Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia V6T2B5, Canada
Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia V6T2B5, Canada.
J Neurosci. 2024 Jun 5;44(23):e1670232024. doi: 10.1523/JNEUROSCI.1670-23.2024.
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, ∼10× 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它们的上下文和情绪状态。行为的语境调制可能依赖于诸如前扣带皮层(ACC)等区域,这些区域可以混合有关情绪/自主状态和行为的信息。本研究的目的是了解当在不同的情绪状态下执行时,ACC 神经元对行为的表示如何发生变化。开发了一个机器学习技术的流水线,用于对雄性大鼠的视频中的复杂、自发行为进行分类和分类。这个被称为分层无监督行为发现工具(HUB-DT)的流水线,在一项任务中发现了一系列具有统计学差异的行为,该任务中的动机显著结果是在创建三个独特的“情绪环境”的试验块中传递的。HUB-DT 能够检测到特定于每个情绪环境的行为,并且能够识别和分离与行为和情绪环境相关的神经信号的部分。总体而言,在依赖于上下文的方式下,响应行为的神经元数量约为以固定方式响应行为的神经元数量的 10 倍,这突出了情绪状态对基于详细肢体运动学分析精确定义的行为表示的极端影响。这种调制类型可能是一种关键机制,它允许 ACC 根据情绪状态和上下文需求来修改行为输出。