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A population receptive field model of the magnetoencephalography response

Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using function...

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Autores principales: Kupers, Eline R., Edadan, Akhil, Benson, Noah C., Zuiderbaan, Wietske, de Jong, Maartje C., Dumoulin, Serge O., Winawer, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631249/
https://www.ncbi.nlm.nih.gov/pubmed/34509622
http://dx.doi.org/10.1016/j.neuroimage.2021.118554
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author Kupers, Eline R.
Edadan, Akhil
Benson, Noah C.
Zuiderbaan, Wietske
de Jong, Maartje C.
Dumoulin, Serge O.
Winawer, Jonathan
author_facet Kupers, Eline R.
Edadan, Akhil
Benson, Noah C.
Zuiderbaan, Wietske
de Jong, Maartje C.
Dumoulin, Serge O.
Winawer, Jonathan
author_sort Kupers, Eline R.
collection PubMed
description Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject’s pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.
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spelling pubmed-86312492021-12-01 A population receptive field model of the magnetoencephalography response Kupers, Eline R. Edadan, Akhil Benson, Noah C. Zuiderbaan, Wietske de Jong, Maartje C. Dumoulin, Serge O. Winawer, Jonathan Neuroimage Article Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject’s pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps. 2021-09-10 2021-12-01 /pmc/articles/PMC8631249/ /pubmed/34509622 http://dx.doi.org/10.1016/j.neuroimage.2021.118554 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Kupers, Eline R.
Edadan, Akhil
Benson, Noah C.
Zuiderbaan, Wietske
de Jong, Maartje C.
Dumoulin, Serge O.
Winawer, Jonathan
A population receptive field model of the magnetoencephalography response
title A population receptive field model of the magnetoencephalography response
title_full A population receptive field model of the magnetoencephalography response
title_fullStr A population receptive field model of the magnetoencephalography response
title_full_unstemmed A population receptive field model of the magnetoencephalography response
title_short A population receptive field model of the magnetoencephalography response
title_sort population receptive field model of the magnetoencephalography response
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631249/
https://www.ncbi.nlm.nih.gov/pubmed/34509622
http://dx.doi.org/10.1016/j.neuroimage.2021.118554
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