Department of Statistical Methods, University of Zaragoza, Zaragoza, Spain; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
Neuroimage Clin. 2024;42:103604. doi: 10.1016/j.nicl.2024.103604. Epub 2024 Apr 8.
Depression is an incapacitating psychiatric disorder with increased risk through adolescence. Among other factors, children with family history of depression have significantly higher risk of developing depression. Early identification of pre-adolescent children who are at risk of depression is crucial for early intervention and prevention. In this study, we used a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9-10 years at baseline), we applied advanced machine learning methods to predict depression risk at the two-year follow-up from the baseline assessment, using a set of comprehensive multimodal neuroimaging features derived from structural MRI, diffusion tensor imaging, and task and rest functional MRI. Prediction performance underwent a rigorous cross-validation method of leave-one-site-out. Our results demonstrate that all brain features had prediction scores significantly better than expected by chance, with brain features from rest-fMRI showing the best classification performance in the high-risk group of participants with parental history of depression (N = 625). Specifically, rest-fMRI features, which came from functional connectomes, showed significantly better classification performance than other brain features. This finding highlights the key role of the interacting elements of the connectome in capturing more individual variability in psychopathology compared to measures of single brain regions. Our study contributes to the effort of identifying biological risks of depression in early adolescence in population-based samples.
抑郁症是一种使人丧失能力的精神障碍，在青少年时期风险增加。在其他因素中，有抑郁症家族史的儿童患抑郁症的风险显著更高。早期识别有患抑郁症风险的青春期前儿童对于早期干预和预防至关重要。在这项研究中，我们使用了来自青少年大脑认知发展（ABCD）研究的大型纵向样本（经过成像质量控制后有 2658 名参与者，基线时年龄在 9-10 岁之间），我们应用了先进的机器学习方法，从基线评估开始，使用从结构 MRI、弥散张量成像和任务及静息功能 MRI 中提取的一组综合多模态神经影像学特征，预测两年后的抑郁风险。预测性能经过了严格的留一站点外交叉验证方法。我们的结果表明，所有的大脑特征的预测评分都明显优于随机预期，来自静息 fMRI 的大脑特征在有抑郁父母病史的高风险组参与者（N=625）中表现出最好的分类性能。具体来说，来自功能连接组的静息 fMRI 特征表现出比其他大脑特征更好的分类性能。这一发现强调了连接组的相互作用元素在捕捉精神病理学中的个体变异性方面比单一脑区测量更具关键作用。我们的研究有助于在基于人群的样本中确定青少年早期抑郁的生物学风险。
