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A Complex Systems Approach to Causal Discovery in Psychiatry.

Publication ,  Journal Article
Saxe, GN; Statnikov, A; Fenyo, D; Ren, J; Li, Z; Prasad, M; Wall, D; Bergman, N; Briggs, EC; Aliferis, C
Published in: PLoS One
2016

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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Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2016

Volume

11

Issue

3

Start / End Page

e0151174

Location

United States

Related Subject Headings

  • Wounds and Injuries
  • Systems Analysis
  • Psychiatry
  • Models, Psychological
  • Humans
  • General Science & Technology
  • Cluster Analysis
  • Child
  • Adolescent
 

Citation

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Saxe, G. N., Statnikov, A., Fenyo, D., Ren, J., Li, Z., Prasad, M., … Aliferis, C. (2016). A Complex Systems Approach to Causal Discovery in Psychiatry. PLoS One, 11(3), e0151174. https://doi.org/10.1371/journal.pone.0151174
Saxe, Glenn N., Alexander Statnikov, David Fenyo, Jiwen Ren, Zhiguo Li, Meera Prasad, Dennis Wall, Nora Bergman, Ernestine C. Briggs, and Constantin Aliferis. “A Complex Systems Approach to Causal Discovery in Psychiatry.PLoS One 11, no. 3 (2016): e0151174. https://doi.org/10.1371/journal.pone.0151174.
Saxe GN, Statnikov A, Fenyo D, Ren J, Li Z, Prasad M, et al. A Complex Systems Approach to Causal Discovery in Psychiatry. PLoS One. 2016;11(3):e0151174.
Saxe, Glenn N., et al. “A Complex Systems Approach to Causal Discovery in Psychiatry.PLoS One, vol. 11, no. 3, 2016, p. e0151174. Pubmed, doi:10.1371/journal.pone.0151174.
Saxe GN, Statnikov A, Fenyo D, Ren J, Li Z, Prasad M, Wall D, Bergman N, Briggs EC, Aliferis C. A Complex Systems Approach to Causal Discovery in Psychiatry. PLoS One. 2016;11(3):e0151174.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2016

Volume

11

Issue

3

Start / End Page

e0151174

Location

United States

Related Subject Headings

  • Wounds and Injuries
  • Systems Analysis
  • Psychiatry
  • Models, Psychological
  • Humans
  • General Science & Technology
  • Cluster Analysis
  • Child
  • Adolescent