Estimating the symptom structure of bipolar disorder via network analysis: Energy dysregulation as a central symptom.
Richard J McNallyDonald J RobinaughThilo DeckersbachLouisa G SylviaAndrew A NierenbergPublished in: Journal of abnormal psychology (2021)
Using network analysis, we estimated the structure of relations among manic and depressive symptoms, respectively, in 486 patients (59% women; age: M = 37, SD = 12.1) with bipolar disorder prior to their entering a clinical trial. We computed three types of networks: (a) Gaussian graphical models (GGMs) depicting regularized partial correlations, (b) regression-based GGMs depicting nonregularized partial correlations, and (c) directed acyclic graphs (DAGs) via a Bayesian hill-climbing algorithm. Low energy and elevated energy were consistently identified as central nodes in the GGMs and as key parent nodes in the DAGs. Across analyses, pessimism about the future and depressed mood were the symptoms most strongly associated with suicidal thoughts and behavior. These exploratory analyses provide rich information about how bipolar disorder symptoms relate to one another, thereby furnishing a foundation for investigating how bipolar disorder symptoms may operate as a causal system. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
- bipolar disorder
- network analysis
- major depressive disorder
- depressive symptoms
- clinical trial
- sleep quality
- end stage renal disease
- newly diagnosed
- patient reported
- ejection fraction
- chronic kidney disease
- sentinel lymph node
- machine learning
- prognostic factors
- social support
- type diabetes
- deep learning
- magnetic resonance
- randomized controlled trial
- emergency department
- patient reported outcomes
- early stage
- skeletal muscle
- open label
- squamous cell carcinoma
- health information
- current status
- insulin resistance
- radiation therapy
- metabolic syndrome
- phase iii
- placebo controlled