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The high degree of comorbidity among mental disorders is a major concern. Symptoms of mental disorders are often causally related to each other (e.g., insomnia causes concentration problems). Recently, researchers have endeavored to create network models of the relationships between symptoms, both within and across disorders. Symptoms that connect two different mental disorders are called "bridge symptoms". Unfortunately, no formal quantitative methods for identifying bridge symptoms exist. Accordingly, we developed four metrics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. We first tested the fidelity of our metrics in predicting bridge nodes in a series of 12,000 simulated conditions. Averaged across all conditions, the metrics achieved a sensitivity of 92.3% and a specificity of 84.9%. Furthermore, we simulated the contagion of mental disorders to demonstrate the effectiveness of eliminating detected bridge nodes in preventing the spread of mental disorders. Eliminating nodes based on bridge metrics was more effective than eliminating nodes high on traditional centrality metrics in preventing comorbidity. Finally, we applied our algorithms to 19 empirical comorbidity networks from the present literature and discussed implications of this comprehensive analysis.
Keywords: mental health, comorbidity, graph theory, network analysis, psychopathology
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