Many neuropsychiatric disorders, including obsessive-compulsive disorder, mood disorders and addiction, involve compromised evaluative and decision-making processes, and maladaptive learned associations. A computational framework that has proven useful in reconciling these different clinical symptoms is reinforcement learning (RL), which dictates how to optimally interact with the environment to maximize potential benefits and mitigate negative consequences. Work over the past several decades has revealed that frontostriatal brain circuits are key mechanistic components of RL. However, the precise nature of the interaction between the frontal cortex and the striatum, and how this communication is achieved, remains unclear. The current proposal focuses on orbitofrontal cortex (OFC) and the caudate nucleus (CN). OFC assigns values to stimuli in our environment, which enables us to make optimal decisions. However, it contains little information about potential motor responses. Our hypothesis is that OFC transfers value information to CN, where it in can be used to select the choice response that will lead to the highest value outcome. We hypothesize that this communication occurs via a phase reset of the local field potential in the theta band at the time of the choice. To test this hypothesis, we will simultaneously record both single neurons and local field potentials from OFC and CN in awake, behaving animals trained to perform an RL task. We will particularly focus on the spatiotemporal dynamics in LFPs between regions, and their relationship to local neuronal computations. We will test the causal role of OFC theta in enabling frontostriatal communication by applying frequency specific microstimulation to OFC while simultaneously recording neural activity in CN. Finally, we will determine whether we can manipulate RL processes using `closed-loop' control, in which we use neural measurements of OFC theta to control the application of microstimulation to CN. Taken together, the results of this proposal will provide convergent correlative and causal evidence for the role of OFC and CN in RL, as well as determine the mechanism by which the two areas communicate. In addition, it will lay the groundwork for future BMI approaches focused on frontolimbic interventions to manipulate maladaptive associations.
This project focuses on understanding how cortical theta rhythms between orbitofrontal cortex and the caudate nucleus enable reinforcement learning. Specifically, we will determine how these two regions interact by measuring and manipulating electrical activity in both regions, including patterned microstimulation controlled by brain activity. Dysfunction of orbitofrontal cortex is strongly implicated in neuropsychiatric disorders that involve dysfunctional reinforcement learning, such as obsessive-compulsive disorder, mood disorders and addiction, so understanding these mechanisms could yield novel therapeutic approaches.
| Wallis, Joni D (2018) Decoding Cognitive Processes from Neural Ensembles. Trends Cogn Sci 22:1091-1102 |
