Neural Predictors of Initiating Alcohol Use During Adolescence.
Neural Predictors of Initiating Alcohol Use During Adolescence.
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DOI：
10.1176/appi.ajp.2016.15121587
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发表时间：
2017-02-01
期刊：
影响因子：
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通讯作者：
Tapert SF
中科院分区：
文献类型：
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作者：
Squeglia LM;Ball TM;Jacobus J;Brumback T;McKenna BS;Nguyen-Louie TT;Sorg SF;Paulus MP;Tapert SF
在美国，未成年人饮酒被广泛认为是青少年的主要公共健康和社会问题。能够在高危儿童开始大量饮酒之前识别他们，可能会对临床和公共卫生产生巨大影响；然而，很少有研究探索青少年物质使用的个人层面的前体。这项前瞻性研究使用机器学习和人口学、神经认知和神经成像数据来预测18岁之前开始使用酒精。参与者(N=137)是健康的物质天真青少年(12-14岁)，他们接受了神经心理测试和结构和功能磁共振成像(sMRI和fMRI)，然后每年进行跟踪调查。到18岁时，70名年轻人(51%)开始使用中到重度酒精，67人仍然不使用酒精。随机森林分类基于人口统计学、神经心理学、sMRI和fMRI数据生成了个人酒精使用结果预测。最终的随机森林模型准确率为74%，具有良好的敏感度(74%)和特异度(73%)，包括34个导致18岁之前饮酒的预测因素，包括几个人口统计和行为因素(男性、较高的社会经济地位、早期约会、更多的外在行为、积极的酒精预期)、执行功能较差、大脑皮质变薄和大脑广泛分布区域的大脑激活较少。纳入神经心理学、sMRI和fMRI数据显著提高了模型的预测精度。高危青少年的鉴定未经临床验证。它的价值在于研究如何解决导致早期饮酒的大脑机制。
Underage drinking is widely recognized as a leading public health and social problem for adolescents in the United States. Being able to identify at-risk children before they initiate heavy alcohol use could have immense clinical and public health implications; however, few investigations have explored individual-level precursors of adolescent substance use. This prospective investigation used machine learning with demographic, neurocognitive, and neuroimaging data in substance-naïve adolescents to predict alcohol use initiation by age 18. Participants (N=137) were healthy substance-naïve adolescents (ages 12–14) who underwent neuropsychological testing and structural and functional magnetic resonance imaging (sMRI and fMRI), then were followed annually. By age 18, 70 youth (51%) initiated moderate-to-heavy alcohol use and 67 remained non-users. Random forests classification generated individual alcohol use outcome predictions based on demographic, neuropsychological, sMRI, and fMRI data. The final random forests model was 74% accurate, with good sensitivity (74%) and specificity (73%) and included 34 predictors contributing to alcohol use by age 18, including several demographic and behavioral factors (being male, higher socioeconomic status, early dating, more externalizing behaviors, positive alcohol expectancies), worse executive functioning, and thinner cortices and less brain activation in diffusely distributed regions of the brain. Inclusion of neuropsychological, sMRI, and fMRI data significantly increased the prediction accuracy of the model. Identification of at-risk youth is not validated for clinical use. Its value is for research to address brain mechanisms that predispose to early drinking.