# Frontostriatal Rhythms Underlying Reinforcement Learning.

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2022 · $379,722

## Abstract

Project summary
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.

## Key facts

- **NIH application ID:** 10401263
- **Project number:** 5R01MH117763-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Joni D Wallis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $379,722
- **Award type:** 5
- **Project period:** 2018-07-23 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401263

## Citation

> US National Institutes of Health, RePORTER application 10401263, Frontostriatal Rhythms Underlying Reinforcement Learning. (5R01MH117763-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10401263. Licensed CC0.

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