@article{ref1,
title="Listening to the data: computational approaches to addiction and learning",
journal="Journal of neuroscience",
year="2023",
author="Wilkinson, Courtney S. and Luján, Miguel Á and Hales, Claire and Costa, Kauê M. and Fiore, Vincenzo G. and Knackstedt, Lori A. and Kober, Hedy",
volume="43",
number="45",
pages="7547-7553",
abstract="Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.<p /> <p>Language: en</p>",
language="en",
issn="0270-6474",
doi="10.1523/JNEUROSCI.1415-23.2023",
url="http://dx.doi.org/10.1523/JNEUROSCI.1415-23.2023"
}