December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Computational modeling of traveling waves using MEG-EEG in human
Author Affiliations & Notes
  • Laetitia Grabot
    Université de Paris, INCC UMR 8002, CNRS, F-75006 Paris, France
  • Garance Merholz
    Université de Paris, INCC UMR 8002, CNRS, F-75006 Paris, France
  • Jonathan Winawer
    Department of Psychology, New York University, New York, NY 10003, United States
    Center for Neural Science, New York University, New York, NY 10003, United States
  • David Heeger
    Department of Psychology, New York University, New York, NY 10003, United States
    Center for Neural Science, New York University, New York, NY 10003, United States
  • Laura Dugué
    Université de Paris, INCC UMR 8002, CNRS, F-75006 Paris, France
    Institut Universitaire de France (IUF), Paris, France
  • Footnotes
    Acknowledgements  This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement N° 852139 to Laura Dugué).
Journal of Vision December 2022, Vol.22, 3511. doi:https://doi.org/10.1167/jov.22.14.3511
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      Laetitia Grabot, Garance Merholz, Jonathan Winawer, David Heeger, Laura Dugué; Computational modeling of traveling waves using MEG-EEG in human. Journal of Vision 2022;22(14):3511. https://doi.org/10.1167/jov.22.14.3511.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The role of brain oscillations in various cognitive functions including visual perception is extensively studied. However, their spatial organization is rarely scrutinized. Recent studies suggest that brain oscillations can travel across the cortex. Mesoscopic waves, traveling within cortical areas, are mainly observed with invasive measurements (e.g., electrocorticography), which limits their investigation. Measuring traveling waves non-invasively in human, such as with magneto- and electro-encephalography (MEG, EEG), is particularly challenging due to technical and biophysical constrains (e.g., source summation, volume conduction). To address these issues, we developed a novel model-based neuroimaging approach. First, in a two-stage computational model, (1) the putative neural sources of a propagating 5Hz-oscillation were modeled within the early visual region (V1) using individual retinotopic mapping from functional MRI recordings (encoding model); and (2) the modeled sources were projected onto the MEG-EEG sensor space to predict the resulting MEG-EEG signal (forward biophysical head model). Second, we tested our model by comparing its predictions against the MEG-EEG signal obtained when participants viewed a radial visual stimulus consisting of a black-and-white sinusoidal wave oscillating at 5Hz and propagating from the center to the periphery of the screen. This “traveling” stimulus was used to elicit a 5Hz-neural oscillation traveling across the retinotopic space. A “standing” stimulus, oscillating at the same frequency with the same phase across the visual field, was used as control. Correlations on amplitude and phase between predicted and measured data revealed a good performance of the model. Crucially, the model was able to distinguish MEG-EEG recordings while participants viewed a traveling stimulus compared to a standing stimulus. Our model aims at bridging the gap between mesoscopic (neuronal populations) and macroscopic (full brain recordings) scales, to facilitate a better understanding of the functional role of brain oscillations for cognition.

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