# Diagnostic and prognostic biomarkers for subtypes of addiction-related circuit dysfunction

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $603,973

## Abstract

Project Summary
Substance use disorders (SUDs) are increasing in prevalence and are already a leading cause of disability,
due in part to the fact that our understanding of the underlying pathophysiology is incomplete. Like most
neuropsychiatric syndromes, SUDs are highly heterogeneous, and distinct mechanisms may be operative in
some individuals but not in others, even within a single diagnostic category. Furthermore, SUDs frequently co-
occur with depression, anxiety, and other psychiatric syndromes, complicating efforts to identify molecular and
circuit-level mechanisms, and disentangle them from those involved in mood and anxiety disorders. Diagnostic
heterogeneity is thus a fundamental obstacle to developing better treatments, identifying biomarkers for
quantifying risk for different forms of addiction, and predicting treatment response and relapse. Recently, we
developed and validated an approach to discovering and diagnosing subtypes of depression using fMRI
measures of functional connectivity, which in turn predicted subtype-specific clinical symptom profiles and
treatment outcomes. Here, in response to PAR-18-062, we propose a secondary data analysis that would
extend this approach to SUDs, leveraging multiple deeply characterized and large-scale neuroimaging
datasets. Our central hypothesis is that individual differences in mechanisms underlying impairments in
response inhibition and salience attribution (iRISA) are mediated by distinct forms of dysfunctional connectivity
in addiction-related circuits, which in turn interact and give rise to distinct neurophysiological addiction
subtypes. In Aim 1, we will use statistical clustering and machine learning methods to delineate these subtypes
and optimize classifiers (fMRI biomarkers) for diagnosing them in individual patients, focusing initially on
cocaine addiction. In Aim 2, we will validate these subtype-specific biomarkers by first replicating them in a
new dataset and then evaluating their longitudinal stability and predictive utility. In Aim 3, we will test whether
subtype-specific circuit mechanisms generalize to mediate iRISA functions in other forms of addiction, and
define their interactions with distinct mechanisms mediating anhedonia and anxious arousal in patients with
comorbid depression and anxiety.

## Key facts

- **NIH application ID:** 10177987
- **Project number:** 5R01DA047851-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Rita Z Goldstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $603,973
- **Award type:** 5
- **Project period:** 2019-08-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10177987

## Citation

> US National Institutes of Health, RePORTER application 10177987, Diagnostic and prognostic biomarkers for subtypes of addiction-related circuit dysfunction (5R01DA047851-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10177987. Licensed CC0.

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