Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-26T00:23:49.160Z Has data issue: false hasContentIssue false

Taking an engineer's view: Implications of network analysis for computational psychiatry

Published online by Cambridge University Press:  06 March 2019

A. David Redish
Affiliation:
Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455. [email protected]
Rebecca Kazinka
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN 55455. [email protected]
Alexander B. Herman
Affiliation:
Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455. [email protected]

Abstract

An engineer's viewpoint on psychiatry asks: What are the failure modes that underlie psychiatric dysfunction? And: How can we modify the system? Psychiatry has made great strides in understanding and treating disorders using biology; however, failure modes and modification access points can also exist extrinsically in environmental interactions. The network analysis suggested by Borsboom et al. in the target article provides a new viewpoint that should be incorporated into current theoretical constructs, not placed in opposition to them.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edition (DSM-5). American Psychiatric Association.Google Scholar
Borsboom, D., Cramer, A. O., Schmittmann, V. D., Epskamp, S. & Waldorp, L. J. (2011) The small world of psychopathology. PLoS One 6(11):e27407. Available at: https://doi.org/10.1371/journal.pone.0027407.Google Scholar
Flagel, S. B., Pine, D. S., Ahmari, S. E., First, M. B., Friston, K. J., Mathys, C., Redish, A. D., Schmack, K., Smoller, J. W. & Thapar, A. (2016) A novel framework for improving psychiatric diagnostic nosology. In: Computational psychiatry: New perspectives on mental illness, ed. Redish, A. D. & Gordon, J. A., Ch. 10, pp. 167–99. MIT Press.Google Scholar
Frances, A. (2014) Resuscitating the biopsychosocial model. The Lancet Psychiatry 1:496–97.Google Scholar
Huys, Q. J., Maia, T. V. & Frank, M. J. (2016) Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience 19(3):404–13. doi: 10.1038/nn.4238.Google Scholar
Insel, T. R. (2014) The NIMH Research Domain Criteria (RDoC) project: Precision medicine for psychiatry. American Journal of Psychiatry 171(4):395–97.Google Scholar
MacDonald, A. W. III, Zick, J. L., Netoff, T. I. & Chafee, M. V. (2016) The computation of collapse: Can reliability engineering shed light on mental illness? In: Computational psychiatry: New perspectives on mental illness, ed. Redish, A. D. & Gordon, J. A., Ch. 9, pp. 153–66. MIT Press.Google Scholar
Marcks, B. A., Weisberg, R. B., Edelen, M. O. & Keller, M. B. (2010) The relationship between sleep disturbance and the course of anxiety disorders in primary care patients. Psychiatry Research 178(3):487–92.Google Scholar
McClelland, J. L., McNaughton, B. L. & O'Reilly, R. C. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review 102(3):419–57.Google Scholar
National Institute of Mental Health. (2018) Research Domain Criteria (RDoC). Available at: https://www.nimh.nih.gov/research-priorities/rdoc.Google Scholar
Ohayon, M. M. & Roth, T. (2003) Place of chronic insomnia in the course of depressive and anxiety disorders. Journal of Psychiatric Research 37(1):915.Google Scholar
Payne, J. D. & Nadel, L. (2004) Sleep, dreams, and memory consolidation: The role of the stress hormone cortisol. Learning and Memory 11(6):671–78.Google Scholar
Petry, N. M. (2011) Contingency management for substance abuse treatment. Routledge.Google Scholar
Rangel, A., Camerer, C. & Montague, P. R. (2008) A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience 9(7):545–56. Available at: https://www.nature.com/articles/nrn2357.Google Scholar
Rasch, B. & Born, J. (2013) About sleep's role in memory. Physiological Reviews 93(2):681766.Google Scholar
Redish, A. D. (2013) The mind within the brain: How we make decisions and how those decisions go wrong. Oxford University Press.Google Scholar
Redish, A. D. & Gordon, J. A. (2016) Computational psychiatry: New perspectives on mental illness. MIT Press.Google Scholar
Redish, A. D., Jensen, S. & Johnson, A. (2008) A unified framework for addiction: vulnerabilities in the decision process. Behavioral and Brain Sciences 31:415–87.Google Scholar
Regier, P. S. & Redish, A. D. (2015) Contingency management and deliberative decision-making processes. Frontiers in Psychiatry 6: article 76. (Online publication). doi: 10.3389/fpsyt.2015.00076. Available at: https://www.frontiersin.org/articles/10.3389/fpsyt.2015.00076/full.Google Scholar
Seibold, B. (2015) A mathematical introduction to traffic flow theory. IPAM Tutorials. Available at: http://helper.ipam.ucla.edu/publications/tratut/tratut_12985.pdf.Google Scholar
Shay, J. (1994) Achilles in Vietnam: Combat trauma and the undoing of character. Simon and Schuster.Google Scholar
Swedo, S. E., Rapoport, J. L., Leonard, H., Lenane, M. & Cheslow, D. (1989) Obsessive–compulsive disorder in children and adolescents: Clinical phenomenology of 70 consecutive cases. Archives of General Psychiatry 46:335–41.Google Scholar
Talarico, J. M. & Rubin, D. C. (2003) Confidence, not consistency, characterizes flashbulb memories. Psychological Science 14(5):455–61.Google Scholar