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学统计领域的独立研究者，重点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来研究该领域。该职业发展奖将为实现这一目标提供至关重要的支持。颁奖期间提出的职业发展活动将包括抑郁症和心血管反应性功能磁共振成像研究的科学和计算模型培训，以及精神病学研究中因果推理的咨询。未来五年有两个主要的研究目标。首先，候选人建议采用功能数据分析（FDA）领域的统计方法来检查多个功能过程中潜在的因果动态协变。将通过使用这些统计方法在功能设置中实现调节器和中介器框架以及将这些方法应用于正式因果模型（例如结构方程和动态因果模型）来进行因果推论。其次，这些技术将在抑郁症和心血管反应性的功能磁共振成像研究中得到验证并应用。这些应用的具体研究目标是：1）探索抑郁和非抑郁受试者大脑激活模式的潜在调节因素； 2）对与抑郁和心血管反应相关的专门大脑区域之间的功能整合进行建模； 3）检查精神病理变量对情绪​​刺激和压力源的大脑激活模式的调节作用； 4）将特定大脑区域的激活模式与心血管对压力源的反应联系起来。拟议的研究计划将导致创建用于检查功能过程动态变化的尖端统计方法并将其应用于精神病学研究。此外，虽然最初的应用将集中在功能磁共振成像研究上，但这些统计方法将广泛适用于一般精神病学研究。