Reward, Control & Decision-Making in Cannabis Use Disorder: Insights from Functional MRI

Exploring the Utility of a Functional Magnetic Resonance Imaging (fMRI) Cannabis Cue-Reactivity Paradigm in Treatment Seeking Adults with Cannabis Use Disorder

Introduction

Functional magnetic resonance imaging (fMRI) studies examining cue-reactivity in cannabis use disorder (CUD) to date have either involved non-treatment seeking participants or been small. We addressed this gap by administering an fMRI cue-reactivity task to CUD participants entering two separate clinical trials.

Methods

Treatment-seeking participants with moderate or severe CUD had behavioral craving measured at baseline via the Marijuana Craving Questionnaire (MCQ-SF). They additionally completed a visual cannabis cue-reactivity paradigm during fMRI following 24-hours of abstinence from cannabis. During fMRI, the Blood Oxygen Level Dependent (BOLD) signal was acquired while participants viewed cannabis-images or matched-neutral-images. BOLD responses were correlated with the MCQ-SF using a General Linear Model.

Results

N=65 participants (32% female; mean age 30.4±9.9SD) averaged 46.3±15.5SD on the MCQ-SF. When contrasting cannabis-images vs. matched-neutral-images, participants showed greater BOLD response in bilateral ventromedial prefrontal, dorsolateral prefrontal, anterior cingulate, and visual cortices, as well as the striatum. Similarly, there was stronger task-based functional-connectivity (tbFC) between the medial prefrontal cortex and both the amygdala and the visual cortex. There were no significant differences in either activation or tbFC between studies or between sexes. Craving negatively correlated with BOLD response in the left ventral striatum (R 2 =-0.25; p =0.01).

Conclusions

We found that, among two separate treatment-seeking CUD groups, cannabis cue-reactivity was evidenced by greater activation and tbFC in regions related to executive function and reward processing, and craving was negatively associated with cue-reactivity in the ventral striatum. Future directions include examining if pharmacological, neuromodulatory, or psychosocial interventions can alter corticostriatal cue-reactivity.

Repetitive transcranial magnetic stimulation as a potential treatment approach for cannabis use disorder

The expanding legalization of cannabis across the United States is associated with increases in cannabis use, and accordingly, an increase in the number and severity of individuals with cannabis use disorder (CUD). The lack of FDA-approved pharmacotherapies and modest efficacy of psychotherapeutic interventions means that many of those who seek treatment for CUD relapse within the first few months. Consequently, there is a pressing need for innovative, evidence-based treatment development for CUD. Preliminary evidence suggests that repetitive transcranial magnetic stimulation (rTMS) may be a novel, non-invasive therapeutic neuromodulation tool for the treatment of a variety of substance use disorders (SUDs), including recently receiving FDA clearance (August 2020) for use as a smoking cessation aid in tobacco cigarette smokers. However, the potential of rTMS for CUD has not yet been reviewed. This paper provides a primer on therapeutic neuromodulation techniques for SUDs, with a particular focus on reviewing the current status of rTMS research in people who use cannabis. Lastly, future directions are proposed for rTMS treatment development in CUD, with suggestions for study design parameters and clinical endpoints based on current gold-standard practices for therapeutic neuromodulation research.

Residual Effects of Cannabis Use on Effort-Based Decision-Making

OBJECTIVE

Acute Δ9-tetrahydrocannabinol (THC) administration in humans (Lawn etal., 2016) and rats (Silveira, Adams, Morena, Hill, & Winstanley, 2016) has been associated with decreased effort allocation that may explain amotivation during acute cannabis intoxication. To date, however, whether residual effects of cannabis use on effort-based decision-making are present and observable in humans have not yet been determined. The goal of this study was to test whether prolonged cannabis use has residual effects on effort-based decision-making in 24-hr abstinent cannabis using adults.

METHOD

We evaluated performance on the Effort Expenditure for Reward Task (EEfRT) in 41 adult cannabis users (mean age = 24.63 years, 21 males) and 45 nonusers (mean age = 23.90 years, 19 males). A mixed 2x3x3 ANOVA with age as a covariate was performed to examine the effect of group, probability of winning, and reward amount on EEfRT performance. EEfRT performance was operationalized as % of trials for which the hard (vs. easy) condition was chosen. Pearson's correlations were conducted to test the relationship between EEfRT performance and measures of cannabis use, anhedonia and motivation.

RESULTS

We found that cannabis users selected hard trials significantly more than nonusers regardless of win probability or reward level. Frequency of cannabis use was positively correlated with amount of % hard trials chosen. There were no significant correlations between % hard trials chosen, self-reported anhedonia, or motivation.

CONCLUSIONS

These results suggest that unlike acute effects, residual effects of cannabis following 24 hrs of abstinence are associated with greater effort allocation during effort-based decision-making.

Decision Making and Impulsivity in Young Adult Cannabis Users

Aims: Chronic cannabis users show impairments on laboratory measures of decision making which reflect risk factors for initiation and continued use of cannabis. However, the differential sensitivity of these tasks to cannabis use has not been established. Moreover, studies to date have often lacked assessment of psychiatric histories and use of other illicit substances, both of which may influence decision making outcomes. The current study aimed to address these limitations by (1) including multiple types of decision making tasks, (2) implementing the Probabilistic Reversal Learning Task, a measure of decision making under uncertainty, for the first time in cannabis users, (3) including young adult cannabis users with no other psychiatric disorders, and (4) conducting urinalysis to exclude users of other illicit drugs.

Methods: Thirty-three current cannabis users without comorbid psychiatric disorders and 35 cannabis non-users completed behavioral measures of decision-making (Iowa Gambling Task), reward discounting (Delay Discounting Task), choice-outcome learning (the Probabilistic Reversal Learning Task) and a questionnaire assessment of impulsivity (Barratt Impulsiveness Scale).

Results: Relative to non-users, cannabis users demonstrated greater preference for immediate vs. delayed rewards on the Delay Discounting Task, made fewer advantageous decisions on the Iowa Gambling Task, and endorsed greater impulsivity on the Barratt Impulsiveness Scale scales. Cannabis users and non-users showed comparable performance on the Probabilistic Reversal Learning Task. Frequency of past-month cannabis use and total years of use did not predict decision making or impulsivity.

Conclusions: Young adult cannabis users demonstrated higher discounting rates and impairments in learning cost-benefit contingencies, while reversal learning was unaffected. Self-reported impulsivity was elevated as well. None of these measures correlated with current or lifetime estimates of cannabis use, arguing against a dose-relationship. Interventions that target improvement in affected components of decision making may be helpful in reducing cannabis use and relapse.

The Influence of Cannabis and Nicotine Co-use on Neuromaturation: A Systematic Review of Adolescent and Young Adult Studies

Accumulating evidence suggests that the use of cannabis and nicotine and tobacco-related products (NTPs) during the adolescent years has harmful effects on the developing brain. Yet, few studies have focused on the developing brain as it relates to the co-administration of cannabis and NTPs, despite the high prevalence rates of co-use in adolescence. This review aims to synthesize the existing literature on neurocognitive, structural neuroimaging, and functional neuroimaging outcomes associated with cannabis and NTP co-use. A systematic search of peer-reviewed articles resulted in a pool of 1107 articles. Inclusion criteria were 1) data-based study; 2) age range of 13 to 35 years or, for preclinical studies, nonadult subjects; 3) cannabis and NTP group jointly considered; and 4) neurocognitive, structural neuroimaging, or functional neuroimaging as an outcome measure. Twelve studies met inclusion criteria. Consistent with the literature, cannabis and nicotine were found to have independent effects on cognition. The available research on the co-use of cannabis and NTPs demonstrates a potential nicotine-related masking effect on cognitive deficits associated with cannabis use, yet there is little research on co-use and associations with neuroimaging indices. In neuroimaging studies, there is preliminary evidence for hippocampal volume differences in co-users and a lack of evidence for co-use differences related to nucleus accumbens activity during reward processing. Notably, no structural neuroimaging studies were found to examine the combined effects of nicotine and cannabis in adolescent-only populations. Further research, including longitudinal studies, is warranted to investigate the influence of cannabis and NTP co-use on maturation.

The short-term and long-term effects of cannabis on cognition: recent advances in the field

The aim of this review is to discuss the most recent evidence for the short-term and long-term effects of cannabis on cognition. The evidence that cannabis intoxication is associated with short-term impairment across several basal cognitive domains, including learning and (episodic) memory, attentional control, and motor inhibition is increasing. However, evidence regarding the effects of long-term heavy cannabis use on cognition remains equivocal. Cannabis research suffers from difficulties in measuring cannabis exposure history, poor control over potential subacute effects, and heterogeneity in cognitive measures and sample composition. Multidisciplinary collaborations and investment in studies that help overcome these difficulties should be prioritized.