Network-Based Discovery of Opioid Use Vulnerability in Rats Using the Bayesian Stochastic Block Model

Frontiers in Psychiatry

Dongjun Chung¹  Carter Allen¹  Leah C. Solberg Woods²  Peter W. Kalivas³  Brittany N. Kuhn³  Analyse T. Roberts³  Ayteria D. Crow³  Gary Hardiman⁴  Nazzareno Cannella⁵  Massimo Ubaldi⁵  Veronica Lunerti⁵  Roberto Ciccocioppo⁵ 

[1] Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States;Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States;Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States;School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom;School of Pharmacy, University of Camerino, Camerino, Italy;

关键词: clustering;    community detection;    Bayesian model;    opioid use disorder;    network analysis;    stochastic block model;   

DOI  :  10.3389/fpsyt.2021.745468

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown