High-THC Cannabis Smoke Impairs Incidental Memory Capacity in Spontaneous Tests of Novelty Preference for Objects and Odors in Male Rats

Abstract

Working memory is an executive function that orchestrates the use of limited amounts of information, referred to as working memory capacity, in cognitive functions. Cannabis exposure impairs working memory in humans; however, it is unclear whether Cannabis facilitates or impairs rodent working memory and working memory capacity. The conflicting literature in rodent models may be at least partly because of the use of drug exposure paradigms that do not closely mirror patterns of human Cannabis use. Here, we used an incidental memory capacity paradigm where a novelty preference is assessed after a short delay in spontaneous recognition-based tests. Either object or odor-based stimuli were used in test variations with sets of identical [identical stimuli test (IST)] and different [different stimuli test (DST)] stimuli (three or six) for low-memory and high-memory loads, respectively. Additionally, we developed a human-machine hybrid behavioral quantification approach which supplements stopwatch-based scoring with supervised machine learning-based classification. After validating the spontaneous IST and DST in male rats, 6-item test versions with the hybrid quantification method were used to evaluate the impact of acute exposure to high-Δ⁹-tetrahydrocannabinol (THC) or high-CBD Cannabis smoke on novelty preference. Under control conditions, male 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-memory load. Odor-based recognition deficits were seen under both low-memory and high-memory loads only following high-THC smoke exposure. Ultimately, these data show that Cannabis smoke exposure impacts incidental memory capacity of male rats in a memory load-dependent, and stimuli-specific manner.

Keywords: cannabinoid; machine learning; recognition memory.

Figure 1.

The validation and establishment of the IST and DST with objects and odors. A, A picture of an example object set-up is shown. Objects are displayed in six positions in a white-corrugated plastic box. B, A picture of an example odor set-up is shown. Odors are displayed in six positions in a white-corrugated plastic box. C, An example of an object stimuli. D, Example of an odor stimuli. E, Object interaction was measured using DRs to evaluate novelty preference using 3-objects and 6-objects. Male rats explore the novel object significantly more than the familiar objects in the IST and DST with both 3-objects and 6-objects. No differences in novelty preference or exploration times are seen between the IST and DST, or between 3-object and 6-object versions. F, Odor interaction was also measured using DRs to evaluate novelty preference using 3-odors and 6-odors. Male rats explore the novel odor significantly more than the familiar odors in the IST and DST with both 3-odor and 6-odor. No differences in novelty preference or exploration times are seen between the IST and DST, or between the 3-odor and 6-odor versions. Data are represented as mean ± SEM.

Figure 2.

Experimental overview for acute Cannabis exposure and behavioral classifier training. A, Schematic representation of the experimental design. Male Long–Evans rats (n = 48) were used for this study. Using a repeated measures experimental design, each rat was exposed to high-THC Cannabis smoke, low-THC Cannabis smoke, and an Air Control condition. Male rats were exposed 20 min before the start of behavioral testing. Each male rat either underwent the 6-object IST and 6-object DST, or the 6-odor IST and 6-odor DST. The order in which the IST and DST were performed was randomized. Rat behavior was quantified using traditional stopwatch scoring and by automated supervised machine learning-based behavioral analysis. Suboptimal supervised machine learning predictions were replaced by stopwatch scoring, constituting a hybrid scoring approach. B, Illustration of the point-of-interest configuration used for pose-estimation analysis. We chose the number and position of points in accordance with the SimBA eight-point configuration. SimBA requires a standardized and specific position (and number) of points. Users should decide what SimBA configuration will be used (single animal, multianimal, point number) before network training with DeepLabCut. C, Visualization of the relative feature importance of the four features clusters. In short, the 40 most important features were systematically categorized into distinct clusters, then we summed the feature importance’s of individual features within each cluster. The raw features importance log is included under “assessment + logs” for each classifier within our GitHub repository. D, Classifier performance metrics for the object (top) and odor (bottom) models. Test frames were randomly extracted from the dataset (20% test, 80% train). E, Classifier performance metrics for the object (top) and odor (bottom) models. Test bouts were randomly extracted from the dataset (20% test, 80% train). See Extended Data Figures 2-1, 2-2, 2-3, and 2-4 for more information regarding the supervised machine learning approach and validation. This figure was created using BioRender.

Figure 3.

Comparison between human stopwatch and supervised machine-learning generated output. A, Correlation matrix between methods of quantifying rat-object interaction. This comparison was made between supervised machine-learning (SML), human-stopwatch (HS), and region-of-interest (ROI)-generated interaction times. Interaction times by object was quantified using each scoring method, then the correlation between interaction DRs was assessed. B, Correlation matrix between methods of quantifying rat-odor interaction. Interaction times by odor was quantified using each scoring method, then the correlation between interaction DRs was assessed. C, Criteria used to rank automated classification. Each video was manually viewed for accurate classification, where a verification rank was assigned based on objective criteria. D, Frequency of verification rank assignment by type of stimuli. Videos with a verification rank less than three were excluded from final analysis and replaced by human stopwatch scoring. Approximately 80% of object videos and 60% of odor videos met inclusion criteria, respectively. E, Correlation between human stopwatch and SML-generated DRs on object videos meeting inclusion criteria, indicating a moderate-to-high correlation (r₍₁₀₉₎ = 0.83, p < 0.0001). F, Correlation between human stopwatch and SML-generated DRs on odor videos meeting inclusion criteria, indicating a moderate-to-high correlation (r₍₇₇₎ = 0.87, p < 0.0001). See Extended Data Figures 3-1 and 3-2 for additional information regarding the scoring and the ranking of videos by Cannabis treatment.

Figure 4.

High-THC Cannabis smoke exposure impacts novelty preference under high-memory (DST) loads using object stimuli, with no impact on distance traveled, frequency of item visitation, or approach latencies. A, An example IST with objects is visualized, showing six identical objects in the sample phase, with a novel object introduced after a 1-min delay in the test phase. B, A DST with objects variation is shown, with an identical test progression, but instead starts with six different objects in the sample phase. C, Interaction measured as time spent with an object was generated using the human-machine hybrid scoring approach and visualized using a discrimination ratio for both variations using object stimuli. No difference in treatment groups is seen in the 6-object IST (n = 64). In the 6-object DST (n = 66), a significant decrease in novelty preference is seen in the SW group in contrast to the AC group (p = 0.04). D, The mean novel approach latency in the 6-object IST (n = 72) and 6-object DST (n = 69) variations is shown to be consistent between treatment groups. E, To illustrate the frequency of visitations to the novel object in comparison to the familiar objects, bout counts are visualized using a discrimination ratio. A preference for novel visitations is seen in the 6-object IST (n = 65) AC and SW groups, with no difference in item visitations in the 6-object DST (n = 66). F, The distance traveled (cm) in the 6-object IST (n = 72) and 6-object DST (n = 69) variations is comparable across treatment groups. Data represents mean ± SEM, *p < 0.05. Abbreviations: high-THC Cannabis smoke (SW); high-CBD Cannabis smoke (TI); Air Control (AC). This figure was created using BioRender.

Figure 5.

High-THC Cannabis smoke exposure impacts novelty preference under high-memory (DST) and low-memory (IST) loads using odor stimuli, with no impact on distance traveled, frequency of item visitation, or approach latencies. A, Example IST with odors is visualized, showing six identical items in the sample phase, with a novel odor introduced after a 1-min delay in the test phase. B, A DST with odors variation is shown, with an identical task progression, but instead starts with six different odors in the sample phase. C Interaction measured as time spent with an odor was generated using the human-machine hybrid scoring approach and visualized using a discrimination ratio for both variations using odor stimuli. In the 6-odor IST (n = 75), a significant decrease in novelty preference is seen in the AC group in comparison to the SW group (p = 0.046). Whereas in the 6-odor DST (n = 73), a significant decrease in novelty preference is seen in the SW group from both the AC (p = 0.023) and TI (p = 0.046) groups. D, The mean novel approach latency in the 6-odor IST (n = 79) and 6-odor DST (n = 73) variations is shown to be consistent between treatment groups. E, To illustrate the frequency of visitations to the novel odor in comparison to the familiar odors, bout counts are visualized using a discrimination ratio. No differences between treatment groups or 6-odor IST (n = 79) and 6-odor DST (n = 73) are seen. F, Distance traveled (cm) in the 6-odor IST (n = 79) and 6-odor DST (n = 73) variations is comparable across treatment groups. Data represents mean ± SEM, *p < 0.05. Abbreviations: high-THC Cannabis smoke (SW); high-CBD Cannabis smoke (TI); Air Control (AC). This figure was created using BioRender.

Figure 6.

Boli count following smoke exposure treatment. A significant increase in the number of boli recorded was observed following Cannabis smoke exposure in comparison to the Air Control (AC) condition. However, no difference between Skywalker (SW) or Treasure Island (TI) groups was recorded. ****p < 0.001. high-THC Cannabis smoke (SW); high-CBD Cannabis smoke (TI); Air Control (AC).