Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers.

ABSTRACT: Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed-effects model framework, we introduce subject- and biomarker-specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying di

SUBMITTER: Sun M 

PROVIDER: S-EPMC8605929 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature