Effect of Acute Alcohol Consumption in a Novel Rodent Model of Decision Making.

Background: Alcohol use, especially at high consumption levels, can lead to irrational decision-making. In humans, this can lead to harmful outcomes often seen in the context of driving under the influence and or aggressive behavior. To date, the field is lacking comprehensive animal models to examine the impact of alcohol use on decision making in rodents, particularly to examine sex differences in choice behavior. To address this issue, the present study examined the effects of acute alcohol consumption during a behavioral approach-avoidance task that captures momentary changes in decision-making behavior and choice selection in female and male rats. Methods: Our team has developed a novel behavioral protocol involving a concurrent choice to consume four different concentrations of alcohol and sucrose combinations. During the task, female or male rats can approach or avoid drinking solutions in four distinct corners of our test apparatus. The solutions were prepared in inverse concentrations (higher sucrose was paired with lower alcohol and vice versa) so that the rodents pursue minimal alcohol use by consuming the higher sucrose concentrations or higher concentrations of alcohol by drinking the lower sucrose concentrations. The animals also have the option to avoid drinking alcohol by not approaching any of the drinking cups. Behavior and choice were tracked during task performance involving different solution concentrations of alcohol and sucrose. Results: The choice of consuming different concentrations of alcohol or sucrose resulted in sex-dependent differences in an approach-avoid trade-off pattern of behavior that was sensitive to different concentrations of alcohol/sucrose combinations. Notably, males were greatly affected by the introduction of alcohol into the task environment, approaching higher alcohol concentrations significantly more often than the non-alcohol containing options. In contrast, females choice patterns and task performance were largely unchanged during alcohol and non-alcohol containing tasks. Regardless of sex, we identify a novel method for identifying individual subject decision-making abnormalities during and after alcohol consumption. Conclusions: This research reveals a novel approach for examining the effects of acute alcohol exposure during a trade-off task, with decision patterns being more impacted by alcohol use in males as compared to females. We also offer the field a novel approach for identifying individual abnormalities in decision making behavior with the presentation of alcohol. Future research can explore these abnormal patterns in both acute and chronic alcohol conditions to develop methods for identifying subjects at-risk for developing an alcohol use disorder and the deleterious impact of alcohol on rational decision making.

Model of a striatal circuit exploring biological mechanisms underlying decision-making during normal and disordered states.

Decision-making requires continuous adaptation to internal and external contexts. Changes in decision-making are reliable transdiagnostic symptoms of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a mathematical space for decision-making computations depending on context, and how the matrix compartment defines action value depending on the space. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model reframes the role of the striosomal circuit in neuroeconomic and disorder-affected decision-making. Highlights: Striosomes prioritize decision-related data used by matrix to set action values. Striosomes and matrix have different roles in the direct and indirect pathways. Abnormal information organization/valuation alters disorder presentation. Variance in data prioritization may explain individual differences in disorders. eTOC: Beck et al. developed a computational model of how a striatal circuit functions during decision-making. The model unifies and extends theories about the direct versus indirect pathways. It further suggests how aberrant circuit function underlies decision-making phenomena observed in neuropsychiatric disorders.

RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making.

Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders.

Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex.

Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.

Thalamic regulation of frontal interactions in human cognitive flexibility.

Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.

Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.