Cannabis Use Dependence, Severity, and Visual Object Memory Laura M. Campisi
11341629
Psychobiology, University of Amsterdam
Supervisors: Janna Cousijn, dr University of Amsterdam
Lauren Kuhns, PhD University of Amsterdam
Bachelor Thesis Final Version 01-02-2020
Abstract
Cannabis is the most commonly used illicit drug in the world. Consequently, cannabis use dependence (CUD) is seen in larger numbers, even though its effects on cognition is barely researched. Evidence suggesting morphological and connective changes in the brain in cannabinoid receptor rich areas shows overlap with areas related to memory. Memory changes in cannabis users compared to controls have been found, but specifically visual object memory (VOM) research is limited. This information lead to the following research questions: do individuals with cannabis use disorder perform worse on immediate and delayed recall in a VOM task compared to non-using controls? In the CUD group, is performance on immediate and delayed VOM related to the severity of their CUD? Expected are worse performance on a VOM task for the CUD group and that a higher severity within the CUD group predicts a lower VOM task score. Twenty-four individuals with CUD and fifteen non-using controls took the both the immediate and delayed short Visual Object Learning Task (sVOLT). CUD severity was determined by using the Mini International Neuropsychiatric Interview. Comparison between CUD and control groups suggested no effect of cannabis on either short- or long-term VOM performance. A regression analyses controlling for
intelligence quotient (IQ) with CUD severity as a predictor for sVOLT scores again provided no evidence for the idea that a higher CUD severity is linked to worse VOM. Further studies should focus on long-term effects of cannabis on VOM, as well as on a more long-term VOM in cannabis dependence.
Keywords: cannabis, cannabis use dependence, visual object memory, severity, visual object learning task
Cannabis use dependence, severity, and visual object memory
Introduction
Cannabis is an illicit substance; the most used of its kind in the world (Kroon, Kuhns, Hoch, & Cousijn, 2019), with around 4% of the world population having used it for
recreational purposes and roughly 22 million people are diagnosed with cannabis abuse or dependence. The use for adolescents is even higher, with 40% of college students in America using occasionally (Young et al., 2002). Due to cannabis being widely used and the debate on whether or not it is save, there has been increased research into its effects on the brain,
cognition, bodily responses, et cetera. CUD has effects on different areas of the brain and a few of those same areas are involved in memory function. This has been the reason to
investigate memory performance in cannabis dependence, and in this study, more specifically, visual object memory (VOM) and raises the question: Would cannabis use dependence
(CUD) have a negative effect on visual object memory, and would the severity of the CUD matter too?
In drug dependence, there is a compulsion to take the drugs and no inhibition to stop taking, because negative withdrawal symptoms occur otherwise (Koob & Volkow, 2010). How this works exactly is currently being investigated. However, the amount of research is still limited and not conclusive enough to determine the severity of prolonged cannabis use. Thus far, research showed that cannabis binds to the cannabinoid-1 (CB1) receptor, which is found in different areas of the brain (Bhattacharyya & Schoeler, 2013a). There, the binding activates a G protein, which in turn decreases cyclic AMP levels. Cyclic AMP levels are amongst other things responsible for potassium and calcium channel opening or closing. These, in turn, have the ability to alter activation of cells and thus systems. If cannabis use has this ability, changes in brain activation, behaviour and cognition are prone to be shown in users. The brain areas with the highest CB1 receptor levels are the hippocampus, cerebellum,
pre-frontal cortex, nucleus accumbens, amygdala, basal ganglia, ventral tegmentum area, and the substantia nigra (Koob & Volkow, 2010; Nader & Sanchez, 2018). The latter two explains the positive association users have towards cannabis. Frequent cannabis use can negatively alter these regions. Yücel et al. (2008) showed, for example, that hippocampal volume was significantly lowered in heavy and regular male cannabis users compared to their matched controls, as well as great amygdala reduction. Differences in brain activity have also been linked to cannabis use, with the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex showing less activation across multiple studies (Nader & Sanchez, 2018).
Studies also showed that there is a difference between frequent cannabis users and cannabis dependent individuals. Dependent users showed smaller left and right hippocampal volumes, compared to both controls and non-dependent users, in multiple sites around the world (Chye, Lorenzetti, et al., 2019a). Chye et al. (2017b) found hippocampal volume reduction in dependent users, but not in non-dependent users, compared to controls. The caudate nucleus had a larger volume, whereas hippocampus and cerebellum volumes were smaller in CUD as found by Lorenzetti et al. (2020). A meta-analysis on the orbitofrontal cortex also highlights that dependent cannabis users had a significantly smaller volume compared to non-dependent user (Chye et al., 2017a). The cerebellum and caudate are more related to the motivation and reward processing in dependent individuals thus has more to do with the drug dependence itself. The hippocampus, however, is related to learning and
memory. A smaller hippocampal volume in CUD would lead to the idea that these individuals would have a harder time with their memory. Considering these differences even between dependence and frequent use, combined with limited available research into dependence, more research focused on the effect of cannabis use dependence and its consequences is a necessity.
Other studies on the effects of cannabis have focused on more cognitive and behavioural aspects. Attention, perception, motor skills, and many more have been researched. A review by Randolph, Turull, Margolis and Tau (2013) summarized several studies where there seems to be an attentional bias towards cannabis related cues in users, as well as overall worse sustained and complex attention. Multiple cognitive assessments showed lowered accuracy and intelligence test scores for heavy cannabis users, due to lowered complex attention.
Furthermore, cognition research also focused on memory. Working memory was poorer for individuals taking cannabis as well.
Working memory is one of many memory sub-types. Memory is very important in our day to day life, thinking about educational and work related events, but also simpler aspects, such as navigation or common knowledge. Since these aspects fall under different types of memories, most research is focused on a specific sub-part. A lot of these experiments have found negative changes in memory in cannabis users. Verbal memory was worsened in young cannabis users comparable to long-term heavy adult users (Solowij et al., 2011; Yücel et al., 2008), with similar results found in current heavy users compared to former users and controls (Pope, Gruber, Hudson, Huestis, & Yurgelun-Todd, 2001).
However, there are some memory types which have not yet or barely been researched. Given that many memory types have been shown to be altered in cannabis use, there should be investigation into whether this change is limited to certain types or most memories. Thus, we need to fill this gap.
Visual object memory (VOM) is one of these memory subtypes. It is the ability to store, remember and recall a newly seen object. This could be a completely new type of object, or a different version of an already known one. VOM is important to experience new things in life and to continue developing the brain and its knowledge of the world. VOM has
been investigated in a limited amount up until now. Animal research with mice who explored a maze filled with objects, found great response in the ACC when the mice recognized those same objects after some time, indicating ACC involvement in long-term object memory (Weible, Rowland, Monaghan, Wolfgang, & Kentros, 2012). Human research into VOM did indicate that dependent users of cannabis performed worse on the Benton Visual Retention Task, though not on more visuospatial oriented tasks compared to controls (Randolph et al., 2013; Schwartz, Gruenewald, Klitzner, & Fedio, 1989).
There are many brain areas involving memory, including the prefrontal cortex, hippocampus, basal ganglia, and anterior cingulate cortex. The hippocampus was related to different types of memory including verbal, episodic, and working memory. Recall tasks showed activity in the anterior cingulate and lateral prefrontal cortex (Bhattacharyya & Schoeler, 2013b).
Accidentally, some of these areas overlap with a number of brain areas altered in cannabis use, as discussed. If the areas that play important roles in VOM are different in individuals with frequent or even dependent cannabis use, it could give reason to believe VOM itself could be different.
The severity of a substance use (dependence) is negatively associated with neuronal activity, brain structure, and cognitive problems (Kroon et al., 2019). Aloi et al. (2018) found higher cannabis use severity was related to worse behavioural performance compared to controls, and unexpectedly with higher cingulate cortex and parietal lobe activity. They explained the higher activity was due to the need of stronger activation to perform the tasks well, since normal levels of activation were paired with lower performance. Cousijn et al. (2013) found a positive correlation between cannabis severity and attentional bias and inhibition towards cannabis related words. Adding severity as a predictor in drug use also helps to give insight into whether the data is a result of addition or the drug itself. If a higher
severity predicts the effects, it can be assumed that the result is in the level of drug dependence/exposure, and not addiction overall since there would be no difference in the latter. Because, if addiction itself is the mediator of the results, how severely one is addicted should not matter.
Combining our knowledge of cannabis effects on certain brain regions, their overlap with brain regions involved in memory, and memory performance, the questions of this thesis were formed: (1) Do individuals with cannabis use disorder perform worse on immediate and delayed recall in a VOM task compared to non-using controls? (2) In the CUD group, is performance on immediate and delayed VOM related to the severity of their CUD?
It is hypothesized that CUD participants would perform worse on the VOM tasks when comparing with controls, focussing on the number of correct answers, for immediate recall as well as delayed recall, based on the effects cannabis has on the brain areas involving memory. For severity, the expectation is the higher the CUD score, the worse the accuracy (Lima et al., 2019). Studies suggested IQ plays an important part in the ability to perform well on these tasks (REFS; (Nader & Sanchez, 2018), which is why IQ scores will be set as a fixed control in the regression analysis.
Materials and methods
Participants. The hypothesis of this thesis was tested by using forty-three participants in total (twenty-eight CUD, fifteen control), with ages from eighteen to thirty (μ = 21.26, σ = 2.85). The CUD and control participants were screened on the following inclusion criteria, after online and flyer wise recruitment: 1) being fluent in Dutch, 2) not having (had) any regular drug use other than cannabis, alcohol or cigarettes. Exclusion for both groups were: 1) used drugs other than cannabis, alcohol, and cigarettes in the month prior to testing, 2) had a history of/or currently having any axis-1 disorders other than depression and anxiety, 3) using
any medication affecting the central nervous system, 4) had a neurological disorder, 5) had severe brain damage, 6) an Alcohol Use Disorders Identification Test (AUDIT) score of more than ten, 7) other serious medical conditions.
The participants in the control group had to have used cannabis one to twenty-five times in their life, but not in the past three months prior to the first session, nor more than two times last year. The CUD group had to have a minimum CUD score of two on the Mini International Neuropsychiatric Interview (MINI) (Lecrubier et al., 1997) in order to meet the criteria for cannabis use disorder, cannabis use of at least six times a week for the past twelve months prior to the first session, and no active plan to lessen or quit using, or seek treatment. See Table 1 for an overview of the in/exclusion criteria for the two groups.
Some of the exclusion criteria were included because this study is part of a bigger study with more tasks, questionnaires, MRI scanning, and bio-sampling, called the Joint Study by Cousijn, J. and Filbey, F. (Figure 1).
Procedure. The experimental session of the larger study took approximately four hours in total. The first part consisted of introduction, consent and additional information forms, the IQ tests, bio-sample gathering, and practice tasks. Afterwards, the participant went into the MRI scanner, where they performed three tasks as well. Part two included the Stroop task, MINI CUD, two questionnaires, Penn Computerized Neuropsychological Test (CNP) (University of Pennsylvania, 2003), and hair sampling took place. All this was redone a year after the first
Table 1. In- and exclusion criteria per group.
CUD CONTROL
INCLUSION Cannabis use six to seven times a week for at least twelve months
One to twenty-five lifetime cannabis use
Minimum MINI CUD score of two
EXCLUSION Active plans to reduce or quit cannabis use, or seek treatment
No cannabis use three months prior to testing Cannabis use of over two times in the past year
session. The parts where this sub-research was included were both screenings, and part one and two of testing.
Assessment. IQ scores were based on two parts of the Wechsler Adult Intelligence Scale-IV (WAIS-IV) (Wechsler, 2008): the matrix and the vocabulary, taken in Dutch. For the matrix, there were twenty-two test items assessing their logical thinking. Scoring was either zero for incorrect answers, or one if correct. The vocabulary subtest had twenty-six items. Scores were zero if incorrect, one if partly correct, and two if perfectly answered. Final scores used for both subtests were scaled with age. The WAIS-IV is shown to be very reliable and valid, with both having a reliability score of 0.90 or higher for the different subtest (Fan et al., 2019).
CUD scores were based on the MINI CUD, a short diagnostic interview of 13
questions, taken in person, see appendix A for the questionnaire. Severity was then rated in 4 groups: none, mild, moderate or severe. The MINI has been shown to be effective in its time, average of 20 minutes, reliable and valid, with drug dependence having a kappa (κ) of 0.81 (Lecrubier et al., 1997).
Figure 1. Joint Study complete overview. The flow of the whole Joint Study. Included in Part
One and Part Two are the items in this research taken place in those moments.
Online screening Phone screening
Part one: - WAIS-IV - Demographics - Urine collection and
drug test MRI Part two: - MINI CUD - Questionnaires - Penn CNP One-year follow up
Participants took the PENN Computerized Neuropsychological test on a computer via the official site. The short Visual Object Learning Test (sVOLT) and the sVOLT delayed are part of a six piece Penn CNP protocol in standard order, with twenty minutes between the sVOLT and sVOLT delayed.The Penn CNP showed great reliability in measuring accuracy on the VOLT tests, with a significant efficiency of 0.44 (Moore et al,2015). Thus, the PENN CNB is a reliable and quick way to test for VOM.
In the sVOLT, ten three-dimensional items are shown for 5000 milliseconds each in a non-random order, see Figure 2 for an example. The items are triangles, squares, pentagons, hexagons, and octagons. All figures, test and control, have a blue two-dimensional figure inside them. Both shapes and their positions compared to one another must be remembered. Immediately after all items are shown, there is a new set of items. This set consist of twenty items, including the ten they previously saw and ten new ones of a similar composition, in a random order. For each of these items, the participants had to choose between four possible statements on the question ‘Did you see the figure before?’: definitely yes, probably yes, probably no or definitely no. This has to be done within twenty seconds, or the trial will be registered as incorrect and the next figure is shown automatically. In the second part, the sVOLT delayed, there are twenty items shown in random order, again including those ten which should have been remembered. The same four possible statements are given, with the same time range of twenty second per trial.
Via a questionnaire, demographic characteristics were obtained. And a general
questionnaire about drug use was taken for information on several aspects of use, such as age of onset or types of drugs taken.
The Beck Depression Index (BDI) is a questionnaire to measure depression symptoms using 21 questions on feelings and thoughts. The participants are given four multiple choice options. Comparing the scoring of multiple depression questionnaires, psychologists view, and the BDI, the BDI has a scoring consistency between 0.75 and 0.92 (Abdel-Rahman et al., 2011; Beck, Guth, Steer, & Ball, 1997) showing its reliability.
The State-Trait Anxiety Inventory (STAI) is a self-reported questionnaire, from which only the trait questions are used, which consist of 20 statements on current feelings and possible worries. These statements were then answered with four options ranging from ‘not at all’ to ‘very much so’. The STAI is reliable and a good measurement to test current anxiety (van der Ploeg, 1982).
Adults Attention Deficit Hyperactivity Disorder Self-report Scale (ASRS) is a questionnaire using a five-point Likert scale to answer six questions whose results can
Figure 2. sVOLT and sVOLT delayed testing phase. Shown is one of the
twenty figures presented in the testing phase of both sVOLT and sVOLT delayed, where the participant has to answer one of four shown options in relationship to previously seen figures.
indicate a person’s ADHD levels. With a sensitivity of 0.84 and specificity of 0.66, the ASRS is able to capture most cases correctly (Van de Glind et al., 2013).
Alcohol Use Disorders Identification Test, also using a five-point Likert-scale with 10 questions, determines whether the individuals alcohol use is problematic. With a sensitivity ranging from 0.13-0.99 (Bohn, Babor, & Kranzler, 1990), the AUDIT is highly reliable to give an accurate indication on alcohol use.
Analysis. Data was checked for assumptions of normal distribution and equal variance before performing an independent t-test to determine the effect of cannabis use disorder on the Penn CNP performance. The total number of positive correct answers was taken as accuracy measurements. The data was also checked if assumptions were met before performing the multiple linear regression, to see if CUD severity had an effect on the accuracy in the Penn CNP performance. The CUD score was set as a predictor variable and IQ levels were
controlled for in the regression analysis. CUD scores test for severity differences, and the IQ was added since Swagerman et al. (2016) determined this factor plays a role in the
performance of the VOLT.
Results
Sample characteristics. A number of participants were excluded before analysis for having tested positive on other drugs than cannabis, alcohol, and cigarettes, which can intervene with results (Ross et al., 2020) and was also an exclusion criterion. Participants 1021, 1119, and 1121 were excluded. Participant 1103 was also removed due to lack of time to perform the Penn CNP, leaving twenty-four CUD participants. As shown in Table 2, the CUD group has more males compared to the control group (male = 0, female = 1). With a significant
difference, it can be seen that the CUD group has a lower WAIS-IV based IQ score, as well as fewer years of education. This, combined with previous research on the influence of
intelligence on cognitive tasks, was another reason to control for IQ. There is no significant difference on the BDI, even though the control group scoring normal levels (M = 7.13, SD = 5.29) and the CUD group scoring for mild mood disturbances (M = 11.92, SD = 9.12). None of the participants in the control group were smokers, whereas more than half of the CUD did smoke. AUDIT scores revealed no significant difference between CUD (M = 6.16, SD = 2.94) and control (M = 4.87, SD = 2.47), despite CUD scoring higher and close to harmful alcohol use (score of seven).
Analysis. As assumptions of variance were met and the number of participants is large enough to assume normality, an independent two sample t-test between the CUD and control group on the sVOLT score was performed. There was no significant effect found between the two groups, t (36) = 0.799, p = 0.215, d = 0.25. The same test was done for the sVOLT delayed, where again no significance between the groups was found, t (34) = 0.256, p = 0.3999, d = 0.07. These results indicate that there is no support that cannabis use disorder has an effect on visual object memory, both short- and long-term (Figure 3).
Table 2. Sample characteristics.
CUD (n=24) Control (n=15) Sex, mean (SD) 0.28 (0.46) * 0.6 (0.51) Age, mean (SD) 21.12 (2.64) 21.47 (3.42) IQ, mean (SD) 15.32 (3.92) ** 19.67 (3.60) Education, mean (SD) 15.80 (2.40) * 17.13 (2.13) ADHD, mean (SD) 10.04 (2.91) 8.73 (2.15) BDI, mean (SD) 11.92 (9.12) 7.13 (5.29) STAI-T, mean (SD) 38.44 (11.53) 37.4 (8.02) AUDIT, mean (SD) 6.16 (2.94) 4.87 (2.47)
Cigarette use, mean (SD) 0.64 (0.49) ** 0 (0)
All considered sample characteristics means, standard deviations, and significant levels. For sex, 0 is male, 1 is female. *p < 0.05, ** p < 0.01
When predicting the sVOLT score with multiple regression analysis, CUD score was not a significant predictor (B = -0.30, p-value = 0.34), nor was IQ (B = -0.03, p-value = 0.88) (Table 3). For the sVOLT delayed score, CUD (B = -0.26, p-value = 0.29) and IQ (B = 0.07, p-value = 0.62) were no significant predictors either (Table 4). These results suggest that neither CUD severity nor IQ are predictors of short- or long-term visual object memory performance.
Table 3. Regression table short VOLT.
B SE B t p
Intercept 17.346 3.433 5.054 5.28*e-05
CUD -0.300 0.309 -0.973 0.342
IQ -0.026 0.169 -0.151 0.881
Given for all coefficients are the beta, standard error of the beta, t-value, p-value.
Table 4. Regression table short VOLT delayed.
B SE B t p
Intercept 15.862 2.658 5.968 6.35*e-06
CUD -0.261 0.239 -1.090 0.288
IQ 0.067 0.131 0.510 0.615
Given for all coefficients are the beta, standard error of the beta, t-value and p-value.
Figure 3. short VOLT comparisons between control and CUD groups. In the left image, the mean score of control (orange) and CUD
(green) is shown for the immedeate short VOLT. The right image shows the short VOLT delayed means of the control (orange) and CUD (green) groups.
Discussion
The aim of this study was to investigate whether having cannabis use dependence is
connected to performance of visual object memory. Comparison give no indication to assume a significant difference between the CUD and control group on VOLT performance, both short- and long-term, discarding the hypothesis. Furthermore, the aim was to determine whether CUD severity, when controlling for IQ levels, would predict visual object memory performance within the CUD group. Again, there was no support found for the assumption that higher severity correlates with worse performance on a VOM task.
In contrast to the study of Schwartz et al. (1989), who did see a visual retention deficit in heavy users, this study did not see a difference between CUD and controls. This contrast might be explained by several reason. Schwartz et al. (1989) used a different type of visual memory test, the Benton Visual Retention Test. The tasks looks quite different from the sVOLT: there is a figure shown and afterwards, four figures that look similar to the first, but slightly different in terms of orientation and relationship of the individual shapes to each other, are shown. One of them is exactly like the learned figure, whereas the others are slightly altered, and the participants had to choose the correct one. This differs from the sVOLT where participants simply had to say whether or not they saw the figure shown
before. This might test a different type of VOM. The age limitations, between 14 and 16 years of, of the Schwartz study was also rather different compared to ours. Their age group was made up of adolescents, whereas this study tested with adults. A potential reason for the difference in results could be that the brains of adolescents are far more vulnerable to
influences from outside then those of adults. Their brains are not fully developed and have the ability to make changes in connections much faster than those of adults who’s brains are more matured and less flexible. Furthermore, the participants in the Schwartz study were willing to get professional help and go to a rehabilitation facility, since that is where their participants
were recruited. This might indicate that the addiction of those participants had gotten out of hand and they were suffering from really negative side-effects. Perhaps, in those stages, memory would be more prominently worsened, explaining their results. In contrast, most participants in this study didn’t report noticing many negative effects of cannabis, at least not large enough to negatively affect their daily life, as reported in the MINI. Specific reports on memory problems were scored with an average of 2.9 on a 5-point Likert scale retrieved from the Cannabis Use Identification Test (Adamson et al., 2010) in the CUD group, which
indicates monthly memory problems and was significantly different compared to the control group (W = 37.5, p = 3.6-6). Additionally, memory problems scored using the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013)
questionnaire with average of 1.7 on a 5-point Likert scale indicated occasionally having memory problems.
Other explanations for the results contrasting the hypotheses is that there has been inconsistency in results with memory performance and substance abuse. Where one study does find, for example, a working memory deficit, others do not see this effect. A longitudinal study found no difference between heavy cannabis users and controls, in both the first and the three year follow-up measurement when looking at working memory performance, as well as working memory network activation (Janna Cousijn et al., 2014). Even morphologic brain changes in cannabis are inconsistent. Weiland et al. (2015) found no significant difference between users and non-users in the nucleus accumbens, amygdala, hippocampus, and
cerebellum. Using high-resolution MRI scans and controlling for many variables at once gave these results, from which can be concluded that very similar groups and high tech machines might lead to different results showing no effect of cannabis. Chye et al. (2019b) performed a large investigation with data from multiple studies where they also controlled for many variables: intracranial volume, imaging site, gender, age, IQ, alcohol use, and cigarette use.
They did not find a difference in cortical thickness, surface area, and gyrification index when comparing cannabis and controls, cannabis dependent and cannabis non-dependent users, and early versus late onset in cannabis use. Alterations to the brain of cannabis dependent users also varies depending on the geographic location. The previously mentioned study of Chye et al. (2019b) also found differences in hippocampal volume between the three sites where their data was received. This could indicate that results differ depending which country they were conducted and might explain the result in other studies versus no result here. The
inconsistency in findings gives rise to the question whether or not any of those results are actually due to the cannabis or other factors.
Furthermore, the exact brain areas where VOM is located are not that thoroughly researched, making it difficult to know how much of these areas overlap with cannabis related areas in reality. It could be possible that some aspects of VOM are located elsewhere in the brain than where cannabis has had an influence, leaving enough information available to not notice any changes in VOM.
Limitations. There are some limitations to this study. To start, the number of participants is not equal in the two groups. This might have a negative effect on the results, considering less data is available in the control group. Having a larger sample size all together would also be wise, since this would increase effect size and the larger the sample group the better the representation of the population. Other inequalities between the two groups can also be found, which might be considered as confounding variables. Cigarette use, sex, education, and IQ might be of real importance. Consulting Chye et al. (2019b), controlling for even more variables might results in different outcomes as well. Within the CUD group when looking at severity as a predictor, age of onset of use might also make a difference in data interpretation (Pope et al., 2003). Future studies could correct these errors, by controlling for equal sex and
number of participants in each group, as well as matching the participants on other aspects. This would eliminate possible group differences effects.
Future research. Further studies into this subject should also consider long-term investigation into the same effect. Any type of research into long-term effects of cannabis is very important to conclusively determine the possible negative effects for those who use regularly, or are addicted or dependent. The long-term research can be focused in two ways. One of which is more long-term VOM. This research gave no indication for differences when looking at VOM delayed recall within half an hour of learning new images, but perhaps when testing days or weeks later there would be a differences between CUD and control. Testing moments can then be, for example, immediate, after a few hours, after two days, and after a week, to test for all these time frames if VOM is connected to CUD. Secondly, research can focus on the long-term effects of CUD on VOM. This can be achieved by doing a longitudinal study where a group of cannabis dependent users are being followed for a longer period of time and testing for VOM at the beginning of the study as well as years later. Looking into within subjects can give more insight into whether memory is an acute cannabis effect or if it is lasting deficit also. This would already be possible with the data collected by the Joint Study when it is finished. Besides following the same CUD individual, there can be a comparison between current dependent users and those who have recovered from their dependence.
Despite not finding effects of CUD on VOM, research indicates possibilities in this area and more research is needed be conclusive. Long-term effects and more insight into the conflicting results between different studies should be the aim of studies to come.
References
Abdel-Rahman, E. M., Balogun, S. A., Kepple, A., Ma, J. Z., Turgut, F., Kovesdy, C. P., & Balogun, R. A. (2011). Beck Depression Inventory and survival in elderly hemodialysis patients. Nephrology Dialysis Transplantation, 26(6), 2064–2065.
https://doi.org/10.1093/ndt/gfr086
Adamson, S. J., Kay-Lambkin, F. J., Baker, A. L., Lewin, T. J., Thornton, L., Kelly, B. J., & Sellman, J. D. (2010). An improved brief measure of cannabis misuse: The Cannabis Use Disorders Identification Test-Revised (CUDIT-R). Drug and Alcohol Dependence, 110(1–2), 137–143. https://doi.org/10.1016/j.drugalcdep.2010.02.017
Aloi, J., Blair, K. S., Crum, K. I., Meffert, H., White, S. F., Tyler, P. M., … Blair, R. J. R. (2018). Adolescents show differential dysfunctions related to Alcohol and Cannabis Use Disorder severity in emotion and executive attention neuro-circuitries. NeuroImage: Clinical, 19, 782–792. https://doi.org/10.1016/j.nicl.2018.06.005
American Psychiatric Association. (2013). Diagnostic and
statistical manual of mental disorders (5th ed.). Washington, DC: Author.
Beck, A. T., Guth, D., Steer, R. A., & Ball, R. (1997). Screening for major depression disorders in medical inpatients with the Beck Depression Inventory for Primary Care. Behaviour Research and Therapy, 35(8), 785–791. https://doi.org/10.1016/S0005-7967(97)00025-9
Bhattacharyya, S., & Schoeler, T. (2013). The effect of cannabis use on memory function: an update. Substance Abuse and Rehabilitation, 11. https://doi.org/10.2147/sar.s25869
Bohn, M. J., Babor, T. F., & Kranzler, H. R. (1990). The Alcohol Use Disorders Identification Test ( AUDIT ): Validation of a Screening Instrument for Use in Medical Settings * ’ UMEROUS assessment methods been designed.
Ceccarini, J., Kuep per, R., Kemels, D., Van Os, J., Henquet, C., & Van Laere, K. (2015). [18F]MK-9470 PET measurement of cannabinoid CB1 receptor availability in chronic cannabis users. Addiction Biology, 20(2), 357–367. https://doi.org/10.1111/adb.12116 Chye, Y., Lorenzetti, V., Suo, C., Batalla, A., Cousijn, J., Goudriaan, A. E., … Solowij, N.
(2019a). Alteration to hippocampal volume and shape confined to cannabis dependence: a multi‐site study. Addiction Biology, 24(4), 822–834. https://doi.org/10.1111/adb.12652 Chye, Y., Solowij, N., Suo, C., Batalla, A., Cousijn, J., Goudriaan, A. E., … Yücel, M.
(2017a). Orbitofrontal and caudate volumes in cannabis users: a multi-site mega-analysis comparing dependent versus non-dependent users. Psychopharmacology, 234(13), 1985– 1995. https://doi.org/10.1007/s00213-017-4606-9
Chye, Y., Suo, C., Lorenzetti, V., Batalla, A., Cousijn, J., Goudriaan, A. E., … Yücel, M. (2019b). Cortical surface morphology in long-term cannabis users: A multi-site MRI study. European Neuropsychopharmacology, 29(2), 257–265.
https://doi.org/10.1016/j.euroneuro.2018.11.1110
Chye, Y., Suo, C., Yücel, M., den Ouden, L., Solowij, N., & Lorenzetti, V. (2017b). Cannabis-related hippocampal volumetric abnormalities specific to subregions in dependent users. Psychopharmacology, 234(14), 2149–2157.
https://doi.org/10.1007/s00213-017-4620-y
Cousijn, J., Watson, P., Koenders, L., Vingerhoets, W. A. M., Goudriaan, A. E., & Wiers, R. W. (2013). Cannabis dependence, cognitive control and attentional bias for cannabis words. Addictive Behaviors, 38(12), 2825–2832.
Cousijn, J., Vingerhoets, W. A. M., Koenders, L., de Haan, L., van den Brink, W., Wiers, R. W., & Goudriaan, A. E. (2014). Relationship between working-memory network function and substance use: a 3-year longitudinal fMRI study in heavy cannabis users and controls. Addiction Biology, 19(2), 282–293. https://doi.org/10.1111/adb.12111 Fan, H.-Z., Zhu, J.-J., Wang, J., Cui, J.-F., Chen, N., Yao, J., … Zou, Y.-Z. (2019). Four-Subtest Index-based Short Form of WAIS-IV: Psychometric Properties and Clinical Utility. Archives of Clinical Neuropsychology, 34, 81–88.
https://doi.org/10.1093/arclin/acy016
Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction.
Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 35(1), 217–238. https://doi.org/10.1038/npp.2009.110 Kroon, E., Kuhns, L., Hoch, E., & Cousijn, J. (2019). Heavy cannabis use, dependence and
the brain: a clinical perspective. Addiction, add.14776. https://doi.org/10.1111/add.14776 Lecrubier, Y., Sheehan, D. V., Weiller, E., Amorim, P., Bonora, I., Sheehan, K., Janavs J,
Dunbar, G. C. (1997). The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: Reliability and validity according to the CIDI. European Psychiatry, 12(5), 224–231. https://doi.org/10.1016/S0924-9338(97)83296-8 Lorenzetti, V., Chye, Y., Suo, C., Walterfang, M., Lubman, D. I., Takagi, M., … Solowij, N.
(2020). Neuroanatomical alterations in people with high and low cannabis dependence. Australian & New Zealand Journal of Psychiatry, 000486741985907.
https://doi.org/10.1177/0004867419859077
Moore, T. M., Reise, S. P., Gur, R. E., Hakonarson, H., & Gur, R. C. (2015). Psychometric properties of the Penn Computerized Neurocognitive Battery. Neuropsychology, 29(2), 235–246. https://doi.org/10.1037/neu0000093
Nader, D. A., & Sanchez, Z. M. (2018, January 2). Effects of regular cannabis use on neurocognition, brain structure, and function: a systematic review of findings in adults. American Journal of Drug and Alcohol Abuse. Taylor and Francis Ltd.
https://doi.org/10.1080/00952990.2017.1306746
Pope, H. G., Gruber, A. J., Hudson, J. I., Cohane, G., Huestis, M. A., & Yurgelun-Todd, D. (2003). Early-onset cannabis use and cognitive deficits: What is the nature of the association? Drug and Alcohol Dependence, 69(3), 303–310.
https://doi.org/10.1016/S0376-8716(02)00334-4
Pope, H. G., Gruber, A. J., Hudson, J. I., Huestis, M. A., & Yurgelun-Todd, D. (2001). Neuropsychological performance in long-term cannabis users. Archives of General Psychiatry, 58(10), 909–915. https://doi.org/10.1001/archpsyc.58.10.909
The University of Pennsylvania Computerized Neuropsychological Test Battery
Documentation Interface (2003). PennCNP.penn Retrieved December 8, 2019, from https://penncnp.med.upenn.edu/
Randolph, K., Turull, P., Margolis, A., & Tau, G. (2013). Cannabis and Cognitive Systems in Adolescents. Adolescent Psychiatry, 3(2), 135–147.
https://doi.org/10.2174/2210676611303020004
Ross, J. M., Ellingson, J. M., Rhee, S. H., Hewitt, J. K., Corley, R. P., Lessem, J. M., & Friedman, N. P. (2020). Investigating the causal effect of cannabis use on cognitive function with a quasi-experimental co-twin design. Drug and Alcohol Dependence, 206, 107712. https://doi.org/10.1016/j.drugalcdep.2019.107712
Schwartz, R. H., Gruenewald, P. J., Klitzner, M., & Fedio, P. (1989). Short-term Memory Impairment in Cannabis-Dependent Adolescents. American Journal of Diseases of Children, 143(10), 1214–1219. https://doi.org/10.1001/archpedi.1989.02150220110030
Solowij, N., Jones, K. A., Rozman, M. E., Davis, S. M., Ciarrochi, J., Heaven, P. C. L., … Yücel, M. (2011). Verbal learning and memory in adolescent cannabis users, alcohol users and non-users. Psychopharmacology, 216(1), 131–144.
https://doi.org/10.1007/s00213-011-2203-x
Swagerman, S. C., De Geus, E. J. C., Kan, K. J., Van Bergen, E., Nieuwboer, H. A., Koenis, M. M. G., … Boomsma, D. I. (2016). The computerized neurocognitive battery:
Validation, aging effects, and heritability across cognitive domains. Neuropsychology, 30(1), 53–64. https://doi.org/10.1037/neu0000248
Van de Glind, G., van den Brink, W., Koeter, M. W. J., Carpentier, P. J., van Emmerik-van Oortmerssen, K., Kaye, S., … Levin, F. R. (2013). Validity of the Adult ADHD Self-Report Scale (ASRS) as a screener for adult ADHD in treatment seeking substance use disorder patients. Drug and Alcohol Dependence, 132(3), 587–596.
https://doi.org/10.1016/j.drugalcdep.2013.04.010
Van der Ploeg, H. M. (1982). De Zelf-Beoordelings Vragenlijst angst. Tijdschrift Voor Psychiatrie, 24(9), 576–588. Retrieved from
http://tijdschriftvoorpsychiatrie.be/issues/368/articles/2438
Wechsler, D. (2008). WAIS-IV: Wechsler adult intelligence scale. San Antonio, Tex: Pearson. Weible, A. P., Rowland, D. C., Monaghan, C. K., Wolfgang, N. T., & Kentros, C. G. (2012).
Neural correlates of long-term object memory in the mouse anterior cingulate cortex. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 32(16), 5598–5608. https://doi.org/10.1523/JNEUROSCI.5265-11.2012
Weiland, X. J., Thayer, R. E., Depue, X. E., Sabbineni, A., Bryan, A. D., & Hutchison, K. E. (2015). Daily Marijuana Use Is Not Associated with Brain Morphometric Measures in
Adolescents or Adults. Journal of Neuroscience, 35(4), 1505–1512. https://doi.org/10.1523/JNEUROSCI.2946-14.2015
Young, S. E., Corley, R. P., Stallings, M. C., Rhee, S. H., Crowley, T. J., & Hewitt, J. K. (2002). Substance use, abuse and dependence in adolescence: Prevalence, symptom profiles and correlates. Drug and Alcohol Dependence, 68(3), 309–322.
https://doi.org/10.1016/S0376-8716(02)00225-9
Yücel, M., Solowij, N., Respondek, C., Whittle, S., Fornito, A., Pantelis, C., & Lubman, D. I. (2008). Regional brain abnormalities associated with long-term heavy cannabis use. Archives of General Psychiatry, 65(6), 694–701.
Appendix A
M.I.N.I. 7.0.0 – CUD Only
J1 Heb je in het afgelopen jaar cannabis gebruikt? NEE Einde
JA Ga naar J2
J2 De volgende vragen gaan over je cannabis gebruikt in het afgelopen jaar:
a. Gebruikte je meer cannabis dan je van plan was op de momenten dat je cannabis gebruikte?
NEE JA
……… b. Heb je herhaaldelijk willen proberen je cannabisgebruik onder controle te krijgen
of te verminderen? NEE
JA
……… Heb je geprobeerd je cannabisgebruik onder controle te krijgen of te minderen, maar zonder succes?
NEE JA
……… Één van beide JA? Codeer JA.
NEE JA
c. Besteedde je een substantiële hoeveelheid tijd aan het verkrijgen van cannabis, het gebruiken ervan of het herstellen van je cannabis gebruik op dagen waarop je cannabis gebruikte?
NEE JA
……… d. Had je een sterk verlangen of behoefte om cannabis te gebruiken?
NEE JA
……… e. Besteedde je minder tijd aan jouw verantwoordelijkheden op werk, op school, of
thuis, omdat je herhaaldelijk cannabis gebruikte?
NEE JA
……… f. Bleef je cannabis gebruiken op het moment dat dit problemen veroorzaakte met
familie of anderen?
NEE JA
……… g. Ben je herhaaldelijk onder de invloed van cannabis geweest in situaties waarin je
anderen hierdoor fysiek in gevaar bracht, bijvoorbeeld tijdens het autorijden, motorrijden, besturen van een machine, varen, etc.?
JA
……… h. Bleef je cannabis gebruiken ook al was het duidelijk dat cannabis psychologische
of fysieke problemen veroorzaakte of verergerde?
NEE JA
……… i. Ben je met belangrijk werk, sociale activiteiten of hobby’s gestopt of ben je deze
minder gaan doen vanwege je cannabis gebruik?
NEE JA
……… j. Moest je meer cannabis gebruiken om hetzelfde gevoel te ervaren dat je ervaarde
toen je begon met gebruiken of had je het gevoel dat je minder voelde dan toen je begon met gebruiken terwijl je dezelfde hoeveelheid bleef gebruiken?
NEE JA
……… Codeer NEE als cannabis wordt voorgeschreven en gebruikt onder medische supervisie.
NEE JA
k. 1 Had je één of meerdere van de volgende ontwenningsverschijnselen als je je cannabis gebruik verminderde (3 of meer codeer YES):
o Prikkelbaarheid, boosheid of agressie o Angst of nervositeit
o Problemen met slapen
o Verlies van eetlust of gewichtsverlies o Onrust
o Depressieve gevoelens
o Serieuze last van één van de volgende problemen: o Maagpijn
o Trillende of shakende ledematen o Zweten
o Opvliegers (plotseling gevoel van warmte) o Rillingen
o Hoofdpijn NEE
JA
……… k. 2 Heb je cannabis gebruikt om ontwenningsverschijnselen te voorkomen of
verminderen? NEE
JA
……… Als er JA geantwoord is op J2K1 of J2K2 codeer dan JA (Deze tellen samen als één vraag).
NEE JA
Evaluatie
Is er minimaal 2 keer JA geantwoord op de J2 vragen?
NEE JA
………
CUD specificatie:
Geen: <2 van de J2 symptomen JA
Mild: 2-3 van de J2 symptomen JA
Moderate: 4-5 van de J2 symptomen JA Severe: 6 of meer J2 symptomen JA
