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Dynamic networks of PTSD symptoms during conflict

Published online by Cambridge University Press:  28 February 2018

Talya Greene ,
Marc Gelkopf ,
Sacha Epskamp  and
Eiko Fried
Talya Greene*
Affiliation:
Department of Community Mental Health, University of Haifa, Haifa, Israel
Marc Gelkopf
Affiliation:
Department of Community Mental Health, University of Haifa, Haifa, Israel NATAL, Israel Trauma Center for Victims of Terror and War, Tel Aviv, Israel
Sacha Epskamp
Affiliation:
Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
Eiko Fried
Affiliation:
Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
*
Author for correspondence: Talya Greene, E-mail: [email protected]
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Abstract

Background

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict.

Methods

Intensive longitudinal assessment data were collected during the Israel–Gaza War in July–August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network.

Results

Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms.

Conclusions

This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

Keywords

ESMintensive longitudinal assessmentmultilevel VARnetwork analysisposttraumatic stresstraumawar
Type
Original Articles
Information
Psychological Medicine , Volume 48 , Issue 14 , October 2018 , pp. 2409 - 2417
Copyright
Copyright © Cambridge University Press 2018 

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References

Afzali, MH, Sunderland, M, Batterham, PJ, Carragher, N, Calear, A and Slade, T (2017a) Network approach to the symptom-level association between alcohol use disorder and posttraumatic stress disorder. Social Psychiatry and Psychiatric Epidemiology 52(3), 329–339.Google Scholar
Afzali, MH, Sunderland, M, Teesson, M, Carragher, N, Mills, K and Slade, T (2017b) A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: the role of overlapping symptoms. Journal of Affective Disorders 208, 490–496.Google Scholar
APA (2013). Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Washington, DC: American Psychiatric Association.Google Scholar
Armey, MF, Schatten, HT, Haradhvala, N and Miller, IW (2015) Ecological momentary assessment (EMA) of depression-related phenomena. Current Opinion in Psychology 4, 21–25.Google Scholar
Armour, C, Tsai, J, Durham, TA, Charak, R, Biehn, TL, Elhai, JD et al. (2015) Dimensional structure of DSM-5 posttraumatic stress symptoms: support for a hybrid anhedonia and externalizing behaviors model. Journal of Psychiatric Research 61, 106–113.Google Scholar
Armour, C, Fried, EI, Deserno, MK, Tsai, J and Pietrzak, RH (2017) A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in US military veterans. Journal of Anxiety Disorders 45, 49–59.Google Scholar
Bak, M, Drukker, M, Hasmi, L and van Os, J (2016) An n = 1 clinical network analysis of symptoms and treatment in psychosis. PLoS ONE 11, e0162811.Google Scholar
Beard, C, Millner, A, Forgeard, M, Fried, E, Hsu, K, Treadway, M et al. (2016) Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine 46, 3359.Google Scholar
Birkeland, MS and Heir, T (2017) Making connections: exploring the centrality of posttraumatic stress symptoms and covariates after a terrorist attack. European Journal of Psychotraumatology 8, 1333387.Google Scholar
Blevins, CA, Weathers, FW, Davis, MT, Witte, TK and Domino, JL (2015) The posttraumatic stress disorder checklist for DSM-5 (PCL-5): development and initial psychometric evaluation. Journal of Traumatic Stress 28, 489–498.Google Scholar
Bolger, N and Laurenceau, J (2013) Intensive Longitudinal Methods. An Introduction to Diary and Experience Sampling Research. New York: Guilford.Google Scholar
Bolger, N, Davis, A and Rafaeli, E (2003) Diary methods: capturing life as it is lived. Annual Review of Psychology 54, 579–616.Google Scholar
Bonanno, GA and Mancini, AD (2012) Beyond resilience and PTSD: mapping the heterogeneity of responses to potential trauma. Psychological Trauma: Theory, Research, Practice, and Policy 4, 74.Google Scholar
Borsboom, D (2017) A network theory of mental disorders. World Psychiatry 16, 5–13.Google Scholar
Bos, F, Snippe, E, de Vos, S, Hartmann, J, Simons, C, van der Krieke, L et al. (2017) Can We jump from cross-sectional to dynamic interpretations of networks? Implications for the network perspective in psychiatry. Psychotherapy and Psychosomatics 86, 175.Google Scholar
Bryant, RA, Nickerson, A, Creamer, M, O'Donnell, M, Forbes, D, Galatzer-Levy, I et al. (2015) Trajectory of post-traumatic stress following traumatic injury: 6-year follow-up. The British Journal of Psychiatry 206, 417–423.Google Scholar
Bryant, RA, Creamer, M, O'Donnell, M, Forbes, D, McFarlane, AC, Silove, D et al. (2017). Acute and chronic posttraumatic stress symptoms in the emergence of posttraumatic stress disorder: a network analysis. JAMA Psychiatry 74(2), 135–142.Google Scholar
Chun, CA (2016) The expression of posttraumatic stress symptoms in daily life: a review of experience sampling methodology and daily diary studies. Journal of Psychopathology and Behavioral Assessment 38, 406–420.Google Scholar
David, SJ, Marshall, AJ, Evanovich, EK and Mumma, GH (2017) Intraindividual dynamic network analysis–implications for clinical assessment. Journal of Psychopathology and Behavioral Assessment, 1–14.Google Scholar
Doron-LaMarca, S, Niles, BL, King, DW, King, LA, Pless Kaiser, A and Lyons, MJ (2015) Temporal associations among chronic PTSD symptoms in US combat veterans. Journal of Traumatic Stress 28, 410–417.Google Scholar
Ebner-Priemer, UW and Kubiak, T (2010) The decade of behavior revisited: future prospects for ambulatory assessment. European Journal of Psychological Assessment 26, 151–153.Google Scholar
Ebner-Priemer, UW and Trull, TJ (2009) Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment 21, 463.Google Scholar
Epskamp, S, Waldorp, LJ, Mõttus, R and Borsboom, D (2016) Discovering psychological dynamics: the Gaussian graphical model in cross-sectional and time-series data. arXiv:1609.04156.Google Scholar
Epskamp, S, van Borkulo, CD, van der Veen, DC, Servaas, MN, Isvoranu, A-M, Riesse, H et al. (2017) Personalized network modeling in psychopathology: the importance of contemporaneous and temporal connections. Clinical Psychological Science. https://doi.org/10.1177/2167702617744325Google Scholar
Fisher, AJ (2015) Toward a dynamic model of psychological assessment: implications for personalized care. Journal of Consulting and Clinical Psychology 83, 825.Google Scholar
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.Google Scholar
Fried, EI and Cramer, AO (2017) Moving forward: challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science 12(6), 999–1020.Google Scholar
Fried, EI, Epskamp, S, Nesse, RM, Tuerlinckx, F and Borsboom, D (2016) What are'good'depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders 189, 314–320.Google Scholar
Fried, EI, Eidhof, M, Palic, S, Costantini, G, Huisman-van Dijk, H, Bockting, CL et al. (2017 a) Replicability and generalizability of PTSD networks: a cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clinical Psychological Science [accepted].Google Scholar
Fried, EI, van Borkulo, CD, Cramer, AO, Boschloo, L, Schoevers, RA and Borsboom, D (2017 b) Mental disorders as networks of problems: a review of recent insights. Social Psychiatry and Psychiatric Epidemiology 52, 1–10.Google Scholar
Gelkopf, M, Lapid, L, Grinapol, S, Werbeloff, N, Carlson, E and Greene, T (2017) Peritraumatic reaction courses during War: gender, serious mental illness, and exposure. Psychiatry: Interpersonal & Biological Processes.Google Scholar
Granger, CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society 424–438.Google Scholar
Greene, T (under review) Blame, PTSD and DSM-5: An urgent need for clarification. European Journal of Psychotraumatology.Google Scholar
Greene, T, Gelkopf, M, Grinapol, S, Werbeloff, N, Carlson, E and Lapid, L (2017) Trajectories of traumatic stress symptoms during conflict: a latent class growth analysis. Journal of Affective Disorders.Google Scholar
Hamaker, E (2012) Why researchers should think “within-person”: a paradigmatic rationale. Handbook of research methods for studying daily life, 43–61.Google Scholar
Hamaker, EL and Wichers, M (2017) No time like the present: discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science 26, 10–15.Google Scholar
Isvoranu, A-M, Borsboom, D, van Os, J and Guloksuz, S (2016) A network approach to environmental impact in psychotic disorder: brief theoretical framework. Schizophrenia Bulletin 42, 870–873.Google Scholar
Kelley, HH and Michela, JL (1980) Attribution theory and research. Annual Review of Psychology 31, 457–501.Google Scholar
Knefel, M, Tran, US and Lueger-Schuster, B (2016) The association of posttraumatic stress disorder, complex posttraumatic stress disorder, and borderline personality disorder from a network analytical perspective. Journal of Anxiety Disorders 43, 70–78.Google Scholar
Lapid Pickman, L, Greene, T and Gelkopf, M (2017) Sense of threat as a mediator of peritraumatic stress symptom development during wartime: an experience sampling study. Journal of Traumatic Stress 30, 372–380.Google Scholar
Levine, SZ and Leucht, S (2016) Identifying a system of predominant negative symptoms: network analysis of three randomized clinical trials. Schizophrenia Research 178, 17–22.Google Scholar
Marshall, GN, Schell, TL, Glynn, SM and Shetty, V (2006) The role of hyperarousal in the manifestation of posttraumatic psychological distress following injury. Journal of Abnormal Psychology 115, 624.Google Scholar
McNally, RJ (2016) Can network analysis transform psychopathology? Behaviour Research and Therapy 86, 95–104.Google Scholar
McNally, RJ, Robinaugh, DJ, Wu, GW, Wang, L, Deserno, MK and Borsboom, D (2015) Mental disorders as causal systems a network approach to posttraumatic stress disorder. Clinical Psychological Science 3, 836–849.Google Scholar
Mitchell, K, Wolf, E, Bovin, M, Lee, L, Green, J, Rosen, R, et al. (2017) Network models of DSM-5 posttraumatic stress disorder: implications for ICD-11. Journal of Abnormal Psychology.Google Scholar
Myin-Germeys, I, Oorschot, M, Collip, D, Lataster, J, Delespaul, P and van Os, J (2009) Experience sampling research in psychopathology: opening the black box of daily life. Psychological Medicine 39, 1533–1547.Google Scholar
Nelson, B, McGorry, PD, Wichers, M, Wigman, JT and Hartmann, JA (2017) Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiatry 74, 528–534.Google Scholar
Pietrzak, RH, Tsai, J, Armour, C, Mota, N, Harpaz-Rotem, I and Southwick, SM (2015) Functional significance of a novel 7-factor model of DSM-5 PTSD symptoms: results from the national health and resilience in veterans study. Journal of Affective Disorders 174, 522–526.Google Scholar
Santangelo, PS, Ebner-Priemer, UW and Trull, TJ (2013) Experience sampling methods in clinical psychology. The Oxford Handbook of Research Strategies for Clinical Psychology, 188–210.Google Scholar
Schell, TL, Marshall, GN and Jaycox, LH (2004) All symptoms are not created equal: the prominent role of hyperarousal in the natural course of posttraumatic psychological distress. Journal of Abnormal Psychology 113, 189.Google Scholar
Schuurman, NK, Ferrer, E, de Boer-Sonnenschein, M and Hamaker, EL (2016) How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods 21, 206.Google Scholar
Spiller, TR, Schick, M, Schnyder, U, Bryant, RA, Nickerson, A and Morina, N (2017) Symptoms of posttraumatic stress disorder in a clinical sample of refugees: a network analysis. European Journal of Psychotraumatology 8, 1318032.Google Scholar
Sullivan, CP, Smith, AJ, Lewis, M and Jones, RT (2018) Network analysis of ptsd symptoms following mass violence. Psychological Trauma: Theory, Research, Practice, and Policy 10(1), 58.Google Scholar
Trull, TJ and Ebner-Priemer, U (2013) Ambulatory assessment. Annual Review of Clinical Psychology 9, 151.Google Scholar
Walz, LC, Nauta, MH and aan het Rot, M (2014) Experience sampling and ecological momentary assessment for studying the daily lives of patients with anxiety disorders: a systematic review. Journal of Anxiety Disorders 28, 925–937.Google Scholar
Weathers, F, Litz, B, Keane, T, Palmieri, P, Marx, B and Schnurr, P (2013) The PTSD checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD. www.ptsd.va.gov.Google Scholar
Wichers, M, Groot, PC, Psychosystems, E and Group, E (2016) Critical slowing down as a personalized early warning signal for depression. Psychotherapy and Psychosomatics 85, 114–116.Google Scholar
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