Neural responses associated with cue‐reactivity in frequent cannabis users

Abstract

Cue-reactivity reflects enhanced motivational processing underlying continued substance use and relapse in substance use disorders. Substance use disorders are associated with greater cue-reactivity in orbitofrontal cortex, anterior cingulate cortex, striatum, ventral tegmental area and amygdala. Here we examine whether this also holds for frequent cannabis users. Using functional magnetic resonance imaging (fMRI), neural responses to neutral and cannabis-related cues were compared between frequent cannabis users (n = 31), sporadic cannabis users (n = 20) and cannabis-naive controls (n = 21). In addition, fMRI findings were correlated with the level of cannabis use, problem severity and craving. In frequent users compared with sporadic users and controls, cannabis images engendered higher activation than control images in the ventral tegmental area. Activation of the orbitofrontal cortex, anterior cingulate cortex and striatum was only higher in a subgroup of frequent users with high compared with low-problem severity. Activity in the right putamen and right dorsolateral prefrontal cortex correlated negatively with subjective craving in frequent users. Activity was not correlated with level of cannabis use. These findings indicate that cannabis cues activate areas associated with addiction pathology in frequent cannabis users compared with sporadic users and controls. However, among frequent users, cue-reactivity seems to be primarily associated with problem severity, not with amount of cannabis use. These findings imply neural cue-reactivity as a tool in assessing problem severity of cannabis use.

The dynamics of cannabis use and dependence

“…For example, a study from the neurobiological field regarding endophenotypic predictors showed, analogous to our results, that primarily problem severity -not amount of cannabis usewas associated with activity in reward-related brain areas in response to visual cannabis cues among frequent users [59].…”

“…Measurement errors are inherent to survey data. When several indicators of a concept are available, a latent class Markov model enables estimation of misclassification probabilities of "latent" variables represented by the measured indicators [59]. Using the CIDI dependence diagnosis (yes/no) and the total SDS score at T0, T1 and T2 as indicators; three latent variables of cannabis dependence were constructed with PanMark 3.2 software [60;61].…”

“…Therefore, alcohol use and alcohol related problems were assessed at baseline with the Alcohol Use Disorders Identification Test (AUDIT); a screening instrument used to detect alcohol abuse and dependence with excellent psychometric qualities [59]. For ecstasy, cocaine, and amphetamines, questions on lifetime use (dichotomous), most recent use (last month/ last 12 months/ before), and frequency of use in the past year [rarely/ less than monthly/ monthly/ weekly/ (near) daily] as well as age of onset were included.…”

Cannabis Abstinence During Treatment and One-Year Follow-Up: Relationship to Neural Activity in Men

“…Regardless of cause, a compelling hypothesis is that these disruptions may be at least partially reversible with prolonged abstinence or treatment. Clinically, the regulation of craving involves cognitive control and recruitment of dorsolateral prefrontal cortex (Kober et al, 2010), in which activity negatively correlates with craving (Cousijn et al, 2012;Kober et al, 2010). In addition, response inhibition (a component of cognitive control) and its neural underpinnings may be modifiable with monetary incentives in adolescent cannabis users (Chung et al, 2011), suggesting that these skills and their underlying neural functions may be trainable and adaptable.…”

Three-Year Course of Cannabis Dependence and Prediction of Persistence

“…We therefore feel that there must be factors other than cannabis use and characteristics of cannabis dependence that determine the course of cannabis dependence. Potential candidates for the prediction of the course of cannabis dependence include genetic vulnerability, gene-environment interactions, gene expression and endophenotypic characteristics [57,58,59,60]. However, in the current study (large sample, many predictors from different domains, excellent follow-up rate) we were not able to identify more independent predictors of persistence: a long list of potential predictors, assessed with adequate instruments, explained only a small proportion of the variance in the 3-year course of cannabis dependence (pseudo R 2 = 8.8%).…”