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

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.

Persistent Valence Representations by Ensembles of Anterior Cingulate Cortex Neurons.

The anterior cingulate cortex (ACC) responds to outcomes of a positive or negative valence, but past studies typically focus on one valence or the other, making it difficult to know how opposing valences are disambiguated. We recorded from ACC neurons as rats received tones followed by aversive, appetitive or null outcomes. The responses to the different tones/outcomes were highly inter-mixed at the single neuron level but combined to produce robust valence-specific representations at the ensemble level. The valence-specific patterns far outlasted the tones and outcomes, persisting throughout the long inter-trial intervals (ITIs) and even throughout trial blocks. When the trials were interleaved, the valence-specific patterns abruptly shifted at the start of each new trial. Overall the aversive trials had the greatest impact on the neurons. Thus within the ACC, valence-specificity is largely an emergent property of ensembles and valence-specific representations can appear quickly and persist long after the initiating event.

How Much Does Movement and Location Encoding Impact Prefrontal Cortex Activity? An Algorithmic Decoding Approach in Freely Moving Rats.

Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other types of task-relevant information has proven challenging. To determine the extent to which movement and location information is relevant to mPFC neurons, tetrodes were used to record neuronal activity while limb positions, poses (i.e., recurring constellations of limb positions), velocity, and spatial locations were simultaneously recorded with two cameras every 200 ms as rats freely roamed in an experimental enclosure. Regression analyses using generalized linear models revealed that more than half of the individual mPFC neurons were significantly responsive to at least one of the factors, and many were responsive to more than one. On the other hand, each factor accounted for only a very small portion of the total spike count variance of any given neuron (<20% and typically <1%). Machine learning methods were used to analyze ensemble activity and revealed that ensembles were usually superior to the sum of the best neurons in encoding movements and spatial locations. Because movement and location encoding by individual neurons was so weak, it may not be such a concern for single-neuron analyses. Yet because these weak signals were so widely distributed across the population, this information was strongly represented at the ensemble level and should be considered in population analyses.

Abrupt changes in the patterns and complexity of anterior cingulate cortex activity when food is introduced into an environment.

Foraging typically involves two distinct phases, an exploration phase where an organism explores its local environment in search of needed resources and an exploitation phase where a discovered resource is consumed. The behavior and cognitive requirements of exploration and exploitation are quite different and yet organisms can quickly and efficiently switch between them many times during a foraging bout. The present study investigated neural activity state dynamics in the anterior cingulate sub-region of the rat medial prefrontal cortex (mPFC) when a reliable food source was introduced into an environment. Distinct and largely independent states were detected using a Hidden Markov Model (HMM) when food was present or absent in the environment. Measures of neural entropy or complexity decreased when rats went from exploring the environment to exploiting a reliable food source. Exploration in the absence of food was associated with many weak activity states, while bouts of food consumption were characterized by fewer stronger states. Widespread activity state changes in the mPFC may help to inform foraging decisions and focus behavior on what is currently most prominent or valuable in the environment.