Mapping differential brain structures for psychiatric problems has been challenging due to a lack of regional convergence and poor replicability in previous brain-behavior association studies. By leveraging two independent large cohorts of neurodevelopment, the ABCD and Generation R Studies (total N = 11271), we implemented an unsupervised machine learning technique with a highly stringent generalizability test to identify reliable brain-behavior associations across diverse domains of child psychiatric problems. Across all psychiatric symptoms measured, one multivariate brain-behavior association was found, reflecting a widespread reduction of cortical surface area correlated with higher child attention problems. Crucially, this association showed marked generalizability across different populations and study protocols, demonstrating potential clinical utility. Moreover, the derived brain dimension score predicted child cognitive and academic functioning three years later and was also associated with polygenic scores for ADHD. Our results indicated that attention problems could be a phenotype for establishing promising multivariate neurobiological prediction models for children across populations. Future studies could extend this investigation into different development periods and examine the predictive values for assessment of functioning, diagnosis, and disease trajectory in clinical samples.

由于在以前的大脑-行为关联研究中缺乏区域收敛和可复制性差，因此绘制精神问题的差异大脑结构一直具有挑战性。通过利用两个独立的大型神经发育队列，即 ABCD 和 Generation R 研究 （总 N = 11271），我们实施了一种无监督的机器学习技术，具有高度严格的泛化性测试，以确定儿童精神问题不同领域的可靠大脑行为关联。在所有测量的精神症状中，发现了一个多变量大脑行为关联，反映了皮质表面积的广泛减少与儿童注意力问题的增加相关。至关重要的是，这种关联在不同人群和研究方案中显示出明显的普遍性，证明了潜在的临床效用。此外，得出的大脑维度评分可预测三年后儿童的认知和学业功能，并且还与 ADHD 的多基因评分相关。我们的结果表明，注意力问题可能是为不同人群的儿童建立有前途的多变量神经生物学预测模型的表型。未来的研究可以将这项调查扩展到不同的发展时期，并检查评估临床样本的功能、诊断和疾病轨迹的预测价值。