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.
A general framework for causal inference in functional magnetic resonance imaging (fMRI) is developed, using the potential outcomes notation widely used in the statistical literature. The framework and methods developed will be useful for studying brain networks, neurosurgical planning and the development of brain-based biomarkers. We also expect the methods to be useful in other applied contexts where high dimensional longitudinal data are collected and/or responses are measured with error.
|Chen, Shaojie; Huang, Lei; Qiu, Huitong et al. (2017) Parallel group independent component analysis for massive fMRI data sets. PLoS One 12:e0173496|
|Kudela, Maria; Harezlak, Jaroslaw; Lindquist, Martin A (2017) Assessing uncertainty in dynamic functional connectivity. Neuroimage 149:165-177|
|Webb-Vargas, Yenny; Chen, Shaojie; Fisher, Aaron et al. (2017) Big Data and Neuroimaging. Stat Biosci 9:543-558|
|Chén, Oliver Y; Crainiceanu, Ciprian; Ogburn, Elizabeth L et al. (2017) High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics :|
|Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina et al. (2017) Group-regularized individual prediction: theory and application to pain. Neuroimage 145:274-287|
|Choe, Ann S; Nebel, Mary Beth; Barber, Anita D et al. (2017) Comparing test-retest reliability of dynamic functional connectivity methods. Neuroimage 158:155-175|
|Li, Shanshan; Chen, Shaojie; Yue, Chen et al. (2016) A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data. Front Neurosci 10:15|
|Nguyen, Trang Quynh; Webb-Vargas, Yenny; Koning, Ina M et al. (2016) Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention. Struct Equ Modeling 23:368-383|
|Choe, Ann S; Jones, Craig K; Joel, Suresh E et al. (2015) Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years. PLoS One 10:e0140134|
|Lindquist, Martin A; Mejia, Amanda (2015) Zen and the art of multiple comparisons. Psychosom Med 77:114-25|
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