Listening to the Data: Computational Approaches to Addiction and Learning.
| Autor: | Wilkinson CS; Department of Psychology, University of Florida, Gainesville, Florida 32611 c.wilkinson@ufl.edu., Luján MÁ; Department of Neurobiology, University of Maryland, School of Medicine, Baltimore, Maryland 21201., Hales C; Department of Psychology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada., Costa KM; National Institute on Drug Abuse Intramural Research Program, Baltimore, Maryland 21224., Fiore VG; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York 10029., Knackstedt LA; Department of Psychology, University of Florida, Gainesville, Florida 32611., Kober H; Departments of Psychiatry, Psychology, and Neuroscience, Yale University, New Haven, Connecticut 06511. |
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| Jazyk: | angličtina |
| Zdroj: | The Journal of neuroscience : the official journal of the Society for Neuroscience [J Neurosci] 2023 Nov 08; Vol. 43 (45), pp. 7547-7553. |
| DOI: | 10.1523/JNEUROSCI.1415-23.2023 |
| Abstrakt: | 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. (Copyright © 2023 the authors.) |
| Databáze: | MEDLINE |
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