Conditional Gaussian graphical model for estimating personalized disease symptom networks

The co-occurrence of symptoms may result from the direct interactions between these symptoms and the symptoms can be treated as a system. In addition, subject-specific risk factors (eg, genetic variants, age) can also exert external influence on the system. In this work, we develop a covariate-dependent conditional Gaussian graphical model to obtain personalized symptom networks. The strengths of network connections are modeled as a function of covariates to capture the heterogeneity among individuals and subgroups of individuals. We assess the performance of our proposed method by simulation studies and an application to a large natural history study of Huntington's disease to investigate the networks of symptoms in multiple clinical domains (motor, cognitive, psychiatric) and identify important brain imaging biomarkers that are associated with the connections. We show that the symptoms in the same clinical domain interact more often with each other than cross domains and the psychiatric subnetwork is the densest network. We validate the findings using the subjects' symptom measurements at follow-up visits.