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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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Related in: MedlinePlus

The CHIDS Causal Network After Node Removal by BC Rank.The CHIDS network after the sequential removal of 15 nodes by BC rank.
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pone.0151174.g008: The CHIDS Causal Network After Node Removal by BC Rank.The CHIDS network after the sequential removal of 15 nodes by BC rank.

Mentions: Another way to visualize the response of the CHIDS network to random vs. targeted (BC rank) attack is by examining the remaining CHIDS network after these two respective forms of challenge are completed, and to compare the remaining network under these two conditions to the CHIDS network before challenge, as shown in Fig 5. The remaining CHIDS network after random removal of 15 nodes is shown in Fig 7. As can be seen, the CHIDS network has largely kept its integrity, losing only 9 of 111 nodes. On the other hand, this same network, when challenged by removal based on BC rank, fragments into 11 components and loses all semblance of its structure (Fig 8).


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)

The CHIDS Causal Network After Node Removal by BC Rank.The CHIDS network after the sequential removal of 15 nodes by BC rank.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0151174.g008: The CHIDS Causal Network After Node Removal by BC Rank.The CHIDS network after the sequential removal of 15 nodes by BC rank.
Mentions: Another way to visualize the response of the CHIDS network to random vs. targeted (BC rank) attack is by examining the remaining CHIDS network after these two respective forms of challenge are completed, and to compare the remaining network under these two conditions to the CHIDS network before challenge, as shown in Fig 5. The remaining CHIDS network after random removal of 15 nodes is shown in Fig 7. As can be seen, the CHIDS network has largely kept its integrity, losing only 9 of 111 nodes. On the other hand, this same network, when challenged by removal based on BC rank, fragments into 11 components and loses all semblance of its structure (Fig 8).

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