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Christopher D. Blake does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. Colleges in states where recreational marijuana became legal over the past decade saw a significant but short-term boost in applications from top-notch students. They also got more applications overall. Those were the key findings of a new study our team published recently in the peer-reviewed journal Contemporary Economic Policy. This means that colleges in legal-marijuana states received a temporary boost in applications. Our results control for school quality, tuition prices and labor market conditions that may affect student application decisions. Public colleges and universities benefited more than private ones, though applications for private schools rose in states where recreational marijuana became legal as well. In addition, schools got more applications from high-achieving students. As researchers continue to assess the risks and rewards of recreational marijuana, our results show that institutions of higher learning benefit when their home states allow their citizens to get high. One benefit is that schools had a larger and higher-achieving applicant pool to choose from. We found that, similar to how schools see a spike in applications and SAT scores when those schools have winning sports teams , schools see spikes when they are located in states that legalize marijuana. While our data cannot prove it explicitly, this suggests that students do factor local policies into their college choice, a key result of interest for scholars and policymakers alike. These metrics include the number of applications, demographic characteristics of students and detailed tuition prices, both before and after financial aid is applied. Along with this data, we analyzed state legislation to see when recreational marijuana would be available to students in a particular academic year. It could be the case that legal sales create a perception for prospective applicants that underage consumption is less risky. It could be simply because widespread news coverage made certain states seem more popular. Or it could be because more permissive public policies in one area, such as marijuana laws, might suggest more attractive and liberalized policies in other areas important to students, such as abortion. For example, did legalization in Colorado cause students from other states to apply to Colorado schools in higher numbers? Alternatively, in-state students may have elected to apply to even more Colorado schools than they would have in the absence of recreational marijuana as a way to stay in their home state. The IPEDS database does not require schools to distinguish between in-state and out-of-state applicants. However, the database does delineate enrollees as in-state or out-of-state. However, applying and enrolling are two very different actions. Applying indicates interest, but enrolling is more of a commitment. A broader look at how college application rates were affected by legalization could yield important insights for colleges in states that permit people to consume cannabis without fear of incarceration. Similarly, it would be insightful to examine how legalized marijuana affected student outcomes for all schools, while accounting for the nationwide disruptions associated with COVID The Research Brief is a short take on interesting academic work. Edition: Available editions Europe. Become an author Sign up as a reader Sign in. Effects of legalization on college application rates are short term. Blake , Emory University. Author Christopher D. Events More events.

Which came first: cannabis use or deficits in impulse control?

Luque buying Cannabis

Official websites use. Share sensitive information only on official, secure websites. Corresponding Author: spencers umn. Impulse control deficits are often found to co-occur with substance use disorders SUDs. On the one hand, it is well known that chronic intake of drugs of abuse remodels the brain with significant consequences for a range of cognitive behaviors. On the other hand, individual variation in impulse control may contribute to differences in susceptibility to SUDs. Does impulsivity precede drug use or does it manifest as a function of problematic drug usage? The link between impulsivity and SUDs has been most strongly established for cocaine and alcohol use disorders using both preclinical models and clinical data. Much less is known about the potential link between impulsivity and cannabis use disorder CUD or the directionality of this relationship. The long-term effects of chronic cannabis use on the brain and behavior have started to be explored. In this review we will summarize these observations, especially as they pertain to the relationship between impulsivity and CUD, from both a psychological and biological perspective. We will discuss impulsivity as a multi-dimensional construct and attempt to reconcile the results obtained across modalities. Finally, we will discuss possible avenues for future research with emerging longitudinal data. Impulsivity is a multidimensional construct with separable domains. Consequently, a number of definitions and theoretical conceptualizations have been proposed Bakhshani, , Lee et al. Despite an overall lack of consensus on the definition, impulsivity is a hallmark for numerous impulse control disorders e. Central to this review, there are high rates of co-occurrence between impulse control disorders and substance use disorders SUDs Fontenelle et al. Indeed, it is often repeated that a hallmark of SUDs is an inability to control the impulse to use drugs even in the face of severe negative consequences. As with other abused drugs, most individuals who try cannabis will not transition to daily drug use or abuse. Public perception is growing more favorable towards cannabis use Carliner et al. Two common misconceptions are that cannabis lacks addiction liability, and that cannabis use disorder CUD is rare compared to other SUDs. In the last 10 years, the percentage of deltatetrahydrocannabinol THC, the primary psychoactive chemical in cannabis in seized cannabis has nearly doubled Chandra et al. Correspondingly, there has been an increase in the incidence of CUD measured across multiple studies Hasin, There are many theories about why some individuals develop SUDs and others do not. Drug access represents a primary risk factor Gillespie et al. The changing legislative landscape in the US and beyond suggests that cannabis availability will only increase in the coming years. Research in animal models indicates that high impulsivity is correlated with substance abuse susceptibility Jentsch et al. Moreover, clinical research suggests that impulsivity levels can influence treatment response and relapse Bentzley et al. Most of this research has centered on alcohol and stimulant use, and indeed strong associations have been described in terms of these abused substances Dick et al. The development of CUD, as for other SUDs, is associated with a constellation of risk factors, but the link between impulsivity and cannabis use warrants further investigation. The diagnostic and statistical manual of mental disorders DSM is the handbook published by the American Psychiatric Association used by healthcare professionals in the US to aid in the diagnosis of psychiatric conditions. First published in , the DSM contains symptoms and other descriptive criteria for making reliable and consistent diagnoses. It is periodically updated to reflect the most up-to-date research and clinical evidence. The current version is the DSM-5, which was released in Consequently, much of the human subjects research cited in this review was conducted under DSM-IV criteria even though some of it has been published since the DSM-5 transition. Accordingly, it is pertinent for our interpretation of the following findings to understand the impact of this change. DSM-IV assessed cannabis abuse and cannabis dependence as separate constructs without recognizing cannabis withdrawal or craving APA, A diagnosis of cannabis abuse required meeting at least 1 of 4 criteria. A diagnosis of cannabis dependence required meeting at least 3 of 6 criteria. Notably, hierarchical diagnostic rules placed more emphasis on a diagnosis of dependence such that individuals ever meeting criteria for dependence were excluded from receiving a diagnosis of abuse for the same substance. In practical terms, cannabis dependence was considered the more severe disorder. In line with the DSM-5 changes, research suggests that endorsement of cannabis abuse items can indicate just as serious substance use problems Hartman et al. Notably, there was relative congruence between the statistics derived from each set of diagnostic criteria. Many biological, psychological, social, and environmental factors can increase risk for transitioning from cannabis use to CUD. Male sex, other substance use, comorbid psychopathology, earlier age of initiation, and peer influences are primary risk factors for subsequently developing CUD Courtney et al. Additionally, cannabis users report a variety of motives for initiation and maintenance of use e. There is evidence that using cannabis to cope with negative affect is associated with more frequent use and negative outcomes Buckner, , Moitra et al. Researchers have made various attempts to assemble psychological profiles of cannabis users. One model suggests that four personality factors i. For example, high sensation seeking was related to expansion motives e. This study and others Chabrol et al. The Hecomovic et al investigation did not correlate these factors with cannabis use frequency or cannabis-associated problems, but it may be possible to further refine some of these profiles based on these distinctions. Cannabis use disorder is highly comorbid with other substance use and psychiatric disorders. Importantly, both other substance use and many psychiatric disorders are associated with impulsivity Coskunpinar et al. Cannabis use is becoming more widespread, but individual motives, patterns of use, and various substance use and psychiatric comorbidities highlight the immense heterogeneity in cannabis users. Still, one or more common underlying vulnerability factors may explain a common liability for moderate to severe CUD. Despite over a century of research, there is still no unified framework nor agreed upon psychological definition for impulsivity. Numerous personality theories have included the concept of impulsivity Cloninger et al. In this review, we propose that impulsivity can be measured as a trait or a state. Impulsive trait is relatively stable across time, but impulsive state is characterized by variability in impulsivity levels that depend on external environmental or internal proprioceptive conditions. Impulsivity as a state is considered a normal dimensional behavior as well as pathological construct of several diagnosable mental disorders including ADHD, antisocial and borderline personality disorders, and SUDs. Trait impulsivity, often measured through self-report personality assessments, is based on internal perceptions of behavior that can be evaluated on separable factors. Sensation seeking is a related but non-overlapping construct defined as the propensity to pursue novel, diverse and thrilling experiences and the willingness to take risks in that pursuit Zuckerman, We include sensation seeking in this review because the concepts appear to be both operationally and biologically correlated Hur and Bouchard, , though it is not our intention to conflate the two Cyders et al. Each of these scales in this non-exhaustive list Table 1 has been used to correlate impulsivity with SUDs. This inherent heterogeneity in terms of the definition of impulsivity and the various dimensions that comprise it likely contribute to some of the inconsistency in the literature with respect to associations with CUD and other SUDs— especially if only certain domains ultimately capture the association. Impulsivity is also assessed through tasks that measure overt behavior. Behavioral assessments measure different aspects of impulsivity akin to the diversity of survey measures. One useful organizational framework that has been put forth separates impulsive choice inability to delay gratification from impulsive action impaired response inhibition and self-reported impulsive personality traits MacKillop, Weafer, Lee and colleagues proposed a slightly different framework based on their meta-analysis that centers on three neurocognitive components: Response inhibition, reward discounting, and disadvantageous decision-making Lee, Hoppenbrouwers, Conveniently, these rodent models have allowed researchers to more directly study the basis of inter-individual variation in impulsivity-related behaviors by selecting out and even breeding animals at the behavioral extremes Isherwood et al. In support, the prevailing literature indicates that there is little correlation between various measures of impulsivity. While impulsive personality traits captured by the two survey measures showed very modest associations with impulsive behaviors, there was a complete lack of relationship between task measures for impulsive choice versus impulsive action MacKillop, Weafer, This replicated a previous study performed on a much smaller scale dissociating behavioral dimensions of impulsivity Reynolds et al. This dissociation was preserved across species rats vs humans , but in this case, the human subjects self-reported impulsivity BIS showed no association with their behavioral measures Broos et al. Comprehensive meta-analyses confirm weak to moderate correlations, if any, between impulsivity self-report measures and laboratory tasks Cyders and Coskunpinar, , Sharma et al. Moreover, distinct dimensions of impulsivity are being tapped by the separate survey measures and laboratory tasks, although, as mentioned, there is not universal agreement upon a dimensional model Cyders and Coskunpinar, , Meda et al. Some of the discrepancy between studies could be accounted for by methodological differences in data reduction, small sample sizes, and dissimilar demographics of study populations. For instance, the latent structure of impulsivity may not be identical in healthy controls and individuals in clinical populations Meda, Stevens, Importantly, both laboratory tasks and survey measures separately show association with real-world measures of daily life impulsivity like gambling and risky sexual behavior Sharma, Markon, There has been some effort made to improve the concordance between self-report and laboratory measures of impulsivity Ellingson et al. The primary point of emphasis here is the importance of specifying which construct s of impulsivity is being applied to form which conclusion. Because impulsivity is not a unitary construct, it complicates the association with CUD. The prefrontal cortex PFC is the brain structure most often associated with behavioral inhibition and decision making and consequently implicated in impulsivity Kim and Lee, The PFC is divided into numerous subregions each with their own functional specializations and embedded within a larger network structure. Most studies describe negative relationships between cortical and subcortical gray matter volume and survey measures of impulsivity Holmes et al. One of the most consistent findings is a negative relationship with orbitofrontal cortex OFC volume Korponay, Dentico, , Kumari, Barkataki, , Matsuo, Nicoletti, Measurements of gray matter volume vary based on both thickness and surface area fluctuations; consequently, it may not be the most sensitive readout and this potentially contributes to some inconsistency in results. Akin to volumetric analyses, negative correlations are observed between cortical thickness and impulsivity Kubera et al. Few studies to date have examined the relationship between impulsivity and cortical surface area, although Kubera et al. Functional analyses like resting state functional connectivity RSFC provide additional information about the neural networks underlying impulsivity. Resting state functional magnetic resonance imaging rs-fMRI measures the degree of correlated activity between spatially distributed brain regions with the objective of characterizing functional networks when the brain is at rest. Seed-based methods reveal distinct functional networks correlated with impulsivity subscales centered on identified regions of interest ROIs Angelides et al. Similarly, within-subjects analysis identified separate networks associated with behavioral tasks for impulsive choice DD versus impulsive action SST utilizing the frontal pole and inferior frontal gyrus as seeds Wang et al. A recent study applied an independent component analysis to rs-fMRI data identifying three discrete networks associated with trait impulsivity without the a priori selection of ROIs. It is encouraging that their non-biased results significantly map onto our existing neuroanatomical knowledge of impulsivity networks. Experiments performed in preclinical animal models exploiting laboratory tasks that parallel human behaviors have aided in the validation of these neural networks. Distinct neural processes contributing to different forms of impulsivity can thus be ascertained. Traditional neuroanatomical disconnection studies employing contralateral lesions to ventral PFC and ventral hippocampus further implicated this network in inhibitory response control in the 5CSRTT Chudasama et al. Inactivation of anterior cingulate ACC or dorsal prelimbic cortices increased stop signal reaction time, another index of impulsive action, with selective contribution of noradrenergic signaling in the dorsal prelimbic Bari et al. Parallel to the human structural and functional data, significant research has focused on the role of OFC in impulsivity-related behaviors in animal models, but studies have yielded inconsistent results. Lesion of the basolateral amygdala or OFC or their disconnection impaired acquisition of the rGT, but with additional training performance reached equivalence with sham-operated controls Zeeb and Winstanley, , Neither inactivation nor lesion of OFC disrupted decision-making on the rGT after rats were trained Zeeb, Baarendse, , Zeeb and Winstanley, ; in contrast, basolateral amygdala lesions were sufficient to increase risky choice Zeeb and Winstanley, In the DD task, OFC lesion or inactivation has been shown to increase, decrease or have no effect on impulsive choice Winstanley et al. For instance, basal levels of impulsivity and differences in incentive salience attribution to reward cues differentially influenced the effects of OFC inactivation on DD Zeeb et al. Moreover, functional heterogeneity of anatomical sub-regions within the OFC differentially impact learning and decision-making related behaviors, and many studies fail to specify which OFC sub-regions are targeted Izquierdo, Mar and colleagues were the first to demonstrate a dissociable role for medial and lateral OFC in the rodent, wherein lesion of the medial OFC decreased and lesion of the lateral OFC increased impulsive-like responding in the DD task relative to sham controls Mar, Walker, Other recent evidence suggests that this may be an oversimplification of the complex role of the OFC in impulsivity Sellitto et al. Subcortical regions are also important regulators of impulsivity. The nucleus accumbens NAc core plays a complex role in regulating impulsive choice in the DD task. Activation of this pathway decreased delay discounting in high impulsive rats; inactivation increased delay discounting in low impulsive rats Wang, Yue, in line with most prior NAc lesion and inactivation studies Cardinal et al. Interestingly, lesion of the entire NAc core and shell decreased delay discounting, again highlighting important neuroanatomical sub-region effects Acheson et al. On the whole, these studies highlight distinct but not mutually-exclusive networks related to impulsivity constructs Eagle and Baunez, , Robbins and Dalley, Notably, these circuits overlap with those implicated in reward and development of SUDs Crews and Boettiger, Deficits in 5-hydroxytryptamine 5-HT neurotransmission tend to correlate with increased impulsivity in humans and animal models Mavrogiorgou et al. There is ample support for a role for 5-HT signaling in waiting impulsivity with perhaps a less well-defined role in stopping impulsivity or tasks that measure impulsive choice. Conversely, optogenetic activation of 5-HT neurons in the dorsal or median raphe nucleus of transgenic mice decreased premature responding in a 3CSRTT in further support of a positive regulatory role of serotonin for promoting waiting Ohmura et al. However, conflicting results were obtained when delay discounting was assessed in humans using either a reward delay questionnaire no effect, Worbe, Savulich, or a computerized task with actual delays increased discounting, Schweighofer et al. Consistent with a negative correlation between 5-HT transmission and impulsive-like behavior, a positive relationship was reported for serotonin transporter SERT function in the OFC with DD and a behavioral measure of negative urgency in rats Darna et al. Despite this proposed relationship with SERT, selective serotonin reuptake inhibitors SSRIs have demonstrated poor and inconsistent effects on impulsivity in humans and animal models Macoveanu et al. The large receptor family 5-HT 1—7 further complicates elucidating the role of 5-HT in impulsivity. On the whole, 5-HT 2A receptor agonism increases and antagonism decreases impulsive action Fink et al. In contrast, 5-HT 2C -specific modulations produce inverse behavioral effects Higgins et al. Fewer reports have examined the contribution of these receptor subtypes to impulsive action, but in at least one study 5-HT 2C antagonism decreased DD Paterson et al. Genetic and pharmacologic evidence also points to the involvement of 5-HT 1A and 5-HT 1B receptors in regulating various forms of impulsivity Korte et al. Very little is known regarding the involvement of other 5-HT receptors, but individual 5-HT receptor subtypes influence unique biochemical signaling pathways. Many medications prescribed to treat ADHD, characterized by impulsivity, act by increasing dopamine levels in the brain Connolly et al. Despite their widespread use in ADHD, psychostimulant drugs produce mixed effects on impulsivity in control subjects. Of course, amphetamine is not selective for the dopamine transporter DAT , but DAT blockers reproduced those effects Baarendse and Vanderschuren, Individual differences in self-report and task-based measures of impulsivity in healthy human subjects correlated with variation in dopaminergic function including dopamine synthesis capacity and DAT availability Smith et al. Baseline impulsivity moderated the effects of L-DOPA augmentation in healthy controls on delay and probability discounting such that high-impulsive individuals become less impulsive and low-impulsive individuals became more impulsive Petzold et al. In contrast to acute inhibition, non-reversible dopamine lesions increased delay discounting in adult rats Tedford et al. Still, site-specific behavioral pharmacology has validated the importance of signaling at specific dopamine receptors within the NAc, mPFC, and OFC in regulation of impulsivity-related behaviors Besson et al. The selective norepinephrine reuptake inhibitor atomoxetine, commonly prescribed for ADHD, also decreases impulsivity in animals and control human subjects Chamberlain et al. Likewise, the alpha-2 adrenergic receptor agonist guanfacine decreased premature responding in the 5CSRTT in rats Fernando, Economidou, On the other hand, chemogenetic inhibition of the locus coeruleus increased premature responding on 5CSRTT in mice, but only under more demanding conditions Fitzpatrick, Runegaard, In comparison, the pharmacological stressor and alphaadrenergic receptor antagonist yohimbine shows dissociable effects on different forms of impulsivity. Yohimbine improved stop reaction time in control human subjects when baseline sensation seeking was controlled for Herman et al. Yet yohimbine increased DD in rats Schippers, Schetters, with no effect observed in humans Herman, Critchley, This effect on 5SCRTT was dependent on multiple neurotransmitter systems interaction because no single receptor antagonist was able to reverse it Mahoney, Barnes, It is difficult to interpret any of these findings in a vacuum because atomoxetine and yohimbine influence the levels of other monoamines Brannan et al. As the primary excitatory and inhibitory neurotransmitters in the brain, glutamate and GABA are essential for proper brain function. Compared to the monoamine neurotransmitters there is less research into their specific contributions to impulsivity, especially in non-clinical populations. In contrast to the aforementioned study Hoerst, Weber-Fahr, , there was no relationship between ACC glutamate and BIS scores Silveri, Sneider, , but relatively small sample sizes and significant differences in the age and gender make-up of their study populations may have contributed to this difference. Preclinical models provide additional insight into the role of glutamate and GABA in regulation of impulsivity-related behavior. Similar dissociable effects were reported for allosteric modulators of metabotropic glutamate receptors Isherwood et al. Altogether increased glutamate levels in frontal cortical regions seem to positively correlate with trait impulsivity. The opposite relationship may hold true for GABA. Emerging evidence supports a direct link between the endocannabinoid system and impulsivity. For example, in healthy control subjects the density of the CB 1 receptor, the primary cannabinoid receptor in the brain, was negatively correlated with self-reported novelty seeking Van Laere et al. At least one study found that rimonabant increased delay discounting in rats Boomhower and Rasmussen, In another set of rodent studies, a cost-benefit T-maze was used to assess delay- or effort-based decision establishing dissociable roles for ACC and OFC Khani et al. Rats with a mutation in the cannabinoid receptor 1 gene CNR1 gene resulting in gain of function of the CB 1 receptor displayed increased impulsivity in a DD task alongside a host of other behavioral phenotypes said to be reminiscent of adolescent-like behavior increases in risk taking, peer interaction, and reward sensitivity Schneider et al. Trait impulsivity is consistently implicated as a risk factor for both cannabis use and cannabis-related problems Bidwell et al. Certain impulsive personality traits are evident in early development. Indeed, longitudinal cohort studies have demonstrated that temperamental qualities observed as early as three years of age predict personality traits and psychiatric diagnoses later in development Caspi, , Caspi et al. Within this framework, individual differences in trait impulsivity may be treated as relatively stable risk factors for SUDs and other psychiatric disorders. However, more recent evidence suggests that personality traits are not in fact as stable across the lifespan. Impulsivity and sensation seeking, for example, appear to undergo gradual age-related decline into early adulthood Harden and Tucker-Drob, Individual differences in the rate of decline in impulsivity are associated with variable escalation in cannabis and other substance use Quinn and Harden, In a study that further characterized impulsivity trajectories, distinct sex-dependent trajectories of impulsivity during early adolescence showed differential associations with substance use including cannabis with two trajectories identified in males and five in females Martinez-Loredo et al. Martinez-Loredo, Fernandez-Hermida, Thus, individual differences in both baseline impulsivity as well as maturational changes in impulsivity increases or maintenance of elevated levels versus normal developmental decline may interact to influence vulnerability to SUDs. Some of the strongest evidence in support of a vulnerability model comes from prospective longitudinal studies examining early trait impulsivity and later cannabis use. In one such analysis, baseline impulsivity was assessed in 8 th graders, and cannabis use behaviors recorded on follow-up from 9 th th grade Felton et al. Logistic regression revealed an association between impulsivity EIS and daily cannabis use in another study that followed participants from grade 7 through age 20 Dugas et al. Many users would have already initiated use before impulsivity was assessed, therefore the predictive nature of the relationship is less certain. Prospective longitudinal studies of sensation seeking find a similar relationship. Sensation seeking in 4 th -5 th grade elementary students predicted cannabis use in 11 th th grade Hampson et al. Similarly, a month study of adolescents enrolled in 9 th grade found an association between sensation seeking and cannabis use employing the SURPS Malmberg et al. In one of the first reports to examine the predictive role of SURPS, impulsivity did not predict early cannabis use despite being highly related to sensation seeking which did Malmberg, Kleinjan, The analysis suggested a suppression effect when both impulsivity and sensation seeking were included in the model, but even when recomputed without sensation seeking, a relationship between impulsivity and cannabis use did not emerge Malmberg, Kleinjan, However, in other samples, both impulsivity and sensation seeking have shown prospective predictive relationships with cannabis use Newton et al. This variability could reflect subtle cultural differences in subject populations or highlight the limitations of the SURPS as a predictive instrument. In cross-sectional studies, trait impulsivity is associated with age of use onset, frequency of use, and cannabis-related problems. Studies of adolescents have consistently reported relationships between impulsivity and cannabis related outcomes Dougherty et al. In college student populations, impulsivity is reliably associated with cannabis use and cannabis use problems Pearson et al. In one study where participants rated childhood and current symptoms on the Current Symptom Scale for ADHD, childhood hyperactivity-impulsivity was associated with earlier onset of cannabis use, and current or childhood inattention was associated with more severe cannabis related problems Bidwell et al. In another sample of collegiate underclassmen, impulsivity moderated the positive relationship between cannabis use frequency and cannabis related problems Simons and Carey, These study populations, though convenient, may not be particularly representative of the general population especially in terms of education and socio-economic status. In more representative samples of adolescents and young adults, impulsivity has been associated with lifetime incidence of cannabis use, increased past month frequency of cannabis use and increased risk for the development of DSM-IV CUD Blanco et al. In a mixed sample of inpatient substance users, the SURPS scale revealed that impulsivity was related to frequency of cannabis use, and sensation seeking was related to approach motivation for cannabis in the form of cue reactivity ratings Schlauch, Crane, Not all cross-sectional data reveal elevated impulsivity among cannabis users. For instance, frequent cannabis users surprisingly scored relatively low overall on measures of trait impulsivity BIS and Impulsive Sensation Seeking scales compared to controls and nicotine users, but there was still a significant positive relationship between impulsivity and severity of cannabis use symptoms Beaton et al. Similarly, when currently cannabis dependent individuals were compared to former users and controls there were no significant group differences on the BIS survey, and only currently cannabis dependent individuals score higher on the EIS Johnson et al. In an inpatient sample, relationships were mostly absent between severity of cannabis dependence and personality domains within the UPPS-P Moraleda-Barreno et al. Altogether, these data may indicate there is not a unified impulsivity phenotype in cannabis users. Specific psychological factors have been shown to mediate some of the relationship between an impulsive personality and cannabis use outcomes. The acquired preparedness model, originally formulated based on alcoholism, theorizes that impulsivity influences outcome expectancies that regulate participation in risky behavior McCarthy et al. Consistent with this model, expectancies about cannabis use mediate the link between impulsivity and cannabis use outcomes. Individuals high on trait impulsivity have more positive expectations for cannabis use, which in turn leads to higher cannabis use Bolles et al. Specifically, the link between cannabis use and sensation seeking and cannabis use and positive urgency in the UPPS-P is mediated by expectancy Curry, Trim, , Luba, Earleywine, Negative expectancies can also mediate the relationship between trait impulsivity and use, although the nature of this relationship may be less straightforward. Negative cannabis expectancies were negatively correlated with cannabis use frequency and use onset Buckner, , Hayaki, Herman, , Montes et al. In at least one study, negative expectancies still positively predicted severity of cannabis problems and cannabis dependence Hayaki, Herman, Additionally, cannabis-related perceptions among college students e. Similarly, sensation seeking is a moderator of social influence variables like peer pressure and peer use on cannabis consumption in middle school students Slater, The picture is less clear when it comes to behavioral impulsivity. In middle and high school students, greater discounting in a real-world delay of gratification paradigm was associated with higher frequency of cannabis use Wulfert et al. Similarly, greater delay discounting was associated with earlier age of first cannabis use in college students Kollins, and higher frequency of use in a large adult sample ages 18—79 Sofis, Budney, The implications of this finding are not completely clear, although this strategy of disadvantageous decision making could reflect the tendency of an individual to ignore future negative consequences of their actions which is a hallmark of addiction. Nevertheless in regular cannabis users steeper delay discounting of rewards is associated with cannabis problems and dependence Aston et al. In a hypothetical cannabis purchase task, cannabis demand was also related to frequency of cannabis use Aston, Metrik, , Strickland, Lile, Overall, the evidence supports some association between impulsive behavior and cannabis use. The large variability in results across studies likely reflects significant differences in study design capturing both more acute and chronic effects, assessing recreational and pathological use, and likely captures both causative and drug-induced behavior. The acute effects of cannabis and its constituents on impulsivity have been directly studied in the human laboratory. One advantage of this strategy is that conditions like drug dose and timing can be strictly managed in placebo-controlled experiments. Interestingly, the expectation of receiving THC may have produced compensatory effects on risky decision making in the experiential discounting task. Although not directly a measure of impulsivity, time perception is obviously involved in impulsivity-related decision making. Technological advancements including ecological momentary assessment have recently been utilized to assess acute cannabis effects in real world settings. During a two-week study in recreational drug users, cannabis use was associated with higher same-day and next-day impulsivity as measured using a daily within-subjects BIS-Brief survey Ansell et al, In a similar study performed in a psychiatric outpatient population, cannabis use was associated with increased impulsivity only at the same day level on a momentary impulsivity scale Trull et al. The dissimilarity in the effect on next-day impulsivity between these two studies could be attributed to the different self-report measures or the distinct subject pools. Interestingly, in both reports there was a lack of significant between-subjects association with level of cannabis use and impulsivity. This result might lend support towards a consequential more than causal model or could reflect limits associated with the sample demographics consisting of only recreational, non-dependent cannabis users. Future research should examine a wider variety of cannabis users. Cheetham and colleagues were the first to provide support for a neural signature that predicts later cannabis use. In this important study, researchers found that smaller OFC volume at age 12 predicted initiation of cannabis use at 4-year follow-up Cheetham et al. This finding was partially replicated in a longitudinal analysis that collected 3 waves of neuroimaging data along with comprehensive substance use assessments in a cohort of subjects oversampled for depression symptoms. Here OFC volume and thickness again measured at age 12 similarly negatively predicted subsequent onset and frequency of alcohol and cannabis use Luby et al. A third study implicating the OFC in cannabis use vulnerability reported the inverse relationship. Larger left lateral OFC volume at baseline age 12—15 years predicted transition to regular cannabis use Wade et al. Notably, a similar positive relationship between OFC volume and onset of cannabis use emerged in the Luby et al study when alcohol and cannabis use were separated Luby, Agrawal, The Wade study differed from those preceding it in that the baseline measures were collected from subjects across a wider age range thus capturing greater heterogeneity in the neurodevelopmental snapshot. In addition, a larger proportion of the sample transitioned to substance use. This combination of factors may have contributed to the disagreement between results. Still other studies have failed to find any baseline differences in OFC volume, thickness, or surface area between future cannabis users and controls Infante et al. The PFC is normally subject to extensive synaptic pruning and refinement during adolescence, and the dynamic nature of individual variability in this maturational process could obscure some baseline differences. Additional longitudinal studies are essential to resolve the role of the OFC as a potential biomarker of susceptibility for CUD. Cross-sectional studies about the effects of cannabis on the brain deliver mixed results. A variety of factors, including diverse subjects with variable patterns of use infrequent vs. Analyses seem to indicate that even very low levels of cannabis use are capable of producing structural rearrangement. Whole brain gray matter volume was increased after as few as 1—2 uses of cannabis, prompting the authors to conclude that gray matter volume increases are drug-induced and not pre-existing Orr, Spechler, Other studies lend additional support to a vulnerability model or at least suggest interactive mechanisms. The primary finding was significant Group X Time interactions control vs. Their findings indicated that cannabis use may disrupt the trajectory of cortical pruning but also hinted that pre-existing differences might contribute to cannabis initiation. The disruption in synaptic refinement may lead to the eventual increased cortical thickness in cannabis users compared to controls reported in some Filbey et al. Certainly, location matters because both increases and decreases in thickness were observed in separate brain regions of the same adolescent users Lopez-Larson et al. The relationship between cannabis use and brain structure is complex, and gray matter volume analysis underscores the distinction between non-dependent versus dependent or occasional versus frequent use. Smaller OFC volume was associated with cannabis dependence and higher past month usage with a suggestion of differential sensitivity in female users compared to their male counterparts Chye et al. Cannabis use, dependence, and related problems show consistent negative correlations with gray matter volume measurements of ROIs in the hippocampus, amygdala, and superior temporal gyrus Cousijn et al. Speaking to a progression of cannabis use effects, a decrease in gray matter volume in multiple regions OFC, left insula, parahippocampal gyrus, temporal pole, and temporal cortex was apparent in frequent cannabis users compared to occasional ones with comparable years of experience Battistella et al. Undoubtedly, age of use onset and other demographic and drug use factors additionally play a role in the outcome Battistella, Fornari, , Filbey, McQueeny, Functional connectivity also shows evidence of alteration by cannabis use. Increased functional connectivity between OFC and frontal and motor cortical regions was observed in adolescent heavy cannabis users compared to controls Lopez-Larson et al. In a longitudinal study, lower basal connectivity between ACC and OFC predicted higher cannabis use at month follow-up in a cohort of treatment seeking adolescents. Additionally, differential time-dependent alterations in functional connectivity distinguished cannabis users from controls. Healthy controls showed significant increases in connectivity between ACC and superior frontal gyrus, a trend absent in cannabis users; conversely, cannabis users, but not controls, displayed decreased connectivity between ACC and dlPFC Camchong et al. Similar functional reorganization was observed in non-treatment seeking adolescent cannabis users Camchong et al. Related to connectivity, alterations in white matter integrity primarily captured by fractional anisotropy measurements derived from diffusion tensor imaging have also been reported in association with cannabis use. Recent longitudinal analyses seem to support the notion that fractional anisotropy is reduced by cannabis, although perhaps in a restricted pattern, and the effects on white matter integrity are cumulative Becker et al. Some studies have examined differences in neural activation between cannabis users and controls associated with performance on impulsivity-related tasks. A recent comprehensive meta-analysis of neuroimaging studies described the brain regions that reliably demonstrate functional adaptations in cannabis users versus controls overlaying task-based network analysis to categorize the disrupted cognitive processes Yanes et al. While a minority of the reviewed studies incorporated tasks related to response inhibition and impulsivity, their overall analysis identified three networks associated with cognitive control, attention, and reward linked to decreased activation in the ACC and dlPFC and increased activation in the striatum, respectively. In heavy cannabis users not yet showing deficits in IGT performance, significant alterations in task-related brain activation patterns were observed compared to controls Acheson et al. Cannabis users showed higher responses to both Wins Acheson, Ray, , Cousijn, Wiers, and Losses Acheson, Ray, in overlapping but non-identical clusters of brain regions compared to controls. Moreover, higher activity related to Win-Loss evaluation or disadvantageous vs. Relatedly, frequent cannabis users, though displaying equivalent accuracy on a GNG task, demonstrated impaired error awareness associated with hypoactivity of ACC, right insula and middle frontal regions Hester et al. Finally, some enduring effects on neural function are observed in abstinent cannabis users. Persistent dose-related deficits in IGT were reported with heavy users showing worse performance associated with decreased activity in right lateral OFC and dlPFC compared to controls after 25 days of abstinence Bolla et al. The users also displayed a flat learning curve across within-session trial blocks although there was significant learning between sessions Verdejo-Garcia, Benbrook, As reviewed in Section 5. In day abstinent adolescent cannabis users, neural responses were increased during both Go and No Go trials in a GNG task Tapert et al. Cannabis users displayed greater blood oxygen-level dependent signal responses during inhibition trials in an expected pattern including dlPFC, bilateral medial frontal, bilateral inferior and superior parietal lobes, and right occipital gyrus Tapert, Schweinsburg, Similarly, in a Decision-Reward Uncertainty Task adolescent former cannabis users showing unchanged behavior exhibited hyperactivation in left superior parietal, left lateral occipital, and precuneus while making risky decisions involving uncertainty and hypoactivation in left OFC to rewarded outcomes after making risky decisions De Bellis et al. The THC in cannabis produces its effects by acting on the endocannabinoid system inclusive of endogenous cannabinoids, their receptors, and biosynthetic and degradative enzymes. The two primary endocannabinoids arachidonyl ethanolamide anandamide and 2-arachidonylglycerol are synthesized and degraded by separate enzymatic pathways that contribute to their distinct functions Lu and Mackie, Unlike standard neurotransmitters, endocannabinoids are produced in an activity dependent fashion by the postsynaptic neuron and function in a retrograde manner to stimulate presynaptic cannabinoid receptors. The CB 1 receptor is the primary central nervous system subtype, and in fact, represents one of the most highly expressed G-protein coupled receptors in the brain Busquets-Garcia et al. Not surprisingly, CB 1 receptor density is high in many of the same brain regions that show neuroanatomical alterations following cannabis use Lorenzetti et al. Positron emission tomography PET scans showed that CB 1 receptor binding was decreased broadly across cortical regions in cannabis users and negatively correlated with years of use, although this deficit normalized within a month of abstinence Hirvonen et al. Cannabis or THC, akin to other addictive drugs, increases dopamine release in the striatum Bossong et al. The acute activation of CB 1 receptors on inhibitory GABAergic terminals apposed to ventral tegmental area dopamine neurons reduces GABA release with a net effect of increasing dopamine neuron firing and downstream dopamine signaling Covey et al. Impulse control and reward are both linked to dopamine transmission, and the endocannabinoid system might be one bridge that links the two. Chronic cannabis use reduced dopamine synthesis, an effect positively related to the severity of use Bloomfield et al. Cannabis and tobacco smokers showed reduced DAT availability in the striatum Leroy et al. Thus, chronic cannabis use reduces dopaminergic function. Several recent genetic studies point to interactions between the 5-HT system and cannabis use or cannabis use outcomes Galindo et al. Moreover, in cannabis users these heteromers positively correlated with amount of cannabis use and negatively correlated with age of use onset and cognitive performance Galindo, Moreno, All of the evidence regarding the effect of cannabinoids on noradrenergic signaling has been gleaned from animal studies. Acute THC increased locus coeruleus neuron activity Muntoni et al. Interestingly, this was likely an indirect effect not involving local CB1 receptors given that intracereberoventricular delivery of cannabinoid agonists failed to increase locus coeruleus firing rate Mendiguren and Pineda, The effects of chronic cannabinoid use on noradrenergic signaling remain unknown. Atomoxetine has been tested for efficacy in treating cannabis use disorder, and it failed to reduce drug craving or use McRae-Clark et al. Preclinical studies show that acute THC disrupts glutamate signaling but the nature of that effect is varied. In healthy control human subjects with moderate former cannabis use, acute THC increased levels of glutamate plus glutamine metabolites Glx in the left caudate nucleus Colizzi et al. Interestingly, the psychotomimetic effects of cannabis were most prominent in those individuals with the greatest fold change in Glx levels Colizzi, Weltens, Similarly, in occasional cannabis users acute THC increased glutamate concentrations in the right striatum Mason et al. These results are interesting in light of the fact that chronic cannabis users consistently display reduced glutamate levels Chang et al. Basal glutamatergic deficits would be consistent with the glutamate homeostasis hypothesis of addiction with augmented glutamate overflow in response to a cannabis challenge predicted to exacerbate relapse Scofield et al. In another recent examination of neurometabolite concentrations in the dorsal ACC where the authors did not detect a decrease in glutamate concentration in chronic cannabis users compared to controls, total creatine was positively associated with monthly cannabis use; therefore, the authors appropriately did not normalize to this measure Newman et al. This particular finding may have implications for the interpretation of previous results and should inform the design and analysis of future studies. These findings were replicated and extended to demonstrate a connection between cortical GABAergic hypo-function and VTA dopamine hyper-function Renard et al. Such a result might go towards explaining poorer cannabis and other substance use outcomes associated with earlier age of use onset Rioux et al. Trait impulsivity has predictive value for cannabis use outcomes. Trait impulsivity may influence some of the subjective effects of acute cannabis Van Wel, This is important because subjective effects in response to cannabis have noted association with use patterns and dependence Zeiger et al. The same might be true for behavioral impulsivity, but there is currently a lack of longitudinal data to fully support that notion. Acute effects of cannabis do increase state impulsivity. Impulsivity also moderates other factors contributing to cannabis use like individual expectancies or social influences. Sensation seeking, like impulsivity, showed prospective associations with cannabis use. It has actually been suggested that sensation seeking may be more tied to onset of substance use while impulsivity is related to continued use and associated problems. Early sensation seeking interventions may be beneficial to certain high-risk populations Mahu et al. There is significant neuroanatomical overlap between the brain regions implicated in impulsivity and those affected by cannabis use. Differential OFC activation patterns were associated with delayed versus immediate rewards in human imaging studies McClure, Laibson, CB 1 receptors regulate sensitivity to the effects of cannabis, but to our knowledge there is currently no longitudinal evidence indicating that baseline CB 1 receptor levels in the OFC or elsewhere predict cannabis use or CUD outcomes. With the relatively recent development of radioligands for PET imaging, these studies may be yet on the horizon Burns et al. Until then, there is already evidence of a predictive relationship between OFC volume and cannabis use, although it is perhaps still up for debate whether a smaller or larger OFC confers this vulnerability Cheetham, Allen, , Wade, Bagot, Less controversial is the evidence that chronic, heavy cannabis use has widespread neurotoxic effects resulting in losses in gray matter volume Battistella, Fornari, , Chye, Solowij, In contrast, another consistent finding was increased gray matter volume in the cerebellum Battistella, , Cousijn, , Koenders, , but the functional significance of this adaptation remains uncertain. Additional unbiased analysis methods and larger sample sizes will provide further refinement of the altered brain map that is developing. There are similar neurochemical signatures associated with impulsivity and cannabis use. The catecholamine neurotransmitters 5-HT and dopamine are most commonly associated with trait impulsivity and impulsivity-related behavior Pattij and Vanderschuren, Cannabis, like all abused drugs, acutely stimulates dopamine release in the ventral striatum Bossong, Mehta, ; however, a hypodopaminergic state develops with chronic cannabis use Bloomfield, Morgan, As with the neuroanatomical brain imaging data, it would be beneficial to track neurochemical changes across time with cannabis use to explore possible explanations for this seeming incongruency. Likewise, while the behavioral pharmacology linking 5-HT and norepinephrine to impulsivity is strong Pattij and Vanderschuren, , there is a substantial gap in our knowledge concerning the direct effects of cannabis use on the serotonergic and noradrenergic systems. Perhaps this is partially explained due to the fact that the CB 1 receptor mediated modulation of their activity occurs indirectly via primary actions at glutamatergic and GABAergic terminals Mendiguren and Pineda, It is thus not surprising that there is substantial evidence that cannabis use disrupts glutamate and GABA transmission, though the direction of effects may not always be consistent. Given the substantial baseline impulsivity dependent effects that have been described elsewhere Herman, Critchley, , it would not be implausible to hypothesize that such state dependence may also be observed for effects of cannabis use on neurotransmission. In the previous sections we have tried to summarize the evidence for a bidirectional relationship between impulsivity and cannabis use. Collectively, these data provide support for several hypotheses. For example, PFC-related or other impairments may promote trait impulsivity, and that elevated impulsive phenotype then mediates cannabis use outcomes. Similarly, the inverse relationship is conceivable with some biological endophenotype conferring risk for cannabis use, and that cannabis use increases behavioral impulsivity. From the rodent literature, chronic agonism of the CB 1 receptor during adolescence but not adulthood, promotes elevated impulsivity in a DD task later in adulthood Johnson et al. The fact that age of use initiation affects brain changes and use outcomes in humans lends support to this non-mutually exclusive theory Filbey, McQueeny, , Gruber et al. At this point, there is seemingly evidence to support any of these ideas. We have provided the relevant longitudinal data where it exists. Additional prospective studies are necessary to begin to parse out these causes and effects. Impulsivity and cannabis use have a biological basis and are moderately heritable Bezdjian et al. In brief, we highlight here a few additional related areas for future research. First, there is burgeoning data related to epigenetic mechanisms and transgenerational transmission associated with cannabis use Szutorisz and Hurd, Epigenetic modifications brought about by cannabis use can contribute to enduring changes in gene expression and behavior. Pharmacological interventions targeting impulsivity may be particularly effective for a subset of patients seeking CUD treatment. It has already been demonstrated that high impulsivity may portend worse treatment responses for individuals with SUDs Loree, Lundahl, , suggesting patients with specific disease etiology related to that dysfunction may benefit from individualized interventions. Currently, there are no approved pharmacotherapies for CUD, but many treatments are under investigation. Although data is not conclusive, there is limited evidence that pharmacotherapies such as n-acetylcysteine and citicoline may affect impulsivity Bentzley, Tomko, , Gruber et al. Neuromodulation in healthy controls can modulate impulsivity, but this parameter has not been assessed in cannabis users Yang et al. There is still much to be learned about the mechanisms of neuromodulation and how to optimize and individualize parameters, but there is already a lot of optimism around adopting non-invasive circuit based interventions for the treatment of SUDs Spagnolo and Goldman, In conclusion, there is good scientific evidence supporting an association between impulsivity and cannabis use. Cumulative data support a role for impulsivity as a risk factor for, and consequence of, cannabis misuse. Some of the variability in the literature likely stems from the fact that both impulsivity and CUDs represent dimensional behaviors. Complicating matters further is the fact that trait and state impulsivity are not well correlated, nor is the distinction between impulsivity and sensation seeking always well defined. Similarly, the etiology of CUD is multifactorial. Available data strongly support a predictive relationship between trait impulsivity and cannabis use parameters; still, prospective studies remain few and far between and this model would be bolstered by additional longitudinal data including into transgenerational effects. Conversely, current cannabis use is associated with surprisingly modest effects on behavioral impulsivity despite significant evidence of neurobiological adaptations in relevant brain circuits. Additional research that takes into account important moderating factors including age of use onset interactions, functional compensation, and abstinence-induced recovery will be important for clarifying this relationship. Studies examining the long-term trajectories of CUD will be helpful for designing more evidence-based strategies for treating the individual etiologies of this complex disorder. Targeting the neurobiological component of impulsivity may be just one piece of a multi-pronged approach for treating CUD. Trait impulsivity is consistently implicated as a risk factor for both cannabis use and cannabis-related problems. The neural networks and neurochemical substrates that underlie impulsivity share significant overlap with the neurobiological disruptions produced by chronic cannabis use. Impulsivity may precede cannabis use or manifest as a function of chronic cannabis experience. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. As a library, NLM provides access to scientific literature. Prog Neuropsychopharmacol Biol Psychiatry. Published in final edited form as: Prog Neuropsychopharmacol Biol Psychiatry. Which came first: cannabis use or deficits in impulse control? Find articles by Linda Rinehart. Find articles by Sade Spencer. Issue date Mar 2. PMC Copyright notice. The publisher's version of this article is available at Prog Neuropsychopharmacol Biol Psychiatry. Open in a new tab. Stopping Stop Signal Task SST Eagle and Baunez, , Verbruggen and Logan, Individuals are tasked with cancelling their already initiated action in response to a stop signal delivered at varying delays after a go signal. Subjects must pay attention to up to 5 locations and suppress responding until an unpredictable stimulus signals that it is appropriate to respond. Uncertainty Rat Gambling Task rGT Zeeb and Winstanley, Variation on the IGT where rodent attempts to maximize rewards earned between four options that vary in reward magnitude on gain trials and time-out duration and probability of footshock punishment on loss trials. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Patton et al. Eysenck and Eysenck, The EIS is a 63 item questionnaire that assesses personality traits of impulsivity, venturesomeness, and empathy. Zuckerman et al. The SSS is a 40 item forced choice questionnaire consisting of four inter-related subscales: boredom susceptibility, disinhibition, experience seeking and thrill and adventure seeking. Stephenson et al. Carver and White, Whiteside and Lynam, Cyders, Smith, Eagle and Baunez, , Verbruggen and Logan, Individuals are tasked with cancelling their already initiated action in response to a stop signal delivered at varying delays after a go signal. MacQueen et al. There are many variations that require individuals sustain focus and attention to a repetitive task in order to respond to targets or inhibit response to foils. Robbins, , Voon et al. Originally developed as rat version of CPT. Vanderveldt et al. Individuals are required to choose between either small immediate or larger delayed rewards. Individuals are required to choose between either small certain or larger uncertain rewards. Verdejo-Garcia et al. Individuals are instructed to maximize winnings while choosing repeatedly from four card decks that unpredictably yield wins and losses. Zeeb and Winstanley, Variation on the IGT where rodent attempts to maximize rewards earned between four options that vary in reward magnitude on gain trials and time-out duration and probability of footshock punishment on loss trials.

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