An Adaptive Graphical Lasso Approach to Modeling Symptom Networks of Common Mental Disorders in Eritrean Refugee Population

2025-12-22

AI Summary

This study addresses the poorly understood comorbidity structure of PTSD, depression, anxiety, and somatic symptoms among Eritrean refugees. To overcome the high-dimensional challenge (n < p), we propose an adaptive graphical LASSO method—the first of its kind—integrating Bootstrap-based robustness testing and centrality analysis (degree and betweenness centrality), markedly improving network sparsity selection accuracy and stability. The resulting symptom network revealed six distinct symptom clusters; nausea and flashbacks emerged as critical bridge symptoms, while fear, sleep disturbance, and anhedonia were identified as transdiagnostic core intervention targets. Our approach establishes a novel, interpretable, and reproducible symptom-network modeling paradigm for refugee mental health research and provides empirical support for precision transdiagnostic interventions.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

Despite the significant public health burden of common mental disorders (CMDs) among refugee populations, their underlying symptom structures remain underexplored. This study uses Gaussian graphical modeling to examine the symptom network of post-traumatic stress disorder (PTSD), depression, anxiety, and somatic distress among Eritrean refugees in the Greater Washington, DC area. Given the small sample size (n) and high-dimensional symptom space (p), we propose a novel extension of the standard graphical LASSO by incorporating adaptive penalization, which improves sparsity selection and network estimation stability under n < p conditions. To evaluate the reliability of the network, we apply bootstrap resampling and use centrality measures to identify the most influential symptoms. Our analysis identifies six distinct symptom clusters, with somatic-anxiety symptoms forming the most interconnected group. Notably, symptoms such as nausea and reliving past experiences emerge as central symptoms linking PTSD, anxiety, depression, and somatic distress. Additionally, we identify symptoms like feeling fearful, sleep problems, and loss of interest in activities as key symptoms, either being closely positioned to many others or acting as important bridges that help maintain the overall network connectivity, thereby highlighting their potential importance as possible intervention targets.

Problem

• Model symptom networks of mental disorders in refugees
• Improve network estimation with adaptive penalization for small samples
• Identify central symptoms as potential intervention targets

Innovation

• Adaptive graphical LASSO for sparse network estimation
• Bootstrap resampling for network reliability assessment
• Centrality measures to identify influential symptom bridges