Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip

IntroductionInternalizing disorders (depression, anxiety, somatic symptom disorder) are among the most common mental health conditions that can substantially reduce daily life function. Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies.
MethodsWe analyzed ~6,000 candidate predictors from multiple knowledge domains (cognitive, psychosocial, neural, biological) contributed by children of late elementary school age (9–10 yrs) and their parents in the ABCD cohort to construct individual-level models predicting the later (11–12 yrs) onset of depression, anxiety and somatic symptom disorder using deep learning with artificial neural networks. Deep learning was guided by an evolutionary algorithm that jointly performed optimization across hyperparameters and automated feature selection, allowing more candidate predictors and a wider variety of predictor types to be analyzed than the largest previous comparable machine learning studies.
ResultsWe found that the future onset of internalizing disorders could be robustly predicted in early adolescence with AUROCs ≥~0.90 and ≥~80% accuracy.
DiscussionEach disorder had a specific set of predictors, though parent problem behavioral traits and sleep disturbances represented cross-cutting themes. Additional computational experiments revealed that psychosocial predictors were more important to predicting early adolescent internalizing disorders than cognitive, neural or biological factors and generated models with better performance. Future work, including replication in additional datasets, will help test the generalizability of our findings and explore their application to other stages in human development and mental health conditions.

引言：
内化障碍（internalizing disorders）包括抑郁症、焦虑症、躯体症状障碍，是最常见的精神健康疾病之一，可显著降低个体日常功能水平。早青春期是内化障碍患病率快速上升的关键发育阶段，厘清预测其发病的特异性因素，对制定干预与预防策略具有重要参考意义。

研究方法：
我们对ABCD队列（ABCD cohort）中9~10岁的小学学龄晚期儿童及其家长提供的、涵盖认知、社会心理、神经、生物学等多个知识领域的约6000个候选预测因子进行分析，采用基于人工神经网络（artificial neural networks）的深度学习技术构建个体水平模型，以预测后续11~12岁阶段抑郁症、焦虑症及躯体症状障碍的发病情况。本研究采用进化算法（evolutionary algorithm）指导深度学习流程，可同时完成超参数优化与自动化特征选择，相较于此前规模最大的同类机器学习（machine learning）研究，能够纳入更多候选预测因子并分析更广泛的预测因子类型。

研究结果：
本研究发现，在早青春期阶段可稳健预测未来内化障碍的发病情况，受试者工作特征曲线下面积（AUROC）≥约0.90，预测准确率≥约80%。

讨论：
每种障碍均存在专属的预测因子集，而家长的问题行为特质与睡眠障碍为跨障碍的共同核心主题。额外开展的计算实验表明，相较于认知、神经或生物学因素，社会心理预测因子对早青春期内化障碍的预测价值更高，所构建的模型性能更优。未来可通过在更多数据集上开展重复验证，检验本研究发现的可推广性，并探索其在人类发育其他阶段及其他精神健康疾病中的应用。