Brain Networks and Adolescent Alcohol Use.-猫眼课题宝

DOI：

10.1001/jamapsychiatry.2023.2949

发表时间：

2023-08

期刊：

JAMA psychiatry

影响因子：

25.8

通讯作者：

中科院分区：

医学1区

文献类型：

作者：

Sarah W. Yip;S. Lichenstein;Q. Liang;B. Chaarani;Alecia D. Dager;Godfrey Pearlson;T. Banaschewski;A. Bokde;S. Desrivières;Herta Flor;A. Grigis;P. Gowland;A. Heinz;R. Brühl;J. Martinot;M. P. Martinot;E. Artiges;F. Nees;D. P. Orfanos;T. Paus;L. Poustka;S. Hohmann;Sabina Millenet;J. Fröhner;M. Smolka;N. Vaidya;H. Walter;R. Whelan;G. Schumann;H. Garavan

青春期酒精滥用是青年残疾和死亡的主要原因，与酒精使用障碍的风险较高有关。酒精滥用风险的大脑机制可能为预防和干预工作提供信息。目的采用数据驱动的方法，识别酒精滥用的神经标志物，特别考虑神经发育的性别差异。设计、设置和参与者纵向多部位功能磁共振成像（fMRI）数据收集于14岁和19岁，用于评估与当前和未来酒精使用风险相关的全脑功能组织模式，通过酒精使用障碍识别测试（AUDIT）进行测量。主要数据由IMAGEN联盟收集，这是一项关于青少年神经发育的欧洲多中心研究。使用在美国进行的大学酒精消费的单点研究中获得的数据进一步测试模型的普遍性。主要样本是1359名具有神经影像学、表型和酒精使用数据的青少年的发育队列。在一个单独的114人的队列中进一步评估模型的普遍性。主要结果和测量脑行为模型准确性，定义为模型预测值与保留测试数据中实际AUDIT评分之间的对应关系，Bonferroni在每个时间点运行的模型数量上进行校正，双尾α < .008，通过排列测试确定。结果在1359例受试者中，平均（SD）年龄为14.42（0.40）岁，729例（54%）为女性。数据驱动的全脑连接方法确定了与未来和当前AUDIT定义的酒精使用风险脆弱性相关的网络（主要结局，如上所述，未来：ρ，0.22; P < .001和现在：ρ，0.27; P < .001）。结果进一步表明，性别差异的大脑行为模型的准确性，如女性模型始终优于男性模型。具体而言，女性专用模型识别网络赋予未来和当前的严重性，奖励和抑制功能磁共振成像任务期间获得的数据的脆弱性。相比之下，男性只有模型成功地准确识别网络使用的数据在抑制控制，但不奖励任务，表明域特异性的酒精使用风险网络的男性青少年。结论和相关性这些数据表明，集中在抑制控制过程的干预措施可能是有效的，在打击男性青少年的酒精使用风险，但抑制和奖励相关的过程可能与女性青少年的酒精使用行为。他们进一步确定了青少年饮酒风险的新网络，可用于识别处于风险中的青少年并为干预工作提供信息。

Importance Alcohol misuse in adolescence is a leading cause of disability and mortality in youth and is associated with higher risk for alcohol use disorder. Brain mechanisms underlying risk of alcohol misuse may inform prevention and intervention efforts. Objective To identify neuromarkers of alcohol misuse using a data-driven approach, with specific consideration of neurodevelopmental sex differences. Design, Setting, and Participants Longitudinal multisite functional magnetic resonance imaging (fMRI) data collected at ages 14 and 19 years were used to assess whole-brain patterns of functional organization associated with current and future alcohol use risk as measured by the Alcohol Use Disorder Identification Test (AUDIT). Primary data were collected by the IMAGEN consortium, a European multisite study of adolescent neurodevelopment. Model generalizability was further tested using data acquired in a single-site study of college alcohol consumption conducted in the US. The primary sample was a developmental cohort of 1359 adolescents with neuroimaging, phenotyping, and alcohol use data. Model generalizability was further assessed in a separate cohort of 114 individuals. Main Outcomes and Measures Brain-behavior model accuracy, as defined by the correspondence between model-predicted and actual AUDIT scores in held-out testing data, Bonferroni corrected across the number of models run at each time point, 2-tailed α < .008, as determined via permutation testing. Results Among 1359 individuals in the study, the mean (SD) age was 14.42 (0.40) years, and 729 individuals (54%) were female. The data-driven, whole-brain connectivity approach identified networks associated with vulnerability for future and current AUDIT-defined alcohol use risk (primary outcome, as specified above, future: ρ, 0.22; P < .001 and present: ρ, 0.27; P < .001). Results further indicated sex divergence in the accuracies of brain-behavior models, such that female-only models consistently outperformed male-only models. Specifically, female-only models identified networks conferring vulnerability for future and current severity using data acquired during both reward and inhibitory fMRI tasks. In contrast, male-only models were successful in accurately identifying networks using data acquired during the inhibitory control-but not reward-task, indicating domain specificity of alcohol use risk networks in male adolescents only. Conclusions and Relevance These data suggest that interventions focusing on inhibitory control processes may be effective in combating alcohol use risk in male adolescents but that both inhibitory and reward-related processes are likely of relevance to alcohol use behaviors in female adolescents. They further identify novel networks of alcohol use risk in youth, which may be used to identify adolescents who are at risk and inform intervention efforts.

Brain Networks and Adolescent Alcohol Use.

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