ArticleFrontiers in psychiatry2021
Network-Based Discovery of Opioid Use Vulnerability in Rats Using the Bayesian Stochastic Block Model.
Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Large-scale behavioral characterization of oxycodone self-administration in heterogeneous stock rats reveals initial analgesic effects are associated with addiction-like behaviors.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- High incidence of estrous cycle irregularities in heterogeneous stock (HS) rats is associated with footshock-resistant cocaine intake.Psychopharmacology · 2026Article
- A Challenge To The Assumption That Short- versus Long-Access Groups of Opioid Users Represent Distinct Phenotypes.bioRxiv : the preprint server for biology · 2025Article
- Automated pipeline for operant behavior phenotyping for high-throughput data management, processing, and visualization.NPP - digital psychiatry and neuroscience · 2025Article
- Predicting individual treatment response in alcohol use disorders: a reverse translational proof-of-concept study.Translational psychiatry · 2025Article
- Listening to the Data: Computational Approaches to Addiction and Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Review
- Ten Points to Improve Reproducibility and Translation of Animal Research.Frontiers in behavioral neuroscience · 2022Article
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Authors and funding
12 authors.
Funding
Abstract
Opioid use disorder is a psychological condition that affects over 200,000 people per year in the U.S., causing the Centers for Disease Control and Prevention to label the crisis as a rapidly spreading public health epidemic. The behavioral relationship between opioid exposure and development of opioid use disorder (OUD) varies greatly between individuals, implying existence of sup-populations with varying degrees of opioid vulnerability. However, effective pre-clinical identification of these sub-populations remains challenging due to the complex multivariate measurements employed in animal models of OUD. In this study, we propose a novel non-linear network-based data analysis workflow that employs seven behavioral traits to identify opioid use sub-populations and assesses contributions of behavioral variables to opioid vulnerability and resiliency. Through this analysis workflow we determined how behavioral variables across heroin taking, refraining and seeking interact with one another to identify potentially heroin resilient and vulnerable behavioral sub-populations. Data were collected from over 400 heterogeneous stock rats in two geographically distinct locations. Rats underwent heroin self-administration training, followed by a progressive ratio and heroin-primed reinstatement test. Next, rats underwent extinction training and a cue-induced reinstatement test. To enter the analysis workflow, we integrated data from different cohorts of rats and removed possible batch effects. We then constructed a rat-rat similarity network based on their behavioral patterns and implemented community detection on this similarity network using a Bayesian degree-corrected stochastic block model to uncover sub-populations of rats with differing levels of opioid vulnerability. We identified three statistically distinct clusters corresponding to distinct behavioral sub-populations, vulnerable, resilient and intermediate for heroin use, refraining and seeking. We implement this analysis workflow as an open source R package, named mlsbm.
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