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 (e.g., 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 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 the important brain imaging biomarkers associated with the connections. We show that the symptoms in the same clinical domain interact more often with each other than across domains and psychiatric subnetwork is the most dense one. We validate the findings using subjects’ measurements from follow-up visits.