The cortical-striatal brain circuitry is heavily implicated in drug-use. As such, the present study investigated the functional role of cortical-striatal circuitry in modulating alcohol self-administration. Given that a functional role for the nucleus accumbens core (AcbC) in modulating alcohol-reinforced responding has been established, we sought to test the role of cortical brain regions with afferent projections to the AcbC: the medial prefrontal cortex (mPFC) and the insular cortex (IC). Long-Evans rats were trained to self-administer alcohol (15% alcohol (v/v)+2% sucrose (w/v)) during 30 min sessions. To test the functional role of the mPFC or IC, we utilized a chemogenetic technique (hM4D-Designer Receptors Activation by Designer Drugs) to silence neuronal activity prior to an alcohol self-administration session. Additionally, we chemogenetically silenced mPFC→AcbC or IC→AcbC projections, to investigate the role of cortical-striatal circuitry in modulating alcohol self-administration. Chemogenetically silencing the mPFC decreased alcohol self-administration, while silencing the IC increased alcohol self-administration, an effect absent in mCherry-Controls. Interestingly, silencing mPFC→AcbC projections had no effect on alcohol self-administration. In contrast, silencing IC→AcbC projections decreased alcohol self-administration, in a reinforcer-specific manner as there was no effect in rats trained to self-administer sucrose (0.8%, w/v). Additionally, no change in self-administration was observed in the mCherry-Controls. Together these data demonstrate the complex role of the cortical-striatal circuitry while implicating a role for the insula-striatal circuit in modulating ongoing alcohol self-administration.

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Light Imaging Detection and Ranging (LiDAR) systems generate point cloud imagery by using laser light to measure distance to a surface and then combine numerous points to create a three-dimensional (3-D) image. Since early adaptations, LiDAR is now common in aerial and subterranean geographical surveying and autonomous vehicle operations. The transportation industry uses LiDAR to monitor roadway quality, which can allow hazardous roadway corrosion to be spotted and repaired before endangering drivers. However, a leading issue with LiDAR availability is the respectively high price point for effective systems, therefore preventing widespread usage. Previous work at fabrication of a low-cost LiDAR system generated high resolution 3-D imagery but was faulted by limited portability and a long run-time while also finding issues with gimbal translation and C++ programming. This effort improves the prior work by combining a touchscreen Graphical User Interface (GUI) with a rangefinder (Garmin LiDAR-Lite v3HP) powered by Raspberry Pi 4 Model B hardware. The rangefinder is housed in a 3-D printed gimbal mount that translates via two stepper motors and driver board. The system runs via a Python script that allows the user to select varying levels of resolution on the GUI prior to data collection onto a Secure Digital card or a file accessible through an internet connection. Like the previous work, data output is in Cartesian coordinates through a .xyz file format with a MATLAB script used to create a point cloud and two-dimensional image with a depth gradient. Overall, a more efficient, easier to use, and accurate LiDAR system was created that offers various resolution levels for under the cost of $500.

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