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The effects of acute cannabis with and without cannabidiol on neural reward anticipation in adults and adolescents

Open AccessPublished:October 27, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.10.004

      Abstract

      Background

      Adolescents may respond differently to cannabis than adults, yet no functional magnetic resonance imaging (fMRI) study has examined acute cannabis effects in this age-group. We investigated the neural correlates of reward anticipation after acute exposure to cannabis in adolescents and adults.

      Methods

      This was a double-blind, placebo-controlled, randomized, crossover experiment. Forty-seven adolescents (n=24, 12 females, 16-17 years) and adults (n=23, 11 females, 26-29 years), matched on cannabis use frequency (0.5-3 days/week), completed the Monetary Incentive Delay task during fMRI after inhaled cannabis with 0.107 mg/kg THC (‘THC’) (8 mg THC for a 75 kg person) or THC plus 0.320 mg/kg CBD (‘THC+CBD’) (24 mg CBD for a 75 kg person), or placebo cannabis (‘PLA’). We investigated reward anticipation activity with whole-brain analyses and region of interest (ROI) analyses in right and left ventral striatum, right and left anterior cingulate cortex, and right insula.

      Results

      THC reduced anticipation activity compared to placebo in the right (P=.005, d=0.49) and left (P=.003, d=0.50) ventral striatum, and right insula (P=.01, d=0.42). THC+CBD reduced activity compared to placebo in the right ventral striatum (P=.01, d=0.41) and right insula (P=.002, d=0.49). There were no differences between ‘THC’ and ‘THC+CBD’ and no significant Drug*Age-Group effect, supported by Bayesian analyses. There were no significant effects in the whole-brain analyses.

      Conclusions

      In weekly cannabis users, cannabis suppresses the brain’s anticipatory reward response to money and CBD does not moderate this effect. Furthermore, the adolescent reward circuitry is not differentially sensitive to acute effects of cannabis on reward anticipation.

      Keywords

      Introduction

      Cannabis is the third most commonly used controlled substance worldwide, after alcohol and nicotine (

      United Nations Office on Drugs and Crime (2021): World Drug Report 2021.

      ). With the currently changing legal landscape, it is crucial to know how cannabis use affects the brain and cognition of both adolescents and adults.
      The major psychoactive effects of cannabis are ascribed to Δ⁹-tetrahydrocannabinol (THC), which acts as a partial agonist of CB1 cannabinoid receptors (CB1Rs). Acute THC has widespread effects on brain activity and neurocognitive function mediated by CB1Rs on gamma-aminobutyric acid (GABA)ergic and glutamatergic neurons in the cortex, hippocampus, basal ganglia, and cerebellum (
      • Bloomfield M.A.P.
      • Hindocha C.
      • Green S.F.
      • Wall M.B.
      • Lees R.
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      The neuropsychopharmacology of cannabis: A review of human imaging studies.
      ,
      • Ramaekers J.G.
      • Mason N.L.
      • Kloft L.
      • Theunissen E.L.
      The why behind the high: determinants of neurocognition during acute cannabis exposure.
      ,
      • Glass M.
      • Dragunow M.
      • Faull R.L.
      Cannabinoid receptors in the human brain: a detailed anatomical and quantitative autoradiographic study in the fetal, neonatal and adult human brain.
      ,
      • Herkenham M.
      • Lynn A.B.
      • Little M.D.
      • Johnson M.R.
      • Melvin L.S.
      • de Costa B.R.
      • et al.
      Cannabinoid receptor localization in brain.
      ). Cannabidiol (CBD), typically the second most abundant phytocannabinoid, has low affinity for CB1Rs but may attenuate CB1R agonist effects as a negative allosteric modulator. There is some evidence that CBD can attenuate the acute anxiogenic and psychotomimetic effects of THC, though findings are not consistent (
      • Freeman A.M.
      • Petrilli K.
      • Lees R.
      • Hindocha C.
      • Mokrysz C.
      • Curran H.V.
      • et al.
      How does cannabidiol (CBD) influence the acute effects of delta-9-tetrahydrocannabinol (THC) in humans? A systematic review.
      ).
      Cannabis use typically starts in adolescence and is more prevalent among adolescents and young adults than other age-groups (

      European Monitoring Centre for Drugs and Drug Addiction (2020): European Drug Report.

      ). In 2021, the annual prevalence was estimated at 16% among 15-year-olds in England (

      NHS Digital Lifestyles Team (2021): Smoking, Drinking and Drug Use among Young People in England 2021.

      ), down from 19% in 2018 (

      NHS Digital Lifestyles Team (2018): Smoking, Drinking and Drug Use among Young People in England 2018 [NS].

      ), and 17% of 15-16-year-olds in the United States (

      Miech RA, Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE, Patrick ME (2022): Monitoring the Future national survey results on drug use, 1975-2021: Volume I, Secondary school students. Ann Arbor: Institute for Social Research, The University of Michigan.

      ), down from 28% in 2020 (

      Miech RA, Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE, Patrick ME (2021): Monitoring the Future national survey results on drug use, 1975-2020: Volume I, Secondary school students. Ann Arbor: Institute for Social Research, The University of Michigan.

      ). Adolescence is an important period of socio-emotional, cognitive, and neural development, including maturation of the endocannabinoid system (
      • Lubman D.I.
      • Cheetham A.
      • Yücel M.
      Cannabis and adolescent brain development.
      ,
      • Giedd J.N.
      Structural magnetic resonance imaging of the adolescent brain.
      ,
      • Giedd J.N.
      • Blumenthal J.
      • Jeffries N.O.
      • Castellanos F.X.
      • Liu H.
      • Zijdenbos A.
      • et al.
      Brain development during childhood and adolescence: a longitudinal MRI study.
      ,
      • Viveros M.
      • Llorente R.
      • Suarez J.
      • Llorente-Berzal A.
      • Lopez-Gallardo M.
      • Rodriguez de Fonseca F.
      The endocannabinoid system in critical neurodevelopmental periods: sex differences and neuropsychiatric implications.
      ,
      • Bossong M.G.
      • Niesink R.J.
      Adolescent brain maturation, the endogenous cannabinoid system and the neurobiology of cannabis-induced schizophrenia.
      ,
      • Schneider M.
      Puberty as a highly vulnerable developmental period for the consequences of cannabis exposure.
      ). As such, adolescents may respond differently to acute cannabis compared with adults. However, only two previous controlled experiments have compared the acute effects of cannabis in these two age-groups. Mokrysz et al. (
      • Mokrysz C.
      • Freeman T.P.
      • Korkki S.
      • Griffiths K.
      • Curran H.V.
      Are adolescents more vulnerable to the harmful effects of cannabis than adults? A placebo-controlled study in human males.
      ) found that twenty 16-17-year-old male cannabis users (median use 11 days/month) showed weaker subjective, memory, and psychotomimetic effects, along with reduced satiety and impaired inhibition, compared to twenty 24-28-year-old male users (8 days/month) after 0.107 mg/kg inhaled THC. Using an older sample with less cannabis use (1-20 total days/lifetime), Murray et al. (
      • Murray C.H.
      • Huang Z.
      • Lee R.
      • de Wit H.
      Adolescents are more sensitive than adults to acute behavioral and cognitive effects of THC.
      ) found increased sensitivity to the effects of 7.5 and 15 mg oral THC on reaction time, stop-signal response accuracy, and time perception in twelve 18-20-year-olds compared with twelve 30-40-year-olds. There were no age-group differences in the effect of THC on working memory, response inhibition, cardiovascular measures, or subjective effects. They also found that THC decreased the amplitude of the event-related potential (ERP) P300 component during an auditory oddball task in the adolescents but not the adults, during electroencephalography (EEG).
      In another recent investigation from the same study, Murray et al. (
      • Murray C.H.
      • Glazer J.E.
      • Lee R.
      • Nusslock R.
      • de Wit H.
      Δ9-THC reduces reward-related brain activity in healthy adults.
      ) examined the effect of oral THC on ERPs during an EEG-adapted Monetary Incentive Delay (MID) task. Both doses of THC reduced the amplitude of a component related to outcome evaluation (RewP) during reward feedback, and the high dose (15 mg) reduced the P300 component as well as a component related to affective processing (LPP) during hits compared with misses. There were no effects on reward anticipation. Only two functional magnetic resonance imaging (fMRI) studies have assessed neural reward anticipation after acute THC exposure (
      • Skumlien M.
      • Langley C.
      • Lawn W.
      • Voon V.
      • Curran H.V.
      • Roiser J.P.
      • et al.
      The acute and non-acute effects of cannabis on reward processing: A systematic review.
      ). Both administered 6 mg inhaled THC or placebo to young adult male cannabis users (4-52 days/year) and examined reward anticipation with the MID task. While Van Hell et al. (
      • van Hell H.H.
      • Jager G.
      • Bossong M.G.
      • Brouwer A.
      • Jansma J.M.
      • Zuurman L.
      • et al.
      Involvement of the endocannabinoid system in reward processing in the human brain.
      ) found no effect of THC on neural anticipation activity in 11 participants, Jansma et al. (
      • Jansma J.M.
      • van Hell H.H.
      • Vanderschuren L.J.
      • Bossong M.G.
      • Jager G.
      • Kahn R.S.
      • et al.
      THC reduces the anticipatory nucleus accumbens response to reward in subjects with a nicotine addiction.
      ) found that THC decreased activity in the nucleus accumbens (NAc) – a key reward processing region (
      • Oldham S.
      • Murawski C.
      • Fornito A.
      • Youssef G.
      • Yucel M.
      • Lorenzetti V.
      The anticipation and outcome phases of reward and loss processing: A neuroimaging meta-analysis of the monetary incentive delay task.
      ) – in 10 nicotine dependent participants, but not in 11 participants who were not nicotine dependent. Crucially, none of these studies included adolescents below 18 years of age. One previous study has explored adolescent vulnerability to the long-term effects of cannabis on reward processing, and found that adolescents were neither more or less vulnerable to cannabis-related differences in neural reward anticipation or feedback on the MID task (

      Skumlien M, Mokrysz C, Freeman TP, Wall MB, Bloomfield M, Lees R, et al. (2022): Neural responses to reward anticipation and feedback in adult and adolescent cannabis users and controls Neuropsychopharmacology.

      ). However, the differential effects of acute cannabis in adolescents and adults have never been investigated.
      Notably, both previous fMRI studies investigating the effect of acute cannabis on reward anticipation had small samples and consequently low power, and neither included female participants (
      • van Hell H.H.
      • Jager G.
      • Bossong M.G.
      • Brouwer A.
      • Jansma J.M.
      • Zuurman L.
      • et al.
      Involvement of the endocannabinoid system in reward processing in the human brain.
      ,
      • Jansma J.M.
      • van Hell H.H.
      • Vanderschuren L.J.
      • Bossong M.G.
      • Jager G.
      • Kahn R.S.
      • et al.
      THC reduces the anticipatory nucleus accumbens response to reward in subjects with a nicotine addiction.
      ). The effects of acute cannabis on reward processing therefore remain unclear. Additionally, neither of these studies explored the potential modulatory effects of CBD. CBD is available as an over-the-counter health supplement in many countries, yet its effects on brain and cognition are poorly understood. In one previous study, 600 mg of oral CBD did not alter the neural correlates of reward anticipation (
      • Lawn W.
      • Hill J.
      • Hindocha C.
      • Yim J.
      • Yamamori Y.
      • Jones G.
      • et al.
      The acute effects of cannabidiol on the neural correlates of reward anticipation and feedback in healthy volunteers.
      ). However, 10 mg inhaled CBD has been found to partially modulate the impact of THC on effort expenditure for reward (
      • Lawn W.
      • Freeman T.P.
      • Pope R.A.
      • Joye A.
      • Harvey L.
      • Hindocha C.
      • et al.
      Acute and chronic effects of cannabinoids on effort-related decision-making and reward learning: an evaluation of the cannabis 'amotivational' hypotheses.
      ), neural responses to music (
      • Freeman T.P.
      • Pope R.A.
      • Wall M.B.
      • Bisby J.A.
      • Luijten M.
      • Hindocha C.
      • et al.
      Cannabis Dampens the Effects of Music in Brain Regions Sensitive to Reward and Emotion.
      ), and connectivity in the limbic striatum (
      • Wall M.B.
      • Freeman T.P.
      • Hindocha C.
      • Demetriou L.
      • Ertl N.
      • Freeman A.M.
      • et al.
      Individual and combined effects of cannabidiol and Δ(9)-tetrahydrocannabinol on striato-cortical connectivity in the human brain.
      ). Finally, and most crucially, no previous controlled experiments have investigated the effects of acute cannabis in adolescents using fMRI (
      • Bloomfield M.A.P.
      • Hindocha C.
      • Green S.F.
      • Wall M.B.
      • Lees R.
      • Petrilli K.
      • et al.
      The neuropsychopharmacology of cannabis: A review of human imaging studies.
      ,
      • Skumlien M.
      • Langley C.
      • Lawn W.
      • Voon V.
      • Curran H.V.
      • Roiser J.P.
      • et al.
      The acute and non-acute effects of cannabis on reward processing: A systematic review.
      ). Considering that adolescents use cannabis at higher rates than adults (

      European Monitoring Centre for Drugs and Drug Addiction (2020): European Drug Report.

      ,

      NHS Digital Lifestyles Team (2021): Smoking, Drinking and Drug Use among Young People in England 2021.

      ,

      National Institute on Drug Abuse (2020): Monitoring the Future Study: Trends in Prevalence of Various Drugs.

      ), and may show resilience or vulnerability to the acute and non-acute effects of cannabis (
      • Lubman D.I.
      • Cheetham A.
      • Yücel M.
      Cannabis and adolescent brain development.
      ,
      • Schneider M.
      Puberty as a highly vulnerable developmental period for the consequences of cannabis exposure.
      ,
      • Mokrysz C.
      • Freeman T.P.
      • Korkki S.
      • Griffiths K.
      • Curran H.V.
      Are adolescents more vulnerable to the harmful effects of cannabis than adults? A placebo-controlled study in human males.
      ,
      • Murray C.H.
      • Huang Z.
      • Lee R.
      • de Wit H.
      Adolescents are more sensitive than adults to acute behavioral and cognitive effects of THC.
      ), this is a critical gap in the research base.
      In the current study we compared reward anticipation on the MID task during fMRI in 24 adolescent and 23 adult cannabis users (0.5-3 days/week) after acute exposure to THC with CBD (‘THC+CBD’), THC without CBD (‘THC’), and placebo (‘PLA’). We performed whole-brain analyses and region of interest (ROI) analyses in key reward regions. We proposed the following, pre-registered (

      Skumlien M, Hall D, Mokrysz C, Freeman TP, Wall MB, Ofori S, et al. (2021): Neural response to reward anticipation after acute exposure to cannabis with and without cannabidiol in adults and adolescents. https://osf.io/s7ryn/.

      ) hypotheses:
      • 1.
        Both active cannabis conditions will reduce reward anticipation activity in all ROIs compared to placebo.
      • 2.
        CBD will attenuate the effect of THC, such that there will be lower reward anticipation activity in all ROIs during ‘THC’ than ‘THC+CBD’.
      • 3.
        There will be an interaction between drug and age-group, with a greater difference between ‘THC’ and ‘PLA’ for adults compared to adolescents. This hypothesis was based on the previously published results by Mokrysz et al. (
        • Mokrysz C.
        • Freeman T.P.
        • Korkki S.
        • Griffiths K.
        • Curran H.V.
        Are adolescents more vulnerable to the harmful effects of cannabis than adults? A placebo-controlled study in human males.
        ) demonstrating adolescent resilience to some acute effects of THC.
      Methods and Materials
      We present data from the CannTeen-Acute study. Full details on trial procedures and outcomes are found in the study protocol (

      Lawn W, Mokrysz C, Ofori S, Trinci K, Borissova A, Petrilli K, et al. (2021): Do adolescents and adults differ in their acute subjective, behavioural, and neural responses to cannbis, with and without cannabidiol? CANNTEENA. https://osf.io/z638r/.

      ). This study was categorized as not a clinical trial by the UK Medicines & Healthcare products Regulatory Agency, as it is not attempting to research the diagnosis, prevention, or treatment of a disease. Nonetheless, it was registered on clinicaltrials.gov 20/04/2021, ID NCT04851392.

      Participants

      Participants were 24 adults (26-29 years, mean=27.8 years, 12 females) and 24 adolescents (16-17 years, mean=17.2 years, 12 females), recruited from the greater London area using online advertisements and word-of-mouth. This was a per-protocol analysis; thus drop-outs were replaced and recruitment continued until 48 participants had completed all three study sessions (Figure 1). Participants had to use cannabis between 0.5 and 3 days per week, averaged over the past three months, and use frequency was matched between the two age-groups. The range of 0.5-3 days/week was to ensure that participants were likely to tolerate the drug well without unexpected adverse events, whilst minimizing potential tolerance effects. Adult users were excluded if they had used cannabis regularly prior to the age of 18, to ensure they had not used cannabis during this key developmental window that might confer vulnerability to the harmful effects of cannabis. Participants also had to be physically healthy and not receiving treatment for any mental health condition. Inclusion and exclusion criteria are presented in Table S1 in the Supplement. Ethical approval was obtained from the University College London (UCL) ethics committee, project ID 5929/005. The study was conducted in line with the Declaration of Helsinki, and all participants provided written informed consent to participating.
      Figure thumbnail gr1
      Figure 1Trial profile. Other reasons for dropping out included scheduling conflicts, personal reasons, and no reason given. COVID-related restrictions were primarily due to lockdowns in March 2020 (after which the study was paused for seven months) and restrictions from January 2021.

      Design

      We employed a double-blind, placebo-controlled, randomized, crossover design, with three drug conditions: ‘PLA’, ‘THC’, and ‘THC+CBD’. Drug order was balanced for all participants, and within both age-groups and genders. Within these groups participants were randomly allocated to drug order using blocked randomization written by TPF and HVC, with blocks of 12 participants.

      Materials

      Reward anticipation was assessed with the Monetary Incentive Delay (MID) task (
      • Knutson B.
      • Westdorp A.
      • Kaiser E.
      • Hommer D.
      FMRI visualization of brain activity during a monetary incentive delay task.
      ). The current version of the task included win and neutral trials, but no loss trials. Details are presented in Supplemental Methods. Additional measures and covariates are presented in Supplemental Methods.

      Procedure

      The drug administration sessions were completed at the Invicro clinical imaging facility, Hammersmith Hospital, London, between 11th March 2019 and 16th June 2021. Participants completed an instant saliva drugs test (Alere DDSV 703 or ALLTEST DSD-867MET/C) and a Lion Alcometer 500 breathalyser and self-reported abstinence at the start of all sessions, to confirm no recent use of alcohol (≥24 hours cut-off), or cannabis or other illicit drugs (all ≥72 hours cut-off). Additional details are in the Supplemental Methods and the full drug administration session schedule is presented in Figure S1.
      Dried medical cannabis flower was obtained from Bedrocan, The Netherlands, and imported under a UK Home Office License. Three cannabis products were used: “Bedrocan” (20.2% THC, 0.1% CBD), “Bedrolite” (0.4% THC, 8.5% CBD), and “Bedrobinol” (no THC or CBD). Participants inhaled vaporized active cannabis containing 0.107 mg/kg THC during ‘THC’ (e.g., 8 mg THC/1.6 standard THC units (
      • Freeman T.P.
      • Lorenzetti V.
      A standard THC unit for reporting of health research on cannabis and cannabinoids.
      ) for a person weighing 75 kg) or 0.107 mg/kg THC plus 0.320 mg/kg CBD during ‘THC+CBD’ (e.g., 24 mg CBD for a person weighing 75 kg), or placebo cannabis. The cannabis was vaporised using a Volcano Medic Vaporiser (Storz and Bickel, Tuttlingen, Germany) at 210C. Participants inhaled two “balloons” within nine minutes each, using standardised timings. The balloon was covered in an opaque bag so the contents were not visible. This method has been shown to be safe (
      • Mokrysz C.
      • Freeman T.P.
      • Korkki S.
      • Griffiths K.
      • Curran H.V.
      Are adolescents more vulnerable to the harmful effects of cannabis than adults? A placebo-controlled study in human males.
      ,
      • Morgan C.J.A.
      • Freeman T.P.
      • Hindocha C.
      • Schafer G.
      • Gardner C.
      • Curran H.V.
      Individual and combined effects of acute delta-9-tetrahydrocannabinol and cannabidiol on psychotomimetic symptoms and memory function.
      ) and produce similar pulmonary and plasma cannabinoid levels to smoked cannabis, but with lower expired carbon monoxide levels (
      • Abrams D.I.
      • Vizoso H.P.
      • Shade S.B.
      • Jay C.
      • Kelly M.E.
      • Benowitz N.L.
      Vaporization as a smokeless cannabis delivery system: a pilot study.
      ,
      • Hazekamp A.
      • Ruhaak R.
      • Zuurman L.
      • van Gerven J.
      • Verpoorte R.
      Evaluation of a vaporizing device (Volcano) for the pulmonary administration of tetrahydrocannabinol.
      ,
      • Lanz C.
      • Mattsson J.
      • Soydaner U.
      • Brenneisen R.
      Medicinal Cannabis: In Vitro Validation of Vaporizers for the Smoke-Free Inhalation of Cannabis.
      ).
      Unmasked staff blinded the drugs. The placebo cannabis matched the active cannabis in appearance and smell, and all experimental researchers and participants were blinded to treatment allocation. The minimum washout period between drug sessions was 72 hours, the mode was 7 days, and the maximum was 51 days (
      • Heuberger J.A.
      • Guan Z.
      • Oyetayo O.O.
      • Klumpers L.
      • Morrison P.D.
      • Beumer T.L.
      • et al.
      Population pharmacokinetic model of THC integrates oral, intravenous, and pulmonary dosing and characterizes short- and long-term pharmacokinetics.
      ,
      • Taylor L.
      • Gidal B.
      • Blakey G.
      • Tayo B.
      • Morrison G.
      A Phase I, Randomized, Double-Blind, Placebo-Controlled, Single Ascending Dose, Multiple Dose, and Food Effect Trial of the Safety, Tolerability and Pharmacokinetics of Highly Purified Cannabidiol in Healthy Subjects.
      ). Blood samples were taken from participants to quantify plasma levels of THC and CBD (see Supplemental Methods).

      MRI Data Acquisition

      MRI data were collected with 3.0 T Siemens Verio and Trio scanners (the Verio scanner was decommissioned part-way through data collection). Participants always completed all three sessions on the same scanner, and an equal number of participants in each gender and age-group were scanned with each scanner (n=36 on Verio, n=12 on Trio). T2* images were acquired using a multiband gradient echo Echo-Planar Imaging (EPI) sequence (
      • Demetriou L.
      • Kowalczyk O.S.
      • Tyson G.
      • Bello T.
      • Newbould R.D.
      • Wall M.B.
      A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI.
      ). T1-weighted structural images were acquired using a Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence (
      • Jack Jr., C.R.
      • Bernstein M.A.
      • Fox N.C.
      • Thompson P.
      • Alexander G.
      • Harvey D.
      • et al.
      The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.
      ). The acquisition sequences and all other aspects of the set-up (behavioral task, response boxes, etc.) were identical for both scanners. Full MRI acquisition parameters are in Supplemental Methods.

      MRI Data Pre-processing and First-level Analysis

      Pre-processing and first-level fMRI analyses were performed in FSL (
      • Smith S.M.
      • Jenkinson M.
      • Woolrich M.W.
      • Beckmann C.F.
      • Behrens T.E.
      • Johansen-Berg H.
      • et al.
      Advances in functional and structural MR image analysis and implementation as FSL.
      ), with the fMRI Expert Analysis Tool (FEAT) (
      • Woolrich M.W.
      • Behrens T.E.
      • Beckmann C.F.
      • Jenkinson M.
      • Smith S.M.
      Multilevel linear modelling for FMRI group analysis using Bayesian inference.
      ,
      • Woolrich M.W.
      • Ripley B.D.
      • Brady M.
      • Smith S.M.
      Temporal autocorrelation in univariate linear modeling of FMRI data.
      ). Structural high-resolution images were pre-processed using the fsl_anat script provided with FSL. Functional images were realigned with MCFLIRT (motion correction FMRIB linear image registration tool) (
      • Jenkinson M.
      • Bannister P.
      • Brady M.
      • Smith S.
      Improved optimization for the robust and accurate linear registration and motion correction of brain images.
      ) and normalised to MNI-152 (Montreal Neurological Institute) space with FNIRT (FMRIB’s nonlinear registration tool), using a 10 mm warp resolution and 12 degrees of freedom. Spatial smoothing was carried out using a 6 mm full-width at half-maximum Gaussian kernel. Raw functional image series, movement estimates, and registration were carefully inspected for each participant.
      There were two explanatory variables (EVs): Anticipation of win outcomes (Anticipate-win; EV1) and anticipation of neutral outcomes (Anticipate-neutral; EV2). These were implemented in a General Linear Model, by convolving their respective onsets with a gamma function model of the hemodynamic response. Motion parameters (standard + temporal derivatives + squared + quadratic) and temporal derivatives were included as regressors-of-no-interest. The FILM pre-whitening procedure was used to account for temporal autocorrelation, and a high-pass filter (100 s cut-off) was used to remove low-frequency noise. Reward anticipation was examined with the Anticipate-win > Anticipate-neutral contrast [1 -1 0 0 0 0].

      Statistical Analyses

      Analyses and hypotheses were pre-registered to the Open Science Framework (

      Skumlien M, Hall D, Mokrysz C, Freeman TP, Wall MB, Ofori S, et al. (2021): Neural response to reward anticipation after acute exposure to cannabis with and without cannabidiol in adults and adolescents. https://osf.io/s7ryn/.

      ). Power calculations are presented in Supplemental Methods. Behavioral and ROI analyses were performed with R 3.6.2 (

      R Core Team (2019): R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

      ), using the rstatix and BayesianFactor packages (

      Kassambara A (2021): rstatix: Pipe-Friendly Framework for Basic Statistical Tests. 0.7.0 ed.

      ,

      Morey RD, Rouder JN (2018): BayesFactor: Computation of Bayes factors for common designs. 0.9.12-14.2 ed.

      ). One adult female did not complete the MID task during ‘THC+CBD’ and was excluded from analyses, leaving 23 adults.
      The main behavioral outcome on the MID task was mean reaction times (RTs) for win and neutral trials. This was analyzed in a linear mixed model with Trial-Type (win, neutral) and Drug (‘PLA’, ‘THC’, ‘THC+CBD’) as within-subjects factors, Age-Group (adult, adolescent) as the between-subjects factor, and mean-centered covariates weekly cigarette/roll-up tobacco use (yes/no), depression, and scanner (Supplemental Methods). These covariates were chosen a priori due to their putative interaction with cannabis use and reward processing (
      • Balodis I.M.
      • Potenza M.N.
      Anticipatory reward processing in addicted populations: a focus on the monetary incentive delay task.
      ,
      • Fergusson D.M.
      • Boden J.M.
      • Horwood L.J.
      Cannabis use and other illicit drug use: testing the cannabis gateway hypothesis.
      ,
      • Millar S.R.
      • Mongan D.
      • O'Dwyer C.
      • Long J.
      • Smyth B.P.
      • Perry I.J.
      • et al.
      Correlates of patterns of cannabis use, abuse and dependence: evidence from two national surveys in Ireland.
      ,
      • Patton G.C.
      • Coffey C.
      • Carlin J.B.
      • Degenhardt L.
      • Lynskey M.
      • Hall W.
      Cannabis use and mental health in young people: cohort study.
      ). In fact, tobacco/nicotine use has been shown to influence the association between cannabis use and neural reward anticipation both acutely (
      • Jansma J.M.
      • van Hell H.H.
      • Vanderschuren L.J.
      • Bossong M.G.
      • Jager G.
      • Kahn R.S.
      • et al.
      THC reduces the anticipatory nucleus accumbens response to reward in subjects with a nicotine addiction.
      ) and non-acutely (

      Skumlien M, Mokrysz C, Freeman TP, Wall MB, Bloomfield M, Lees R, et al. (2022): Neural responses to reward anticipation and feedback in adult and adolescent cannabis users and controls Neuropsychopharmacology.

      ). An unstructured covariance structure was used. As hit rates (% hit targets) were calibrated to 50% these were not analyzed.
      Group-level fMRI analyses were performed with FMRIBs local analysis of mixed effects (FLAME). Cluster-level statistics were used, with a cluster-defining threshold of Z=3.1 (p=0.001) and a multiple test corrected cluster-extent threshold of a=0.05. Mean blood-oxygen-level-dependent responses during reward anticipation were examined in separate whole-brain one-sample t-tests for ‘PLA’, ‘THC’, and ‘THC+CBD’. The main effect of Drug and the Drug*Age-Group interaction were investigated with a 3X2 mixed measures analyses of variance (ANOVA). The design setup in FSL does not allow for a between-subjects main effect to be examined simultaneously, as this causes rank deficiency of the design matrix. Therefore, we performed subject-level fixed effects analyses averaging the three drug conditions for each participant, and then passed these results up to a separate group-level FLAME independent-samples t-test analysis with Age-Group as a factor.
      ROIs were the right and left ventral striatum, right and left anterior cingulate cortex (ACC), and right insula. These were selected based on a large meta-analysis of the MID task (
      • Oldham S.
      • Murawski C.
      • Fornito A.
      • Youssef G.
      • Yucel M.
      • Lorenzetti V.
      The anticipation and outcome phases of reward and loss processing: A neuroimaging meta-analysis of the monetary incentive delay task.
      ) and a previous study of MID reward processing in adult and adolescent cannabis users and controls (

      Skumlien M, Mokrysz C, Freeman TP, Wall MB, Bloomfield M, Lees R, et al. (2022): Neural responses to reward anticipation and feedback in adult and adolescent cannabis users and controls Neuropsychopharmacology.

      ). Six mm radius spheres were constructed around coordinates with peak Z-values or activation likelihood estimates (Table S2), and unstandardized b-values were extracted from the lower-level contrasts. Separate 2X3 mixed measures analyses of covariance (ANCOVAs) were performed for each ROI with Drug, Age-Group, and mean-centered covariates cigarette/roll-up tobacco use, depression, and scanner. All two-way Drug interactions were included. Null Drug main effects were followed up with paired-samples Bayesian tests of ‘PLA’ vs. ‘THC’ and ‘THC’ vs. ‘THC+CBD’. Null Drug*Age-Group interactions were followed up with independent-samples Bayesian tests comparing adults and adolescents on difference scores for ‘THC’ vs. ‘PLA’. A scaled-information prior of r=.707 was used, and Jeffreys-Zellner-Siow Bayes factors (BF01) above 3 were interpreted as meaningful (
      • Wagenmakers E.J.
      • Wetzels R.
      • Borsboom D.
      • van der Maas H.L.
      Why psychologists must change the way they analyze their data: the case of psi: comment on Bem (2011).
      ). Finally, correlations between ‘THC’ minus ‘PLA’ difference scores for reward anticipation responses in every ROI and days/week of cannabis use, lifetime days of use, and dependence were computed.

      Results

      Participant characteristics are displayed in Table 1. Plasma concentrations of THC and CBD are displayed in Figure 2. Full trial results on primary outcome measures, blinding, and adverse events will be reported elsewhere.
      Table 1Participant characteristics
      Adolescents (n = 24)Adults (n = 23)Group differencesTest statistic
      Demographics and covariates
      Sex
      Female12 (50%)11 (48%)
      Male12 (50%)12 (52%)
      Age in years17.17 (0.43), 16.50-17.9227.78 (1.06), 26.33-29.58Adolescents < Adultst28.67=44.51, P<.001
      Ethnicity
      White17 (71%)18 (78%)
      Mixed4 (17%)1 (4%)
      Asian1 (4%)2 (9%)
      Black02 (9%)
      Other1 (4%)0
      Prefer not to say1 (4%)0
      Maternal education
      Below undergraduate degree8 (33%)8 (35%)
      Undergraduate degree or above16 (67%)15 (65%)
      BDI10.38 (8.55), 0-285.43 (6.56), 0-22Adolescents > Adultst45=2.22, P=.03
      SUPPS-P48.17 (7.51), 34-6142.57 (9.02), 30-64Adolescents > Adultst45=2.32, P=.03
      Alcohol use, days/week0.56 (0.62), 0-2.502.10 (1.72), 0-6Adolescents < Adultst27.39=4.04, P<.001
      Alcohol units/week5.39 (8.24), 0-35.5012.58 (9.89), 0-31.99Adolescents < Adultst45=2.71, P=.009
      Tobacco use, days/week2.33 (2.05), 0-71.20 (1.56), 0-6.25Adolescents > Adultst45=2.13, P=.04
      Hours since last nicotine use
      Includes participants who reported having used nicotine in the past week.
      ‘PLA’36.73 (41.04), 1-146, n=1680.75 (34.71), 32-154, n=10Adolescents < Adultst24=2.82, P=.01
      ‘THC’24.90 (30.75), 0.1-93, n=1552.78 (36.48), 12-130, n=9t22=2.01, P=.06
      ‘THC+CBD’37.46 (45.81), 0.5-169, n=1752.54 (37.94), 1.5-141, n=12t27=0.94, P=.36
      Other illicit drug use, monthly use
      Yes2 (8%)2 (9%)
      No22 (92%)21 (91%)
      Cannabis use
      Days/week of use1.41 (0.77), 0.25-3.501.50 (0.75), 0.50-2.75t45=0.42, P=.67
      Grams used on a day of use0.81 (0.56), 0.25-2.500.52 (0.52), 0.10-2.00t45=1.84, P=.07
      Days since last use
      ‘PLA’6.04 (8.06), 2.90-43.005.13 (3.47), 3.00-19.00t45=0.50, P=.62
      ‘THC’8.01 (9.72), 3.00-51.007.41 (4.31), 3.33-18.00t45=0.27, P=.79
      ‘THC+CBD’5.46 (2.48), 3.10-12.006.91 (5.34), 2.88-26.00t45=1.21, P=.24
      Age of first ever use14.55 (1.03), 11.92-16.0818.30 (2.60), 14.00-24.42Adolescents < Adultst28.51=6.47, P<.001
      Lifetime days of use153.67 (89.97), 11-418560.35 (640.27), 136-3172Adolescents < Adultst22.83=3.02, P=.006
      CUDIT-R10.17 (3.14), 5-167.35 (3.31), 3-15Adolescents > Adultst45=2.99, P=.004
      Abbreviations: BDI, Beck Depression Inventory; CBD, cannabidiol; CUD, Cannabis Use Disorder; CUDIT-R, Cannabis Use Disorder Identification Test – Revised; DSM, Diagnostic and Statistical Manual of Mental Disorders; PLA, placebo; SUPPS-P, Short UPPS-P Impulsive Behavior Scale; THC, Δ9-tetrahydrocannabinol.
      Note. For continuous data mean (SD) and range are shown. For categorical data, n (%) is shown. Age-group differences were investigated with independent samples t-tests. Two participants had used cannabis <72 hours prior to a drug administration session, in breach of abstinence rules. However, as they were unable to reschedule their sessions, lead experimenters made the decision to continue with the session considering the abstinence requirement was not severely violated (<3 hours).
      a Includes participants who reported having used nicotine in the past week.
      Figure thumbnail gr2
      Figure 2Plasma concentrations of THC and CBD by Drug and Age-Group. A, Δ⁹-tetrahydrocannabinol (THC) plasma levels (ng.ml-1). B, cannabidiol (CBD) plasma levels (ng.ml-1). The blood sample was taken 30 minutes after the start of drug administration, immediately before scanning. Bars represent means with dots indicating individual participant values, and error bars represent standard errors. Differences in THC and CBD levels for placebo, ‘THC’, and ‘THC+CBD’ conditions were investigated with paired samples t-tests. Differences between adolescents and adults within each Drug condition were investigated with independent-samples t-tests. Data were missing for four adolescents and one adult for the placebo condition, four adolescents for the ‘THC’ condition, and two adolescents and one adult for the ‘THC+CBD’ condition.
      Descriptive statistics and full results of the behavioral analyses are presented in tables S3 and S4. There was a significant effect of Trial-Type, with higher lower RTs (mean difference 6 ms, P<.001) for win trials than neutral trials. There were no significant effects of Drug and Age-Group.
      Brain regions were labelled using the Harvard-Oxford cortical and subcortical structural atlases (
      • Desikan R.S.
      • Ségonne F.
      • Fischl B.
      • Quinn B.T.
      • Dickerson B.C.
      • Blacker D.
      • et al.
      An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
      ,
      • Frazier J.A.
      • Chiu S.
      • Breeze J.L.
      • Makris N.
      • Lange N.
      • Kennedy D.N.
      • et al.
      Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder.
      ,
      • Makris N.
      • Goldstein J.M.
      • Kennedy D.
      • Hodge S.M.
      • Caviness V.S.
      • Faraone S.V.
      • et al.
      Decreased volume of left and total anterior insular lobule in schizophrenia.
      ). The whole-brain analysis revealed reward anticipation activity in a large network comprising the striatum, insula, thalamus, anterior cingulate and paracingulate cortex, and prefrontal cortex (Figure S2 and Table S5). There were no significant effects of Drug, Age-Group, or Drug*Age-Group. Exploratory paired-samples t-tests were performed with Z=2.3 (p<0.05, cluster-corrected) to compare the drug conditions. These showed lower activity during ‘THC’ and ‘THC+CBD’ compared to ‘PLA’ in a network comprising the dorsal and ventral striatum, paracingulate cortex, insula, frontal pole, and orbitofrontal cortex (Figure 3 and Table S6). There were no significant differences between ‘THC’ and ‘THC+CBD’.
      Figure thumbnail gr3
      Figure 3Differences in reward anticipation between drug conditions. Significant differences in reward anticipation between the placebo (‘PLA’), Δ⁹-tetrahydrocannabinol (‘THC’), and Δ⁹-tetrahydrocannabinol + cannabidiol (‘THC+CBD’) conditions in whole-brain paired-samples t-tests across Age-Group (n=47). Cluster-defining threshold was 2.3. Images are presented in radiological orientation, such that left on the image is the right hemisphere.
      Results of the ROI analyses are displayed in Figure 4 and Table S7. Unadjusted models are presented in Table S8. There was a significant main effect of Drug for right ventral striatum (P=.009, ηp2=.11), left ventral striatum (P=.02, ηp2=.09), and right insula (P=.003, ηp2=.13). Post hoc paired samples t-tests showed significantly greater activity during ‘PLA’ than ‘THC’ for right ventral striatum (P=.005, d=0.49), left ventral striatum (P=.003, d=0.50), and right insula (P=.01, d=0.42). There was significantly greater activity during ‘PLA’ than ‘THC+CBD’ for right ventral striatum (P=.01, d=0.41) and right insula (P=.002, d=0.49), but not left ventral striatum (P=.17, d=0.24). There were no significant differences between ‘THC’ and ‘THC+CBD’, and no significant Drug effects in the ACC. These findings were supported by Bayesian analyses (Table S9).
      Figure thumbnail gr4
      Figure 4Region of interest reward anticipation activity by Drug and Age-Group Abbreviations: ACC, anterior cingulate cortex; R, right; L, left. Bars represent mean beta-values with dots indicating individual participant values, and error bars represent standard errors.
      There was a significant main effect of Age-Group for all ROIs except left ACC, with adolescents activating more than adults (Figure 4 and Table S7). However, there were no significant Drug*Age-Group effects. This was supported by Bayesian analyses for ‘THC’ minus ‘PLA’ in all ROIs (Table S9). None of the correlations were significant (Table S10).

      Discussion

      This is the first fMRI study to investigate the effects of acute cannabis in adolescents, and consequently also the first to compare adults and adolescents after acute cannabis administration. We found that active cannabis, in comparison to placebo, attenuated reward anticipation brain activity in key reward-related regions, including the ventral striatum, in people who used cannabis 0.5-3 days/week. Age-group did not moderate the effect of cannabis on the neural correlates of reward anticipation. Finally, CBD did not modulate the effect of THC.

      THC Reduces Activity in the Brain’s Reward System

      The current results are partially consistent with those of Jansma et al. (
      • Jansma J.M.
      • van Hell H.H.
      • Vanderschuren L.J.
      • Bossong M.G.
      • Jager G.
      • Kahn R.S.
      • et al.
      THC reduces the anticipatory nucleus accumbens response to reward in subjects with a nicotine addiction.
      ) who found that THC attenuated reward anticipation activity in the NAc in nicotine dependent participants. This effect was not found in non-nicotine dependent participants or by van Hell et al. (
      • van Hell H.H.
      • Jager G.
      • Bossong M.G.
      • Brouwer A.
      • Jansma J.M.
      • Zuurman L.
      • et al.
      Involvement of the endocannabinoid system in reward processing in the human brain.
      ), although both these studies had markedly smaller samples relative to the current study. THC has also been found to acutely attenuate ERPs during the feedback phase of the MID task (
      • Murray C.H.
      • Glazer J.E.
      • Lee R.
      • Nusslock R.
      • de Wit H.
      Δ9-THC reduces reward-related brain activity in healthy adults.
      ), ventral striatal responses to music listening (
      • Freeman T.P.
      • Pope R.A.
      • Wall M.B.
      • Bisby J.A.
      • Luijten M.
      • Hindocha C.
      • et al.
      Cannabis Dampens the Effects of Music in Brain Regions Sensitive to Reward and Emotion.
      ), and functional connectivity in the limbic striatum (
      • Wall M.B.
      • Freeman T.P.
      • Hindocha C.
      • Demetriou L.
      • Ertl N.
      • Freeman A.M.
      • et al.
      Individual and combined effects of cannabidiol and Δ(9)-tetrahydrocannabinol on striato-cortical connectivity in the human brain.
      ), relative to placebo. Thus, our results along with some previous evidence suggest that acute THC reduces activity in the brain’s reward system.
      Notably, our participants used cannabis roughly twice as frequently as those of van Hell et al. (
      • van Hell H.H.
      • Jager G.
      • Bossong M.G.
      • Brouwer A.
      • Jansma J.M.
      • Zuurman L.
      • et al.
      Involvement of the endocannabinoid system in reward processing in the human brain.
      ) and Jansma et al. (
      • Jansma J.M.
      • van Hell H.H.
      • Vanderschuren L.J.
      • Bossong M.G.
      • Jager G.
      • Kahn R.S.
      • et al.
      THC reduces the anticipatory nucleus accumbens response to reward in subjects with a nicotine addiction.
      ) (roughly 1.5-2 days/month), and much more frequently than those of Murray et al. (
      • Murray C.H.
      • Glazer J.E.
      • Lee R.
      • Nusslock R.
      • de Wit H.
      Δ9-THC reduces reward-related brain activity in healthy adults.
      ) (1-20 days/life). Level of cannabis use is important given that repeated exposure can increase the tolerance to acute effects (
      • Colizzi M.
      • Bhattacharyya S.
      Cannabis use and the development of tolerance: a systematic review of human evidence.
      ,
      • Mason N.L.
      • Theunissen E.L.
      • Hutten N.
      • Tse D.H.Y.
      • Toennes S.W.
      • Jansen J.F.A.
      • et al.
      Reduced responsiveness of the reward system is associated with tolerance to cannabis impairment in chronic users.
      ). However, we found no correlation between days per week of use and ‘THC’ minus ‘PLA’ difference scores in any ROI (Table S10). Moreover, as we did find an acute effect of cannabis in this study, 0.5-3 days per week of cannabis use cannot fully attenuate acute effects of THC on the reward system through a putative tolerance mechanism.
      Lastly, it is not known whether the present acute effects persist into abstinence. In one longitudinal investigation, Martz et al. found that cannabis use predicted attenuated reward anticipation activity in the NAc in 108 young adults after ≥48 hours of abstinence (
      • Martz M.E.
      • Trucco E.M.
      • Cope L.M.
      • Hardee J.E.
      • Jester J.M.
      • Zucker R.A.
      • et al.
      Association of Marijuana Use With Blunted Nucleus Accumbens Response to Reward Anticipation.
      ), indicating some convergence between acute and long-term effects. This is also similar to what has been found in other substance use and gambling disorders (
      • Balodis I.M.
      • Potenza M.N.
      Anticipatory reward processing in addicted populations: a focus on the monetary incentive delay task.
      ,
      • Luijten M.
      • Schellekens A.F.
      • Kühn S.
      • Machielse M.W.
      • Sescousse G.
      Disruption of Reward Processing in Addiction: An Image-Based Meta-analysis of Functional Magnetic Resonance Imaging Studies.
      ). However, Skumlien et al. did not find an association between cannabis use and reward anticipation in a recent cross-sectional study of 125 adults and adolescents after ≥12 hours of abstinence (

      Skumlien M, Mokrysz C, Freeman TP, Wall MB, Bloomfield M, Lees R, et al. (2022): Neural responses to reward anticipation and feedback in adult and adolescent cannabis users and controls Neuropsychopharmacology.

      ). More longitudinal research is needed to unpack long-term, chronic associations while users are not intoxicated.

      CBD Does Not Modulate the Effect of THC

      There were no differences between ‘THC’ and ‘THC+CBD’ on any outcome, which was supported by Bayesian analyses, confirming that CBD did not modulate the effect of THC. Thus, although high dose pre-administration of CBD has been previously shown to attenuate anxiogenic and psychotomimetic effects of THC (
      • Freeman A.M.
      • Petrilli K.
      • Lees R.
      • Hindocha C.
      • Mokrysz C.
      • Curran H.V.
      • et al.
      How does cannabidiol (CBD) influence the acute effects of delta-9-tetrahydrocannabinol (THC) in humans? A systematic review.
      ,
      • Englund A.
      • Morrison P.D.
      • Nottage J.
      • Hague D.
      • Kane F.
      • Bonaccorso S.
      • et al.
      Cannabidiol inhibits THC-elicited paranoid symptoms and hippocampal-dependent memory impairment.
      ,
      • Bhattacharyya S.
      • Morrison P.D.
      • Fusar-Poli P.
      • Martin-Santos R.
      • Borgwardt S.
      • Winton-Brown T.
      • et al.
      Opposite effects of delta-9-tetrahydrocannabinol and cannabidiol on human brain function and psychopathology.
      ), the present study did not find an effect neural reward anticipation. Of note, both THC and CBD were successfully absorbed and observed in plasma. Moreover, cannabinoid levels did not differ between adolescents and adults in the ‘THC’ condition, which contrasts with some previous findings from preclinical studies in rodents (
      • Torrens A.
      • Roy P.
      • Lin L.
      • Vu C.
      • Grimes D.
      • Inshishian V.C.
      • et al.
      Comparative Pharmacokinetics of Δ(9)-Tetrahydrocannabinol in Adolescent and Adult Male and Female Rats.
      ,
      • Torrens A.
      • Vozella V.
      • Huff H.
      • McNeil B.
      • Ahmed F.
      • Ghidini A.
      • et al.
      Comparative Pharmacokinetics of Δ(9)-Tetrahydrocannabinol in Adolescent and Adult Male Mice.
      ). However, adults did have slightly higher CBD levels in the ‘THC+CBD’ condition. Additionally, in line with some (
      • Arkell T.R.
      • Lintzeris N.
      • Kevin R.C.
      • Ramaekers J.G.
      • Vandrey R.
      • Irwin C.
      • et al.
      Cannabidiol (CBD) content in vaporized cannabis does not prevent tetrahydrocannabinol (THC)-induced impairment of driving and cognition.
      ), but not all existing research (
      • Withey S.L.
      • Bergman J.
      • Huestis M.A.
      • George S.R.
      • Madras B.K.
      THC and CBD blood and brain concentrations following daily administration to adolescent primates.
      ), THC concentrations were higher in the ‘THC+CBD’ condition than in the ‘THC’ condition (Figure 2). This deserves further exploration in future studies.

      Adolescents Are Not Differentially Sensitive to the Acute Effects of THC on Reward Anticipation

      Crucially, this is the first controlled experiment to examine the acute effects of cannabis in adolescents using fMRI. Adolescents had higher reward anticipation activity across Drug conditions in all but one ROI (left ACC), which converges with some previous studies showing striatal hyperactivity in adolescents during reward processing (
      • Silverman M.H.
      • Jedd K.
      • Luciana M.
      Neural networks involved in adolescent reward processing: An activation likelihood estimation meta-analysis of functional neuroimaging studies.
      ,
      • Richards J.M.
      • Plate R.C.
      • Ernst M.
      A systematic review of fMRI reward paradigms used in studies of adolescents vs. adults: the impact of task design and implications for understanding neurodevelopment.
      ,
      • Galvan A.
      • Hare T.A.
      • Parra C.E.
      • Penn J.
      • Voss H.
      • Glover G.
      • et al.
      Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents.
      ). However, adolescents and adults did not differ in their neural responses to active cannabis in any ROI, which was confirmed by Bayesian analyses. Thus, our results suggest that the reward system is not more or less sensitive to disruption by a moderate dose of acute cannabis at age 16-17 years than at age 26-29 years. Previous research in the CannTeen study has also not revealed different associations between chronic cannabis use and reward processing in adolescents and adults (

      Skumlien M, Mokrysz C, Freeman TP, Wall MB, Bloomfield M, Lees R, et al. (2022): Neural responses to reward anticipation and feedback in adult and adolescent cannabis users and controls Neuropsychopharmacology.

      ,
      • Skumlien M.
      • Mokrysz C.
      • Freeman T.P.
      • Valton V.
      • Wall M.B.
      • Bloomfield M.
      • et al.
      Anhedonia, apathy, pleasure, and effort-based decision-making in adult and adolescent cannabis users and controls.
      ). Nonetheless, other cognitive or psychological processes could still be differently affected by acute cannabis in these two age-groups, and should be explored in future studies.
      Of note, the age-group comparison is somewhat limited by the significantly higher number of lifetime days of cannabis use in the adults compared with the adolescents (Table 1). Prolonged cannabis use may lead to increased tolerance to the acute effects of THC (
      • Colizzi M.
      • Bhattacharyya S.
      Cannabis use and the development of tolerance: a systematic review of human evidence.
      ,
      • Mason N.L.
      • Theunissen E.L.
      • Hutten N.
      • Tse D.H.Y.
      • Toennes S.W.
      • Jansen J.F.A.
      • et al.
      Reduced responsiveness of the reward system is associated with tolerance to cannabis impairment in chronic users.
      ), which could have cancelled out the hypothesized greater vulnerability to these effects in the adult age-group. This limitation is difficult to avoid as adults typically have a longer history of cannabis use than adolescents, although we did restrict the adult group to people who had not used cannabis regularly prior to age 18. Relatedly, adolescents had significantly higher scores on the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) than the adults, suggesting greater levels of cannabis use problems in this group. Adolescent cannabis users are consistently found to have greater risk of developing dependence than adult users, even with similar levels of use (
      • Chen C.-Y.
      • O’Brien M.S.
      • Anthony J.C.
      Who becomes cannabis dependent soon after onset of use? Epidemiological evidence from the United States: 2000–2001.
      ,
      • Chen C.-Y.
      • Storr C.L.
      • Anthony J.C.
      Early-onset drug use and risk for drug dependence problems.
      ,
      • Ehlers C.L.
      • Gizer I.R.
      • Vieten C.
      • Gilder D.A.
      • Stouffer G.M.
      • Lau P.
      • et al.
      Cannabis dependence in the San Francisco Family Study: age of onset of use, DSM-IV symptoms, withdrawal, and heritability.
      ,
      • Lawn W.
      • Mokrysz C.
      • Lees R.
      • Trinci K.
      • Petrilli K.
      • Skumlien M.
      • et al.
      The CannTeen Study: Cannabis use disorder, depression, anxiety, and psychotic-like symptoms in adolescent and adult cannabis users and age-matched controls.
      ,
      • Leung J.
      • Chan G.C.K.
      • Hides L.
      • Hall W.D.
      What is the prevalence and risk of cannabis use disorders among people who use cannabis? a systematic review and meta-analysis.
      ,
      • Lopez-Quintero C.
      • Pérez de los Cobos J.
      • Hasin D.S.
      • Okuda M.
      • Wang S.
      • Grant B.F.
      • et al.
      Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).
      ). Crucially, the two age-groups were matched on days per week of cannabis use. Moreover, we did not find a significant correlation between lifetime days of use or CUDIT-R scores and ‘THC’ minus ‘PLA’ difference scores in any ROI (Table S10), suggesting that neither were associated with the impact of THC on reward function.

      Limitations

      One limitation of this study concerned the restricted age range of participants. It is possible that younger adolescents with less developed reward systems respond differently to THC than adults. However, ethical considerations prevent controlled experiments of acute drug effects in this age-group. Future work should also further examine the effect of acute cannabis on the consummatory phase of reward processing, which could include the feedback phase of the MID task (
      • Murray C.H.
      • Glazer J.E.
      • Lee R.
      • Nusslock R.
      • de Wit H.
      Δ9-THC reduces reward-related brain activity in healthy adults.
      ,
      • van Hell H.H.
      • Jager G.
      • Bossong M.G.
      • Brouwer A.
      • Jansma J.M.
      • Zuurman L.
      • et al.
      Involvement of the endocannabinoid system in reward processing in the human brain.
      ,
      • Jansma J.M.
      • van Hell H.H.
      • Vanderschuren L.J.
      • Bossong M.G.
      • Jager G.
      • Kahn R.S.
      • et al.
      THC reduces the anticipatory nucleus accumbens response to reward in subjects with a nicotine addiction.
      ). Finally, although our sample size greatly exceeds that of previous studies with similar aims, this study was not powered to detect small effects.

      Conclusions

      In this placebo-controlled, randomized, crossover trial, we found blunted reward anticipation activity in key reward regions after acute active cannabis compared to placebo. Adolescents and adults did not show different neural responses to acute cannabis. There was also no evidence of a modulatory effect of CBD. These findings demonstrate that cannabis suppresses the brain’s anticipatory reward response to money, CBD does not modulate this effect, and adolescents are neither more sensitive nor more resilient to the acute effects of cannabis on neural reward anticipation.

      Acknowledgements

      This study was funded by a grant from the Medical Research Council, MR/P012728/1, to HVC and TPF. MS is funded by an Aker Scholarship from the Aker Foundation. AB is funded for this study by a fellowship from the National Institute for Health Research (NIHR) UCLH Biomedical Research Centre and currently supported by a NIHR Academic Clinical Felloship. BJS receives funding from the Lundbeck Foundation and the Leverhulme Trust, CL is funded by a Wellcome Trust Collaborative Award 200181/Z//15/Z, and their research is conducted within the NIHR Cambridge Biomedical Research Centre (Mental Health Theme and Neurodegeneration Theme) and the NIHR MedTech in vitro diagnostics Co-operative. HVC is supported by grants from the UK MRC (MR/P012728/1), UK Department of Health, and by the NIHR UCLH Biomedical Research Centre. The funding sources had no role in the design and conduct of the study collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
      Michael Bloomfield PhD was a trial physician. We thank Denisa Clisu MSci, Zarah Rahim Haniff MSc, Elsa Clinton MSc, Tiernan Coughlan MSc, and Teodora Perju MSci for assisting with data collection during their time as undergraduate or graduate students on the study. We are also thankful to the clinical staff at Invicro, and to all participants of the CannTeen study.
      Disclosures
      B.J.S. consults for Cambridge Cognition. HVC has consulted for Janssen Research and Development. MBW’s primary employer is Invicro LLC, a contract research organization which performs commercial research for the pharmaceutical and biotechnology industries. Remaining authors report no biomedical financial interests or potential conflicts of interest.
      Data Sharing Statement
      The data are not available.

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