# Network Dynamics of Negative and Positive Valence Systems in Decision Making

> **NIH NIH F30** · DUKE UNIVERSITY · 2022 · $42,089

## Abstract

ABSTRACT
Evaluating risk and reward potential in the execution of motivated behaviors is important in decision-making.
Positive valence systems in the brain encode positive stimuli and play a key role in motivation, reward
expectance, and appetitive behavior. Negative valence systems, on the other hand, encode negative stimuli
such as fear and anxiety, and drive avoidance. Critically, an imbalance in these valence systems is thought to
underlie many core symptoms in Major Depressive Disorder (MDD). Recent studies have shown that the brain
regions responsible for encoding these divergent valence systems have anatomical and functional overlap.
This raises the hypothesis that differences in network-level activity involving these overlapping areas may
discriminate information of positive and negative valence. Here, I propose to employ in vivo recordings of
electrical activity across multiple brain regions concurrently as mice perform a behavioral task designed to
probe both reward and aversion. This task, modeled after the classic elevated plus maze and sucrose
preference tasks, will directly quantify the impact of anxiogenic stimuli on reward-motivated behavior. Using
machine-learning techniques, I will then generate neural models that reflect the network-level activity engaged
during the performance of this task. I anticipate that this strategy with discover an independent network that
corresponds with the positive valence system, and another independent network that corresponds with the
negative valence system. I also anticipate that I will discover a network that directly integrates network-level
activity in these two systems to drive decisions making. Lastly, the structure of these networks will be validated
in a cohort of mice that will be subjected to chronic social defeat stress. A validated model of MDD, chronic
social defeat stress induces increased anxiety-like phenotypes and decreased reward drive in a subset of mice
(stress-susceptible mice) while only increasing anxiety-like phenotypes in other animals (stress-resilient mice).
Thus, successful completion of the proposed work will lead to a network-level understanding of positive and
negative valence systems. Furthermore, the framework discovered through this study has the potential to
facilitate the development of new revolutionary approaches for diagnosis and treatment of MDD.

## Key facts

- **NIH application ID:** 10382218
- **Project number:** 5F30MH118888-04
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Dalton N. Hughes
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $42,089
- **Award type:** 5
- **Project period:** 2019-03-01 → 2022-10-17

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10382218

## Citation

> US National Institutes of Health, RePORTER application 10382218, Network Dynamics of Negative and Positive Valence Systems in Decision Making (5F30MH118888-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10382218. Licensed CC0.

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