Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions

Autores
Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Bekinschtein, Tristán Andrés; Rowe, James B.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Brain function can be conceived as a hierarchy of generative models that optimizes predictions of sensory inputs and minimizes “surprise.” Each level of the hierarchy makes predictions of neural events at a lower level in the hierarchy, which returns a prediction error when these expectations are violated. We tested the generalization of this hypothesis to multiple sequential deviations, and we identified the most likely organization of the network that accommodates deviations in temporal structure of stimuli. Magnetoencephalography of healthy human participants during an auditory paradigm identified prediction error responses in bilateral primary auditory cortex, superior temporal gyrus, and lateral prefrontal cortex for deviation by frequency, intensity, location, duration, and silent gap. We examined the connectivity between cortical sources using a set of 21 generative models that embedded alternate hypotheses of frontotemporal network dynamics. Bayesian model selection provided evidence for two new features of functional network organization. First, an expectancy signal provided input to the prefrontal cortex bilaterally, related to the temporal structure of stimuli. Second, there are functionally significant lateral connections between superior temporal and/or prefrontal cortex. The results support a predictive coding hypothesis but go beyond previous work in demonstrating the generalization to multiple concurrent stimulus dimensions and the evidence for a temporal expectancy input at the higher level of the frontotemporal hierarchy. We propose that this framework for studying the brain's response to unexpected events is not limited to simple sensory tasks but may also apply to the neurocognitive mechanisms of higher cognitive functions and their disorders.
Fil: Phillips, Holly N.. University of Cambridge; Reino Unido
Fil: Blenkmann, Alejandro Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina
Fil: Hughes, Laura E.. University of Cambridge; Reino Unido
Fil: Bekinschtein, Tristán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cambridge; Reino Unido
Fil: Rowe, James B.. University of Cambridge; Reino Unido
Materia
Dynamic causal modeling
Magnetoencephalography
Mismatch negativity
Prediction and prediction error
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:11336/17795

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spelling Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensionsPhillips, Holly N.Blenkmann, Alejandro OmarHughes, Laura E.Bekinschtein, Tristán AndrésRowe, James B.Dynamic causal modelingMagnetoencephalographyMismatch negativityPrediction and prediction errorhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Brain function can be conceived as a hierarchy of generative models that optimizes predictions of sensory inputs and minimizes “surprise.” Each level of the hierarchy makes predictions of neural events at a lower level in the hierarchy, which returns a prediction error when these expectations are violated. We tested the generalization of this hypothesis to multiple sequential deviations, and we identified the most likely organization of the network that accommodates deviations in temporal structure of stimuli. Magnetoencephalography of healthy human participants during an auditory paradigm identified prediction error responses in bilateral primary auditory cortex, superior temporal gyrus, and lateral prefrontal cortex for deviation by frequency, intensity, location, duration, and silent gap. We examined the connectivity between cortical sources using a set of 21 generative models that embedded alternate hypotheses of frontotemporal network dynamics. Bayesian model selection provided evidence for two new features of functional network organization. First, an expectancy signal provided input to the prefrontal cortex bilaterally, related to the temporal structure of stimuli. Second, there are functionally significant lateral connections between superior temporal and/or prefrontal cortex. The results support a predictive coding hypothesis but go beyond previous work in demonstrating the generalization to multiple concurrent stimulus dimensions and the evidence for a temporal expectancy input at the higher level of the frontotemporal hierarchy. We propose that this framework for studying the brain's response to unexpected events is not limited to simple sensory tasks but may also apply to the neurocognitive mechanisms of higher cognitive functions and their disorders.Fil: Phillips, Holly N.. University of Cambridge; Reino UnidoFil: Blenkmann, Alejandro Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Hughes, Laura E.. University of Cambridge; Reino UnidoFil: Bekinschtein, Tristán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cambridge; Reino UnidoFil: Rowe, James B.. University of Cambridge; Reino UnidoSociety for Neuroscience2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/17795Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Bekinschtein, Tristán Andrés; Rowe, James B.; Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions; Society for Neuroscience; Journal of Neuroscience; 35; 25; 6-2015; 9255-92640270-64741529-2401enginfo:eu-repo/semantics/altIdentifier/doi/10.1523/JNEUROSCI.5095-14.2015info:eu-repo/semantics/altIdentifier/url/http://www.jneurosci.org/content/35/25/9255info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4478247/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 Técnicas2025-09-29T09:50:07Zoai:ri.conicet.gov.ar:11336/17795instacron: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 09:50:07.664CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
title Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
spellingShingle Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
Phillips, Holly N.
Dynamic causal modeling
Magnetoencephalography
Mismatch negativity
Prediction and prediction error
title_short Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
title_full Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
title_fullStr Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
title_full_unstemmed Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
title_sort Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions
dc.creator.none.fl_str_mv Phillips, Holly N.
Blenkmann, Alejandro Omar
Hughes, Laura E.
Bekinschtein, Tristán Andrés
Rowe, James B.
author Phillips, Holly N.
author_facet Phillips, Holly N.
Blenkmann, Alejandro Omar
Hughes, Laura E.
Bekinschtein, Tristán Andrés
Rowe, James B.
author_role author
author2 Blenkmann, Alejandro Omar
Hughes, Laura E.
Bekinschtein, Tristán Andrés
Rowe, James B.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Dynamic causal modeling
Magnetoencephalography
Mismatch negativity
Prediction and prediction error
topic Dynamic causal modeling
Magnetoencephalography
Mismatch negativity
Prediction and prediction error
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Brain function can be conceived as a hierarchy of generative models that optimizes predictions of sensory inputs and minimizes “surprise.” Each level of the hierarchy makes predictions of neural events at a lower level in the hierarchy, which returns a prediction error when these expectations are violated. We tested the generalization of this hypothesis to multiple sequential deviations, and we identified the most likely organization of the network that accommodates deviations in temporal structure of stimuli. Magnetoencephalography of healthy human participants during an auditory paradigm identified prediction error responses in bilateral primary auditory cortex, superior temporal gyrus, and lateral prefrontal cortex for deviation by frequency, intensity, location, duration, and silent gap. We examined the connectivity between cortical sources using a set of 21 generative models that embedded alternate hypotheses of frontotemporal network dynamics. Bayesian model selection provided evidence for two new features of functional network organization. First, an expectancy signal provided input to the prefrontal cortex bilaterally, related to the temporal structure of stimuli. Second, there are functionally significant lateral connections between superior temporal and/or prefrontal cortex. The results support a predictive coding hypothesis but go beyond previous work in demonstrating the generalization to multiple concurrent stimulus dimensions and the evidence for a temporal expectancy input at the higher level of the frontotemporal hierarchy. We propose that this framework for studying the brain's response to unexpected events is not limited to simple sensory tasks but may also apply to the neurocognitive mechanisms of higher cognitive functions and their disorders.
Fil: Phillips, Holly N.. University of Cambridge; Reino Unido
Fil: Blenkmann, Alejandro Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina
Fil: Hughes, Laura E.. University of Cambridge; Reino Unido
Fil: Bekinschtein, Tristán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cambridge; Reino Unido
Fil: Rowe, James B.. University of Cambridge; Reino Unido
description Brain function can be conceived as a hierarchy of generative models that optimizes predictions of sensory inputs and minimizes “surprise.” Each level of the hierarchy makes predictions of neural events at a lower level in the hierarchy, which returns a prediction error when these expectations are violated. We tested the generalization of this hypothesis to multiple sequential deviations, and we identified the most likely organization of the network that accommodates deviations in temporal structure of stimuli. Magnetoencephalography of healthy human participants during an auditory paradigm identified prediction error responses in bilateral primary auditory cortex, superior temporal gyrus, and lateral prefrontal cortex for deviation by frequency, intensity, location, duration, and silent gap. We examined the connectivity between cortical sources using a set of 21 generative models that embedded alternate hypotheses of frontotemporal network dynamics. Bayesian model selection provided evidence for two new features of functional network organization. First, an expectancy signal provided input to the prefrontal cortex bilaterally, related to the temporal structure of stimuli. Second, there are functionally significant lateral connections between superior temporal and/or prefrontal cortex. The results support a predictive coding hypothesis but go beyond previous work in demonstrating the generalization to multiple concurrent stimulus dimensions and the evidence for a temporal expectancy input at the higher level of the frontotemporal hierarchy. We propose that this framework for studying the brain's response to unexpected events is not limited to simple sensory tasks but may also apply to the neurocognitive mechanisms of higher cognitive functions and their disorders.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/17795
Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Bekinschtein, Tristán Andrés; Rowe, James B.; Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions; Society for Neuroscience; Journal of Neuroscience; 35; 25; 6-2015; 9255-9264
0270-6474
1529-2401
url http://hdl.handle.net/11336/17795
identifier_str_mv Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Bekinschtein, Tristán Andrés; Rowe, James B.; Hierarchical organization of frontotemporal networks for the prediction of stimuli across multiple dimensions; Society for Neuroscience; Journal of Neuroscience; 35; 25; 6-2015; 9255-9264
0270-6474
1529-2401
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1523/JNEUROSCI.5095-14.2015
info:eu-repo/semantics/altIdentifier/url/http://www.jneurosci.org/content/35/25/9255
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4478247/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Society for Neuroscience
publisher.none.fl_str_mv Society for Neuroscience
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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