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

从内表型概况预测高危青少年抑郁症的发作

• 批准号: 10051424
• 负责人: Christian Anthony Webb
• 金额: $ 53.29万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-11 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10051424
• 关键词: Address; Adolescence; Adolescent; Adult; Age; Anhedonia; Behavior; Behavior assessment; Behavioral; Characteristics; Childhood; Clinical; Cluster Analysis; Complex; Conflict (Psychology); Control Groups; Data; Depressed mood; Development; Diagnosis; Dimensions; Disease; Disease remission; Early identification; Emotional; Epidemiology; Equipment and supply inventories; Evaluation; Female; Functional Magnetic Resonance Imaging; Future; Goals; Heterogeneity; Individual; Intervention; Link; Literature; Machine Learning; Major Depressive Disorder; Maps; Measures; Mental Depression; Modeling; National Institute of Mental Health; Negative Valence; Neurobiology; Neurotic Disorders; Online Systems; Onset of illness; Outcome; Parents; Participant; Patient Self-Report; Phase; Positive Valence; Predictive Value; Prevalence; Recording of previous events; Research; Research Domain Criteria; Rewards; Risk; Risk Marker; Sampling; Sensitivity and Specificity; Strategic Planning; Subgroup; Surrogate Markers; Symptoms; System; Teenagers; Telephone; Testing; Youth; algorithm development; anxiety symptoms; base; behavior measurement; clinically relevant; cognitive control; cognitive system; cost; depressive symptoms; endophenotype; experience; follow-up; indexing; kernel methods; machine learning algorithm; male; neural correlate; pleasure; prediction algorithm; prospective; recruit; relating to nervous system; single episode major depressive disorder; socioeconomics; trait

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.

项目摘要/摘要 重度抑郁症（MDD）在儿童时期并不常见，但越来越普遍 在青少年期间。到18岁时，约有15％的青少年将至少经历一集 MDD，女性遭受一集的可能性是男性的两倍。这种发展激增 抑郁症的抑郁症在有MDD病史的父母的青少年中尤其高，近一半 在青春期结束时发展该疾病。尽管有这些流行病学的发现以及 与MDD相关的负面后果，关于神经和行为的数据差不多 异常会议可能会冒着未来抑郁症的风险。预测MDD的能力 在发作之前 处于危险中的年轻人的干预措施与NIMH战略计划密切相符。 为了解决这些差距，以父母的身份增加了12-15岁的青少年以增加MDD发作风险 MDD的历史以及没有父母抑郁病史的对照样本将完成基线 MDD的复制内表型（神经质，Anhedonia，Anhedonia，Anhedonia， 认知控制定义）。越来越多的证据和我们的初步数据表明，这些内表型是 相对稳定的特质风险标记，具有非重叠的神经底物，并在前且前瞻性 预测抑郁症发作。该项目有三个目标。首先，我们将评估这些神经相关性 青少年样本中的三种内型型（n = 148），其中一半的MDD风险较高（AIM 1）。 其次，在24个月的随访阶段，将每6个月通过电话与参与者联系， 采取了评估症状变化的措施。分析将测试行为和中性 内表型测量前瞻性预测随访阶段抑郁症状的发作。 重要的是，要评估增量预测有效性，我们将测试每种内型型号是否测量 预测未来的抑郁症状超出相关临床，家族/人口统计以及 发育变量以前与未来抑郁症的风险有关（AIM 2）。第三，我们将测试是否 多元机器学习模型编码行为和神经内表型标记以及 临床，家庭/人口和发育特征可用于预测特定主体的风险 未来的抑郁症发作具有足够高的灵敏度和特异性，可以在临床上有用（AIM 3）。批判性， 机器学习的最新进展允许发展算法，以预测个人的风险 级别，以及众多预测指标的集成，而不是依靠可能在 隔离，具有有限的临床可用预测价值。总的来说，结果有望提高我们的能力 为了预测抑郁症状的发作，并最终告知免费的可用的开发 基于Web的风险计算器，用于预测特定主题的抑郁症风险。