■注意缺陷多动障碍(ADHD)的外化障碍,对立的反抗障碍(ODD),和行为障碍(CD)在青春期很常见,并且是成人精神病理学的有力预测因子。虽然可以治疗,大量的诊断重叠使干预计划复杂化。了解哪些因素可以预测每种疾病的发作并消除其不同预测因素的歧义具有很大的翻译兴趣。
■我们分析了9-10岁儿童及其父母的5,777个多模式候选预测因子,以预测ADHD的未来发作,ODD,和CD在2年随访。我们使用创新的AI算法优化的深度学习来联合优化模型训练,执行自动特征选择,并构建11-12岁时疾病发作和所有流行病例的个体水平预测,并在候选预测因子仅限于神经指标时检查相对预测性能。
■多模态模型达到了86-97%的精度,0.919-0.996AUROC,在坚持测试中,准确率和召回率为82-97%,看不见的数据在纯神经模型中,预测性能大幅下降,但仍实现了约80%的准确性和AUROC。父母的侵略性和外化特征独特地区分了ODD的发作,而边缘系统的结构性MRI指标是针对CD的。睡眠障碍的心理社会措施,父母的心理健康和行为特征,学校的表现在所有疾病中都被证明是有价值的。在纯神经模型中,强调了皮质下区域的结构和功能MRI指标以及皮质-皮质下连通性。总的来说,我们确定了准确性和最终预测因子重要性之间的强相关性.
■使用AI优化的深度学习可以使用多模态特征对早期青少年外化障碍的发作产生高度准确的个体水平预测。虽然外化障碍在青少年中经常并发,某些预测因子特定于ODD或CD的发作多动症.据我们所知,这是第一项机器学习研究,通过相同的设计和参与者队列来预测所有三种主要的青少年外部化障碍的发作,以实现直接比较,分析>200个多模态特征,并包括许多类型的神经成像指标。未来在外部验证数据中测试我们的观察结果的研究将有助于进一步测试这些发现的普遍性。
UNASSIGNED: The externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest.
UNASSIGNED: We analyzed 5,777 multimodal candidate predictors from children aged 9-10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11-12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics.
UNASSIGNED: Multimodal models achieved ~86-97% accuracy, 0.919-0.996 AUROC, and ~82-97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance.
UNASSIGNED: Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings.