Analysis of operant self-administration behaviors with supervised machine learning: Protocol for video acquisition and pose estimation analysis using DeepLabCut and Simple Behavioral Analysis (SimBA).

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.

Cocaine reward and reinstatement in adolescent versus adult rodents.

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.

Effects of moderate ethanol consumption as a function of n-6:n-3 dietary ratio on lipid profile, inflammation, and liver function in mice.

Objective: It is critical to understand how moderate ethanol exposure interacts with dietary components such as essential fatty acids to influence inflammatory processes underlying CVD pathogenesis. The purpose of this study was to examine the effects of moderate ethanol consumption and dietary n-6:n-3 fatty acid composition on markers associated with CVD in mice.

Methods: Twenty-three C57BL/6J mice consumed an 18% ethanol solution or 26.