The Roles of Inflammatory and Glutamatergic Processes in the Neurodevelopmental Mechanisms Underlying Adolescent Depression

炎症和谷氨酸能过程在青少年抑郁症神经发育机制中的作用

• 批准号: 9933235
• 负责人: TIFFANY CHEING HO
• 金额: $ 4.11万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9933235
• 关键词: Address; Administrative Supplement; Adolescent; Age; Area; Behavior; Cause of Death; Classification; Clinical; Data; Depressive disorder; Diffusion Magnetic Resonance Imaging; Enrollment; Ensure; Etiology; Feeling suicidal; Fostering; Functional Magnetic Resonance Imaging; Funding; Gender; Glutamates; Goals; Grant; Growth; Inflammation; Inflammatory; Infrastructure; Interview; Investigation; Laboratories; Logistic Regressions; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Maintenance; Measurement; Measures; Mediating; Modeling; National Institute of Mental Health; Nature; Neurobiology; Patient Self-Report; Pattern; Phenotype; Prevalence; Process; Questionnaires; Recording of previous events; Research; Research Personnel; Research Training; Rest; Risk; Risk Factors; Role; Sampling; Sleep; Social Interaction; Statistical Methods; Stress; Structure; Suicide; Technology; Testing; Time; United States; adolescent suicide; base; child depression; classification algorithm; cognitive testing; comparison group; cytokine; digital; epidemiologic data; falls; feature detection; high dimensionality; high risk; high-risk adolescents; immune function; improved; longitudinal dataset; machine learning algorithm; mobile application; multidimensional data; multimodality; neurobiological mechanism; neuroimaging; novel; parent grant; psychosocial; real time monitoring; suicidal; suicidal behavior; suicidal risk; suicide attempter; trend

ABSTRACT Suicidal thoughts and behaviors (STBs) are growing more prevalent among adolescents; despite these alarming trends, researchers have been hampered in their efforts to identify antecedents of STBs because of the transient nature of suicidal impulses that are unlikely to be captured during a clinical or laboratory assessment. Furthermore, research in this area has focused primarily on time-invariant factors (e.g., gender) and self-disclosed information, which greatly limits our understanding of the neurobiological and psychosocial mechanisms underlying the etiology and maintenance of STBs. Advances in real-time monitoring technology, including mobile apps, provide an unprecedented opportunity to continuously measure key behaviors relevant for understanding suicide risk (e.g., social interactions, sleep) outside of the laboratory for the purposes generating digital phenotypes of STBs. Moreover, statistical approaches such as machine learning are ideal for handling high-dimensional data across different constructs (e.g., clinical, digital, neurobiological) and are increasingly being used in the context of improving prediction of STBs. Thus, the overarching goal of this supplement is to collect and integrate digital phenotypes with neurobiological phenotypes in a machine learning framework to identify multi-level factors associated with the etiology and maintenance of STBs in a high-risk sample: depressed adolescents. Specifically, we will build on the existing infrastructure of the parent grant—which focuses on characterizing the stress-related neurobiological trajectories using a multi-level approach in a sample of depressed adolescents—by seeking to identify multi-level predictors and trajectories of STBs in this high-risk sample and to compute deviations from normative phenotypes and trajectories computed from a low-risk comparison group. We will use machine learning algorithms to identify the constellation of factors that best predict likelihood of engaging in STBs by Time 3 among the depressed adolescents (Aim 1); we will also identify the factors that best predict trajectories of STBs based on changes from Time to Time 3 among the depressed adolescents (Aim 2); we will also test whether deviations from normative phenotypes and trajectories (computed from data in the healthy controls) are better predictors of STBs (Aim 3). In accordance with NOT-MH-19-026 (“Administrative Supplements for NIMH Grants to Expand Suicide Research”), this approach addresses current barriers in our understanding of the mechanisms of action underlying suicide risk by collecting ecologically valid measurements of suicide-relevant behaviors and by fostering advanced statistical methods for multi-level and cross-construct integration.

摘要 自杀的想法和行为（STBs）在青少年中越来越普遍;尽管如此， 令人担忧的趋势，研究人员一直在努力确定STBs的前身，因为 自杀冲动的短暂性，在临床或实验室中不太可能被捕获 考核此外，该领域的研究主要集中在时不变因素（例如，性别） 和自我披露的信息，这极大地限制了我们对神经生物学和心理社会学的理解。 STBs的病因学和维持机制。实时监测技术的进步， 包括移动的应用程序，提供了一个前所未有的机会，可以持续衡量关键的相关行为 为了理解自杀风险（例如，社会交往，睡眠）在实验室外的目的 生成STB的数字表型。此外，机器学习等统计方法非常适合 处理跨不同构造的高维数据（例如，临床、数字、神经生物学）， 越来越多地被用于改善STB的预测。因此，这一总体目标 补充是在机器中收集并整合数字表型与神经生物学表型 学习框架，以确定多层次的因素与病因学和维护性传播疾病， 高风险样本：抑郁青少年。具体而言，我们将在母公司现有基础设施的基础上进行建设 格兰特-这侧重于表征压力相关的神经生物学轨迹使用多层次的 方法在一个样本的抑郁症-通过寻求确定多层次的预测和轨迹 在这个高风险样本中的STB，并计算偏离规范表型和轨迹 从低风险对照组计算。我们将使用机器学习算法来识别 最能预测抑郁症患者在时间3前从事STBs的可能性的因素星座 青少年（目标1）;我们还将根据变化确定最能预测STB轨迹的因素 从时间到时间3在抑郁的青少年（目标2）;我们还将测试是否偏离 正常表型和轨迹（从健康对照组的数据计算）是更好的预测因子， STB（目标3）。根据NOT-MH-19-026（“NIMH赠款扩展的行政补充 自杀研究”），这种方法解决了我们理解自杀机制的现有障碍。 通过收集自杀相关行为的生态有效测量结果， 通过培养先进的统计方法进行多层次和跨结构的整合。