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dc.contributor.author Bedi, Gillinder
dc.contributor.author Cecchi, Guillermo Alberto
dc.contributor.author Fernandez Slezak, Diego
dc.contributor.author Carrillo, Facundo
dc.contributor.author Sigman, Mariano
dc.contributor.author de Wit, Harriet
dc.date.available 2017-12-01T21:22:39Z
dc.date.issued 2014-04
dc.identifier.citation Bedi, Gillinder; Cecchi, Guillermo Alberto; Fernandez Slezak, Diego; Carrillo, Facundo; Sigman, Mariano; et al.; A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects; Nature Publishing Group; Neuropsychopharmacology; 39; 10; 4-2014; 2340-2348
dc.identifier.issn 0893-133X
dc.identifier.uri http://hdl.handle.net/11336/29509
dc.description.abstract Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique ‘window’ into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; ‘ecstasy’) and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness.
dc.format application/pdf
dc.language.iso eng
dc.publisher Nature Publishing Group
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject ECSTASY
dc.subject MDMA
dc.subject METHAMPHETAMINE
dc.subject SPEECH
dc.subject SEMANTIC ANALYSES
dc.subject MACHINE LEARNING
dc.subject.classification Otras Ciencias Biológicas
dc.subject.classification Ciencias Biológicas
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2017-06-09T14:19:29Z
dc.journal.volume 39
dc.journal.number 10
dc.journal.pagination 2340-2348
dc.journal.pais Reino Unido
dc.description.fil Fil: Bedi, Gillinder. Columbia University; Estados Unidos
dc.description.fil Fil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil Fil: Fernandez Slezak, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil Fil: Carrillo, Facundo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil Fil: Sigman, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil Fil: de Wit, Harriet. Columbia University; Estados Unidos
dc.journal.title Neuropsychopharmacology
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/npp.2014.80
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/npp201480
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138742/


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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)