# Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $211,875

## Abstract

Project Summary
Studies in substance use disorders (SUDs) have identified a profound inter-subject variability, where a wide
variety of multifaceted, dissociable behavioral phenotypes are correlated with addiction development and
symptomatology. Even apparently incompatible behavioral expressions, such as impulsivity and inelasticity
or perseverance, have been found to co-occur in SUDs and to signal similar addiction vulnerabilities.
However, few neural or computational mechanisms have been described so far to account for such
seemingly contradicting findings and individual differences, thus hindering the development of
individualized diagnosis and treatment. The overarching goal of this project is to validate a new model of
addiction using Nicotine Use Disorder (NUD) as test case. We propose to expand on previous theories to
provide a more comprehensive neuro-computational framework of addiction that includes phenotypic
variability and co-occurrence of impulsivity and perseverance, characterized in terms of effective
connectivity in cortico-striatal circuits. The scientific premise for this project is grounded in decades of
human and non-human animal work which have demonstrated the roles played in addiction by the ventral
and dorsal corticostriatal systems, respectively responsible for goal oriented and habitual behavior. The
simulation of the neural dynamics in these two circuits has allowed our model to describe addiction on two
independent dimensions. On a first dimension, addictive drugs such as nicotine result in increased circuit
gain and state transition stability in both ventral and dorsal cortico-striatal systems, amplifying preliminary
evidence (impulsivity) and making choice selections become inelastic due to a feedback effect
(perseverance). On a second dimension, which is not necessarily affected by drug exposure, our models
converge in suggesting that this gain-related over-stability of both cortico-striatal circuits is aggravated by
the presence of a “dominance” of either of the two circuit over the other. In aim 1, we will validate the model
prediction that high circuit gain predicts greater behavioral impulsivity and perseverance. In aim 2 we will
validate the model prediction that the balance between the two cortico-striatal circuits predicts drug use
severity. Circuit gain and circuit balance will be tested in NUD individuals (n=32) and healthy controls
(n=32), tasked with decision-making tasks. Circuit gain will be measured in terms of effective connectivity
between cortical and striatal areas, within each circuit, and estimated with the use of Dynamic Causal
Modelling (DCM). Circuit balance will be estimated using DCM for the ventro-dorsal effective connectivity,
to establish dominance on a gradient. This proof-of-concept project can provide a new computational
framework for drug addiction, and a quantitative model to characterize clinical heterogeneity, eventually
informing individualized treatments.

## Key facts

- **NIH application ID:** 9980853
- **Project number:** 5R21DA049243-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Xiaosi Gu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $211,875
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9980853

## Citation

> US National Institutes of Health, RePORTER application 9980853, Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance (5R21DA049243-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9980853. Licensed CC0.

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