A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects

Autores
Bedi, Gillinder; Cecchi, Guillermo Alberto; Fernandez Slezak, Diego; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Bedi, Gillinder. Columbia University; Estados Unidos
Fil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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
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
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
Fil: de Wit, Harriet. Columbia University; Estados Unidos
Materia
Ecstasy
Mdma
Methamphetamine
Speech
Semantic Analyses
Machine Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/29509

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network_name_str CONICET Digital (CONICET)
spelling A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug EffectsBedi, GillinderCecchi, Guillermo AlbertoFernandez Slezak, DiegoCarrillo, FacundoSigman, Marianode Wit, HarrietEcstasyMdmaMethamphetamineSpeechSemantic AnalysesMachine Learninghttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Abused 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.Fil: Bedi, Gillinder. Columbia University; Estados UnidosFil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Slezak, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carrillo, Facundo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sigman, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Wit, Harriet. Columbia University; Estados UnidosNature Publishing Group2014-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/29509Bedi, 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; 2340-23480893-133XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/npp.2014.80info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/npp201480info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138742/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:43Zoai:ri.conicet.gov.ar:11336/29509instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:46:44.181CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
title A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
spellingShingle A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
Bedi, Gillinder
Ecstasy
Mdma
Methamphetamine
Speech
Semantic Analyses
Machine Learning
title_short A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
title_full A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
title_fullStr A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
title_full_unstemmed A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
title_sort A Window into the Intoxicated Mind? : speech as an Index of Psychoactive Drug Effects
dc.creator.none.fl_str_mv Bedi, Gillinder
Cecchi, Guillermo Alberto
Fernandez Slezak, Diego
Carrillo, Facundo
Sigman, Mariano
de Wit, Harriet
author Bedi, Gillinder
author_facet Bedi, Gillinder
Cecchi, Guillermo Alberto
Fernandez Slezak, Diego
Carrillo, Facundo
Sigman, Mariano
de Wit, Harriet
author_role author
author2 Cecchi, Guillermo Alberto
Fernandez Slezak, Diego
Carrillo, Facundo
Sigman, Mariano
de Wit, Harriet
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ecstasy
Mdma
Methamphetamine
Speech
Semantic Analyses
Machine Learning
topic Ecstasy
Mdma
Methamphetamine
Speech
Semantic Analyses
Machine Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Bedi, Gillinder. Columbia University; Estados Unidos
Fil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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
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
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
Fil: de Wit, Harriet. Columbia University; Estados Unidos
description 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.
publishDate 2014
dc.date.none.fl_str_mv 2014-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/29509
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
0893-133X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/29509
identifier_str_mv 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
0893-133X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138742/
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dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
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