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

Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups