No abstract

The TUNL task is an automated touchscreen task used to evaluate the cognitive processes involved in working memory (WM) and spatial pattern separation in rodents. Both rats and mice can be used. To elicit working memory processes, the rodent must distinguish between a sample (familiar) light stimulus and a novel light stimulus after a delay. With a correct selection, the rodent will receive a food reward. A major benefit of TUNL compared to other similar tasks is the circumvention of spatial "mediating strategies" that the rodent may use to supplement or replace working memory processes to complete the task successfully. Each trial is 'unique', as the stimuli are pseudo-randomized between trials in an array of spatial locations. The TUNL task uses a progression of six training steps to teach the rodent the associated rules necessary to complete the full task. Task performance is typically measured by trials completed and by accuracy. Task accuracy can be evaluated across various spatial separations to engage hippocampal-dependent processes involved in spatial pattern separation. The latency between trial responses can also be evaluated, with food reward collection latency as a measure of motivation. The TUNL task can be used to assess working memory and cognitive deficits in rodent models with neurodegenerative and neurological disorders, providing a valuable tool to screen for new treatment options, in addition to assessing basic neurobiology.

Cannabis sativa has gained popularity as a 'natural substance', leading many to falsely assume that it is not harmful. This assumption has been documented amongst pregnant mothers, many of whom consider Cannabis use during pregnancy as benign. The purpose of this study was to validate a Cannabis smoke exposure model in pregnant rats by determining the plasma levels of cannabinoids and associated metabolites in the dams after exposure to either Cannabis smoke or injected cannabinoids. Maternal and fetal cytokine and chemokine profiles were also assessed after exposure. Pregnant Sprague-Dawley rats were treated daily from gestational day 6-20 with either room air, i.p. vehicle, inhaled high-δ9-tetrahydrocannabinol (THC) (17.98% THC, 0.1% cannabidiol [CBD]) smoke, inhaled high-CBD (0.1% THC, 12.83% CBD) smoke, 3 mg/kg i.p. THC, or 10 mg/kg i.p. CBD. Our data reveal that THC and CBD, but not their metabolites, accumulate in maternal plasma after repeated exposures. Injection of THC or CBD was associated with fewer offspring and increased uterine reabsorption events. For cytokines and chemokines, injection of THC or CBD up-regulated several pro-inflammatory cytokines compared to control or high-THC smoke or high-CBD smoke in placental and fetal brain tissue, whereas smoke exposure was generally associated with reduced cytokine and chemokine concentrations in placental and fetal brain tissue compared to controls. These results support existing, but limited, knowledge on how different routes of administration contribute to inconsistent manifestations of cannabinoid-mediated effects on pregnancy. Smoked Cannabis is still the most common means of human consumption, and more preclinical investigation is needed to determine the effects of smoke inhalation on developmental and behavioural trajectories.

Working memory (WM) is an executive function that orchestrates the use of a limited amount of information, referred to as working memory capacity (WMC), in cognitive functions. In humans, Cannabis exposure impairs WM; however, it is unclear if Cannabis facilitates or impairs rodent WM. Existing literature also fails to address the effects of Cannabis exposure on rodent WMC using exposure paradigms that closely mirror patterns of human use. In the present study, WMC of rats was inferred by novelty preference after a short delay in spontaneous recognition-based tests. Either object or odor-based stimuli were used in different variations of the tests that present identical (IOT) and different (DOT) sets of stimuli (3 or 6) for low- and high-cognitive loads, respectively. Additionally, we present a human-machine hybrid (HYB) behavioral quantification approach which supplements stopwatch-based scoring with supervised machine learning (SML)-based classification, enabling behavioral data to be made publicly available. After validating the spontaneous tests, 6-item IOT and DOT tests with the HYB method were used to evaluate the impact of acute exposure to high-THC or high-CBD Cannabis smoke on novelty preference. Under control conditions, rats showed novelty preference in all test variations. We found that high-THC, but not high-CBD, Cannabis smoke exposure impaired novelty preference for objects under a high-cognitive load. Odor-based recognition deficits were seen under both low-, and high-cognitive loads only following high-THC smoke exposure. Ultimately, these data show that Cannabis smoke exposure impacts novelty preference in a load-dependent, and stimuli-specific manner.