The world of Driving under the Influence (DUIs) is not one to mess around in. Anyone familiar with the acronym knows DUIs aren’t good– they’re a criminal offense, they go on your permanent record, and they involve the death of tens of thousands of people each year. DUIs are a rare topic in our society that is generally black and white: you don’t do it. End of story.

However, I want to add just a touch of gray into the discussion, with a specific subset of research that might have implications for DUIs. In this case, I’m talking solely about cannabis related DUIs, the same ones that former NFL player Le’veon Bell didn’t even know were a thing. 

Le’veon Bell’s situation brings up an interesting point, because it is true that a lot less is known about the specifics of cannabis-based intoxication as compared to alcohol. Colloquially, I think many of us have a broad conceptualization of DUIs as a solely alcohol-based impairment, with “under the influence” generating the mental image of someone off a few drinks swerving recklessly between lanes and crashing into a tree. This simply isn’t always the case, however, as there is a solid volume of research establishing that the THC in cannabis, particularly within the first hour after smoking, affects our neurocognitive and motor functioning more than enough to impair driving (Brands et al., 2019; review by Sevingy, 2021; review by Hartman et al., 2013). Furthermore, cannabis-related DUIs are more common than we might think: 12 million (4.7%) Americans self-reported that they drove under the influence of marjuana in 2018, and a 2020 Pandemic Traffic Safety report found that 31.2% of drivers involved in serious injury or fatal crashes in the US were under the influence of cannabis, which was even higher than alcohol (26.9%).

From the research side of things, there is also a deficiency of cannabis-related research in the field of impairment and intoxication. A quick search for “alcohol impairment” on APA Psycinfo reveals 1,415 unique research articles, plus an additional 4,714 unique articles for “alcohol intoxication.” The terms “cannabis impairment” “marjuana impairment” “THC impairment” “cannabis intoxication” “marjuana intoxication” and “THC intoxication” generate only 754 articles combined. 

I’m not writing this blog to endorse driving while high. I’m much more interested in refining and adding nuance to what many believe is a unanimous unequivocal point of view. Through this three part mini-series, I will be using the real-world implication of cannabis DUIs as a framework within which to discuss the dynamic cannabis tolerance research that has taken off in the past fifteen years. One of the reasons to talk about DUIs in the midst of cannabis research is to hold a hovering implication over these graphs and numbers, to connect the innocent statistics and brain mechanisms in their respective fields with the power they hold outside in the “real world.” I will only have accomplished my goal if I leave you with more questions than when you started reading.

Part 1: What is Tolerance?

Cannabis research has almost always been conducted with one really bad assumption. Since the first studies of cannabinoids and their potential effects on humans, almost all experimental research in the field has been carried out upon a participant group of occasional users with a low frequency of lifetime use (Ramaekers et al., 2016; review by Cricq 2020). This implies the assumption that cannabis acts on our body the same whether we’ve smoked it once, or smoked for years. It is only in the last decade and a half that this assumption has begun to be seriously challenged in a growing sub-field known as “tolerance research.”

Back in the late 2000s, tolerance research in relation to cannabis was not the hot-button topic that it is today. However, like many burgeoning fields, there were researchers just a touch ahead of the curve, the ones especially interested in the subject, getting funded and publishing articles just slightly before the others. When it comes to the early days of cannabis tolerance research, Dr. Johannes (J.G.) Ramaekers was one name that kept appearing on every research paper, and it’s relevant to speak a little bit to his background. 

Dr. Ramaekers is a Psychopharmacology and Behavioral Toxicology scientist who has been in the field for decades conducting neurocognitive research. What he’s most known for, however, is the tolerance research his lab has done specifically dealing with cannabis. His most cited article comes from all the way back in 2004, and is titled “Dose Related Risk of Motor Vehicle Crashes After Cannabis Use,” an article that finds among other things that the impairing effects of THC are most prominent (and dangerous) in our subconscious driving behaviors, but do not present the same level of immense risk when it comes to the more complex parts of driving that require our conscious control (Ramaekers et al., 2004). 

What additionally interested me about Dr. Ramaekers are his qualifications– he’s served both on the International Council on Alcohol, Drugs, & Traffic Safety and the Center for Studies on Law in Action at Indiana University. These positions reflect a backdrop of real-world implications found in the results of the cannabis studies he’s worked on, and it shines through in his lab’s research papers, which are peppered with introduction and discussion sections always looking to make potential connections between laboratory results and everyday functioning. 

One of the most important early findings to come out of Dr. Ramaekers’s lab and into the field of cannabis tolerance research can (almost) be summed up in one graph. I’m not going to build up any more suspense, here it is right below you.

What’s going on here?

This graph comes from a Ramaekers et al. (2009) experimental study where the authors were looking to compare neurocognitive performance (memory, executive function, perceptual-motor skills) between light/occasional and heavy/frequent cannabis users after smoking. In the study, a total of 24 participants (12 heavy/frequent smokers and 12 light/occasional smokers) were given either a fat cannabis joint (13% THC) or a placebo joint (no THC). The subjects then filled out various self-reports and completed various neurocognitive simulations and tasks. The graph above shows the performance of these subjects on the Critical Tracking Task (CTT), a test that’s been around in various forms since the mid-60s used to measure psychomotor control and cognitive impairment. In the 2009 version, there’s a virtual bar on a computer screen that moves away from a predefined target position. Participants use a joystick or keyboard to control the bar back to its target position, a task that becomes more difficult as the test continues.

One measure of performance is the frequency at which the subject completely loses control of the bar, which is defined as being over 180° off from the correct movement. This is known as the “critical frequency,” and is represented in radians/second as “lambda-c” is the y-axis in the graph. Essentially, lower values on y-axis closer to -1 represent a greater tendency towards “critical frequency,” i.e. a greater tendency to lose control of the bar in the task. Higher values on the y-axis closer to 1 represent a better control of the bar. 

On the x-axis is time after the cannabis joint was smoked. When looking at the graph, it is most relevant to focus on the data points at the 0-1 hour mark, as the psychoactive, cognitively-impairing component of THC is at its most potent in the first hour (Bidwell et al., 2020; review by Sharma et al., 2012). At this 0-1 mark, there are four key data points. One data point (the dark square) stands out as much lower than the others – this is the reduced performance of occasional smokers in the task. This is expected, as the general consensus amongst cannabis literature is that neurocognitive performance is reduced directly after smoking (review by Kroon et al., 2021, review by Sharma et al., 2012). 

The interesting part of the graph, however, are the other three data points at the 0-1 hr time: these three data points look to be almost identical, stacked on top of each other with a similar critical frequency of 0 rad/sec. It’s hard to tell because of how condensed the data sits on top of each other, but two of these data points (the circles) represent the placebo, or control condition, while the third data point (light square) is showing the frequent cannabis users. 

The similarity of these three data points suggest that frequent users performed just as well as the placebo control groups on these tasks, suggesting that contrary to popular anecdotal opinion, cannabis was not disrupting their mental processing. 

This was a big finding. It indicates that frequent cannabis users have somehow developed a tolerance to THC, almost as if they had built up a superpower to reject the impairing effects of cannabis. 

Here are two more graphs from a different task run by the study, this one a “Stop Signal Task” looking to measure motor impulsivity and inhibitory control. The test instructs participants to make quick keyboard responses to visual signals (i.e. press a key if they see a certain combination of letters), but to immediately stop responding if they receive a “stop signal,” which in this case was a * appearing in one of the four corners of the screen. In the graph on the left, we see a similar result in the 0-1 hour mark as the “Critical Tracking Task,” with the outlier effect of the occasional users performing around 10% worse in terms of response accuracy percentage, but more importantly frequent users performing just as well as the non-smoking placebo participants. On the right side, however, we do see the frequent users displaying a bit of tolerance impairment, as their reaction time to the stop signal is significantly higher than the other three groups. This finding is supported by past research suggesting that long term, consistent cannabis use can impair inhibitory control signals from the prefrontal cortex through the limbic system (Jentsch and Taylor, 1999). If you’ve taken BI374, this can lead to similar delayed or impulsive motor reactions that we saw occurring from the loss of neuromodulatory dopaminergic neurons in the basal ganglia direct/indirect pathways to the thalamus (if you haven’t taken BI374, hang on to the importance of dopamine neurons, because they’ll come back into the story later). 

Fast-forward to the present time, and cannabis tolerance research is no longer a budding trend. Rather, there is a steady and growing subfield of cannabis literature that here’s to stay, and be expanded upon. The widespread fascination in tolerance effects has even caused a branching of interest, to the point researchers simply working on cannabis studies will often include occasional and frequent participant groups as a part of their methodology. There was a particularly striking example of this widespread curiosity with tolerance in a Bidwell et al. (2020) study on cannabis flower and cannabis concentrate users: in a pop-science article posted about the publication, one of the co-authors made sure to add, “People in the high concentration group were much less compromised than we thought they were going to be… If we gave people that high a concentration of alcohol it would have been a different story.”

Thank you so much for checking this out. In part 2, I’m looking to dive deeper into the biological basis of cannabis tolerance itself: what are the reward pathways and neuroadaptations that are playing a role in why frequent THC use changes the way our brain functions.

References

Bidwell, L. C., Ellingson, J. M., Karoly, H. C., York Williams, S. L., Hitchcock, L. N., Tracy, B. L., … & Hutchison, K. E. (2020). Association of naturalistic administration of cannabis flower and concentrates with intoxication and impairment. JAMA psychiatry, 77(8), 787-796. 10.1001/jamapsychiatry.2020.0927

Brands, B., Mann, R. E., Wickens, C. M., Sproule, B., Stoduto, G., Sayer, G. S. & Le Foll, B. (2019). Acute and residual effects of smoked cannabis: Impact on driving speed and lateral control, heart rate, and self-reported drug effects. Drug and alcohol dependence, 205, 107641. 10.1016/j.drugalcdep.2019.107641

Crocq, M. A. (2022). History of cannabis and the endocannabinoid system. Dialogues in clinical neuroscience. 10.31887/DCNS.2020.22.3/mcrocq

Hartman, R. L., & Huestis, M. A. (2013). Cannabis effects on driving skills. Clinical chemistry, 59(3), 478-492. 10.1373/clinchem.2012.194381

Jentsch, J. D., & Taylor, J. R. (1999). Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology, 146, 373-390. 10.1007/PL00005483

Jex, H. R., McDonnell, J. D., & Phatak, A. V. (1966). A“Critical”Tracking Task for Manual Control Research. IEEE Transactions on Human Factors in Electronics, (4), 138-145. 10.1109/THFE.1966.232660

Ramaekers, J. G., Berghaus, G., van Laar, M., & Drummer, O. H. (2004). Dose related risk of motor vehicle crashes after cannabis use: an update. Drugs, driving and traffic safety, 477-499. 10.1016/j.drugalcdep.2003.10.008

Ramaekers, J. G., Kauert, G., Theunissen, E. L., Toennes, S. W., & Moeller, M. R. (2009). Neurocognitive performance during acute THC intoxication in heavy and occasional cannabis users. Journal of psychopharmacology, 23(3), 266-277. 10.1177/0269881108092393

Ramaekers, J. G., Van Wel, J. H., Spronk, D. B., Toennes, S. W., Kuypers, K. P. C., Theunissen, E. L., & Verkes, R. J. (2016). Cannabis and tolerance: acute drug impairment as a function of cannabis use history. Scientific reports, 6(1), 26843. 10.1038/srep26843

Sevigny, E. L. (2021). Cannabis and driving ability. Current opinion in psychology, 38, 75-79. 10.1016/j.copsyc.2021.03.003

Sharma, P., Murthy, P., & Bharath, M. S. (2012). Chemistry, metabolism, and toxicology of cannabis: clinical implications. Iranian journal of psychiatry, 7(4), 149.Wagner, E., Atkins, R. G., Berning, A., Robbins, A., Watson, C., & Anderle, J. (2020). Examination of the traffic safety environment during the second quarter of 2020: Special report (No. DOT HS 813 011). United States. National Highway Traffic Safety Administration. Office of Behavioral Safety Research. 10.21949/1525982