Longitudinal Causal Inferencer for fMRI

描述（由申请人提供）：新的成像方式，如功能性磁共振成像（fMRI），有助于促进我们对人类大脑功能的理解。研究人员有兴趣使用这些技术来研究外部刺激对人脑功能的影响以及不同大脑区域之间的因果关系。对因果问题的关注促使了大量的工作，实证研究人员应用各种统计程序（例如，格兰杰因果关系、动态因果模型、结构方程和有向图模型）与功能磁共振成像数据进行比较，并将结果关联解释为效应，这往往是不恰当的。对于一个因果关系占据中心舞台的领域来说，这是一个主要问题。基于因果关系支持反事实条件陈述的想法，我们使用了在统计文献中广泛使用的潜在结果符号，开发了一个基本框架，用于fMRI研究中的因果推理。我们还扩展了统计文献调解，这主要是采取的情况下，一个治疗，一个单一的调解人和一个单一的反应，多功能调解人的情况下。除了帮助推动功能神经影像学领域的发展外，我们还对因果推理，功能数据分析和纵向数据分析的研究做出了贡献。我们开发的方法与高维数据的因果推理，应用这些功能磁共振成像数据的创伤后应激障碍，热疼痛和社会评价的研究。

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.