A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging.

ABSTRACT: It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm(3). These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do n

SUBMITTER: Bilgel M 

PROVIDER: S-EPMC4912927 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature