Modeling Covariation Brain Function, Health/Depression

协变大脑功能建模，健康/抑郁

• 批准号: 7079853
• 负责人: Wesley Kurt Thompson
• 金额: $ 17.27万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7079853
• 关键词: antidepressants; behavioral /social science research tag; blood pressure; brain mapping; cardiovascular disorder; cardiovascular disorder risk; comorbidity; computational biology; computational neuroscience; computer program /software; emotions; functional magnetic resonance imaging; health behavior; human data; major depression; mathematical model; mental health epidemiology; neural information processing; psychological stressor; psychopathology; psychopharmacology; statistics /biometry

DESCRIPTION (provided by applicant): The applicant's long-term career goal is to become an independent investigator in the field of psychiatric statistics with an emphasis on research into the relationships between depression and physical health. He will dedicate his career to creating and implementing novel statistical methodologies and study designs to research in this area. This career development award will provide crucial support for achieving this goal. The career development activities proposed during the award period will include training in the scientific and computational models of fMRI studies of depression and cardiovascular reactivity and consultations on causal inference in psychiatric research. There are two primary proposed research goals over the next five years. First, the candidate proposes to adapt statistical methodologies from the field of functional data analysis (FDA) for the examination of potentially causal dynamic covariation in multiple functional processes. Causal inferences will be made by using these statistical methodologies to implement the moderator and mediator framework in the functional setting and also by applications of these methodologies to formal causal models, e.g., structural equation and dynamic causal models. Second, these techniques will be validated on and applied to fMRI studies of depression and cardiovascular reactivity. Specific research goals in these applications will be: 1) exploring potential moderators of patterns of brain activation in depressed and non-depressed subjects; 2) modeling functional integration among specialized brain regions related to depression and cardiovascular reactivity; 3) examining the moderating effect of psychopathological variables on patterns of brain activation in response to emotional stimuli and to stressors; and 4) relating patterns of activation in specialized brain regions to cardiovascular responses to stressors. The proposed research program will result in the creation and application to psychiatric research of a cutting-edge body of statistical methodologies for examining dynamic variation in functional processes. Moreover, while the initial applications will focus on fMRI research, these statistical methodologies will have wide applicability to psychiatric research in general.

描述（由申请人提供）：申请人的长期职业目标是成为精神病统计领域的独立研究者，重点是研究抑郁症与身体健康之间的关系。他将致力于创建和实施新颖的统计方法和研究设计，以在这一领域进行研究。该职业发展奖将为实现这一目标提供重要的支持。奖励期间提出的职业发展活动将包括在抑郁症和心血管反应性的功能磁共振成像研究的科学和计算模型中进行培训，以及有关精神病研究因果推断的咨询。在未来五年中，有两个主要的研究目标。首先，候选人提议从功能数据分析（FDA）领域（FDA）中适应统计方法，以检查多个功能过程中潜在的因果动态协方差。通过使用这些统计学方法来实现功能设置中的主持人和中介框架，以及这些方法对正式因果模型的应用，例如结构方程和动态因果模型，进行因果推断。其次，这些技术将在抑郁症和心血管反应性的fMRI研究上进行验证并应用。这些应用程序中的特定研究目标将是：1）探索抑郁和不抑郁的受试者大脑激活模式的潜在主持人； 2）建模与抑郁和心血管反应性有关的专业大脑区域之间的功能整合； 3）检查心理病理变量对响应情绪刺激和压力源的大脑激活模式的调节作用； 4）将专业大脑区域的激活模式与对压力源的心血管反应有关。拟议的研究计划将导致创建和应用于统计学方法的尖端研究，以检查功能过程中的动态变化。此外，虽然最初的应用将集中在fMRI研究上，但这些统计方法将对一般的精神病研究具有广泛的适用性。