Reconfiguration of Behavioral Signals in the Anterior Cingulate Cortex based on Emotional State.

Abstract

Behaviours 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 behaviours. 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 video. This pipeline, termed 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 3 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 behaviour 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 behavioral output based on emotional states and contextual demands.Significance Statement Context and emotional state affect how we see the world and behave in it. Emotional contextualization can be observed at a neural level in the anterior cingulate cortex (ACC). In this study, rats were exposed to events invoking differing emotional responses while we recorded from ensembles of ACC neurons and precisely tracked behaviors using our machine-learning pipeline, 'HUB-DT'. The extent of emotional modulational of behavioral representations by ACC neurons was striking. This modulation may be what allows the ACC to bias decisions and actions based on internal states, and more generally, may offer some insight into how models of network function can more closely reflect what is being represented in ACC.