Behaviors and their execution depend on the context and emotional state in which they are performed. The contextual modulation of behavior likely relies on regions such as the anterior cingulate cortex (ACC) that multiplex information about emotional/autonomic states and behaviors. The objective of the present study was to understand how the representations of behaviors by ACC neurons become modified when performed in different emotional states. A pipeline of machine learning techniques was developed to categorize and classify complex, spontaneous behaviors in male rats from the video. This pipeline, termed Hierarchical Unsupervised Behavioural Discovery Tool (HUB-DT), discovered a range of statistically separable behaviors during a task in which motivationally significant outcomes were delivered in blocks of trials that created three unique "emotional contexts." HUB-DT was capable of detecting behaviors specific to each emotional context and was able to identify and segregate the portions of a neural signal related to a behavior and to emotional context. Overall, ~10x as many neurons responded to behaviors in a contextually dependent versus a fixed manner, highlighting the extreme impact of emotional state on representations of behaviors that were precisely defined based on detailed analyses of limb kinematics. This type of modulation may be a key mechanism that allows the ACC to modify the behavioral output based on emotional states and contextual demands.

行为及其执行取决于执行行为的背景和情绪状态。行为的情境调节可能依赖于前扣带皮层 (ACC) 等区域，该区域复合有关情绪/自主状态和行为的信息。本研究的目的是了解 ACC 神经元的行为表征在不同情绪状态下如何发生变化。开发了一系列机器学习技术来对视频中雄性大鼠复杂的自发行为进行分类。这个管道被称为分层无监督行为发现工具（HUB-DT），在一项任务中发现了一系列统计上可分离的行为，其中在创建三种独特的“情感背景”的试验块中交付了具有动机的显着结果。 HUB-DT 能够检测特定于每种情绪背景的行为，并能够识别和分离与行为和情绪背景相关的神经信号部分。总体而言，以上下文相关方式与固定方式对行为作出反应的神经元数量增加了约 10 倍，这凸显了情绪状态对基于肢体运动学详细分析精确定义的行为表征的极端影响。这种类型的调节可能是一种关键机制，允许 ACC 根据情绪状态和情境需求修改行为输出。