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Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography

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 oddb...

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Autores principales: Phillips, Holly N., Blenkmann, Alejandro, Hughes, Laura E., Kochen, Silvia, Bekinschtein, Tristan A., Cam-CAN, Rowe, James B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Masson 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981429/
https://www.ncbi.nlm.nih.gov/pubmed/27389803
http://dx.doi.org/10.1016/j.cortex.2016.05.001
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author Phillips, Holly N.
Blenkmann, Alejandro
Hughes, Laura E.
Kochen, Silvia
Bekinschtein, Tristan A.
Cam-CAN
Rowe, James B.
author_facet Phillips, Holly N.
Blenkmann, Alejandro
Hughes, Laura E.
Kochen, Silvia
Bekinschtein, Tristan A.
Cam-CAN
Rowe, James B.
author_sort Phillips, Holly N.
collection PubMed
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.
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spelling pubmed-49814292016-09-01 Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography Phillips, Holly N. Blenkmann, Alejandro Hughes, Laura E. Kochen, Silvia Bekinschtein, Tristan A. Cam-CAN Rowe, James B. Cortex Research Report 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. Masson 2016-09 /pmc/articles/PMC4981429/ /pubmed/27389803 http://dx.doi.org/10.1016/j.cortex.2016.05.001 Text en Crown Copyright © 2016 Published by Elsevier Ltd. All rights reserved.
spellingShingle Research Report
Phillips, Holly N.
Blenkmann, Alejandro
Hughes, Laura E.
Kochen, Silvia
Bekinschtein, Tristan A.
Cam-CAN
Rowe, James B.
Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title 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_short Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
title_sort convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography
topic Research Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981429/
https://www.ncbi.nlm.nih.gov/pubmed/27389803
http://dx.doi.org/10.1016/j.cortex.2016.05.001
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