Long-Term Outcomes of Adolescent THC Exposure on Translational Cognitive Measures in Adulthood in an Animal Model and Computational Assessment of Human Data
Bibliographic record
Abstract
Importance: Although perceived as relatively harmless and nonaddictive, adolescent cannabis use significantly increases the likelihood of developing cannabis use disorder in adulthood, especially for high-potency cannabis. Risky decision-making is associated with chronic cannabis use, but given confounds of human studies, it remains unclear whether adolescent cannabis exposure and Δ9-tetrahydrocannabinol (THC) potency specifically predicts risky decision-making or influences cognitive response to the drug later in life. Objective: To leverage a human data set of cannabis users and a rat model to evaluate the long-term outcomes of adolescent THC exposure on adult decision-making and impulse control. Design, Setting, and Participants: This translational rat study tested the link between adolescent THC exposure and adulthood decision-making. A reanalysis of a previously published dataset of human chronic cannabis users was conducted to evaluate decision-making phenotypes. Computational modeling assessed the human and animal results in a single framework. Data were collected from 2017 to 2020 and analyzed from 2020 to 2022. Main Outcomes and Measures: Decision-making was measured by the Iowa Gambling Task (IGT) and Rat Gambling Task (rGT). Impulse control was assessed in the rat model. Computational modeling was used to determine reward and punishment learning rates and learning strategy used by cannabis users and THC-exposed rats. Cell-specific molecular measures were conducted in the prefrontal cortex and amygdala. Results: Of 37 participants, 24 (65%) were male, and the mean (SD) age was 33.0 (8.3) years. Chronic cannabis users (n = 22; mean [SE] IGT score, -5.182 [1.262]) showed disadvantageous decision-making compared with controls (n = 15; mean [SE] IGT score, 7.133 [2.687]; Cohen d = 1.436). Risky choice was associated with increased reward learning (mean [SE] IGT score: cannabis user, 0.170 [0.018]; control, 0.046 [0.008]; Cohen d = 1.895) and a strategy favoring exploration vs long-term gains (mean [SE] IGT score: cannabis user, 0.088 [0.012]; control, 0.020 [0.002]; Cohen d = 2.218). Rats exposed to high-dose THC but not low-dose THC during adolescence also showed increased risky decision-making (mean [SE] rGT score: vehicle, 46.17 [7.02]; low-dose THC, 69.45 [6.01]; high-dose THC, 21.97 [11.98]; Cohen d = 0.433) and elevated reward learning rates (mean [SE] rGT score: vehicle, 0.17 [0.01]; low-dose THC, 0.10 [0.01]; high-dose THC, 0.24 [0.06]; Cohen d = 1.541) during task acquisition. These animals were also uniquely susceptible to increased cognitive impairments after reexposure to THC in adulthood, which was correlated with even greater reward learning (r = -0.525; P < .001) and a shift in strategy (r = 0.502; P < .001), similar to results seen in human cannabis users. Molecular studies revealed that adolescent THC dose differentially affected cannabinoid-1 receptor messenger RNA expression in the prelimbic cortex and basolateral amygdala in a layer- and cell-specific manner. Further, astrocyte glial fibrillary acidic protein messenger RNA expression associated with cognitive deficits apparent with adult THC reexposure. Conclusions and Relevance: In this translational study, high-dose adolescent THC exposure was associated with cognitive vulnerability in adulthood, especially with THC re-exposure. These data also suggest a link between astrocytes and cognition that altogether provides important insights regarding the neurobiological genesis of risky cannabis use that may help promote prevention and treatment efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".