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. Author manuscript; available in PMC: 2022 Oct 1.
Published in final edited form as: Drug Alcohol Depend. 2021 Jul 29;227:108920. doi: 10.1016/j.drugalcdep.2021.108920

Assessment of Transient Dopamine Responses to Smoked Cannabis

Katina C Calakos a,b, Heather Liu c, Yihuan Lu d,e, Jon Mikael Anderson a, David Matuskey a,d,e,f, Nabeel Nabulsi d,e, Yunpeng Ye d,e, Patrick D Skosnik a,g, Deepak Cyril D’Souza a,g, Evan D Morris a,b,c,d,e, Kelly P Cosgrove a,b,d,h, Ansel T Hillmer a,c,d,e,*
PMCID: PMC8464527  NIHMSID: NIHMS1728832  PMID: 34399137

Abstract

Background:

Dopaminergic mechanisms that may underlie cannabis’ reinforcing effects are not well elucidated in humans. This positron emission tomography (PET) imaging study used the dopamine D2/3 receptor antagonist [11C]raclopride and kinetic modelling testing for transient changes in radiotracer uptake to assess the striatal dopamine response to smoked cannabis in a preliminary sample.

Methods:

PET emission data were acquired from regular cannabis users (n=14; 7M/7F; 19-32 years old) over 90 minutes immediately after [11C]raclopride administration (584±95 MBq) as bolus followed by constant infusion (Kbol=105 min). Participants smoked a cannabis cigarette, using a paced puff protocol, 35 minutes after scan start. Plasma concentrations of Δ9-THC and metabolites and ratings of subjective “high” were collected during imaging. Striatal dopamine responses were assessed voxelwise with a kinetic model testing for transient reductions in [11C]raclopride binding, linearized-parametric neurotransmitter PET (lp-ntPET) (cerebellum as a reference region).

Results:

Cannabis smoking increased plasma Δ9-THC levels (peak: 0-10 minutes) and subjective high (peak: 0-30 minutes). Significant clusters (>16 voxels) modeled by transient reductions in [11C]raclopride binding were identified for all 12 analyzed scans. In total, 26 clusters of significant responses to cannabis were detected, of which 16 were located in the ventral striatum, including at least one ventral striatum cluster in 11 of the 12 analyzed scans.

Conclusions:

These preliminary data support the sensitivity of [11C]raclopride PET with analysis of transient changes in radiotracer uptake to detect cannabis smoking-induced dopamine responses. This approach shows future promise to further elucidate roles of mesolimbic dopaminergic signaling in chronic cannabis use. ClinicalTrials.gov Identifier: NCT02817698.

Keywords: PET, [11C]raclopride, cannabis, dopamine

1. Introduction

Cannabis is one of the most widely used psychoactive substances. In 2017, 5.2% of adults aged 18-25 years in the U.S. met criteria for cannabis use disorder (CUD)(SAMHSA, 2018a). New initiation of cannabis use has increased in this demographic, while perceived risk of harm with cannabis use has decreased (SAMHSA, 2018b). Moreover, the potency of cannabis as measured by the content of Δ9-tetrahydrocannabinol (Δ9-THC), its primary psychoactive constituent, has increased from approximately 4% Δ9-THC in the 1980s to 17% Δ9-THC more recently in typical cannabis strains, and may continue to rise (ElSohly et al., 2016). In addition, there is an array of high Δ9-THC cannabis derivatives available (e.g., wax and shatter, upwards of 90% Δ9-THC) (Spindle et al., 2019). These behavioral and biochemical factors combined may yield more highly reinforcing cannabis consumption and consequently increase problematic cannabis use in adults. It is therefore imperative to elucidate neural mechanisms of cannabis reinforcement that likely contribute to escalating cannabis use.

Current literature suggests that a key neurobiological mechanism underlying cannabis reinforcement is Δ9-THC activation of the cannabinoid receptor type 1 (CB1R) (Cooper and Haney, 2008; Cooper and Haney, 2009). CB1R is a presynaptically bound Gi/o-protein coupled receptor that, when activated, triggers intracellular signaling pathways (Devane et al., 1988; Herkenham et al., 1990; Lu and Mackie, 2016; Matsuda et al., 1990). Preclinical studies show that Δ9-THC increases neuron firing in the ventral tegmental area (VTA) (French et al., 1997; Wu and French, 2000) and stimulates dopamine release in the nucleus accumbens (Chen et al., 1990; Chen et al., 1991). Mechanistically, Δ9-THC may activate CB1R on presynaptic GABAergic terminals to disinhibit (i.e., promote) striatal dopamine release from postsynaptic VTA neurons (Bloomfield et al., 2016; Esteban and Garcia-Sevilla, 2012; French, 1997; Sperlágh et al., 2009). Since stimulation of VTA neurons and consequent release of dopamine in striatum underpins the reinforcing effects of many drugs of abuse (Di Chiara and Imperato, 1988), this process is of interest to better understand the reinforcing effects of cannabis.

Δ9-THC-induced dopamine responses can be assessed in vivo with positron emission tomography (PET) brain imaging. Human studies have yielded mixed results; some studies indicate that Δ9-THC elicits a dopaminergic response in striatum (Bossong et al., 2015; Bossong et al., 2009), while others yield null(Barkus et al., 2010; Stokes et al., 2009) or mixed(Kuepper et al., 2013) effects. Key gaps in this literature may contribute to these discrepant results. First, most human imaging studies isolate Δ9-THC and test its effects on dopaminergic signaling, but do not assess dopamine effects in a more natural route of cannabis use (i.e., self-administration of the whole cannabis plant by smoking). Second, cannabis smoking likely induces transient changes in extracellular dopamine, which may not be reliably detected by analysis methods with underlying steady-state assumptions (Sullivan et al., 2013). To address these gaps, this study was designed to assess localized transient dopamine responses to smoked cannabis. The procedures feature the dopamine D2/3 receptor (D2/3R) antagonist radioligand [11C]raclopride and the time-varying linear parametric neurotransmitter PET (lp-ntPET) model(Kim et al., 2014) to assess transient dopamine responses after cannabis smoking. This approach provides sensitivity to short-lived neurotransmitter changes such as those observed with tobacco smoking (Cosgrove et al., 2014).

The primary aim of this study was to assess the spatiotemporal pattern of dopamine response to a mid-scan smoked cannabis cigarette in regular cannabis users. We hypothesized that cannabis smoking would induce a detectable dopamine response in the ventral striatum. Additionally, exploratory analyses assessed associations of magnitude and timing of cannabis-induced striatal dopamine responses with pre-smoking and smoking-related changes in plasma and subjective measures.

2. Methods

2.1. Study Participants

Subjects were recruited from the local community. Participants provided written informed consent after review of the study procedures, approved by the Yale-New Haven Hospital Radiation Safety Committee and the Yale University Human Investigation Committee. During screening for study eligibility, subjects were administered the Structured Clinical Interview for DSM-5, received a medical exam, submitted urine samples for pregnancy testing and drug toxicology, and provided personal and familial demographic information, drug use history, and psychiatric history. Subjects were included if they met DSM-5 criteria for cannabis use disorder or regularly used cannabis ≥5 days per week, had urine samples positive for 11-COOH-Δ9-THC (the inactive metabolite of Δ9-THC), and reported smoking cannabis regularly for ≥1 year. Exclusion criteria included presence of a significant medical condition, historical or current neurological illness, a current axis I diagnosis other than cannabis use disorder and nicotine dependence, a history of significant head trauma, regular or current use of prescribed medications or illicit drugs except cannabis within 6 months prior to scan confirmed by toxicology, magnetic resonance imaging (MRI) contraindications, and pregnancy or lactating status for women. On PET scan day, PET Center staff obtained subjects’ weights, vital signs, and urine samples. Subjects abstained overnight from cannabis by self-report (and cigarettes if tobacco smokers) prior to the scan, verified by carbon monoxide levels ≤11 ppm indicative of no recent smoke inhalation(Hecht and Vogt, 1985; Sandberg et al., 2011).

Fourteen cannabis users (7 women, 7 men; aged 28±4 years) were enrolled in the study (see Supplementary Table 1). Five participants met criteria for mild to severe cannabis use disorder. Five participants were current tobacco smokers who smoked 6±3 cigarettes per day and reported mild nicotine dependence (3.0±1.4 on the Fagerström Test for Nicotine Dependence (Heatherton et al., 1991)). Four participants were former tobacco smokers.

2.2. Cannabis Use and Clinical Measures

Participants’ typical cannabis use behavior (e.g., frequency and quantity of use, dollars spent on cannabis) was assessed during screening. Subjects also reported, on scan day, past-week average cannabis use per day. Participants reported a mixture of smoking methods (e.g., blunts, bowls, joints). To facilitate comparison across subjects, blunts and bowls were converted into units of joints using approximations reported previously (Mariani et al., 2011). Participants also weighed average grams of cannabis used per day, using oregano as a proxy for cannabis, as done previously (Mariani et al., 2011). Additionally, pre-scan plasma concentration of 11-COOH-Δ9-THC was used as a biological index correlated with recent cannabis use (Smith et al., 2018). Withdrawal symptoms and craving for marijuana on scan day, after overnight abstinence, were assessed with the Cannabis Withdrawal Scale (CWS)(Allsop et al., 2011) and the Short-Form Marijuana Craving Questionnaire (MCQ-SF)(Heishman et al., 2009), respectively.

2.3. Imaging Data Acquisition

MRI data were acquired for anatomical localization of [11C]raclopride uptake. MRI data were acquired with a 3 T Prisma Scanner (Siemens Medical Systems, Erlangen, Germany) with a weighted gradient-echo (MPRAGE) sequence featuring the following parameters: (TE=3.3 ms; TI=1,100 ms, TF=2,500 ms, FA=7°). These settings yielded images with 1 mm3 isotropic resolution.

[11C]Raclopride was prepared via 11C-methylation of desmethyl-raclopride as previously described(Gallezot et al., 2014), resulting in high molar activities of 239±102 MBq/nmol at time of injection. PET imaging data were acquired with a High Resolution Research Tomograph (HRRT; Siemens). Head motion data were simultaneously acquired with an optical motion-tracking tool (Vicra, NDI Systems, Waterloo, Canada) worn by the participant. Data acquisition began with a 6-minute transmission scan using a 137Cs source. Emission data were acquired in list mode for 90 minutes beginning concurrently with initial administration of a total of 583.5±94.5 MBq (1.7±1.1 μg) [11C]raclopride given as a bolus followed by a constant infusion with Kbol=105 min as previously validated(Wang et al., 2017). Participants smoked the cannabis cigarette starting at 35 minutes into the scanning procedure.

2.4. Cannabis Challenge and Subjective Effects

Cannabis cigarettes (0.9±0.1 g) contained either 3.7% (n=12) or 5.6% (n=2) Δ9-THC, a dose of approximately 33 or 50 mg per cigarette, 0.01% cannabidiol, and 0.28% or 0.34% cannabinol, respectively. Cigarettes were acquired through NIDA collaboration (RTI International). To standardize smoking topography, participants self-administered cannabis with an adapted Foltin paced smoking protocol (Foltin et al., 1986) during which subjects were verbally cued to inhale for 3 seconds, hold for 5 seconds, and exhale for the remainder of a 30-second interval. Subjects performed at least four 30-second intervals of smoking, and were encouraged to smoke the whole cigarette. To eliminate second-hand smoke for laboratory staff, an air filter (Movex) was positioned in front of the scanner and above the subject’s head for the scan duration. Participants were also asked to rate their subjective feeling of “high” on a scale of 0 (not at all) to 10 (most ever) at t=−5, 5, 15, 30, 45, and 55 minutes relative to the start of cannabis smoking, and immediately after the end of cannabis smoking (3.7 ± 2.1 minutes relative to start of smoking).

2.5. Venous Sampling of Δ9-THC and Metabolites

Blood samples were acquired pre-scanning (1.68±1.42 hours prior to cannabis smoking), at t=−5, 10, 20, 35, 50, and 55 minutes relative to smoking initiation, and immediately after smoking. Plasma concentrations of Δ9-THC and the metabolites 11-OH-Δ9-THC and 11-COOH-Δ9-THC were measured using high performance liquid chromatography/tandem mass spectrometry (LC-MS/MS) (NMS Labs, Horsham, PA, USA). Venous sampling could not be obtained from one subject, and a second subject only had blood withdrawn pre-scan due to technical issues.

2.6. Image Processing and Analysis

Dynamic list-mode PET data were histogrammed into discrete time frames of 3 minutes and reconstructed with the MOLAR algorithm (Carson et al., 2003), including corrections for scatter, attenuation, dead-time, normalization, scanner geometry, and motion using optical motion-tracking data. Two subjects’ PET data were excluded due to failed motion-tracking data acquisition. Reconstructed images were denoised with the HYPR algorithm (Christian et al., 2010) using a 3 mm isotropic Gaussian filter to create the composite image. To transform PET data into MR space, a summed image of the first 10 minutes of PET data was registered to the subject-specific T1-weighted MRI using a mutual information algorithm with six degrees of freedom (FLIRT, FSL 3.2; Analysis Group; FMRIB, Oxford, UK). The native MRI was co-registered to the Montreal Neurological Institute (MNI) template space with a nonlinear transformation algorithm (BioImage Suite; https://www.bioimagesuite.com). Analyses were confined to the whole striatum, the region of high specific [11C]raclopride binding, using a mask including 2,469 voxels. This mask was generated by thresholding pseudo-BPND images for all subjects transformed into template space by pseudo-BPND>1.5, and only including voxels that remained for all participants after applying an erode-dilate function to smooth rough edges.

PET data were analyzed with the lp-ntPET kinetic model (Kim et al., 2014; Normandin et al., 2012). This approach extends the multilinear reference tissue model (Ichise et al., 2003) (a method that estimates rate constants by fitting the observed dynamic radioactivity concentrations from the reference CR and target region/voxel CT) by adding a time-varying term that models transient changes in specific radiotracer binding:

CT(t)=R1CR(t)+k2∫0tCR(u)du−k2a∫0tCT(u)du−γ∫0tCT(u)h(u)du

where R1 is the ratio of delivery rate constants in the target to that in the reference, k2 is the efflux rate constant of nondisplaceable radiotracer in tissue to plasma, k2a is the efflux rate constant of radiotracer from the target tissue to plasma, and γ is the magnitude of peak change in k2a. Here, positive values in γ model transient reductions in specific binding, in this specific context designed to model radiotracer displacement caused by endogenous dopamine release. The time course of the observed transient response, h(u), was modeled with a gamma variate function:

h(u)=(t−tDtP−tD)αexp(α[1−t−tDtP−tD])u(t−tD)

where tD is the start time, tP is the time of maximal effect, u(t) is a step function, and α is a parameter that conveys ‘sharpness’. A library of basis functions was created to use linear fitting methods for rapid parameter estimation. Recent work demonstrated that smaller basis function libraries reduce false positive rates (Liu and Morris, 2020); therefore the implemented library fixed the ‘sharpness’ parameter α=1 and the start time tD to the mid-frame immediately prior to cannabis smoking (tD=34.5 min), with tP values ranging from 37.5 to 76.5 in 3-minute steps, providing 14 basis functions. This implementation optimized the analysis to detect drug effects of cannabis (as opposed to expectation effects, which could have a different tD), and characterized the temporal characteristics of dopamine responses by the single parameter tP while reducing the number of model parameters to avoid over-determined parameter estimates. The corrected Akaike Information Criterion (Sakamoto et al., 1986) (cAIC), with 3 parameters for the MRTM model and 5 parameters for lp-ntPET, was used to determine if the lp-ntPET model significantly improved model fit of the time activity curve at the voxel level (by virtue of lower cAIC score).

Next, a cluster size threshold of 16 voxels was applied based on null simulations (Supplementary Figure 1). Further parametric thresholding required voxels in a cluster to have relatively homogenous tP values as previously implemented (Bevington et al., 2020). In this dataset, this practically meant that tP values for each voxel in a given cluster were within 15 minutes of each other. Finally, to confirm that a detected cluster contained relatively uniform transient dynamics and improve specificity and accuracy of parameter estimates, the average time-activity curve from all voxels included in the cluster was re-analyzed with lp-ntPET. This parameterized the transient changes in specific uptake for an entire cluster of voxels with single values of γ and tP, which quantify the magnitude and timing of peak [11C]raclopride response, respectively. The anatomic location of a cluster was determined as located within defined striatal subregions (Mawlawi et al., 2001) by determining the anatomic striatal subregion with the plurality of voxels from a given cluster.

2.7. Head Motion Considerations

Reductions in specific binding modeled with lp-ntPET alternatively could be explained by head motion. Although the Vicra motion tracking system provides good compensation for head motion, it does not perfectly capture all motion. As an additional check against head motion confounds, the data-driven algorithm Centroid of Distribution (Lu et al., 2020) (COD) was used to estimate residual head motion traces in 3 directions (x, lateral; y, anterior-posterior; z, superior-inferior) after MOLAR reconstruction. These traces were used to assess if the timing of estimated head motion aligned with timing of transient reductions in specific binding for identified clusters, suggesting possible head motion contamination. This was done with a second lp-ntPET modeling analysis performed on time-activity curves from identified clusters, but here the library of basis functions was composed of only the three COD-estimated head motion traces (in each of the x, y, and z directions). A significantly improved model fit indicated temporal alignment of head motion with modeled reductions in specific binding, suggesting that head motion may have contributed to the modeled result for a given cluster. Clusters identified in this way were not considered for subsequent exploratory analyses.

2.8. Statistical Analysis

The effects of cannabis smoking on plasma Δ9-THC and subjective “high” were assessed using paired Wilcoxon rank sum tests for nonnormally distributed data (pre-smoke vs. end-smoke values). To investigate associations of imaging parameters with the effects of cannabis smoking, Pearson’s correlation coefficients were computed for estimates of peak modeled dopamine response magnitude (γ) and timing (tP) in the ventral striatum with post-smoking peak concentration of plasma Δ9-THC, area under the subjective “high”-by-time curve calculated using the trapezoidal method relative to pre-smoke “high” ratings, indices of cannabis use (i.e., past-week joints per day, pre-smoking plasma 11-COOH-Δ9-THC concentration), and withdrawal-related measures after overnight abstinence (i.e., pre-scan scores on the MCQ-SF and CWS). For individuals from whom >1 significant cluster of cannabis-induced dopamine release was detected, the parameter estimates were averaged between the clusters for a singular index of evoked dopamine response for correlative analyses. The significance level was set to p<0.05 (uncorrected) for exploratory analyses. Statistical analyses were performed with R (The R Foundation for Statistical Computing, version 3.6.3).

3. Results

3.1. Participant Demographics

Participants reported smoking 3.4±2.7 joints per day in the week prior to scanning. Participants reported average use of 2.1±1.8 grams per day as measured with an oregano proxy, spending 68±52 U.S. dollars on cannabis per week, smoking 6±1 days per week, and regular cannabis use for 8±5 years. Pre-scan concentrations of 11-COOH-Δ9-THC were 55.2±37.2 ng/mL. On scan day, after 19.5±10.3 hours of self-reported abstinence from cannabis and prior to scanning, subjects scored 13.3±12.4 on the CWS assessment of withdrawal symptoms (possible score range: 0-190), and 44.9±13.3 on the MCQ-SF assessment of craving for marijuana (possible score range: 12-84).

3.2. Cannabis Effects on Plasma Δ9-THC and Subjective High

Participants smoked 8±4 cannabis cigarette puffs during the challenge. Plasma concentration of Δ9-THC significantly increased from pre-smoking to end of smoking (p=0.002, pre: 4.6±5.2 ng/mL; end: 73.3±54.6 ng/mL) (Figure 1A). Peak Δ9-THC levels were observed 0-10 minutes after initiation of smoking for all but one subject (peak: 55 minutes). Plasma 11-OH-Δ9-THC concentrations were not reliably detected in all subjects and consequently were not analyzed in this study. Plasma concentrations of 11-COOH-Δ9-THC mildly increased from baseline levels (p=0.03, baseline: 41.8±31.0 ng/mL; peak: 54.2±36.9 ng/mL) (Figure 1A). Subjective rating of “high” significantly increased (p=0.001) from pre-smoking (all ratings: 0) to end of smoking (3±2) (Figure 1B). Peak ratings of subjective “high” ranged in value from 1-10, and were observed 0-30 minutes after initiation of smoking.

Figure 1. Cannabis-induced change in plasma Δ9-THC and 11-COOH-Δ9-THC, and subjective “high.”.

Figure 1.

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Plasma concentration of Δ9-THC significantly increased from pre-smoking to end of smoking (p=0.002). Plasma concentration of 11-COOH-Δ9-THC slightly increased from pre-smoking to peak levels after smoking (p=0.03) (A). Subjective ratings of “high” (scale: 0-10) increased from pre-smoking to end of smoking (p=0.001) (B). Data represent the fourteen individuals scanned in Panel B, and a subset of eleven individuals with detectable and reliable plasma Δ9-THC or 11-COOH-Δ9-THC levels pre- or post-smoking in panel A. Dotted lines intersect the x-axis at time “0” and indicate time of cannabis smoking initiation. Data points reflect mean across subjects, error bars reflect SEM.

3.3. Assessment of Cannabis-Induced Striatal Dopamine Release

Significant clusters modeled by transient reductions in [11C]raclopride uptake were detected in all twelve scans. A total of 26 clusters were identified, of which 16 were identified in the ventral striatum, as summarized in Table 1. Consideration of possible head motion identified 5 scans with possible head motion contamination (Table 1; Supplementary Figure 2). The subset of seven confirmed motion-free scans included sixteen clusters, of which nine were observed in the ventral striatum (Figure 2). Peak response times (i.e., tP estimates) varied across these clusters, but occurred at early times (≤10 min) after cannabis smoking in approximately half of the clusters. Exploratory analyses examining relationships of modeled peak dopamine response with cannabis smoking effects and clinical measures are summarized in Table 2 and Supplementary Figures 3-5. In this sample, estimated tP in the ventral striatum was significantly associated with pre-scan plasma 11-COOH-Δ9-THC concentration (rp=0.82, p=0.04) (Supplementary Figure 3), and estimated γ in the ventral striatum was “trend”-level inversely associated with pre-scan craving scores (rp=−0.70, p=0.08) (Supplementary Figure 4).

Table 1.

Parametric characterization of significant clusters of cannabis-induced striatal dopamine release detected with lp-ntPET.

Participant
#		 Primary ROI1 Cluster Size (#
voxels)		 tP (min)^ γ (min−1) Suspected
Motion
1 R VS 23 75 0.031 No
1 R Putamen 33 69 0.030 No
1 R Caudate 20 45 0.027 No
2 L VS 33 63 0.032 No
2 R VS 29 75 0.033 No
2 R Putamen 18 42 0.038 No
3 R Putamen 35 45 0.036 No
3 R VS 19 45 0.037 No
4 R VS 18 45 0.042 No
4 R VS 38 42 0.058 No
5 L VS 18 48 0.025 No
6 R VS 46 75 0.039 No
6 R Putamen 24 57 0.028 No
6 L Caudate 19 75 0.035 No
6 R Caudate 31 66 0.030 No
7 R VS 31 60 0.022 No
8 L VS 17 75 0.046 Yes
8 R VS 42 42 0.049 Yes
8 L VS 24 42 0.052 Yes
9 R VS 24 42 0.046 Yes
9 L VS 25 75 0.029 Yes
10 R VS/Caudate 124 75 0.035 Yes
10 L Caudate 27 42 0.041 Yes
11 R VS 36 45 0.031 Yes
11 L Putamen 17 48 0.022 Yes
12 L Putamen 36 39 0.052 Yes
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1

L: left, R: right, VS: ventral striatum. tP: estimated time of maximal cannabis-induced dopamine release. γ: estimated magnitude of cannabis-induced dopamine release.

Figure 2. Cannabis-induced dopamine release in the whole striatum detected with lp-ntPET.

Figure 2.

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Significant clusters of cannabis-induced dopamine release were detected in the caudate, putamen, and ventral striatum of seven participants (4 women, 3 men; aged 28±4 years). The color bar indicates the probability of a participant exhibiting a significant dopamine response after smoking. Brighter clusters indicate a higher number of participants. Coronal slices are shown moving in a posterior direction, with specific location shown by green lines on the transverse slice on the bottom.

Table 2.

Statistical summary for associations of γ and tP in the ventral striatum with plasma, subjective, and clinical measures of interest.

γ t P
r p p r p p
peak plasma Δ9-THC post-smoking (ng/mL) 0.26 0.67 0.21 0.73
area under the “high”-by-time curve 0.25 0.59 −0.54 0.21
past-week joints per day −0.38 0.39 0.39 0.39
pre-scan 11-COOH-Δ9-THC (ng/mL) −0.31 0.55 0.82 0.04
pre-scan MCQ-SF total score −0.70 0.08 0.24 0.60
pre-scan CWS withdrawal score −0.04 0.93 −0.30 0.52
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γ, tP: Modeled peak dopamine response magnitude (γ) and timing (tP), respectively, in the ventral striatum. MCQ-SF: Short-Form Marijuana Craving Questionnaire, CWS: Cannabis Withdrawal Scale. rp: Pearson’s correlation coefficient computed for association between lp-ntPET parameter estimate and corresponding outcome measure. Significance level: p<0.05.

4. Discussion

This imaging study of regular cannabis users identified clusters of brain voxels modeled by transient reductions in [11C]raclopride uptake after cannabis smoking, primarily in the ventral striatum. These results are aligned with our initial hypothesis that cannabis smoking would induce a detectable dopamine response in the ventral striatum. The findings demonstrate the feasibility of measuring transient dopamine responses to cannabis smoking in human neuroimaging studies.

Mesolimbic dopamine projections from the VTA to the nucleus accumbens are key substrates for the reinforcing properties of substances of abuse (Pierce and Kumaresan, 2006). Microdialysis studies with Δ9-THC in rodents demonstrate dopamine release in the ventral striatum (Chen et al., 1990; Chen et al., 1991). The presented data support translation of this literature to regular cannabis users by showing the detection of transient reductions in [11C]raclopride uptake consistent with dopamine release, primarily in ventral striatum. This finding is also in agreement with previous human studies of Δ9-THC administration inducing [11C]raclopride displacement in the anatomically defined limbic striatum(Bossong et al., 2015; Bossong et al., 2009). The majority of ventral striatal clusters were in the right hemisphere, in line with previous reports of lateralized drug- and cue-induced striatal dopamine release (Cosgrove et al., 2014; Oberlin et al., 2013; Oberlin et al., 2015; Wong et al., 2006; Yoder et al., 2016; Yoder et al., 2009). Clusters were also detected in the dorsal striatum, which has been implicated in the shift from recreational to compulsive use of cannabis (Zhou et al., 2018) and other drugs (Everitt and Robbins, 2016). More broadly, individuals with cannabis use disorder exhibit deficits in stimulant-induced striatal dopamine release (van de Giessen et al., 2017), with some evidence for specificity in ventral striatum (Volkow et al., 2014). Taken together, these findings provide human evidence for an important role of dopaminergic signaling in the ventral striatum with cannabis use.

The presented use of a kinetic analysis modeling transient changes in radiotracer uptake is an important innovation that advances the human neuroimaging literature of dopamine responses to cannabis. While human PET studies with D2/D3 radiotracers such as [11C]raclopride are reliably sensitive to dopamine release from psychostimulants such as amphetamine and methylphenidate(Endres et al., 1997), similar potential effects are more mixed for other drugs of abuse such as alcohol, nicotine, and cannabis(Martinez and Narendran, 2010). Yet key differences exist, as the microdialysis literature indicates nearly an order of magnitude greater dopamine response with slower dissipation for clinically relevant doses of amphetamine compared to those of nicotine and cannabinoids (Chen et al., 1991; Di Chiara and Imperato, 1988; Fadda et al., 2006; Ranaldi et al., 1999). The net dopamine efflux following a small stimulus is, in theory, proportional to changes in [11C]raclopride BPND(Endres et al., 1997; Yoder et al., 2004) (the steady-state ratio of specifically bound to free radiotracer). Consequently, perhaps it is not surprising that analyses using BPND yield small effects with a Δ9-THC stimulus thought to produce small, transient increases in extracellular dopamine. This likely contributes to conflicting reports regarding the dopamine response to Δ9-THC in humans. By accounting for transient effects on specific uptake, the implemented lp-ntPET analysis increases sensitivity to dynamic stimuli such as a gambling task (Bevington et al., 2020), smoked tobacco cigarettes (Cosgrove et al., 2014), and presently cannabis smoking. Indeed, the dopamine response to cannabis in this study was detected in similar striatal subdivisions as the previously reported response to tobacco (Cosgrove et al., 2014), with slightly greater magnitude of response. Furthermore, having participants smoke during scanning permitted a more naturalistic study of cannabis effects, and may have contributed to this paradigm’s sensitivity to detect transient dopamine responses to both the smoked cannabis and associative sensory cues during smoking. Thus, lp-ntPET presents an important analysis tool for future human studies of acute dopaminergic effects after smoked cannabis across different study populations.

Smoked cannabis increased plasma concentrations of both Δ9-THC and the metabolite 11-COOH-Δ9-THC to values roughly similar to previous reported levels in daily users (Huestis et al., 1992; Lee et al., 2015; Schwope et al., 2012), and increased subjective “high” ratings similar to previous reports (Chait and Zacny, 1992; Hart et al., 2002; Heishman et al., 1989; Schwope et al., 2012). Since microdialysis data indicate a dose-dependent relationship of Δ9-THC with dopamine efflux (Chen et al., 1990; Chen et al., 1991), exploratory analyses assessed preliminary relationships between γ, the magnitude of peak transient reduction in [11C]raclopride uptake, and plasma Δ9-THC concentrations. Theoretically γ should reflect the magnitude of peak dopamine response. The current sample did not yield a significant linear relationship (Supplementary Figure 5), however previous work indicates a bias in γ as tP increases (Kim et al., 2014), so this relationship may be nonlinear. Exploratory analyses further assessed the relationship of γ with area under the “high”-by-time curve, again finding no significant relationship. While γ may be more precisely estimated with Bayesian approaches (Irace et al., 2020), such an approach is not suitable to the present voxelwise analyses due to computational limitations, and this limitation is partially addressed by the implemented parametric thresholding step to reduces noise, which improves γ estimation. Nonetheless, future work building on this initial sample is needed to fully characterize γ as a proxy for the magnitude of dopamine release and identify relationships of this parameter with plasma and subjective effects.

Careful consideration of head motion is especially noteworthy. In regions of high radiotracer uptake, uncorrected head motion may reduce the apparent radiotracer concentration, which potentially could be modeled by lp-ntPET as a transient reduction in radiotracer uptake. Therefore, COD motion detection was applied to the (optical motion-corrected) list-mode data to identify possible uncorrected motion, which occurred for 5 scans. Since COD performance remains to be optimized for radiotracers that do not exhibit high uptake throughout the whole brain such as [11C]raclopride, it is difficult to conclusively establish whether or not motion interfered with these scans. Out of an abundance of caution we present these data and consider them as possible but unconfirmed dopamine release, while omitting them from exploratory analyses. Whether or not these scans are considered, the primary result remains consistent: cannabis smoking induces clusters of transient reductions in [11C]raclopride uptake, predominantly in the ventral striatum.

The study population consisted of regular cannabis smokers, which was designed to match the route of cannabis administration in this study while honing investigative focus on a population at risk for psychiatric comorbidity and cognitive impairment (Kroon et al., 2020). Since chronic cannabis users exhibit neurobiological adaptations such as lower CB1R availability (Ceccarini et al., 2015; D’Souza et al., 2016; Hirvonen et al., 2011) and compelling evidence for deficits in stimulant-induced striatal dopamine release(van de Giessen et al., 2017; Volkow et al., 2014), we speculate that the dopaminergic response to smoked cannabis may be underestimated in this study. Although we did not find preliminary evidence for an association of recent cannabis use with the magnitude of dopamine response, there was a significant association between longer time to peak dopamine response and higher pre-scan plasma 11-COOH-Δ9-THC concentration indicative of recent cannabis use (Supplementary Figure 3). These relationships are of relevance to explore in a larger sample. Considering the cannabis use frequency of the study population, participants were abstinent overnight from cannabis use to standardize the most recent cannabis exposure. Although cannabis withdrawal is observed as early as 1-3 days after abstinence in heavy cannabis users (Budney et al., 2003), withdrawal symptoms were low in this cohort (CWS of 13.3±12.4) while marijuana craving was moderate (MCQ-SF of 44.9±13.3). Interestingly, there was evidence of a “trend”-level association of higher MCQ-SF total score with lower γ in ventral striatum (Supplementary Figure 4). Finally, given potential effects of cannabis and tobacco co-use on neural systems (Subramaniam et al., 2016) (which may have blunted dopamine responses), and ample literature on sex differences involved in the mechanisms of cannabis use(Calakos et al., 2017), the effects of tobacco smoking and sex are worth exploring in future work. In summary, larger studies are needed to better explore relationships of cannabis-induced dopamine responses with participant sex, tobacco smoking status, recent cannabis use, and marijuana craving on scan day.

This report demonstrates that [11C]raclopride PET can detect dopamine responses to smoked cannabis when using analyses that account for transient changes in radiotracer uptake. The majority of clusters were detected in the ventral striatum, implicating dopamine function in this brain region as important to the pathophysiology of cannabis use. This approach shows promise for future studies elucidating specific roles of dopaminergic signaling in chronic cannabis use.

Supplementary Material

1

Highlights.

  • Dopamine mechanisms of cannabis’ reinforcing effects are not well studied in people

  • Positron Emission Tomography imaging of dopamine D2/D3 receptors was performed

  • Participants smoked a cannabis cigarette while in the scanner

  • Dopamine responses were detected by modeling short-lived reductions in receptor binding

  • The majority of detected clusters were located in ventral striatum

Acknowledgments

We thank the Yale PET Center and our research assistants for scan management and participant recruitment and scheduling. We thank Dr. Richard Carson for his insightful scientific contributions and Ms. Enette Mae Revilla for assistance with COD analyses.

Role of Funding Source

This study was supported by the National Institute on Drug Abuse (Grants R01 DA038832, R03 DA047588), the National Institute on Alcohol Abuse and Alcoholism (Grant K01 AA024788), The U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, and the Wendy U. and Thomas C. Naratil Pioneer Award funded by the Women’s Health Research at Yale. KCC was additionally supported by the National Institutes of Health (Grant T32 NS041228).

Footnotes

Conflict of Interest

The authors report no competing financial disclosures or other conflicts of interest.

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