Graduation Year

2020

Document Type

Thesis

Degree

M.A.

Degree Name

Master of Arts (M.A.)

Degree Granting Department

Psychology

Major Professor

Marina A .Bornovalova, Ph.D.

Committee Member

Brent Small, Ph.D.

Committee Member

Jonathan Rottenberg, Ph.D.

Keywords

Psychopathology,, Comorbidity, Temporal symptom networks

Abstract

In contrast with the latent variable models, network psychometricians have proposed that symptoms co-occur not because of an underlying common cause but because of direct dynamic associations among symptoms. This empirical study aims to elucidate how features of with borderline personality disorder, depression, and anxiety interact with one another and form a network. Specifically, I aimed to identify a potential causal structure among the features of BPD, depression, and anxiety while identifying the most influential features. Participants were 37 undergraduate students between the ages of 18 and 26 recruited from University of South Florida SONA pool. Following baseline assessment, participants were prompted to answer a Qualtrics-based survey of current symptoms of BPD, depression and anxiety twice each day for 40 days. Multi-level time-series network analysis identified a potentially causal structure that may explain how BPD features interact among themselves and with features of other disorders and behave as a network. In the network of BPD features alone, interpersonal difficulties predicted dissociation which then predicted affective fluctuation within-person over time, with dissociation exerting the strongest influence on the network. When depression and anxiety features were included to form transdiagnostic networks, several cross-disorder connections were found highlighting potential pathways to comorbidity. Overall, future -related negative thoughts and feelings, and dissociation were identified as the most influential features across the networks and might be promising targets of intervention.

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