Publications by authors named "Christopher A Reeves"

The use of supervised machine learning to approximate poses in video recordings allows for rapid and efficient analysis of complex behavioral profiles. Currently, there are limited protocols for automated analysis of operant self-administration behavior. We provide methodology to 1) obtain videos of training sessions via Raspberry Pi microcomputers or GoPros 2) obtain pose estimation data using the supervised machine learning software packages DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA) with local high performance computer cluster, 3) comparison of standard MedPC lever response vs quadrant time data generated from pose estimation regions of interest and 4) generation of predictive behavioral classifiers.

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Adolescence is a critical juncture when initiation of drug use intersects with profound developmental changes in the brain. Adolescent drug use increases the risk to develop substance use disorders (SUDs) later in life, but the mechanisms that confer this vulnerability are not understood. SUDs are defined by cycles of use, abstinence, and relapse.

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Adolescents are at increased risk to develop substance use disorders and suffer from relapse throughout life. Targeted weakening of drug-associated memories has been shown to reduce relapse-like behavior in adult rats, however this process has been understudied in adolescents. We aimed to examine whether adolescent-formed, cocaine-associated memories could be manipulated via reconsolidation mechanisms.

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Rationale: Drug use during adolescence results in a life-long risk to develop substance-use disorders. Adolescent rats are sensitive to different drug-associated cues, compared to adults; however, the contribution of adolescent-formed context-drug-associations to elicit relapse-like behavior is underexplored.

Objectives: The present study compared the effect of adolescent vs adult-formed context-drug associations to elicit time-dependent increases in cocaine-seeking behavior.

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