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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The authors would like to thank Carrie Purbeck Trunzo MHA, Robert Lee MS/MA, and Tracy Bethel MPH of Duke University for assisting the authors in understanding and working with the National Child Traumatic Stress Network Core Data Set. We would also like to thank Karestan Koenen PhD of the Harvard University School of Public Health for her help collecting and interpreting the genomic data, and Stephen Porges PhD of the University of North Carolina at Chapel Hill for his help in collecting and interpreting the psychophysiologic data. We are also grateful to the National Institute of Mental Health (grants R21 MH086309 and R01 MH063247) and the Substance Abuse and Mental Health Services Administration (grant U79 SM06128) for supporting this work.
© 2016 Saxe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.