# Brain Networks for Addiction

> **NIH NIH F99** · DUKE UNIVERSITY · 2024 · $42,774

## Abstract

Opioid use disorder (OUD) and overdose rates have seen a stark rise in incidence throughout the last decade
and continue to pose a devastating threat to the victims, families, and communities affected by addiction. The
emergence of a negative emotional state reflecting a motivational withdrawal symptom when access to the drug
is prevented is a core characteristic of drug addiction and is specifically exacerbated with opioid abuse. However,
current treatment options for those suffering from OUD fail to provide therapeutic relief for many individuals and
effectively substitute more addictive opioids for less addictive versions. Given the role withdrawal avoidance
behavior plays in yielding an exaggerated motivational drive for addictive drugs while simultaneously
compromising executive and inhibitory control of decision making, the central goal of this proposal is to elucidate
a neural code underlying opioid withdrawal and its ability to induce relapse. In Aim 1 (F99), I explain the use of
a custom designed multisite electrode to target 13 brain regions to monitor neural dynamics in opioid withdrawing
mice. Neural features extracted from these recordings are used to train a machine learning model to identify
latent representations that can most optimally and parsimoniously explain and reconstruct the original neural
features. Importantly, in a holdout dataset, this model integrates information that can predict if, and when, a
mouse is in opioid withdrawal. This predictive capacity can generalize to a novel cohort of mice and across
multiple opioid drugs. The discovered network is primarily governed by gamma frequency oscillations between
the ventral tegmental area (VTA) and nucleus accumbens shell (NAcS). In the remainder of my predoctoral
training, I will investigate the contribution of this circuit activity in withdrawal behavior. I will develop a novel
optogenetic targeting approach to establish a causal link between activity across this circuit, brain-wide network
interactions, and withdrawal-specific behavioral responses. In Aim 2 (K00), I pivot my focus towards
understanding the cellular contributions of distinct cell types in intravenous self-administration models of opioid
relapse and vulnerability to relapse. I am primarily interested in understanding how distinct cell types within
individual brain regions that are predominantly affected by chronic opioid exposure confer aberrant cellular
physiology, leading dysfunction of cells, circuits, networks, and behavior. Moreover, I outline a plan to expand
my existing technical skills to include transcriptional profiling, monitoring of in vivo cellular dynamics, and new
addiction paradigm. In my Training Plan, I detail past, ongoing, and future efforts to further cultivate my technical
development, professional and career development, written and oral communication, and mentorship and
leadership. This proposal seeks to address BRAIN Initiative's Scientific Review and High Priority Research Areas
...

## Key facts

- **NIH application ID:** 11001054
- **Project number:** 1F99NS135695-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Karim Abdelaal
- **Activity code:** F99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,774
- **Award type:** 1
- **Project period:** 2024-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11001054

## Citation

> US National Institutes of Health, RePORTER application 11001054, Brain Networks for Addiction (1F99NS135695-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/11001054. Licensed CC0.

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