Spatial Bayesian Methods for Task Functional MRI Studies.
BayesfMRI 
The BayesfMRI R package includes the main function BayesGLM, which implements a spatial Bayesian GLM for task fMRI. It also contains a wrapper function BayesGLM_cifti, for CIFTI cortical surface fMRI data.
Citation
If you use BayesfMRI please cite the following papers:
| Name | APA Citation |
|---|---|
| Spatial Bayesian GLM | Mejia, A. F., Yue, Y., Bolin, D., Lindgren, F., & Lindquist, M. A. (2020). A Bayesian general linear modeling approach to cortical surface fMRI data analysis. Journal of the American Statistical Association, 115(530), 501-520. |
| Multi-session Spatial Bayesian GLM | Spencer, D., Yue, Y. R., Bolin, D., Ryan, S., & Mejia, A. F. (2022). Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups. NeuroImage, 249, 118908. |
You can also obtain citation information from within R like so:
citation("BayesfMRI")
Installation
You can install BayesfMRI from CRAN with:
install.packages("BayesfMRI")
Vignette
See this link to view the tutorial vignette.
Important Note on Dependencies:
BayesfMRI depends on the ciftiTools package, which requires an installation of Connectome Workbench. It can be installed from the HCP website.
The INLA package is required, which, due to a CRAN policy, will not be installed automatically. You can obtain it by running install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=FALSE). For more information, see the INLA website. Note: INLA must be installed before installing BayesfMRI.
On Mac platforms, an installation of Xcode is necessary to build the C++ code included in BayesfMRI.