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），有助于提高我们对人类大脑功能的理解。研究人员有兴趣利用这些技术来研究外部刺激对人类大脑功能的影响以及大脑不同区域之间的因果关系。对因果关系问题的关注促使了大量的工作，实证研究人员将各种统计程序（例如，格兰杰因果关系，动态因果模型，结构方程和定向图形模型）应用于fMRI数据，并将结果关联解释为效应，通常是不恰当的。对于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观点，我们使用在统计文献中广泛使用的潜在结果符号来开发fMRI研究中因果推理的基本框架。我们还扩展了关于调解的统计文献，这些文献主要涉及治疗，单一调解人和单一响应的情况，以及多个功能调解人的情况。除了帮助推动功能神经成像领域的发展外，我们还在因果推理，功能数据分析和纵向数据分析方面做出了贡献。我们开发了使用高维数据进行因果推理的方法，并将这些方法应用于创伤后应激障碍、热痛和社会评价研究的功能磁共振成像数据。

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.