The prefrontal cortex (PFC) is essential for cognitive functions such as decision-making, emotional regulation, and attention. Dysfunction in PFC circuitry is implicated in neuropsychiatric disorders, including Alzheimer’s disease, depression, and anxiety. Within the PFC, excitatory glutamatergic neurons and inhibitory GABAergic neurons coordinate activity to maintain proper network function. The excitatory-inhibitory balance is critical for cognitive processing, yet the role of the most abundant GPCR in the brain, the cannabinoid 1 receptor (CB1), in regulating these neuronal populations remains unclear. CB1 receptors are highly expressed across other cortical regions but have the most dense expression in the PFC where they are hypothesized to modulate synaptic transmission and plasticity. To investigate their cell-specific function, we utilized a CRISPR-Cas9 to locally knockout the CB1 receptor specific neuronal populations using a viral cre-dependent driver. This virus was administered in either vesicular GABA transporter (VGAT)-Cre or vesicular glutamate transporter (VGLUT)-Cre animals to select for inhibitory or excitatory neurons, respectively. We assessed CB1 receptor expression using RNAscope in situ hybridization to quantify CB1 mRNA in VGAT-expressing inhibitory neurons and VGLUT-expressing excitatory neurons. Fluorescence microscopy was used to visualize CB1 receptor distribution and determine whether its expression differs between these neuronal populations compared to controls. By mapping CB1 receptor expression and assessing its functional role in these neurons through previous behavioral experiments, this study provided insight into how the endocannabinoid system regulates PFC circuitry. Understanding CB1-mediated modulation of excitatory and inhibitory balance could have broad implications for neuropsychiatric disorders characterized by PFC dysfunction.

Cannabis is the most commonly used drug in America, with 52.2 million individuals (19% of Americans) reporting use in 2021. The primary psychoactive compound, Delta-9-tetrahydrocannabinol (THC), binds to cannabinoid receptors, among the most abundant in the brain. This interaction causes mental and locomotor impairment, contributing to increased motor vehicle crashes in states with legalization. However, a comprehensive baseline for THC’s biophysical effects on behavior and motor function remains lacking. This research aims to establish such a baseline using advanced AI-driven behavioral analysis in mice. Mice received intraperitoneal injections of THC (0.1–30 mg/kg) or a vehicle solution (control). One hour post-injection, each mouse was recorded for 15 minutes in a custom Linear Track designed for dual-view (side and bottom-up) behavioral assessment. Video recordings were analyzed using an AI computer vision model tracking 29 points of interest at 100 fps. The collected data trained a THC behavioral regression AI algorithm to predict doses based on behavioral patterns. Analysis of novel videos revealed a model accuracy with a mean squared error of 0.50, successfully identifying THC-induced impairment. This approach also enabled investigations into specific brain regions mediating THC behaviors through local drug infusion. This study marks the first successful attempt to predict THC dose relative to impairment levels using AI modeling. The research aims to computerize behavioral analysis, developing a preclinical AI model capable of recognizing and predicting THC’s effects with minimal human bias and error. This technology provides a data-driven approach to characterizing subtle behavioral differences, offering potential applications in both research and clinical settings.