  The general linear model (GLM) is a widely popular and convenient tool for
estimating the functional brain response and identifying areas of significant
activation during a task or stimulus. However, the classical GLM is based on a
massive univariate approach that does not explicitly leverage the similarity of
activation patterns among neighboring brain locations. As a result, it tends to
produce noisy estimates and be underpowered to detect significant activations,
particularly in individual subjects and small groups. A recent alternative, a
cortical surface-based spatial Bayesian GLM, leverages spatial dependencies
among neighboring cortical vertices to produce more accurate estimates and
areas of functional activation. The spatial Bayesian GLM can be applied to
individual and group-level analysis. In this study, we assess the reliability
and power of individual and group-average measures of task activation produced
via the surface-based spatial Bayesian GLM. We analyze motor task data from 45
subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also
extend the model to multi-run analysis and employ subject-specific cortical
surfaces rather than surfaces inflated to a sphere for more accurate
distance-based modeling. Results show that the surface-based spatial Bayesian
GLM produces highly reliable activations in individual subjects and is powerful
enough to detect trait-like functional topologies. Additionally, spatial
Bayesian modeling enhances reliability of group-level analysis even in
moderately sized samples (n=45). The power of the spatial Bayesian GLM to
detect activations above a scientifically meaningful effect size is nearly
invariant to sample size, exhibiting high power even in small samples (n=10).
The spatial Bayesian GLM is computationally efficient in individuals and groups
and is convenient to implement with the open-source BayesfMRI R package.
