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A Complex Systems Approach to Causal Discovery in Psychiatry.

Saxe GN, Statnikov A, Fenyo D, Ren J, Li Z, Prasad M, Wall D, Bergman N, Briggs EC, Aliferis C - PLoS ONE (2016)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, New York, United States of America.

ABSTRACT
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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Out-degree Distribution of the CHIDS Network (logarithmic scale).
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pone.0151174.g003: Out-degree Distribution of the CHIDS Network (logarithmic scale).

Mentions: The 111 node/167 link causal network was analyzed for its complex systems properties. Because the causal network is directed, we determined both its in-degree and out-degree scaling properties, based on the distributions of numbers of links entering and leaving nodes, respectively. In Figs 2 and 3, we show these scaling distributions, with logarithmic transformation.


A Complex Systems Approach to Causal Discovery in Psychiatry.

Saxe GN, Statnikov A, Fenyo D, Ren J, Li Z, Prasad M, Wall D, Bergman N, Briggs EC, Aliferis C - PLoS ONE (2016)

Out-degree Distribution of the CHIDS Network (logarithmic scale).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4814084&req=5

pone.0151174.g003: Out-degree Distribution of the CHIDS Network (logarithmic scale).
Mentions: The 111 node/167 link causal network was analyzed for its complex systems properties. Because the causal network is directed, we determined both its in-degree and out-degree scaling properties, based on the distributions of numbers of links entering and leaving nodes, respectively. In Figs 2 and 3, we show these scaling distributions, with logarithmic transformation.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, New York, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus