ArticleAddiction neuroscience2024
Examining a punishment-related brain circuit with miniature fluorescence microscopes and deep learning.
Article in Addiction neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Using miniscopes and deep learning to compare neurobehavioral representations of psychostimulant and opioid self-administration.Addiction neuroscience · 2025Article
- Miniscope Imaging of Nucleus Accumbens Neural Activity in Freely Behaving Rats: Virus Injection, Gradient Index Lens Implantation, Recording Strategies, and Analytical Methods.Current protocols · 2025Article
- Minibox: Custom solo or semi-group housing chambers for long term housing of rats with miniscopes.MethodsX · 2024Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
In humans experiencing substance use disorder (SUD), abstinence from drug use is often motivated by a desire to avoid some undesirable consequence of further use: health effects, legal ramifications, etc. This process can be experimentally modeled in rodents by training and subsequently punishing an operant response in a context-induced reinstatement procedure. Understanding the biobehavioral mechanisms underlying punishment learning is critical to understanding both abstinence and relapse in individuals with SUD. To date, most investigations into the neural mechanisms of context-induced reinstatement following punishment have utilized discrete loss-of-function manipulations that do not capture ongoing changes in neural circuitry related to punishment-induced behavior change. Here, we describe a two-pronged approach to analyzing the biobehavioral mechanisms of punishment learning using miniature fluorescence microscopes and deep learning algorithms. We review recent advancements in both techniques and consider a target neural circuit.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.