# CRCNS Research Proposal: Cortico-amygdalar substrates of adaptive learning

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2022 · $314,193

## Abstract

PROJECT SUMMARY
Individuals with Substance Use Disorders (SUD) often exhibit preferences for risky, uncertain outcomes
and demonstrate inflexible learning. The neural mechanisms of adaptive learning under uncertainty,
however, have been predominantly investigated in human and nonhuman primates with limited
capacity for microcircuit measurements and/or systems-level causal manipulations. Guided by
mechanistic computational models, here we propose precise systems-level manipulation of interactions
between multiple brain areas and imaging of stable neuronal ensembles in rodents, to reveal circuit-
level mechanisms underlying adaptive learning. Previous studies point to the role of basolateral
amygdala (BLA) and several interconnected subregions of the prefrontal cortex (PFC), including
orbitofrontal (OFC) and anterior cingulate cortex (ACC), in adaptive behavior. Based on our recent
modeling and experimental work, we hypothesize that OFC and ACC provide stable representations of
stimulus-outcome (state values) and action-outcome associations (action values), respectively. BLA uses
input from OFC and ACC to estimate volatility in both state and action values to destabilize these
representations, which in turn enables faster adjustments in response to real changes in the
environment. To test this hypothesis we will chemogenetically inhibit projection neurons between these
regions during probabilistic reversal learning of state and action values, and record neuronal ensemble
activity via calcium imaging in each cortical region during learning. The results from manipulating
different pathways and high temporal- and spatial- resolution of calcium imaging data will be used to
identify the relative strength and types of projections, and the representations of reward value in order
to refine our models and subsequently develop circuit-level, spiking network models for adaptive
learning under uncertainty. Given that SUDs are characterized by uncertain, rapidly-changing, and often
extreme reward environments, our proposed aims are pertinent to clinical observations in SUDs and
especially deficits in behavioral adjustments. Altogether, using a combination of detailed computational
modeling and a sophisticated experimental approach, we will reveal the contributions of cortico-
amygdalar circuits to adaptive behavior under uncertainty.

## Key facts

- **NIH application ID:** 10598322
- **Project number:** 3R01DA047870-05S1
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Alicia Izquierdo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $314,193
- **Award type:** 3
- **Project period:** 2018-09-15 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598322

## Citation

> US National Institutes of Health, RePORTER application 10598322, CRCNS Research Proposal: Cortico-amygdalar substrates of adaptive learning (3R01DA047870-05S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10598322. Licensed CC0.

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