TY - GEN AB - Patterns of alcohol consumption during adolescence differ from those observed during adulthood; namely, adolescents are more likely to consume alcohol less frequently, but in larger quantities per occasion, when compared to adults. Importantly, this binge-pattern of drinking carries substantial risks for adverse outcomes, including involvement in motor vehicle accidents, alcohol poisoning, and sexual victimization. Although prior work has examined neural correlates of emergent alcohol use among adolescent populations, the present study takes a novel, data-driven approach by incorporating graph theory metrics of resting-state functional connectivity with predictive modeling via machine learning. To identify risk factors for future binge drinking, a subset of participants were selected from an ongoing prospective longitudinal study (National Consortium on Alcohol and Neurodevelopment in Adolescence). All participants were alcohol-na?ve at baseline (n=150), but 51% (n=77) emerged into binge drinking over the course of four years of follow-up assessments (transitioners), while the rest remained abstinent from alcohol use (controls). AD - Oregon Health and Science University AD - Oregon Health and Science University AD - Oregon Health & Science University, Swedish Neuroscience Institute AD - Oregon Health and Science University AU - Kliamovich, Dakota AU - Morales, Angelica AU - Harman, Gareth AU - Boyd, Stephen DA - 2020 DO - 10.6083/tm70mv665 DO - DOI ID - 8240 KW - Adolescent KW - Alcoholism KW - Binge Drinking KW - Magnetic Resonance Imaging KW - resting-state FMRI KW - graph theory KW - neurodevelopment L1 - https://digitalcollections.ohsu.edu/record/8240/files/Dakota-Kliamovich.pdf L2 - https://digitalcollections.ohsu.edu/record/8240/files/Dakota-Kliamovich.pdf L4 - https://digitalcollections.ohsu.edu/record/8240/files/Dakota-Kliamovich.pdf LA - eng LK - https://digitalcollections.ohsu.edu/record/8240/files/Dakota-Kliamovich.pdf N2 - Patterns of alcohol consumption during adolescence differ from those observed during adulthood; namely, adolescents are more likely to consume alcohol less frequently, but in larger quantities per occasion, when compared to adults. Importantly, this binge-pattern of drinking carries substantial risks for adverse outcomes, including involvement in motor vehicle accidents, alcohol poisoning, and sexual victimization. Although prior work has examined neural correlates of emergent alcohol use among adolescent populations, the present study takes a novel, data-driven approach by incorporating graph theory metrics of resting-state functional connectivity with predictive modeling via machine learning. To identify risk factors for future binge drinking, a subset of participants were selected from an ongoing prospective longitudinal study (National Consortium on Alcohol and Neurodevelopment in Adolescence). All participants were alcohol-na?ve at baseline (n=150), but 51% (n=77) emerged into binge drinking over the course of four years of follow-up assessments (transitioners), while the rest remained abstinent from alcohol use (controls). PB - Oregon Health and Science University PY - 2020 T1 - Predicting adolescent binge drinking from brain networks at rest TI - Predicting adolescent binge drinking from brain networks at rest UR - https://digitalcollections.ohsu.edu/record/8240/files/Dakota-Kliamovich.pdf Y1 - 2020 ER -