# Computational and Neural Modeling of Cue Reactivity in Addiction

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $577,836

## Abstract

Abstract
Substance use disorders (SUD) and obesity are both major public health concerns in the United States, with
an estimated 20.8 million Americans struggling with at least one SUD in 2015 and 78.6 million adults and 12.7
million children who are obese. Cue-elicited craving is a central symptom of both drug addiction and binge
eating and a strong predictor of relapse. Compared to other SUD symptoms, craving is also much more
resistant to treatment. Unfortunately, our understanding of the neurobiological basis of cue-induced craving is
still limited, especially compared to the wealth of existing human neuroimaging data. This is partially due to the
lack of big data collectives (i.e. fMRI studies have mostly been conducted in isolation from each other) as well
as the scarcity of model-based computational analysis in neuroimaging studies on addiction and obesity. The
overarching goal of this project is to use multi-level, model-based computational methods to re-analyze six
existing fMRI datasets that examine cue reactivity and craving across a total of 954 individuals with substance
use or binge eating (59 tobacco smokers, 254 cannabis users, 598 binge drinkers, and 43 binge eating adults).
We will address three timely aims using novel computational modeling methods: 1) conduct Bayesian model-
based analyses to examine the common and distinct computational mechanisms of drug and food craving
across different groups; 2) use dynamic causal modeling to quantify directed coupling between neural regions
involved in cue reactivity shared by or unique to different substance using and binge eating groups; 3) explore
how models of cue-elicited craving are modulated by the severity of substance use and binge eating. Findings
from this project will greatly enhance our understanding of the neural and computational mechanisms
underlying craving and cue reactivity in drug addiction and binge eating. The implication of these results could
be far-reaching, because 1) craving is a common and core phenotype across different substance use and
binge eating groups; 2) these advanced modeling methods could be applied to many other pathologies related
to dysfunctional craving and reward processing; and 3) how these mechanisms differ between more severe
(e.g. SUD) and less severe (e.g. non-SUD) individuals could provide mechanisms that might protect an
individual from developing SUD.

## Key facts

- **NIH application ID:** 10197070
- **Project number:** 5R01DA043695-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Xiaosi Gu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $577,836
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10197070

## Citation

> US National Institutes of Health, RePORTER application 10197070, Computational and Neural Modeling of Cue Reactivity in Addiction (5R01DA043695-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10197070. Licensed CC0.

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