Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.

ABSTRACT: In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that a

SUBMITTER: McDonnell EI 

PROVIDER: S-EPMC11502120 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature