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PLoS One. 2016 Mar 30;11(3):e0151174. doi: 10.1371/journal.pone.0151174. eCollection 2016.

A Complex Systems Approach to Causal Discovery in Psychiatry.

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Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, New York, United States of America.
Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, New York, United States of America.
Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
Systems Medicine in the Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America.
Department of Psychiatry, Duke University School of Medicine, Durham, North Carolina, United States of America.
Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, United States of America.


Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

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