Longitudinal Causal Inferencer for fMRI

fMRI 纵向因果推断器

• 批准号: 8681440
• 负责人: Martin Lindquist
• 金额: $ 28.19万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8681440
• 关键词: Affect; Agreement; Area; Attention; Base of the Brain; Biological Markers; Brain; Brain imaging; Brain region; Bypass; Data; Data Analyses; Development Plans; Equation; Equilibrium; Etiology; Evaluation; Experimental Designs; Functional Magnetic Resonance Imaging; Goals; Human; Imaging technology; Knowledge; Literature; Measures; Mediating; Mediation; Mediator of activation protein; Methodology; Methods; Modeling; Morphologic artifacts; Outcome; Pain; Post-Traumatic Stress Disorders; Procedures; Randomized; Regimen; Research; Research Personnel; Rest; Series; Staging; Stimulus; Structural Models; Techniques; Technology; Time; Transcranial magnetic stimulation; Treatment Protocols; Work; blood oxygenation level dependent response; design; imaging modality; interest; neuroimaging; novel; public health relevance; research study; response; social; treatment effect

DESCRIPTION (provided by applicant): New imaging modalities such as functional magnetic resonance imaging (fMRI) have helped advance our understanding of human brain function. Researchers are interested in using these techniques to study both effects of external stimuli on human brain function and causal relations among different brain regions. The concern with causal issues has prompted much work, with empirical researchers applying various statistical procedures (e.g., Granger causality, dynamic causal models, structural equation and directed graphical models) to fMRI data and interpreting the resulting associations as effects, often inappropriately. This is a major problem for a field where causation occupies center stage. Building on the idea that causal relationships sustain counterfactual conditional statements, we use potential outcomes notation, widely used in the statistical literature, to develop a basic framework for causal inference in fMRI research. We also extend the statistical literature on mediation, which mostly takes up the case of a treatment, a single mediator and a single response, to the case of multiple functional mediators. In addition to helping push the field of functional neuroimaging forward, we contribute to the research on causal inference, functional data analysis and longitudinal data analysis. We develop methods for causal inference with high dimensional data, applying these to fMRI data from studies of post-traumatic stress disorder, thermal pain and social evaluation.

描述（由申请人提供）：新的成像方式（例如功能磁共振成像（fMRI））有助于促进我们对人脑功能的理解。研究人员有兴趣使用这些技术研究外部刺激对不同大脑区域之间人脑功能和因果关系的影响。因果问题的关注促使了很多工作，经验研究人员将各种统计程序（例如Granger因果关系，动态因果模型，结构方程和定向图形模型）应用于fMRI数据并将结果关联解释为效果，通常是不正确的。对于因果关系占据中心阶段的领域而言，这是一个主要问题。基于因果关系维持反事实的条件陈述的观念，我们使用了潜在的结果符号（统计文献中广泛使用）来开发fMRI研究中因果关系的基本框架。我们还将有关调解的统计文献扩展到多个功能介体的情况下，主要占用治疗，单个介体和单个反应的情况。除了帮助推动功能神经影像的领域外，我们还为因果推理，功能数据分析和纵向数据分析做出了贡献。我们开发了使用高维数据的因果推断的方法，将这些方法应用于创伤后应激障碍，热疼痛和社会评估的研究中的fMRI数据。