1. Rogers Memorial Hospital
Presented on the symposium entitled “Envisioning the Clinical Integration of Network Analysis and CBT: New Developments.” McNally, R. J. (Chair). Association of Behavioral and Cognitive Therapies. October, New York, NY.
Comorbid Obsessive-Compulsive Disorder and Depression: A Network Analytic Approach
Richard J. McNally
Patrick Mair
Harvard University
Beth L. Mugno
Bradley C. Riemann
Rogers Memorial Hospital

2. Comorbidity
As many as two-thirds of people with OCD develop major depression
OCD usually precedes onset of depression
Successful behavior therapy for OCD often reduces depression when overall severity is moderate

3. Traditional View of Comorbidity (Cramer et al
Traditional View of Comorbidity (Cramer et al., 2010, Behavioral & Brain Sciences)

4. Network View of Psychiatric Comorbidity (Cramer et al
Network View of Psychiatric Comorbidity (Cramer et al., 2010, Behavioral & Brain Sciences)

5. What is the Network Structure of OCD and Depression?

6. Method Subjects Assessed at intake 408 patients with primary OCD
Rogers Memorial Hospital
Assessed at intake
Yale-Brown Obsessive Compulsive Scale (Y-BOCS)
10 items (0 – 4)
Quick Inventory of Depression Symptomatology –Self-Report (QIDS-SR 16)
16 items (0 -3)

7. Bayesian Network
A Bayesian network analysis returns a directed acyclic graph (DAG)
The “hill climbing” algorithm tests for conditional independence relations among nodes

8. Averaged Bayesian Network
1000 iterations of the network (“bootstrapping samples”)
An edge remains only if its appears in at least 85% of the iterations
The direction of an edge?
X > Y percentage
Y > X percentage

9. Averaged Bayesian Network
The “importance” of an edge
BIC value (Bayesian Information Criterion)
The larger the absolute magnitude, the more “damaging” to model fit if one were to remove the edge from the network
Some edge examples: Sadness > guilt Strength = Direction = 0.903
Comptime > compdistress Strength = Direction = 0.598

10. Results

11. Averaged Bayesian Network

13. Averaged Bayesian Network (“Scutari Method”)

15. Unresolved Issues Candidate causal system?
No important unmeasured variables?
Are DAGs clinically plausible?

16. Thank you!

17. Additional slides that time limits prohibited me from presenting on the NYC ABCT symposium.

18. Edge thickness signifies the probability of an edge’s direction occurring in the direction predicted bootstrap samples and 85% threshold for edge retention (Sachs method)

19. Edge thickness reflects the percentage of time an edge was pointing in the direction depicted in the graph. Thin edges may signify possible cycles.

20. Graphical LASSO Least Absolute Shrinkage and Selection Operator
Edges are partial correlations
Computed via “regularization”
Small edges driven to zero
Likely “false alarms” vanish from the graph
Undirected network

21. 1000 bootstrap samples Estimate the robustness of the edges
Estimate the robustness of the centrality metrics

22. Results

23. Partial Correlation Network (graphical LASSO)

25. Confidence intervals for edge strength from the glasso with OCD and depression symptoms October