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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要重度抑郁症 (MDD) 在儿童时期并不常见，但变得越来越普遍到 18 岁时，大约 15% 的青少年至少经历过一次。 MDD，女性发生这种发育激增的可能性是男性的两倍。在父母有重度抑郁症病史的青少年中，抑郁症发病率尤其高，近一半的人尽管有这些流行病学发现和范围，但在青春期结束时仍会出现这种疾病。与 MDD 相关的负面下游后果，关于神经和行为的数据却少得惊人前瞻性预测 MDD 的能力。在其发病之前对早期识别和针对性治疗具有重要的临床意义为高危青少年部署干预措施，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影响而患 MDD 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 MDD（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是相对稳定的性状风险标记，具有不重叠的神经基质，并且具有先行性和前瞻性该项目有三个目标：首先，我们将评估这些的神经相关性。青少年样本 (n = 148) 中存在三种内表型，其中一半的人患 MDD 的风险较高（目标 1）。其次，在 24 个月的随访阶段，每 6 个月将通过电话联系参与者，评估症状变化的管理措施将测试行为和神经是否发生变化。内表型测量前瞻性地预测随访阶段抑郁症状的发作。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测量是否预测未来的抑郁症状，超越相关的临床、家族/人口统计和先前与未来抑郁症风险相关的发展变量（目标 2）第三，我们将测试是否存在。结合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/人口统计和发育特征，可用于预测受试者特定的风险未来抑郁症发作具有足够高的敏感性和特异性，在临床上有用（至关重要的是，机器学习的最新进展允许开发预测个人风险的算法水平，以及众多预测变量的整合，而不是依赖于单个变量，这些变量可能会总的来说，结果预计将提高我们的能力。预测抑郁症状的发作，并最终为开发免费的、基于网络的风险计算器，用于预测特定主题的抑郁症风险。