Jang Grace, Kragel Philip A
Emory University, Atlanta, GA 30032.
bioRxiv. 2024 Jul 30:2024.07.29.605621. doi: 10.1101/2024.07.29.605621.
The amygdala is a cluster of subcortical nuclei that receives diverse sensory inputs and projects to the cortex, midbrain and other subcortical structures. Numerous accounts of amygdalar contributions to social and emotional behavior have been offered, yet an overarching description of amygdala function remains elusive. Here we adopt a computationally explicit framework that aims to develop a model of amygdala function based on the types of sensory inputs it receives, rather than individual constructs such as threat, arousal, or valence. Characterizing human fMRI signal acquired as participants viewed a full-length film, we developed encoding models that predict both patterns of amygdala activity and self-reported valence evoked by naturalistic images. We use deep image synthesis to generate artificial stimuli that distinctly engage encoding models of amygdala subregions that systematically differ from one another in terms of their low-level visual properties. These findings characterize how the amygdala compresses high-dimensional sensory inputs into low-dimensional representations relevant for behavior.
杏仁核是一组皮质下核团，接收多种感觉输入并投射到皮层、中脑和其他皮质下结构。关于杏仁核对社会和情感行为的贡献已有诸多描述，但对杏仁核功能的总体描述仍难以捉摸。在这里，我们采用一种计算明确的框架，旨在基于杏仁核接收的感觉输入类型开发一个杏仁核功能模型，而不是基于诸如威胁、唤醒或效价等个体概念。通过对参与者观看全长电影时获取的人类功能磁共振成像（fMRI）信号进行特征分析，我们开发了编码模型，该模型可以预测杏仁核活动模式以及自然主义图像诱发的自我报告效价。我们使用深度图像合成来生成人工刺激，这些刺激能够特异性地激活杏仁核亚区域的编码模型，这些亚区域在低水平视觉属性方面系统地彼此不同。这些发现描述了杏仁核如何将高维感觉输入压缩为与行为相关的低维表征。
