1. 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 MH  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.

2. 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

3. 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

4. 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

5. 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

6. 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.

7. McNally et al (2014)

8. 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

9. 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

10. 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)

11. 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

12. Results

13. 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.

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

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

16. Centrality plot for the relative importance network.

17. 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

18. 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

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

20. 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

21. 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

22. Discussion

23. 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

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

25. Centrality stability Betweenness 0.0505 closeness 0.0505
strength
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

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

27. 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)

28. 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]

29. Perturbed and restarted network

30. 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

31. 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

32. 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

33. Thank you