract away entirely from neural mechanisms. They also often overlook the distinct contributions of cue and context in fear extinction and recall. To address these gaps, we present ConFER, a neurally constrained model of fear extinction, recall, and relapse. ConFER integrates findings from the neural fear circuit, modeling distinct pathways for cue and context processing. These pathways independently activate positive and/or negative memory engrams in the basolateral amygdala, competing to determine the fear response. ConFER simulates fear renewal and spontaneous recovery across context combinations, while generating novel, testable predictions. Notably, it predicts counterconditioning may better prevent relapse than extinction in new contexts or after delays. By mechanistically modeling fear relapse, ConFER offers insights to improve exposure therapy outcomes.
暴露疗法是焦虑症的一种标准治疗方法，它依赖于恐惧消退。然而，消退记忆通常局限于学习消退的空间和时间背景，导致在新环境中或延迟后出现恐惧复发。动物研究为人类的恐惧消退提供了见解。将这些发现整合到一个基于神经的框架中，同时为人类生成可测试假设的计算模型，可以弥补这一差距。当前的模型要么专注于神经元水平的活动，限制了其范围，要么完全脱离神经机制。它们还常常忽视线索和背景在恐惧消退和记忆中的不同作用。为了弥补这些差距，我们提出了ConFER，这是一个关于恐惧消退、记忆和复发的神经约束模型。ConFER整合了神经恐惧回路的研究结果，对线索和背景处理的不同途径进行建模。这些途径独立激活基底外侧杏仁核中的正向和/或负向记忆印迹，竞争以确定恐惧反应。ConFER模拟了跨背景组合的恐惧恢复和自发恢复，同时生成了新颖的、可测试的预测。值得注意的是，它预测在新环境中或延迟后，反条件作用可能比消退更能有效地预防复发。通过对恐惧复发进行机制建模，ConFER为改善暴露疗法的效果提供了见解。
