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Electrophysiological implants enable exploration of the relationship between neuronal activity and behavior. These technologies evolve rapidly, with multiple iterations of recording systems developed and utilized. Chronic implants must address a litany of complications, including retention of high signal-to-noise ratio in probes and the ability to withstand excess force over the experimental period. To overcome these issues, we designed a chronic implant for rats. Our comprehensive protocol optimizes the entire implant process, from assembling and testing the probes (Neuropixels) to implantation. In addition to addressing the complications previously mentioned, our implant can vertically adjust probes with micron precision and is con- structed using modular components, allowing it to be easily modified for various research contexts, electro- physiological recording systems, headstages, and probe types. 

Abstract Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context-dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. 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 provides evidence for the central role that striosomes play in neuroeconomic and disorder-affected decision-making. 

Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neu- ropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context- dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. 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 presenta- tion. The model provides evidence for the central role that striosomes play in neuroeconomic and disorder-affected decision-making. 

Action selection is important for species survival. The basal ganglia, a subcortical structure, has long been thought to play a crucial role in action selection and movement initiation. Classical theories suggest that an important role of the striatum, the input region of the basal ganglia, is to select actions to be performed based on cortical projections carrying action information. However, thanks to recent progress in neural recording techniques, new experimental evidence suggests that the striatum does not perform action selection. Rather, the striatum plays an advisory role. Thus the classical theories of the basal ganglia need to be revisited and revised. As a rst step, in this work we hypothesize a new computational role for the striatum. We present a network-level theory in which the striatum transforms cortical action bids into action evaluations. Based on the region’s neural circuitry, we theorize that the role of the striatum is to transform bids to action values that are normalized, contrast-enhanced, orthogonalized, and encoded as continuous values through the use of two separate neuron populations with bipolar tuning and both feedforward and collateral inhibitory mechanisms. We simulate our network and investigate the role of the network components in its dynamics. Finally, we compare the behavior of our network to previous literature on decision-making behavior in rodents and primates. 

Abstract AimsWe sought to explore how acute alcohol exposure alters decision-making in rats performing an approach-avoid decision-making task. Increasing concentrations of alcohol were mixed with decreasing concentrations of sucrose to mimic mixed/sweetened alcoholic beverages. MethodsRats were trained on an apparatus in which different concentrations of sucrose were available in four different corners of the arena. During daily sessions, a tone signaled each trial start, followed by illumination (15 lux, blue LEDs) of a single corner port, indicating the potential availability of sucrose at that location. The rat (one rat per arena, both females and males) then chose to approach the lit corner to have the solution dispensed or avoid it, with no solution being dispensed. We examined how the decisions to pursue sucrose rewards shifted with the addition and subsequent removal of ethanol from the sucrose ports. ResultsMales were greatly affected by the introduction of alcohol into the task environment, shifting their approach preference to solutions containing higher alcohol concentrations rather than maintaining the prior preference for high-sucrose-concentration solutions. In contrast, females’ choice patterns and task performance remained largely unchanged. We also explore a method for identifying changes in decision-making tendencies during and after alcohol consumption within individual subjects. ConclusionsThis research explores the introduction of alcohol in varying concentrations with sucrose solutions during an approach-avoid task, with male decision-making and behavioral patterns significantly impacted. We also explore a novel approach for identifying individual adaptations of decision-making behavior when alcohol becomes available, which could be expanded upon in future research. 

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. 

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.