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

ABSTRACT: 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 recently proposed 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

SUBMITTER: Spencer D 

PROVIDER: S-EPMC9006342 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature