Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients

Lazarov, Amit; Suarez‐Jimenez, Benjamin; Levi, Ofir; Coppersmith, Daniel D L; Lubin, Gad; Pine, Daniel S.; Bar‐Haim, Yair; Abend, Rany; Neria, Yuval

doi:10.1017/s0033291719002034

Cited by 79 publications

(85 citation statements)

References 168 publications

(373 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In both pre-treatment networks, flashbacks emerged as most influential while amnesia had the least expected influence. Overall, the results were comparable to network analyses of similar samples [29,30]. The NetworkCom-parisonTest revealed that the structure of the Clinician Administered PTSD Scale and PTSD Checklist network both changed from pre-to post-treatment (both p < .001), which was supported also by the results of the community analyses (see Additional file 1: TableS2).…”

Section: Ptsd Network and Node Metricssupporting

confidence: 71%

“…Consequentially, there was no missing data among the included participants. Participant characteristics are outlined in Table 1 (for a more detailed description of the original dataset, see [29]). The study was approved by the Ethics Committee of the IDF Medical Corps.…”

Section: Participants and Proceduresmentioning

confidence: 99%

“…Thus, we computed networks based on clinician-evaluated and selfreported symptom assessment (Clinician Administered PTSD Scale and PTSD Checklist, respectively) of the same patients at the same time-point. Third, we repeated these two analyses after removing the "amnesia" item (i.e., difficulties remembering different aspects of the traumatic experience), a known outlier in the phenomenology of PTSD [29,30,73,74]. Finally, to examine the invariance assumption, we assessed the (in)variance of both networks across the two time-points (before and after treatment) by comparing the pre-and post-treatment networks using the Net-workComparisonTest [75] and by conducting community analyses in all four networks using the walktrap algorithm implemented in the EGAnet package [76].…”

Section: Extension Analysesmentioning

confidence: 99%

“…Still, some authors are much more cautious when interpreting centrality indexes and when emphasizing their clinical meaning (e.g.,[28][29]). …”

mentioning

confidence: 99%

See 3 more Smart Citations

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

Spiller

Levi

Neria

et al. 2020

BMC Med

Self Cite

View full text Add to dashboard Cite

Background In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms’ causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis). Methods Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom’s change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement). Results Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses. Conclusions The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

show abstract

Section: Ptsd Network and Node Metricssupporting

confidence: 71%

Section: Participants and Proceduresmentioning

confidence: 99%

Section: Extension Analysesmentioning

confidence: 99%

“…Still, some authors are much more cautious when interpreting centrality indexes and when emphasizing their clinical meaning (e.g.,[28][29]). …”

mentioning

confidence: 99%

See 2 more Smart Citations

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

Spiller

Levi

Neria

et al. 2020

BMC Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the fast-expanding research fields focused on identifying associations between a wide array of symptoms and other factors, and commonly utilizing network models, is the field of post-traumatic stress. Over the past half-decade, numerous studies investigating associations between symptomatology (Mcnally et al, 2015;van Loo et al, 2018), potential risk factors (Armour et al, 2017;Choi et al, 2017;Mancini et al, 2019;Simons et al, 2019) and pathways to comorbidity (Djelantik et al, 2020;Gilbar, 2020;Lazarov et al, 2019;Malgaroli et al, 2018;Price et al, 2019;Vanzhula et al, 2019) have been published and yielded novel results and hypotheses. In response to this rapid research expansion and in an aim to synthesize current findings in the field, a thorough systematic review of the network approach to posttraumatic stress disorder (PTSD) has been recently carried out (Birkeland et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Network Models of Post-traumatic Stress Disorder: A Meta-analysis

Isvoranu¹,

Epskamp²,

Cheung³

2020

Preprint

View full text Add to dashboard Cite

Post-traumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-study heterogeneity into account, and applied this framework to the first-ever meta-analytic study of network models. We analyzed the correlational structures of 52 different samples with a total sample size of n = 29,561, and estimated a single pooled network model underlying the datasets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (1) While several clear symptom-links and interpretable clusters can be identified in the network, most symptoms feature very similar levels of centrality. To this end, aiming to identify central symptoms in PTSD symptom networks may not be fruitful. (2) We identified large between-study heterogeneity, indicating that it should be expected for networks of single studies to not perfectly align with one-another, and meta-analytic approaches are vital for the study of PTSD networks. (3) Nonetheless, we found evidence that networks estimated from single studies may give rise to generalizable results, as our results aligned with previous descriptive analyses of reported network studies, and network models estimated from single samples lead to similar network structures as the pooled network model. We discuss the implications of these findings for both the PTSD literature as well as methodological literature on network psychometrics.

show abstract

Network analysis of PTSD and depressive symptoms in 158,139 treatment‐seeking veterans with PTSD

Duek

Spiller

Pietrzak

et al. 2020

Depression and Anxiety

View full text Add to dashboard Cite

Background: In recent years, a new framework for analyzing and understanding posttraumatic stress disorder (PTSD) was introduced; the network approach. Up until now, network analysis studies of PTSD were largely conducted on small to medium sample sizes (N < 1,000), which might be a possible cause of variability in main findings.Moreover, only a limited number of network studies investigated comorbidity.Methods: In this study, we utilized a large sample to conduct a network analysis of 17 symptoms of PTSD (DSM-IV), and compared it to the result of a second network consisting of symptoms of PTSD and depression (based on Patient Health Questionnaire-9[PHQ-9]). Our sample consisted of 502,036 treatment-seeking veterans, out of which 158,139 had fully completed the assessment of symptoms of PTSD and a subsample of 32,841 with valid PCL and PHQ-9 that was administered within 14 days or less.Results: Analyses found that in the PTSD network, the most central symptoms were feeling distant or cut off from others, followed by feeling very upset when reminded of the event, and repeated disturbing memories or thoughts of the event. In the combined network, we found that concentration difficulties and anhedonia are two of the five most central symptoms. Conclusion:Our findings replicate the centrality of intrusion symptoms in PTSD symptoms' network. Taking into account the large sample and high stability of the network structure, we believe our study can answer some of the criticism regarding stability of cross-sectional network structures.

show abstract

Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients

Cited by 79 publications

References 168 publications

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

Network Models of Post-traumatic Stress Disorder: A Meta-analysis

Network analysis of PTSD and depressive symptoms in 158,139 treatment‐seeking veterans with PTSD

Contact Info

Product

Resources

About