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Attentional bias to cannabis stimuli in people with cannabis use disorder
Marianna Quinones-Valera1, Madeleine I Fraser2, Nadia Solowij3
1Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia.
Background:
Around one in five people who use cannabis develop a cannabis use disorder (CUD) during their lifetime. Attentional bias toward substance-related stimuli is theorised to drive substance-seeking behaviour, contributing to the development of addiction and relapse. There is mixed evidence of cannabis-related attentional bias in individuals with CUD and its association with cannabis quantity, CUD severity, and confounders (e.g., alcohol, nicotine use). We aimed to examine cannabis attentional bias in non-treatment-seeking adults with a CUD (n = 74) versus controls (n = 30), and in relation to levels of cannabis and nicotine use, adjusting for alcohol use.
Methods:
Attentional bias was measured using a visual probe task. To detect attentional bias differences between groups (CUD, controls), we ran linear mixed-effect models with reaction times (RTs) to cannabis versus neutral images as the outcome variable, adjusting for intra-individual variability and stimulus onset asynchrony (SOA, 200/500 ms). Within the CUD group only, we explore the association between attentional bias and cannabis quantity, craving, Cannabis Use Disorder Identification Test - Revised (CUDIT-R) scores, image valence ratings, and nicotine use. All models were adjusted for alcohol use.
Results:
No significant effects were found for group, cannabis quantity, craving, CUDIT-R scores, or nicotine use on attentional bias toward cannabis cues.
Conclusion:
Findings suggest that non-treatment seekers with CUD do not exhibit attentional bias to cannabis cues. Replication in treatment-seeking samples is needed to determine whether attentional bias has clinical relevance, using objective measures (e.g., eye tracking).
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