Neural Representation of Motor Output, Context and Behavioral Adaptation in Rat Medial Prefrontal Cortex During Learned Behavior

Proactive Versus Reactive Control Strategies Differentially Mediate Alcohol Drinking in Male Wistars and P Rats

Problematic alcohol consumption is associated with deficits in decision-making and alterations in prefrontal cortex neural activity likely contribute. We hypothesized that the differences in cognitive control would be evident between male Wistars and a model of genetic risk: alcohol-preferring P rats. Cognitive control is split into proactive and reactive components. Proactive control maintains goal-directed behavior independent of a stimulus, whereas reactive control elicits goal-directed behavior at the time of a stimulus. We hypothesized that Wistars would show proactive control over alcohol seeking whereas P rats would show reactive control over alcohol seeking. Neural activity was recorded from the prefrontal cortex during an alcohol seeking task with two session types. On congruent sessions, the conditioned stimulus (CS+) was on the same side as alcohol access. Incongruent sessions presented alcohol opposite the CS+. Wistars, but not P rats, made more incorrect approaches during incongruent sessions, suggesting that Wistars utilized the previously learned rule. This motivated the hypothesis that neural activity reflecting proactive control would be observable in Wistars but not P rats. While P rats showed differences in neural activity at times of alcohol access, Wistars showed differences prior to approaching the sipper. These results support our hypothesis that Wistars are more likely to engage in proactive cognitive control strategies whereas P rats are more likely to engage in reactive cognitive control strategies. Although P rats were bred to prefer alcohol, the differences in cognitive control may reflect a sequela of behaviors that mirror those in humans at risk for an AUD.

Proactive Versus Reactive Control Strategies Differentially Mediate Alcohol Drinking in Wistar and P rats

Problematic alcohol consumption is associated with deficits in decision-making, and alterations in prefrontal cortex neural activity likely contributes. We hypothesized that differences in cognitive control would be evident between male Wistar rats and a model for genetic risk for alcohol use disorder (alcohol-preferring P rats). Cognitive control can be split into proactive and reactive components. Proactive control maintains goal-directed behavior independent of a stimulus whereas reactive control elicits goal-directed behavior at the time of a stimulus. We hypothesized that Wistars would show proactive control over alcohol-seeking whereas P rats would show reactive control over alcohol-seeking. Neural ensembles were recorded from prefrontal cortex during an alcohol seeking task that utilized two session types. On congruent sessions the CS+ was on the same side as alcohol access. Incongruent sessions presented alcohol opposite the CS+. Wistars, but not P rats, exhibited an increase in incorrect approaches during incongruent sessions, suggesting that Wistars utilized the previously learned task-rule. This motivated the hypothesis that ensemble activity reflecting proactive control would be observable in Wistars but not P rats. While P rats showed differences in neural activity at times relevant for alcohol delivery, Wistars showed differences prior to approaching the sipper. These results support our hypothesis that Wistars are more likely to engage proactive cognitive-control strategies whereas P rats are more likely to engage reactive cognitive control strategies. Although P rats were bred to prefer alcohol, differences in cognitive control may reflect a sequela of behaviors that mirror those in humans at risk for an AUD.

Prefrontal Cortical to Mediodorsal Thalamus Projection Neurons Regulate Posterror Adaptive Control of Behavior

Adaptive control is the online adjustment of behavior to guide and optimize responses after errors or conflict. The neural circuits involved in monitoring and adapting behavioral performance following error are poorly understood. The prefrontal cortex (PFC) plays a critical role in this form of control. However, these brain areas are densely connected with many other regions, and it is unknown which projections are critical for adaptive behavior. Here, we tested the involvement of four distinct dorsal and ventral prefrontal cortical projections to striatal and thalamic target areas in adaptive control. We re-analyzed data from published experiments, using trial-by-trial analyses of behavior in an operant task for attention and impulsivity. We find that male rats slow their responses and perform worse following errors. Moreover, by combining retrograde labeling and chemogenetic silencing, we find that dorsomedial prefrontal pyramidal neurons that project to the lateral nucleus of the mediodorsal thalamus (MDL) are involved in posterror performance and timing of responses, specifically with unpredictable delays until stimulus presentation. Together, these data show that dorsal medial PFC (mPFC) projection neurons targeting the lateral MDT regulate adaptive control to flexibly optimize behavioral responses in goal-directed behavior.

Spike Prediction on Primary Motor Cortex from Medial Prefrontal Cortex during Task Learning

OBJECTIVES

Brain-machine interfaces (BMIs) aim to help people with motor disabilities by interpreting brain signals into motor intentions using advanced signal processing methods. Currently, BMI users require intensive training to perform a pre-defined task, not to mention learning a new task. Thus, it is essential to understand neural information pathways among the cortical areas in task learning to provide principles for designing BMIs with learning abilities. We propose to investigate the relationship between the medial prefrontal cortex (mPFC) and primary motor cortex (M1), which are actively involved in motor control and task learning, and show how information is conveyed in spikes between the two regions on a single-trial basis by computational models.

APPROACH

We are interested in modeling the functional relationship between mPFC and M1 activities during task learning. Six Sprague Dawley rats were trained to learn a new behavioral task. Neural spike data was recorded from mPFC and M1 during learning. We then implement the generalized linear model, the second-order generalized Laguerre-Volterra model, and the staged point-process model to predict M1 spikes from mPFC spikes across multiple days during task learning. The prediction performance is compared across different models or learning stages to reveal the relationship between mPFC and M1 spike activities.

MAIN RESULTS

We find that M1 neural spikes can be well predicted from mPFC spikes on the single-trial level, which indicates a highly correlated relationship between mPFC and M1 activities during task learning. By comparing the performance across models, we find that models with higher nonlinear capacity perform significantly better than linear models. This indicates that predicting M1 activity from mPFC activity requires the model to consider higher-order nonlinear interactions beyond pairwise interactions. We also find that the correlation coefficient between the mPFC and M1 spikes increases during task learning. The spike prediction models perform the best when the subjects become well trained on the new task compared with the early and middle stages. The results suggest that the co-activation between mPFC and M1 activities evolves during task learning, and becomes stronger as subjects become well trained.

SIGNIFICANCE

This study demonstrates that the dynamic patterns of M1 spikes can be predicted from mPFC spikes during task learning, and this will further help in the design of adaptive BMI decoders for task learning.

Integration of value and action in medial prefrontal neural systems

The rodent medial prefrontal cortex (mPFC) plays a key role in regulating cognition, emotion, and behavior. mPFC neurons are activated in diverse experimental paradigms, raising the questions of whether there are specific task elements or dimensions encoded by mPFC neurons, and whether these encoded parameters are selective to neurons in particular mPFC subregions or networks. Here, we consider the role of mPFC neurons in processing appetitive and aversive cues, outcomes, and related behaviors. mPFC neurons are strongly activated in tasks probing value and outcome-associated actions, but these responses vary across experimental paradigms. Can we identify specific categories of responses (e.g., positive or negative value), or do mPFC neurons exhibit response properties that are too heterogeneous/complex to cluster into distinct conceptual groups? Based on a review of relevant studies, we consider what has been done and what needs to be further explored in order to address these questions.