Vandewouw Marlee M, Syed Bilal, Barnett Noah, Arias Alfredo, Kelley Elizabeth, Jones Jessica, Ayub Muhammad, Iaboni Alana, Arnold Paul D, Crosbie Jennifer, Schachar Russell J, Taylor Margot J, Lerch Jason P, Anagnostou Evdokia, Kushki Azadeh
Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
bioRxiv. 2025 May 9:2024.11.28.625869. doi: 10.1101/2024.11.28.625869.
Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning models that used measures of brain structure (thickness, surface area, and volume) to (a) predict clinically significant internalizing problems cross-sectionally (=14,523); and (b) predict subsequent worsening trajectories (using the reliable change index) of internalizing problems (=10,540) longitudinally. A stratified cross-validation scheme was used to tune, train, and test the models, which were evaluated using the area under the receiving operating characteristic curve (AUC). The cross-sectional model performed well across the sample, reaching an AUC of 0.80[95% CI: 0.71,0.88]. For the longitudinal model, while performance was sub-optimal for predicting worsening trajectories in a sample of the general population (AUC=0.66[0.65,0.67]), good performance was achieved in a small, external test set of primarily ND conditions (AUC=0.80[0.78,0.81]), as well as across all ND conditions (AUC=0.73[0.70,0.76]). Deep learning with features of brain structure is a promising avenue for biomarkers of internalizing problems, particularly for individuals who have a higher likelihood of experiencing difficulties.
内化问题（如焦虑和抑郁）与一系列不良后果相关。虽然已知一些内化问题的预测因素（例如，它们经常与神经发育（ND）疾病同时出现），但内化问题的生物学标志物尚未得到很好的理解。在这里，我们使用深度学习这一用于识别复杂和多维度脑-行为关系的强大工具，来预测内化问题的横断面和恶化的纵向轨迹。数据从四个大规模数据集提取：青少年大脑认知发展研究、健康大脑网络、人类连接组计划发展研究和安大略省神经发育网络。我们开发了深度学习模型，该模型使用脑结构测量值（厚度、表面积和体积）来：（a）横断面预测具有临床意义的内化问题（=14,523）；（b）纵向预测内化问题（=10,540）随后的恶化轨迹（使用可靠变化指数）。使用分层交叉验证方案来调整、训练和测试模型，并使用接受者操作特征曲线（AUC）下的面积对模型进行评估。横断面模型在整个样本中表现良好，AUC达到0.80[95%CI：0.71,0.88]。对于纵向模型，虽然在预测一般人群样本中的恶化轨迹时性能次优（AUC=0.66[0.65,0.67]），但在主要为ND疾病的小型外部测试集中（AUC=0.80[0.78,0.81]）以及所有ND疾病中（AUC=0.73[0.70,0.76]）都取得了良好的性能。利用脑结构特征的深度学习是寻找内化问题生物标志物的一条有前途的途径，特别是对于那些经历困难可能性较高的个体。
