Longitudinal surface‐based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS

• Published in: NeuroImage (Orlando, Fla.) Vol. 255; p. 119180
• Main Authors: Mejia, Amanda F., Koppelmans, Vincent, Jelsone-Swain, Laura, Kalra, Sanjay, Welsh, Robert C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.07.2022; Elsevier Limited; Elsevier
• Subjects: Aging; Amyotrophic lateral sclerosis; Amyotrophic Lateral Sclerosis - diagnostic imaging; Bayes Theorem; Bayesian; Bayesian analysis; Biomarkers; Brain architecture; Brain mapping; Brain research; Data smoothing; Disabled Persons; Disease; Disease Progression; Functional magnetic resonance imaging; General linear model; Humans; Linear Models; Longitudinal; Longitudinal studies; Magnetic Resonance Imaging; Mathematical models; Medical imaging; Motor Disorders; Neurodegeneration; Neurodegenerative diseases; Neurodegenerative Diseases - diagnostic imaging; Power; Statistics; United States > US; Michigan; Longitudinal; General linear model; Neurodegeneration; Statistics; Bayesian

•We analyze a rich longitudinal fMRI dataset of 190 scans from ALS and HC subjects•We apply a novel longitudinal spatial Bayesian GLM in native cortical surface space•This approach has high accuracy and power compared with massive univariate approach•We find an inverted U-shaped activation trajector...