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.
| Chén, Oliver Y; Crainiceanu, Ciprian; Ogburn, Elizabeth L et al. (2018) High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics 19:121-136 |
| Solo, Victor; Poline, Jean-Baptiste; Lindquist, Martin A et al. (2018) Connectivity in fMRI: Blind Spots and Breakthroughs. IEEE Trans Med Imaging 37:1537-1550 |
| Mejia, Amanda F; Nebel, Mary Beth; Barber, Anita D et al. (2018) Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage. Neuroimage 172:478-491 |
| Barber, Anita D; Lindquist, Martin A; DeRosse, Pamela et al. (2018) Dynamic Functional Connectivity States Reflecting Psychotic-like Experiences. Biol Psychiatry Cogn Neurosci Neuroimaging 3:443-453 |
| Chen, Shaojie; Liu, Kai; Yang, Yuguang et al. (2017) An M-estimator for reduced-rank system identification. Pattern Recognit Lett 86:76-81 |
| Chen, Shaojie; Huang, Lei; Qiu, Huitong et al. (2017) Parallel group independent component analysis for massive fMRI data sets. PLoS One 12:e0173496 |
| Eklund, Anders; Lindquist, Martin A; Villani, Mattias (2017) A Bayesian heteroscedastic GLM with application to fMRI data with motion spikes. Neuroimage 155:354-369 |
| 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 |
| Sobel, Michael; Madigan, David; Wang, Wei (2017) Causal Inference for Meta-Analysis and Multi-Level Data Structures, with Application to Randomized Studies of Vioxx. Psychometrika 82:459-474 |
Showing the most recent 10 out of 27 publications
