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
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119180

•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 trajectory, depending on ALS progression rate•Initial hyperactivation likely due to loss of inhibition, not functional compensation Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans). Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individual anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory of motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package