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dc.provenanceCONICET-
dc.creatorPhillips, Holly N.-
dc.creatorBlenkmann, Alejandro Omar-
dc.creatorHughes, Laura E.-
dc.creatorKochen, Sara Silvia-
dc.creatorBekinschtein, Tristán Andrés-
dc.creatorCambridge Centre for Ageing and Neuroscience-
dc.creatorRowe, James B.-
dc.date2018-06-11T14:51:05Z-
dc.date2018-06-11T14:51:05Z-
dc.date2016-09-
dc.date2018-06-08T14:27:09Z-
dc.date.accessioned2019-04-29T15:41:27Z-
dc.date.available2019-04-29T15:41:27Z-
dc.date.issued2018-06-11T14:51:05Z-
dc.date.issued2018-06-11T14:51:05Z-
dc.date.issued2016-09-
dc.date.issued2018-06-08T14:27:09Z-
dc.identifierPhillips, 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-
dc.identifier0010-9452-
dc.identifierhttp://hdl.handle.net/11336/48054-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/299621-
dc.descriptionWe 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.-
dc.descriptionFil: Phillips, Holly N.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido-
dc.descriptionFil: 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-
dc.descriptionFil: Hughes, Laura E.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido-
dc.descriptionFil: 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-
dc.descriptionFil: Bekinschtein, Tristán Andrés. University of Cambridge; Reino Unido. Cognition and Brain Sciences Unit; Reino Unido-
dc.descriptionFil: Cambridge Centre for Ageing and Neuroscience. No especifica;-
dc.descriptionFil: Rowe, James B.. Cognition and Brain Sciences Unit; Reino Unido. University of Cambridge; Reino Unido-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherElsevier Masson-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016%2Fj.cortex.2016.05.001-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0010945216301058-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981429/-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.source.urihttp://hdl.handle.net/11336/68808-
dc.subjectDynamic causal modelling-
dc.subjectMismatch negativity-
dc.subjectElectrocorticography-
dc.subjectCognition-
dc.subjectSalud Ocupacional-
dc.subjectCiencias de la Salud-
dc.subjectCIENCIAS MÉDICAS Y DE LA SALUD-
dc.titleConvergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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