A machine learning classifier identified distinct brain activation patterns that perfectly distinguished decisions to purchase cannabis from decisions to decline in daily smokers.
Read this if you're interested in the neuroscience of why quitting cannabis can be difficult.
100% classifier accuracy distinguishing buy vs. decline decisions
What the researchers found
Researchers combined brain imaging with a real-world-like purchasing task where daily cannabis smokers made repeated decisions to buy or decline puffs of cannabis at various prices. One randomly selected decision was actually carried out: if they had chosen to buy, they paid and smoked in the lab.
Using machine learning, a classifier achieved 100% accuracy distinguishing brain patterns during purchase decisions from decline decisions at the individual level. Key brain regions contributing to this neural signature included the dorsal striatum, insula, posterior parietal regions, anterior and posterior cingulate, and dorsolateral prefrontal cortex.
Purchasing behavior followed expected patterns: participants bought more puffs at lower prices and of active cannabis compared to placebo. The findings provide a brain-based framework for understanding drug-related decision making.
Why it matters
Understanding the neural basis of decisions to use drugs could enable measurement of how treatments change decision-making processes in the brain, potentially improving addiction treatment development and evaluation.
The numbers in context
21 daily cannabis smokers participated. 17 purchased cannabis and were included in fMRI analysis. Machine learning classifier achieved 100% accuracy at the individual level. 6 brain regions reliably contributed to the decision signature.
How the study worked
Functional MRI study with a within-subject choice task. Twenty-one daily cannabis smokers made repeated decisions to purchase or decline 1-12 cannabis puffs at prices from $0.25 to $5. One decision was randomly implemented. Machine learning with leave-one-subject-out cross-validation identified discriminating neural patterns in 17 participants who purchased cannabis.
What this study cannot tell us
Very small sample (17 in fMRI analysis). Participants were non-treatment-seeking daily users, so results may not generalize to those trying to quit. The laboratory environment may not fully capture real-world decision contexts. 100% accuracy in a small sample may reflect overfitting.
How to read the evidence
Small fMRI study with innovative methodology but limited sample size. Results need replication.
When this study was published
Published in 2015. This was early work establishing neural signatures of drug-related decision making.
The bigger picture
Addiction is increasingly understood as a disorder of decision-making. This study advances that framework by identifying specific brain signatures associated with the actual choice to use a drug, not just responses to drug cues. This could eventually help test whether behavioral or pharmacological treatments change these decision patterns.
Questions still open
- Would this neural signature hold up in larger samples? Can treatment interventions measurably change these brain decision patterns? Do people who successfully quit cannabis show different neural signatures during cannabis-related decisions?
Common questions
Can brain scans tell if someone will use cannabis?
What brain regions are involved in cannabis purchasing decisions?
Read the original research
An fMRI-Based Neural Signature of Decisions to Smoke Cannabis.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 40(12), 2657-65
Citation
Bedi, Gillinder; Lindquist, Martin A; Haney, Margaret. (2015). An fMRI-Based Neural Signature of Decisions to Smoke Cannabis.. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 40(12), 2657-65. https://doi.org/10.1038/npp.2015.135
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