Post Traumatic Stress Disorder as a Causal System   Nader Amir and Shaan McGhie San Diego State University, San Diego, CA US. Funding :  This project was supported by Grants 1R01MH101118, R01 MH106477  from NIMH Disclosure : Dr. Amir was formerly a part owner of Cognitive Retraining Technologies, LLC (“CRT”), a company that marketed anxiety relief products. Dr. Amir’s ownership interest in CRT was extinguished on January 29, 2016, when CRT was acquired by another entity. Dr. Amir has an interest in royalty income generated by the marketing of anxiety relief products by this entity.

Traditional models of Post Traumatic Stress Disorder (PTSD) Latent construct causes the symptoms that quantify it (DSM­5; APA, 2013). That is, both categorical and dimensional models conceptualize PTSD symptoms as indicators of an underlying latent variable that is measured by these less than psychometrically perfect indicators The goal is therefore: Make the indicator better: more psychometrically sound

Traditional models of Post Traumatic Stress Disorder (PTSD) This has met with limited success We have yet to discover any pathonomonic variables for the latent structures Maybe we need to look harder, better (more biological) indicators Maybe the entire model can be complemented

Causal Systems Model of Post Traumatic Stress Disorder (PTSD) Psychometricians have turned to a different approach to understand mental disorders such as PTSD Mental disorders as causal systems (Borsboom & Cramer, 2013) graphical models for causal relations that can complement conventional models (Greenland, Pearl, & Robins, 1999) Also called influence diagrams relevance diagrams causal networks

Causal System model of PTSD A stressor (trauma) causes a symptom, which may cause other symptoms and in turn be affected by those symptoms These symptoms themselves constitute the mental disorder

Causal System model of PTSD McNally et al. (2014): Examined PTSD symptoms in 362 earthquake survivors (38% met criteria for probable PTSD) Feeling distant from other people was linked to loss of interest in previously enjoyable activities as well as emotional numbing Difficulty sleeping, hypervigilance, and being easily startled were also clustered as interrelated symptoms.

McNally et al (2014)

Current study Replicated and extended these network analyses Used a weighted and directed network The magnitude of the relation is shown through thickness of the line The arrows start at the predictor symptom and end at the predicted symptom Test the accuracy of the network Bayesian network

The symptoms presented in the graphs Distant = feeling of detachment from others Numb = restricted affect Future = sense of foreshortened future Sleep = difficulty falling or staying asleep Startle = exaggerated startled response Lossint = diminished interest in previously enjoyable activities, Avoidth = efforts to avoid thoughts that concern the trauma Avoidact= efforts to avoid activities, places, or people that arouse recollections of the trauma. Hyper = hyperarousal in response to cues Dreams = traumatic dreams Intrusion = intrusive thoughts, memories or images Flash = Flashbacks of the trauma Upset = feeling upset in response to reminders of the trauma Concen = difficulty concentrating Physior = Physiological reactions to reminders of the trauma Anger = feeling irritable or having outbursts

Network Analyses Node Edge Association Network Concentration Network A symptom in the graph Edge lines connecting nodes, indicating a relationship between two nodes (thicker lines means stronger relationships/higher correlations) Association Network simple correlations between symptoms Concentration Network Partial correlations between symptoms Relative Importance network Directed and weighted network (shows direction and magnitude of relationships) of linear models. Uses lmg metric in R package Relaimpo (Grömping, 2006)

Measures of Centrality Betweeness number of times a node is on the shortest path between two other nodes Closeness average distance of one node to the others (higher number means closer together) Strength-out The effect of the node on other nodes

Results

665 students: 17 PTSD symptoms Mean PCL-C score: 32.12, SD = 12.38 15% (106) meet DSM-V criteria for PTSD Fig 6. Association network of simple correlations between PTSD symptoms with a cutoff of minimum .30 correlation.

Fig 8. Concentration network of partial correlations between PTSD symptoms with a threshold of .10

Fig. 9 Relative Importance network of PTSD symptoms, showing direction and magnitude of relationships

Centrality plot for the relative importance network.

Detecting communities of symptoms Possible to over-interpret the visualization of data (Fried, 2016, Psych Network) Most studies use the Fruchterman-Reingold algorithm to create a layout: Nodes with the most connections / highest number of connections in the center of the graph Node placement just one of many equally ‘correct’ ways

Detecting communities of symptoms Better ways: Use Eigen values Use spinglass algorithm detects communities Exploratory Graph Analysis (Golino & Epskamp (2016) currently under development Re-estimates a regularized partial correlation network and uses the walktrap (a random walk) algorithm to find communities unlike eigenvalue decomposition it shows directly what items belong to what clusters

Fig. 11. Identify communities of items in networks using Exploratory Graph Analysis via the R-package EGA and spinglass

But how sure are we? Epskamp, Borsboom & Fried, 2016 We can Estimate of the accuracy of edge-weights, by drawing bootstrapped CI Investigate the stability of (the order of) centrality indices after observing only portions of the data Perform bootstrapped difference tests between edge-weights and centrality indices to test whether these differ significantly from each other

Epskamp, Borsboom & Fried, 2016 Bootstrapped confidence intervals of estimated edge-weights for the estimated network of 17 PTSD symptoms. The red line the sample values Grey area the bootstrapped CIs Each horizontal line represents one edge of the network, ordered from the edge with the highest edge-weight to the edge with the lowest edge-weight

Discussion

What does this imply? Epskamp et al found that: generally large bootstrapped CIs imply that interpreting the order of most edges in the network should be done with care and that upset when reminded of the trauma – upsetting thoughts/images being jumpy – being alert feeling distant – loss of interest are reliably the three strongest edges since their bootstrapped CIs do not overlap with the bootstrapped CIs of any other edges Current study: None did not overlap Highest Hypervigilane- startle intrusion--dreams distant--numb

Centrality stability Stability of centrality indices by estimating network models based on subsets of the data

Centrality stability Betweenness 0.0505 closeness 0.0505 strength 0.361 The CS-coefficient indicates betweenness (CS(cor = 0.7) = 0.05) and closeness (CS(cor = 0.7) = 0.05) are not stable under subsetting cases. Node strength performs better (CS(cor = 0.7) = 0.36) but does not reach the cutoff of 0.5 from simulation studies Thus: order of node strength is interpretable with some care, while the orders of betweenness and closeness are not

Testing for significant difference Edges cannot be shown to significantly differ from one-another

Bayesian network McNally (in press) Parametric method that produces directed acyclical graphs Arrows with direction Lacks cycles (feedback loops) An interdisciplinary area the aim of determining causal inferences from observational data. However, requires additional assumptions (Peal, 2014) Possible? May be (smoking and cancer)

Bayesian Networks, with causal aspirations Each node is printed in square brackets along with all its parents (which are reported after a pipe as a colon-separated list) [flash] [upset|flash] [intrusion|upset] [physior|upset] [avoidth|upset] [dreams|intrusion] [avoidact|avoidth] [amnesia|avoidth] [lossint|avoidact] [distant|lossint] [numb|distant] [concen|distant] [hyper|distant] [future|distant:numb] [sleep|concen] [anger|concen] [startle|hyper]

Perturbed and restarted network

Discussion Replicated and extended the results of McNally et al. (2014) in a larger sample These results suggest that in a large sample, PTSD symptoms are interrelated especially bidirectionally These results suggest that the most central symptoms may be the most important in the disorder and thus, and ideal candidate to target in treatment

Discussion Interventions focusing on social support and interaction for PTSD are likely to influence one symptom cluster, thereby alleviating other symptoms that it affects. These data suggest a potential causal system of symptoms The fact that specific symptoms may give rise to each other highlights a pattern that may lead to their specific chronicity

Discussion However, the results of our estimating Network accuracy suggested some caution Replication and larger samples Better characterized samples may provide more clear and accurate networks Begin to create causal models that can be tested

Thank you