Predicting the onset of depression in at-risk adolescents from endophenotype profiles

项目总结/摘要 重度抑郁症（MDD）在儿童期并不常见，但越来越普遍 在青春期。到18岁时，大约15%的青少年至少经历过一次 MDD，女性发病的可能性是男性的两倍。这种发展的激增， 抑郁症在父母有抑郁症病史的青少年中尤其高，近一半的青少年患有抑郁症。 在青春期结束时患上这种疾病。尽管有这些流行病学调查结果， 与MDD相关的负面下游后果，关于神经和行为的数据少得惊人。 这些异常会给年轻人带来未来抑郁症发作的风险。前瞻性预测MDD的能力 在其发病之前，将具有重要的临床意义， 为高危青年部署干预措施，这与《国家精神卫生战略计划》高度一致。 为了解决这些差距，12-15岁的青少年由于父母的关系而增加了MDD发作的风险。 MDD病史以及父母无抑郁症病史的对照样本将完成基线检查 对MDD的复制内表型（神经质，快感缺乏， 认知控制缺陷）。越来越多的证据和我们的初步数据表明，这些内在表型是 相对稳定的特征样风险标志物，具有非重叠的神经基质，并且先于和前瞻性地 预测抑郁症的发作该项目有三个目标。首先，我们将评估这些神经相关性， 在青少年样本（n = 148）中的三种内表型，其中一半处于MDD的高风险中（目标1）。 第二，在24个月的随访阶段，将每6个月通过电话联系参与者， 管理措施，以评估症状的变化。分析将测试行为和神经 内表型测量前瞻性地预测随访期间抑郁症状的发作。 重要的是，为了评估增量预测有效性，我们将测试每个内表型指标是否 预测未来的抑郁症状超过相关的临床，家族/人口统计学和 以前与未来抑郁风险相关的发展变量（目标2）。第三，我们将测试 多变量机器学习模型，包括行为和神经内表型标记，以及 临床、家族/人口统计学和发育特征，可用于预测受试者特异性的 未来抑郁症发作具有足够高的敏感性和特异性，可用于临床（目标3）。重要的是， 机器学习的最新进展允许开发预测个体风险的算法 水平，以及许多预测因素的整合，而不是依赖于单一的变量， 隔离，具有有限的临床有用的预测价值。总的来说，这些结果有望提高我们的能力， 来预测抑郁症状的发作，并最终告知一个免费提供的， 基于网络的风险计算器，用于预测特定受试者的抑郁风险。

Project Summary/Abstract Major depressive disorder (MDD) is uncommon in childhood, but becomes increasingly prevalent during adolescence. By the age of 18, about 15% of adolescents will have experienced at least one episode of MDD, with females twice as likely than males to have suffered an episode. This developmental surge in depression is especially high among teens who have a parent with a history of MDD, with close to half developing the disorder by the end of adolescence. Despite these epidemiological findings, and the range of negative downstream consequences linked to MDD, there are strikingly little data on the neural and behavioral abnormalities that confer risk for future depression onset in youth. The ability to prospectively predict MDD prior to its onset would have important clinical implications for the early identification of – and targeted deployment of interventions for – at-risk youth, which is strongly aligned with the NIMH Strategic Plan. To address these gaps, adolescents ages 12-15 at increased risk of MDD onset by virtue of a parental history of MDD, as well as a control sample with no parental history of depression, will complete baseline neural (fMRI) and behavioral assessments of replicated endophenotypes of MDD (neuroticism, anhedonia, cognitive control deficits). Growing evidence and our preliminary data suggest that these endophenotypes are relatively stable trait-like risk markers, have non-overlapping neural substrates, and precede and prospectively predict depression onset. The project has three aims. First, we will evaluate the neural correlates of these three endophenotypes in an adolescent sample (n = 148), half of whom are at elevated risk of MDD (Aim 1). Second, during a 24-month follow-up phase, participants will be contacted by phone every 6 months and administered measures to assess changes in symptoms. Analyses will test whether behavioral and neural endophenotype measures prospectively predict onset of depressive symptoms during the follow-up phase. Importantly, to evaluate incremental predictive validity, we will test whether each endophenotype measure predicts future depressive symptoms above and beyond relevant clinical, familial/demographic and developmental variables previously linked with risk of future depression (Aim 2). Third, we will test whether multivariate machine learning models incorporating behavioral and neural endophenotype markers, as well as clinical, familial/demographic, and developmental characteristics, can be used to predict subject-specific risk of future depression onset with sufficiently high sensitivity and specificity to be clinically useful (Aim 3). Critically, recent advances in machine learning allow for the development of algorithms predicting risk at the individual level, as well as the integration of numerous predictors rather than relying on single variables that may, in isolation, have limited clinically-useful predictive value. Collectively, results are expected to advance our ability to predict the onset of depressive symptoms and, ultimately, to inform the development of a freely available, web-based risk calculator for predicting subject-specific depression risk.