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Biosensor approach to psychopathology classification.

Koshelev, M; Lohrenz, T; Vannucci, M; Montague, PR; (2010) Biosensor approach to psychopathology classification. PLoS Comput Biol , 6 (10) , Article e1000966. 10.1371/journal.pcbi.1000966. Green open access

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We used a multi-round, two-party exchange game in which a healthy subject played a subject diagnosed with a DSM-IV (Diagnostic and Statistics Manual-IV) disorder, and applied a Bayesian clustering approach to the behavior exhibited by the healthy subject. The goal was to characterize quantitatively the style of play elicited in the healthy subject (the proposer) by their DSM-diagnosed partner (the responder). The approach exploits the dynamics of the behavior elicited in the healthy proposer as a biosensor for cognitive features that characterize the psychopathology group at the other side of the interaction. Using a large cohort of subjects (n = 574), we found statistically significant clustering of proposers' behavior overlapping with a range of DSM-IV disorders including autism spectrum disorder, borderline personality disorder, attention deficit hyperactivity disorder, and major depressive disorder. To further validate these results, we developed a computer agent to replace the human subject in the proposer role (the biosensor) and show that it can also detect these same four DSM-defined disorders. These results suggest that the highly developed social sensitivities that humans bring to a two-party social exchange can be exploited and automated to detect important psychopathologies, using an interpersonal behavioral probe not directly related to the defining diagnostic criteria.

Type: Article
Title: Biosensor approach to psychopathology classification.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1000966
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1000966
Language: English
Additional information: Copyright: © 2010 Koshelev 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. Funding: This work was supported by a training fellowship from the Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia to MK. MV was partially supported by NIH-NHGRI grant number R01-HG003319, and by NSF-DMS grant number 1007871. PRM was partially supported by NIH R01 grants DA11723 and MH085496. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords: Algorithms, Attention Deficit Disorder with Hyperactivity, Autistic Disorder, Bayes Theorem, Borderline Personality Disorder, Case-Control Studies, Cluster Analysis, Cognition, Computational Biology, Computer Simulation, Depressive Disorder, Major, Humans, Interpersonal Relations, Mental Disorders, Models, Biological, Psychology, Social, Reproducibility of Results, Role Playing, Social Behavior
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
URI: https://discovery.ucl.ac.uk/id/eprint/1378404
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