Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences | CCM, Charitéplatz 1, 10117 Berlin, Germany; Humboldt-Universität zu Berlin, Faculty of Life Sciences, Department of Psychology, Unter den Linden 6, 10099 Berlin, Germany.
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences | CCM, Charitéplatz 1, 10117 Berlin, Germany; Humboldt-Universität zu Berlin, Faculty of Life Sciences, Department of Psychology, Unter den Linden 6, 10099 Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience, Charitéplatz 1, 10117 Berlin, Germany.
Neuroimage Clin. 2023;40:103520. doi: 10.1016/j.nicl.2023.103520. Epub 2023 Sep 30.
Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psychometric variables such as personality traits or mental health comorbidities in a data-driven approach. We analyzed a subset of adolescents who did not have any prior binge drinking experience at age 14 (IMAGEN dataset, n = 555, 52.61% female). Participants underwent structural magnetic resonance imaging at age 14, binge drinking assessments at ages 14 and 22, and psychometric questionnaire assessments at ages 14 and 22. We derived structural brain features from T1-weighted magnetic resonance and diffusion tensor imaging. Using Machine Learning (ML), we predicted binge drinking (age 22) from brain structure (age 14) and used counterbalancing with oversampling to systematically control for 110 + variables from a wide range of social, personality, and other psychometric characteristics potentially associated with binge drinking. We evaluated if controlling for any variable resulted in a significant reduction in ML prediction accuracy. Sensation-seeking (-13.98 ± 1.68%), assessed via the Substance Use Risk Profile Scale at age 14, and uncontrolled eating (-13.98 ± 3.28%), assessed via the Three-Factor-Eating-Questionnaire at age 22, led to significant reductions in mean balanced prediction accuracy upon controlling for them. Thus, sensation-seeking and binge eating could partially explain the prediction of future binge drinking from adolescent brain structure. Our findings suggest that binge drinking and binge eating at age 22 share common neurobiological precursors discovered by the ML model. These neurobiological precursors seem to be associated with sensation-seeking at age 14. Our results facilitate early detection of increased risk for binge drinking and inform future clinical research in trans-diagnostic prevention approaches for adolescent alcohol misuse.
青少年早期的狂饮行为可以通过青少年早期的大脑结构进行预测，准确率超过 70%。我们通过数据驱动的方法研究了这种对酒精滥用行为的准确前瞻性预测是否可以用心理测量变量（如人格特质或心理健康合并症）来解释。我们分析了一组在 14 岁时没有任何狂饮经历的青少年（IMAGEN 数据集，n=555，女性占 52.61%）。参与者在 14 岁时接受了结构磁共振成像，在 14 岁和 22 岁时接受了狂饮评估，在 14 岁和 22 岁时接受了心理测量问卷评估。我们从 T1 加权磁共振和弥散张量成像中得出了结构脑特征。使用机器学习（ML），我们从大脑结构（14 岁）预测狂饮（22 岁），并使用平衡和过采样来系统地控制与狂饮相关的 110 多个变量，这些变量来自广泛的社会、人格和其他心理测量特征。我们评估了控制任何变量是否会导致 ML 预测准确性的显著降低。通过 14 岁时的物质使用风险特征量表评估的感觉寻求（-13.98±1.68%）和通过 22 岁时的三因素饮食问卷评估的不受控制的饮食（-13.98±3.28%），在控制这些因素后，导致平均平衡预测准确性显著降低。因此，感觉寻求和 binge eating 可以部分解释从青少年大脑结构预测未来的 binge drinking。我们的研究结果表明，22 岁时的 binge drinking 和 binge eating 具有 ML 模型发现的共同神经生物学前兆。这些神经生物学前体似乎与 14 岁时的感觉寻求有关。我们的研究结果有助于早期发现 binge drinking 风险增加，并为青少年酒精滥用的跨诊断预防方法提供未来的临床研究信息。
