Biosensor approach to psychopathology classification.

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...

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Autores principales:	Misha Koshelev, Terry Lohrenz, Marina Vannucci, P Read Montague
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/deee870daf8e4ce6bbd1174b648cabb5
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spelling	oai:doaj.org-article:deee870daf8e4ce6bbd1174b648cabb52021-11-18T05:50:53ZBiosensor approach to psychopathology classification.1553-734X1553-735810.1371/journal.pcbi.1000966https://doaj.org/article/deee870daf8e4ce6bbd1174b648cabb52010-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20975934/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We 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.Misha KoshelevTerry LohrenzMarina VannucciP Read MontaguePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 10, p e1000966 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Misha Koshelev Terry Lohrenz Marina Vannucci P Read Montague Biosensor approach to psychopathology classification.
description	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.
format	article
author	Misha Koshelev Terry Lohrenz Marina Vannucci P Read Montague
author_facet	Misha Koshelev Terry Lohrenz Marina Vannucci P Read Montague
author_sort	Misha Koshelev
title	Biosensor approach to psychopathology classification.
title_short	Biosensor approach to psychopathology classification.
title_full	Biosensor approach to psychopathology classification.
title_fullStr	Biosensor approach to psychopathology classification.
title_full_unstemmed	Biosensor approach to psychopathology classification.
title_sort	biosensor approach to psychopathology classification.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/deee870daf8e4ce6bbd1174b648cabb5
work_keys_str_mv	AT mishakoshelev biosensorapproachtopsychopathologyclassification AT terrylohrenz biosensorapproachtopsychopathologyclassification AT marinavannucci biosensorapproachtopsychopathologyclassification AT preadmontague biosensorapproachtopsychopathologyclassification
_version_	1718424821455388672

Biosensor approach to psychopathology classification.

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