# Establishing a Spatial Map of Dopamine Reward Prediction Error Computations and their Function in Distinct Associative Learning Processes Across the Striatum: a Methodological Framework

> **NIH NIH F31** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2024 · $29,092

## Abstract

PROJECT SUMMARY/ABSTRACT. Dopamine (DA) signaling in the striatum, the main input to the basal
ganglia, is critical for instrumental learning, a process involving associations of stimuli, responses, and outcomes.
DA dysfunction results in diverse symptoms in disorders such as obsessive-compulsive disorder, Parkinson’s
Disease, and addiction, which are often attributed to an imbalance in distinct instrumental learning processes.
Anatomically segregated subregions of the striatum are thought to support stimulus-outcome (S-O), stimulus-
response (S-R), and response-outcome (R-O) associations. Further, while the dorsomedial striatum (DMS) is
necessary for flexible goal-directed behavior, the dorsolateral striatum (DLS) supports automatic, outcome-
independent habitual behavior. While dopamine (DA) is typically thought to encode a reward prediction error
(RPE), a teaching signal which drives associative learning, studies suggest that DA release dynamics vary
depending on the target region. However, it is unknown how natural spatiotemporal DA release dynamics support
learning distinct stimulus, response, and outcome associations. These gaps hinder the development of targeted
diagnostics and treatments for dopamine-dysfunction affecting distinct striatum regions.
 This proposed project will make strides toward understanding the functional and computational
significance of spatially varying DA dynamics in distinct associative learning processes. A behavioral paradigm
which requires mice to switch from a cue-dependent S-R strategy to a cue-independent strategy based on recent
actions and outcomes will enable classification of behavior strategy across timescales. This behavioral paradigm
will be combined with a new multi optical fiber photometry method to record DA release dynamics throughout
the volume of the striatum as mice learn and update distinct stimulus, response and outcome contingencies.
This new large-scale, cell-type specific recording method will be applied to establish a spatial map of distinct DA
RPE correlates and can be adapted to record distributed cell-type specific dynamics of any brain region with
high spatiotemporal resolution. Finally, this method will be advanced with a digital mirror device (DMD) to target
light to large, yet spatially precise, regions of the striatum for optogenetic manipulation which mimics the spatial
scale and resolution of natural DA release dynamics.
 Completion of this project will support practical and theoretical training in three main areas: behavioral
testing and analysis, functional circuit analysis, and technology development. Dr. Mark Howe (sponsor) will
provide mentorship and training in in vivo analysis of neural circuits and dynamics. Dr. David Boas (co-sponsor),
the director of the Neurophotonics Center at Boston University, will provide training in the concepts and
techniques used for optical neuro-engineering, which will augment training supported by the NSF
Neurophotonics National Research Trai...

## Key facts

- **NIH application ID:** 10899613
- **Project number:** 5F31NS127536-03
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Eleanor Brown
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $29,092
- **Award type:** 5
- **Project period:** 2022-09-01 → 2025-05-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899613

## Citation

> US National Institutes of Health, RePORTER application 10899613, Establishing a Spatial Map of Dopamine Reward Prediction Error Computations and their Function in Distinct Associative Learning Processes Across the Striatum: a Methodological Framework (5F31NS127536-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10899613. Licensed CC0.

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