BayesBrainMap
Estimate Brain Networks and Connectivity with Population-Derived Priors
Implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods. Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
2026-04-09 windows/windows R-4.5 | BayesBrainMap_0.2.0.zip |
500.7 KiB |