Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography

Autores
Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Kochen, Sara Silvia; Bekinschtein, Tristán Andrés; Cambridge Centre for Ageing and Neuroscience; Rowe, James B.
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical ‘nodes’ in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography – ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.
Fil: Phillips, Holly N.. Cognition and Brain Sciences Unit; Reino Unido. 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 ; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos ; Argentina
Fil: Hughes, Laura E.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido
Fil: Kochen, Sara Silvia. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos ; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; Argentina
Fil: Bekinschtein, Tristán Andrés. University of Cambridge; Reino Unido. Cognition and Brain Sciences Unit; Reino Unido
Fil: Cambridge Centre for Ageing and Neuroscience. No especifica;
Fil: Rowe, James B.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido
Materia
Dynamic Causal Modelling
Mismatch Negativity
Electrocorticography
Cognition
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:11336/48054
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalographyPhillips, Holly N.Blenkmann, Alejandro OmarHughes, Laura E.Kochen, Sara SilviaBekinschtein, Tristán AndrésCambridge Centre for Ageing and NeuroscienceRowe, James B.Dynamic Causal ModellingMismatch NegativityElectrocorticographyCognitionhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical ‘nodes’ in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography – ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.Fil: Phillips, Holly N.. Cognition and Brain Sciences Unit; Reino Unido. 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 ; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos ; ArgentinaFil: Hughes, Laura E.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino UnidoFil: Kochen, Sara Silvia. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos ; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Bekinschtein, Tristán Andrés. University of Cambridge; Reino Unido. Cognition and Brain Sciences Unit; Reino UnidoFil: Cambridge Centre for Ageing and Neuroscience. No especifica;Fil: Rowe, James B.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino UnidoElsevier Masson2016-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/48054Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Kochen, Sara Silvia; Bekinschtein, Tristán Andrés; et al.; Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography; Elsevier Masson; Cortex; 82; 9-2016; 192-2050010-9452CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016%2Fj.cortex.2016.05.001info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0010945216301058info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981429/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 Técnicas2025-10-22T11:04:17Zoai:ri.conicet.gov.ar:11336/48054instacron: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:34982025-10-22 11:04:17.486CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
spellingShingle Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
Phillips, Holly N.
Dynamic Causal Modelling
Mismatch Negativity
Electrocorticography
Cognition
title_short Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title_full Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title_fullStr Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title_full_unstemmed Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title_sort Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
dc.creator.none.fl_str_mv Phillips, Holly N.
Blenkmann, Alejandro Omar
Hughes, Laura E.
Kochen, Sara Silvia
Bekinschtein, Tristán Andrés
Cambridge Centre for Ageing and Neuroscience
Rowe, James B.
author Phillips, Holly N.
author_facet Phillips, Holly N.
Blenkmann, Alejandro Omar
Hughes, Laura E.
Kochen, Sara Silvia
Bekinschtein, Tristán Andrés
Cambridge Centre for Ageing and Neuroscience
Rowe, James B.
author_role author
author2 Blenkmann, Alejandro Omar
Hughes, Laura E.
Kochen, Sara Silvia
Bekinschtein, Tristán Andrés
Cambridge Centre for Ageing and Neuroscience
Rowe, James B.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Dynamic Causal Modelling
Mismatch Negativity
Electrocorticography
Cognition
topic Dynamic Causal Modelling
Mismatch Negativity
Electrocorticography
Cognition
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical ‘nodes’ in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography – ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.
Fil: Phillips, Holly N.. Cognition and Brain Sciences Unit; Reino Unido. 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 ; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos ; Argentina
Fil: Hughes, Laura E.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido
Fil: Kochen, Sara Silvia. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos ; Argentina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; Argentina
Fil: Bekinschtein, Tristán Andrés. University of Cambridge; Reino Unido. Cognition and Brain Sciences Unit; Reino Unido
Fil: Cambridge Centre for Ageing and Neuroscience. No especifica;
Fil: Rowe, James B.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido
description We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical ‘nodes’ in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography – ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.
publishDate 2016
dc.date.none.fl_str_mv 2016-09
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/11336/48054
Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Kochen, Sara Silvia; Bekinschtein, Tristán Andrés; et al.; Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography; Elsevier Masson; Cortex; 82; 9-2016; 192-205
0010-9452
CONICET Digital
CONICET
url http://hdl.handle.net/11336/48054
identifier_str_mv Phillips, Holly N.; Blenkmann, Alejandro Omar; Hughes, Laura E.; Kochen, Sara Silvia; Bekinschtein, Tristán Andrés; et al.; Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography; Elsevier Masson; Cortex; 82; 9-2016; 192-205
0010-9452
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016%2Fj.cortex.2016.05.001
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0010945216301058
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981429/
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 Elsevier Masson
publisher.none.fl_str_mv Elsevier Masson
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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