UC San Diego
Blunted Frontostriatal Blood Oxygen Level-Dependent Signals Predict Stimulant and Marijuana Use.
- Author(s): Blair, MA
- Stewart, JL
- May, AC
- Reske, M
- Tapert, SF
- Paulus, MP
- et al.
Published Web Locationhttps://doi.org/10.1016/j.bpsc.2018.03.005
BACKGROUND:Occasional recreational stimulant (amphetamine and cocaine) use is an important public health problem among young adults because 16% of those who experiment develop stimulant use disorder. This study aimed to determine whether behavioral and/or neural processing measures can forecast the transition from occasional to problematic stimulant use. METHODS:Occasional stimulant users completed a Risky Gains Task during functional magnetic resonance imaging and were followed up 3 years later. Categorical analyses tested whether blood oxygen level-dependent (BOLD) responses differentiated occasional stimulant users who became problem stimulant users (n = 35) from those who desisted from stimulant use (n = 75) at follow-up. Dimensional analyses (regardless of problem stimulant user or desisted stimulant use status; n = 144) tested whether BOLD responses predicted baseline and follow-up stimulant and marijuana use. RESULTS:Categorical results indicated that relative to those who desisted from stimulant use, problem stimulant users 1) made riskier decisions after winning feedback; 2) exhibited lower frontal, insular, and striatal BOLD responses to win/loss feedback after making risky decisions; and 3) displayed lower thalamic but greater temporo-occipital BOLD responses to risky losses than to risky wins. In comparison, dimensional results indicated that lower BOLD signals to risky choices than to safe choices in frontal, striatal, and additional regions predicted greater marijuana use at follow-up. CONCLUSIONS:Taken together, blunted frontostriatal signals during risky choices may quantify vulnerability to future marijuana consumption, whereas blunted frontostriatal signals to risky outcomes mark risk for future stimulant use disorder. These behavioral and neural processing measures may prove to be useful for identifying ultra-high risk individuals prior to onset of problem drug use.
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