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Co-morbid obsessive–compulsive disorder and depression: a Bayesian network approach

Published online by Cambridge University Press:  05 January 2017

R. J. McNally ,
P. Mair ,
B. L. Mugno  and
B. C. Riemann
R. J. McNally*
Affiliation:
Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USA
P. Mair
Affiliation:
Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USA
B. L. Mugno
Affiliation:
OCD Center and Cognitive–Behavioral Therapy Services, Rogers Memorial Hospital, 34700 Valley Road, Oconomowoc, WI 53066, USA
B. C. Riemann
Affiliation:
OCD Center and Cognitive–Behavioral Therapy Services, Rogers Memorial Hospital, 34700 Valley Road, Oconomowoc, WI 53066, USA
*
*Address for correspondence: R. J. McNally, Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USA.(Email: rjm@wjh.harvard.edu)
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Abstract

Background

Obsessive–compulsive disorder (OCD) is often co-morbid with depression. Using the methods of network analysis, we computed two networks that disclose the potentially causal relationships among symptoms of these two disorders in 408 adult patients with primary OCD and co-morbid depression symptoms.

Method

We examined the relationship between the symptoms constituting these syndromes by computing a (regularized) partial correlation network via the graphical LASSO procedure, and a directed acyclic graph (DAG) via a Bayesian hill-climbing algorithm.

Results

The results suggest that the degree of interference and distress associated with obsessions, and the degree of interference associated with compulsions, are the chief drivers of co-morbidity. Moreover, activation of the depression cluster appears to occur solely through distress associated with obsessions activating sadness – a key symptom that ‘bridges’ the two syndromic clusters in the DAG.

Conclusions

Bayesian analysis can expand the repertoire of network analytic approaches to psychopathology. We discuss clinical implications and limitations of our findings.

Keywords

Co-morbiditydepressiongraphical LASSOnetwork analysisobsessive–compulsive disorder
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 7 , May 2017 , pp. 1204 - 1214
Copyright
Copyright © Cambridge University Press 2017 

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