2015
DOI: 10.1016/j.pscychresns.2015.05.007
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Neural activation during risky decision-making in youth at high risk for substance use disorders

Abstract: Risky decision-making, particularly in the context of reward-seeking behavior, is strongly associated with the presence of substance use disorders (SUDs). However, there has been little research on the neural substrates underlying reward-related decision-making in drug-naïve youth who are at elevated risk for SUDs. Participants comprised 23 high-risk (HR) youth with a well-established SUD risk phenotype and 27 low-risk healthy comparison (HC) youth, aged 10–14. Participants completed the balloon analog risk ta… Show more

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Cited by 33 publications
(45 citation statements)
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References 70 publications
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“…Using data from the IMAGEN study, (a functionalstructural imaging study with 2200 European adolescents tested every 2 years between ages 14 and 18), and a machine learning prediction model applied to a subset of the IMAGEN sample, Whelan et al (2014) did not find an association between striatal volume or reward-elicited-striatal-responses, and future binge drinking; rather, future (age 16) binge drinking was predicted by reduced activation during reward anticipation in occipito-temporal and PCC regions, reduced activity in the left temporal pole, and increased activity in bilateral superior frontal gyrus, during reward anticipation at age 14. These findings are corroborated by those of other studies, albeit not all longitudinal in nature, reporting increased activations in frontal regions including the OFC and ventro-medial PFC (regions implicated in coding the subjective value of rewards; Levy & Glimcher, 2012) to be associated with substance use in adolescence (Chung et al, 2011;De Bellis et al, 2013;Hulvershorn et al, 2015). Therefore, rewardrelated processing abnormalities in striatal-frontal circuits, and not simply striatal circuits, appear to be observed in youth at risk of early-onset substance use and misuse.…”
Section: Dual-process Models Of Control In Adolescencesupporting
confidence: 84%
“…Using data from the IMAGEN study, (a functionalstructural imaging study with 2200 European adolescents tested every 2 years between ages 14 and 18), and a machine learning prediction model applied to a subset of the IMAGEN sample, Whelan et al (2014) did not find an association between striatal volume or reward-elicited-striatal-responses, and future binge drinking; rather, future (age 16) binge drinking was predicted by reduced activation during reward anticipation in occipito-temporal and PCC regions, reduced activity in the left temporal pole, and increased activity in bilateral superior frontal gyrus, during reward anticipation at age 14. These findings are corroborated by those of other studies, albeit not all longitudinal in nature, reporting increased activations in frontal regions including the OFC and ventro-medial PFC (regions implicated in coding the subjective value of rewards; Levy & Glimcher, 2012) to be associated with substance use in adolescence (Chung et al, 2011;De Bellis et al, 2013;Hulvershorn et al, 2015). Therefore, rewardrelated processing abnormalities in striatal-frontal circuits, and not simply striatal circuits, appear to be observed in youth at risk of early-onset substance use and misuse.…”
Section: Dual-process Models Of Control In Adolescencesupporting
confidence: 84%
“…Moreover, adults showed greater ACC activation than both adolescents and young adults when anticipating pleasant interoceptive stimuli (65). However, youth at high risk for SUD show greater ACC, prefrontal and AI activation than CON in response to risky decisions involving high potential for negative outcomes (66). Thus, it is unclear what role ACC plays in adolescents.…”
Section: Discussionmentioning
confidence: 99%
“…We used a version of the BART (see Fig. 1), previously validated in neuroimaging studies of healthy individuals (Fukunaga et al, 2012), substance users (Bogg et al, 2012), and at-risk youth (Hulvershorn et al, 2015), developed from the original implementation of the BART for fMRI and utilizing a similar analytic approach (Rao et al, 2008). Acquisition parameters were identical to those used in Bogg et al, 2012 and are reported in Supplementary materials.…”
Section: Methodsmentioning
confidence: 99%