Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-17T15:07:44.864Z Has data issue: false hasContentIssue false
  • English
  • Français

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]
Get access Rights & Permissions [Opens in a new window]

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
Check for updates
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.CrossRefGoogle Scholar
American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders. Washington, DC: American Psychiatric Association.Google Scholar
Bale, TL and Epperson, CN (2015) Sex differences and stress across the lifespan. Nature Neuroscience 18, 1413–1420.CrossRefGoogle ScholarPubMed
Bateman, AW, Gunderson, J and Mulder, R (2015) Treatment of personality disorder. The Lancet 385, 735–743.CrossRefGoogle ScholarPubMed
Beltz, AM and Molenaar, PC (2016) Dealing with multiple solutions in structural vector autoregressive models. Multivariate Behavioral Research 51, 357–373.CrossRefGoogle ScholarPubMed
Beltz, AM, Wright, AG, Sprague, BN and Molenaar, PC (2016) Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment 23, 447–458.CrossRefGoogle Scholar
Boccaletti, S, Latora, V, Moreno, Y, Chavez, M and Hwang, D-U (2006) Complex networks: structure and dynamics. Physics Reports 424, 175–308.CrossRefGoogle Scholar
Bringmann, LF and Eronen, MI (2018) Don't blame the model: reconsidering the network approach to psychopathology. Psychological Review 125, 606–615.CrossRefGoogle ScholarPubMed
Bringmann, LF, PE, ML, Vissers, N, Ceulemans, E, Borsboom, D, Vanpaemel, W, Tuerlinckx, F and Kuppens, P (2016) Assessing temporal emotion dynamics using networks. Assessment 23, 425–435.CrossRefGoogle ScholarPubMed
Brown, TA (2014) Confirmatory Factor Analysis for Applied Research. NY: Guilford Publications.Google Scholar
Epskamp, S, Van Borkulo, CD, Van Der Veen, DC, Servaas, MN, Isvoranu, A-M, Riese, H and Cramer, AO (2018) Personalized network modeling in psychopathology: the importance of contemporaneous and temporal connections. Clinical Psychological Science 6, 416–427.CrossRefGoogle ScholarPubMed
Fisher, AJ and Boswell, JF (2016) Enhancing the personalization of psychotherapy with dynamic assessment and modeling. Assessment 23, 496–506.CrossRefGoogle ScholarPubMed
Fisher, AJ, Reeves, JW, Lawyer, G, Medaglia, JD and Rubel, JA (2017) Exploring the idiographic dynamics of mood and anxiety via network analysis. Journal of Abnormal Psychology 126, 1044–1056.CrossRefGoogle ScholarPubMed
Forbes, MK, Wright, AG, Markon, KE and Krueger, RF (2017) Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology 126, 969–988.CrossRefGoogle ScholarPubMed
Foster, KT, Hicks, BM and Zucker, RA (2018) Positive and negative effects of internalizing on alcohol use problems from childhood to young adulthood: the mediating and suppressing role of externalizing. Journal of Abnormal Psychology 127, 394–403.CrossRefGoogle ScholarPubMed
Fried, EI, Van Borkulo, CD, Cramer, AO, Boschloo, L, Schoevers, RA and Borsboom, D (2017) Mental disorders as networks of problems: a review of recent insights. Social Psychiatry and Psychiatric Epidemiology 52, 1–10.CrossRefGoogle ScholarPubMed
Gates, KM and Molenaar, PC (2012) Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage 63, 310–319.CrossRefGoogle ScholarPubMed
Gates, KM, Molenaar, PC, Hillary, FG, Ram, N and Rovine, MJ (2010) Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. Neuroimage 50, 1118–1125.CrossRefGoogle ScholarPubMed
Hopwood, CJ (2018) Interpersonal dynamics in personality and personality disorders. European Journal of Personality 32, 499–524.CrossRefGoogle Scholar
Hopwood, CJ and Bleidorn, W (2018) Stability and change in personality and personality disorders. Current Opinion in Psychology 21, 6–10.CrossRefGoogle ScholarPubMed
Hopwood, CJ, Kotov, R, Krueger, RF, Watson, D, Widiger, TA, Althoff, RR, Ansell, EB, Bach, B, Michael Bagby, R and Blais, MA (2018) The time has come for dimensional personality disorder diagnosis. Personality and Mental Health 12, 82–86.CrossRefGoogle ScholarPubMed
Houben, M, Van Den noortgate, W and Kuppens, P (2015) The relation between short-term emotion dynamics and psychological well-being: a meta-analysis. Psychological Bulletin 141, 901–930.CrossRefGoogle ScholarPubMed
Kim, J, Zhu, W, Chang, L, Bentler, PM and Ernst, T (2007) Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping 28, 85–93.CrossRefGoogle ScholarPubMed
Krueger, RF, Hopwood, CJ, Wright, AG and Markon, KE (2014) DSM-5 and the path toward empirically based and clinically useful conceptualization of personality and psychopathology. Clinical Psychology: Science and Practice 21, 245–261.Google Scholar
Kudielka, BM and Kirschbaum, C (2005) Sex differences in HPA axis responses to stress: a review. Biological Psychology 69, 113–132.CrossRefGoogle ScholarPubMed
Lane, ST, Gates, KM, Pike, HK, Beltz, AM and Wright, AG (2019) Uncovering general, shared, and unique temporal patterns in ambulatory assessment data. Psychological Methods 24, 54–69.CrossRefGoogle ScholarPubMed
Lupien, SJ, Mcewen, BS, Gunnar, MR and Heim, C (2009) Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience 10, 434–445.CrossRefGoogle ScholarPubMed
Morey, LC and Hopwood, CJ (2013) Stability and change in personality disorders. Annual Review of Clinical Psychology 9, 499–528.CrossRefGoogle ScholarPubMed
Rankin, ED and Marsh, JC (1985) Effects of missing data on the statistical analysis of clinical time Series. Social Work Research and Abstracts 21, 13–16.CrossRefGoogle Scholar
Silverman, MH and Krueger, RF (2018) Taking stock of relationships among personality disorders and other forms of psychopathology. In Livesley, JW and Larstone, R (eds), Handbook of Personality Disorders: Theory, Research, and Treatment. NY: Guilford Publications, pp. 155–168.Google Scholar
Skodol, AE (2008) Longitudinal course and outcome of personality disorders. Psychiatric Clinics of North America 31, 495–503.CrossRefGoogle ScholarPubMed
Trull, TJ and Ebner-Priemer, U (2013) Ambulatory assessment. Annual Review of Clinical Psychology 9, 151–176.CrossRefGoogle ScholarPubMed
Widiger, TA and Simonsen, E (2005) Alternative dimensional models of personality disorder: finding a common ground. Journal of Personality Disorders 19, 110–130.CrossRefGoogle ScholarPubMed
Wright, AG and Hopwood, CJ (2016) Advancing the assessment of dynamic psychological processes. Assessment 23, 399–403.CrossRefGoogle ScholarPubMed
Wright, AG and Simms, LJ (2016) Stability and fluctuation of personality disorder features in daily life. Journal of Abnormal Psychology 125, 641–656.CrossRefGoogle ScholarPubMed
Wright, AGC, Gates, KM, Arizmendi, C, Lane, ST, Woods, WC and Edershile, EA (2019) Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology. Psychological Assessment 31, 502–515.CrossRefGoogle ScholarPubMed
Wright, AG, Beltz, AM, Gates, KM, Molenaar, P and Simms, LJ (2015 a) Examining the dynamic structure of daily internalizing and externalizing behavior at multiple levels of analysis. Frontiers in Psychology 6, 1914.CrossRefGoogle ScholarPubMed
Wright, AG, Calabrese, WR, Rudick, MM, Yam, WH, Zelazny, K, Williams, TF, Rotterman, JH and Simms, LJ (2015 b) Stability of the DSM-5 section III pathological personality traits and their longitudinal associations with psychosocial functioning in personality disordered individuals. Journal of Abnormal Psychology 124, 199–207.CrossRefGoogle ScholarPubMed
Supplementary material: File

Dotterer et al. supplementary material

Dotterer et al. supplementary material

Download Dotterer et al. supplementary material(File)
File 239.3 KB