Exploring the Idiographic Dynamics of Mood and Anxiety via Network Analysis

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Abstract

Individual variation is increasingly recognized as important to psychopathology research. Concurrently, new methods of analysis based on network models are bringing new perspectives on mental (dys)function. This current work analyzed idiographic multivariate time series data using a novel network methodology that incorporates contemporaneous and lagged associations in mood and anxiety symptomatology. Data were taken from 40 individuals with generalized anxiety disorder (GAD), major depressive disorder (MDD), or comorbid GAD and MDD, who answered questions about 21 descriptors of mood and anxiety symptomatology 4 times a day over a period of approximately 30 days. The model provided an excellent fit to the intraindividual symptom dynamics of all 40 individuals. The most central symptoms in contemporaneous systems were those related to positive and negative mood. The temporal networks highlighted the importance of anger to symptomatology, while also finding that depressed mood and worry—the principal diagnostic criteria for GAD and MDD—were the least influential nodes across the sample. The method’s potential for analysis of individual symptom patterns is demonstrated by 3 exemplar participants. Idiographic network-based analysis may fundamentally alter the way psychopathology is assessed, classified, and treated, allowing researchers and clinicians to better understand individual symptom dynamics.

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