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

项目摘要/摘要 重度抑郁障碍(MDD)在儿童时期并不常见，但变得越来越普遍 在青春期。到18岁时，大约15%的青少年将经历至少一次 MDD，女性发病的可能性是男性的两倍。这一发展热潮 在父母有MDD病史的青少年中，抑郁症尤其严重，近一半的人患有抑郁症 在青春期结束时发展成这种障碍。尽管有这些流行病学调查结果，而且 与MDD相关的负面下游后果，关于神经和行为的数据惊人地少 异常增加了年轻人未来抑郁发作的风险。前瞻性预测MDD的能力 在发病之前，对于早期识别和靶向的 部署针对高危青年的干预措施，这与NIMH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原因，MDD发病的风险增加 MDD病史以及没有父母抑郁病史的对照样本将完成基线 神经(FMRI)和行为评估复制的MDD的内表型(神经质、快感缺失、 认知控制缺陷)。越来越多的证据和我们的初步数据表明，这些内表型 相对稳定的特征样风险标记，具有不重叠的神经底物，并且先于和前瞻性地 预测抑郁症的发病。该项目有三个目标。首先，我们将评估这些神经元之间的神经关联 在一个青少年样本中(n=148)有三种内表型，其中一半的人患MDD的风险增加(目标1)。 其次，在为期24个月的跟踪阶段，参与者将每6个月通过电话联系一次，并 采取措施评估症状的变化。分析将测试行为和神经 内表型测量前瞻性地预测在后续阶段抑郁症状的发生。 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测量 预测未来的抑郁症状，超越相关的临床、家族/人口统计和 以前与未来抑郁风险有关的发育变量(目标2)。第三，我们将测试 包含行为和神经内表型标记的多变量机器学习模型，以及 临床、家族/人口学和发育特征可用来预测特定于受试者的风险。 未来抑郁症的发作具有足够高的敏感度和特异度，可用于临床(目标3)。关键是， 机器学习的最新进展使预测个体风险的算法得以发展 水平，以及多个预测值的集成，而不是依赖单个变量，这些变量可能 隔离，对临床有用的预测价值有限。总体而言，预计结果将提高我们的能力 为了预测抑郁症状的出现，并最终告知可免费获得的 基于Web的风险计算器，用于预测特定于受试者的抑郁症风险。

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.