Background: A novel psychopathological approach is the application of network analysis, as it is proposed that symptoms and their interconnections constitute a disease itself, rather than simply being components or outcome factors of disease.
Objective: Using data from the Clinical Research Center for Depression (CRESCEND) Study, this study examined depressive symptoms in elderly patients with major depressive disorder using a network analysis approach.
Methods: Among 135 elderly patients with major depressive disorder who were recruited from the CRESCEND study, we created a network based on individual items from the Hamilton Depression Rating Scale (HAMD), with the nodes being each item (symptom) and the edges being the strength of the association between the items (interconnection). By calculating measures of centrality of each of the nodes, we were able to determine which depressive symptoms were most central (influential) in the network.
Results: The insight item was completely unconnected with other items and it was excluded in terms of network analysis. Thus, a network analysis of the 16 HAMD items estimated that the anxiety psychic item was the most central domain, followed by insomnia (middle of the night), depressive mood, and insomnia (early hours of the morning) items. On the contrary, the retardation item was the most poorly interconnected with the network.
Conclusion: We suggest that our study makes a significant contribution to the literature because we have found that anxiety, depressed mood, and insomnia are most central to the network, indicating that they are the most influential symptoms in major depression in elderly individuals.