Description
Spatial Bayesian Methods for Task Functional MRI Studies.
Description
Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.
README.md
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")
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.
On Mac platforms, an installation of Xcode is necessary to build the C++ code included in BayesfMRI.