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Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample

Published online by Cambridge University Press:  10 October 2019

Hailey L. Dotterer Open the ORCID record for Hailey L. Dotterer [Opens in a new window] ,
Adriene M. Beltz ,
Katherine T. Foster ,
Leonard J. Simms  and
Aidan G. C. Wright
Hailey L. Dotterer
Affiliation:
Department of Psychology, University of Michigan, Ann Arbor, USA
Adriene M. Beltz*
Affiliation:
Department of Psychology, University of Michigan, Ann Arbor, USA
Katherine T. Foster
Affiliation:
Department of Psychology, University of Michigan, Ann Arbor, USA
Leonard J. Simms
Affiliation:
Department of Psychology, University at Buffalo, Buffalo, USA
Aidan G. C. Wright
Affiliation:
Department of Psychology, University of Pittsburgh, Pittsburgh, USA
*
Author for correspondence: Adriene M. Beltz, E-mail: [email protected]
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Abstract

Background

An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning).

Methods

Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress).

Results

Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days.

Conclusions

Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.

Keywords

Connectivityexternalizinginternalizingpersonality disorderperson-specific
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 14 , October 2020 , pp. 2397 - 2405
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Copyright
Copyright © Cambridge University Press 2019

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