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Comorbid Obsessive-Compulsive Disorder and Depression: A Network Analytic Approach

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, 2016. New York, NY.

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Comorbid Obsessive-Compulsive Disorder and Depression: A Network Analytic Approach

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  1. 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, 2016. 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., 2010, Behavioral & Brain Sciences)

  4. 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 • 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

  10. Results

  11. Averaged Bayesian Network

  12. Averaged Bayesian Network(“Scutari Method”)

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

  14. Thank you!

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

  16. 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

  17. 1000 bootstrap samples • Estimate the robustness of the edges • Estimate the robustness of the centrality metrics

  18. Results

  19. Partial Correlation Network (graphical LASSO)

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