多水平预测因子对青少年大脑认知发展（ABCD）研究中抑郁症状的影响。
Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study.
机构信息
Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
出版信息
J Child Psychol Psychiatry. 2022 Dec;63(12):1523-1533. doi: 10.1111/jcpp.13608. Epub 2022 Mar 21.
BACKGROUND
While identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study.
METHODS
A total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up.
RESULTS
Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE = 3.757; R = 0.156; GBT: MAE = 3.761; R = 0.147) and 1-year follow-up (EN: MAE = 4.255; R = 0.103; GBT: MAE = 4.262; R = 0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints.
CONCLUSIONS
Consistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk.
背景
虽然确定青少年抑郁的风险因素对于早期预防和干预至关重要，但大多数研究都试图了解孤立因素的作用，而不是广泛的因素。在这里，我们试图研究多层次的因素，最大限度地预测参与美国青少年大脑与认知发展研究（ABCD）的儿童的抑郁症状。
方法
共有 7995 名来自 ABCD（版本 3.0 发布）的参与者在基线和 1 年随访时提供了完整的数据。抑郁症状采用儿童行为检查表进行测量。预测特征包括儿童人口统计学、环境、结构和静息状态 fMRI 变量、父母的抑郁史和人口统计学特征。我们使用线性（弹性网回归，EN）和非线性（梯度提升树，GBT）预测模型来确定哪些特征集可以最大限度地预测基线时和 1 年随访时的抑郁症状。
结果
线性和非线性模型在预测基线时（EN：MAE=3.757；R=0.156；GBT：MAE=3.761；R=0.147）和 1 年随访时（EN：MAE=4.255；R=0.103；GBT：MAE=4.262；R=0.089）的抑郁症状都取得了类似的结果。父母的抑郁史、更大的家庭冲突和更短的儿童睡眠时间是两种模型中同时和未来儿童抑郁症状的主要预测因素。尽管静息状态 fMRI 特征是相对较弱的预测因素，但尾状核的功能连接始终是与两个时间点的抑郁症状最相关的最强神经特征。
结论
与先前的研究一致，父母的心理健康、家庭环境和儿童的睡眠质量是青少年抑郁的重要风险因素。尾状核的功能连接是抑郁症状的相对较弱的预测因素，但可能代表抑郁风险的生物标志物。
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