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

• Published in: NeuroImage (Orlando, Fla.) Vol. 249; p. 118908
• Main Authors: Spencer, Daniel, Yue, Yu Ryan, Bolin, David, Ryan, Sarah, Mejia, Amanda F.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2022; Elsevier Limited; Elsevier
• Subjects: Adult; Bayes Theorem; Bayesian; Bayesian analysis; Brain research; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiology; Connectome - methods; Connectome - standards; Cortical surface; Efficiency; General linear model; Humans; Linear Models; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Models, Theoretical; Power; Reproducibility of Results; Sparsity; Task fMRI; Task Performance and Analysis; Cortical surface; General linear model; Task fMRI; Bayesian
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.118908

•We present a fast cortical surface-based spatial Bayesian GLM for task fMRI.•Leveraging dependencies along the cortex enhances accuracy and power.•Using HCP retest data, we find more reliable activations in individuals and groups.•Power is dramatically increased compared with “massive univariate” m...