3. BayesfMRI: User-friendly spatial Bayesian modeling for task fMRI

COEX 

Spatial Bayesian models are a powerful way to account for spatial dependencies in fMRI analysis [3]. While massive univariate modeling treats each voxel or vertex as a separate entity, spatial Bayesian models place a multivariate prior distribution on the underlying maps of activation, which encodes the spatial dependence and implicitly smoothes the activation estimates. This avoids ad-hoc data smoothing, which induces spatially dependent noise and can lead to false positive clusters [2]. In addition, spatial Bayesian models can use the joint posterior distribution across brain locations to identify areas of activation. This dramatically increases power to detect activations and facilitates the use of meaningful minimum effect sizes, even in individual-level analysis [5]. The open-source BayesfMRI R package provides a user-friendly interface for spatial Bayesian models for task fMRI analysis.

Importantly, the spatial Bayesian models implemented in BayesfMRI are surface-based and subcortical parcel-constrained. These grayordinates-based models leverage spatial dependencies in a neurobiologically appropriate way and avoid the mixing of signals known to occur with whole-brain volumetric smoothing or spatial modeling [1].