Course program
Course Github page with resources
Slides on spatial Bayesian modeling for powerful task activation analysis
Poster 1325
Poster 1952
Access recordings of all OHBM 2023 educational workshops and Mandy's talk in this workshop
Github repo for the course: https://github.com/wdweeda/ohbm2023_edu_course/
Materials for Mandy's lecture on spatial Bayesian models for powerful task fMRI analysis: Slides and code available here. BayesfMRI R package on GitHub and CRAN.
This talk is based on the following papers:
Spencer D, Yue YR, Bolin D, Ryan S and Mejia AF. Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups. NeuroImage (2022). https://doi.org/10.1016/j.neuroimage.2022.118908
Mejia AF,* Yue Y*, Bolin D, Lindgren F, Lindquist MA. A Bayesian general linear modeling approach to cortical surface fMRI data analysis. Journal of the American Statistical Association (2020): 501-520. https://doi.org/10.1080/01621459.2019.1611582
Access the full paper:
Parlak F, Phạm DĐ, Spencer D, Welsh RC and Mejia AF. Sources of Residual Autocorrelation in Multiband Task fMRI and Strategies for Effective Mitigation. Frontiers in Neuroscience (2023). https://doi.org/10.3389/fnins.2022.1051424
Access the full paper:
Phạm DĐ, McDonald DJ, Ding L, Nebel MB, Mejia AF. Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing. NeuroImage (2023). https://doi.org/10.1016/j.neuroimage.2023.119972