New technologies such as functional magnetic resonance imaging (fMRI) have aided our ability to understand human brain function. Distinguishing causal from spurious relationships is of great interest and importance for both basic neuroscience and applications in medicine, drug development, and clinical practice. Neuroscientists want to know how experimental stimuli affect brain function, how neural activity in different brain regions are causally linked, and how neural activity mediates the relationship between a stimulus and an outcome. Researchers apply various statistical procedures (e.g., Granger causality, dynamic causal models, structural equation models, and directed graphical models) to fMRI data, interpreting the resulting associations as effects, often inappropriately. This is a major problem. The previous funding cycle of this grant laid the groundwork for a principled approach to casual inference in neuroscience research, employing the potential outcomes notation used in the statistical literature on causal inference. Here we seek to extend the frame- work developed there to better study mediation, where neural activity in one or more brain regions mediates a treatment-outcome relationship, and effective connectivity, where activations in different regions are causally linked. To begin, we de?ne new causal effects and construct a new whole brain model that will facilitate the study of localization, mediation, and effective connectivity at the regional level under assumptions more realistic than those typically made in modeling fMRI data. Further, we create a new statistical method for conduct- ing high-dimensional mediation analysis that identi?es networks that may mediate the relationship between a treatment and outcome. Finally, building on the literature on instrumental variables and structural equation mod- els, we develop new methods for studying mediation and effective connectivity after the application of external neurostimulation using recently developed technologies such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) that are starting to see widespread use in both theoretical neuro- science and applied contexts. We apply the methods to fMRI data from studies of post-traumatic stress disorder, thermal pain and social evaluation.
Using the potential outcomes notation widely used in the statistical literature on causal infer- ence we develop new methods for causal inference in functional magnetic resonance imaging (fMRI). Findings obtained using our methods will help to quantify the connected activity among multiple brain regions, examine the role of the brain as a mediator between stimuli and behavioral outcomes, and affect clinical practice. We also expect the methods to be useful in other applied contexts where high dimensional data are collected and causal inferences are of interest, for example, genetics, epidemiology, and environmental science.
| 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 |
| 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 |
| 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
