A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex

Kupers, Eline R; Benson, Noah C.; Winawer, Jonathan

doi:10.1101/2020.04.19.049197

Cited by 7 publications

(6 citation statements)

References 116 publications

(182 reference statements)

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“…The approach of fitting an interpretable encoding model to training data and then performing a model inversion to make predictions about unseen data is well established in the fMRI literature (Casey et al, 2011; Friston et al, 2008; Kay et al, 2008; Mitchell et al, 2008; Naselaris et al, 2009, 2015; Nishimoto et al, 2011; Schoenmakers et al, 2013), but has only seen limited adoption so far in M/EEG (di Liberto et al, 2015; Kupers et al, 2020). Our focus in this paper has been to emphasise the interpretability benefits of this approach – which are often overlooked – and demonstrate how it can be readily extended to time series analysis for data at high temporal resolution in ways that we believe offer significant benefits to conventional timepoint-by-timepoint decoding.…”

Section: Discussionmentioning

confidence: 99%

“…In neuroscience terminology, this amounts to fitting an encoding model, then inverting that encoding model to make predictions through an equivalent decoding model (Friston et al, 2008; Haxby et al, 2014; Naselaris et al, 2011). This approach has been successful in fMRI (Casey et al, 2011; Friston et al, 2008; Kay et al, 2008; Mitchell et al, 2008; Naselaris et al, 2009, 2015; Nishimoto et al, 2011; Schoenmakers et al, 2013) but has only seen quite limited adoption for M/EEG (di Liberto et al, 2015; Kupers et al, 2020). In line with these fMRI works, we propose a linear generative model of stimulus evoked activity based upon the popular General Linear Model (GLM) framework.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG

Higgins

Vidaurre

Kolling

et al. 2021

Preprint

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An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. Whilst EEG and MEG offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion allows predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG

Higgins

Vidaurre

Kolling

et al. 2021

Preprint

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“…Also, our approach is not constrained by cancelation effects of opposite facing dipoles. On the contrary, our approach can be used to investigate the effect of source cancelation on sensor responses by simulating different temporal patterns in visual cortex ( Kupers et al, 2020 ). We first predict neural time series at a millimeter-scale on the cortical surface using local pRF models estimated with fMRI, before predicting sensor responses with the MEG forward model.…”

Section: Discussionmentioning

confidence: 99%

A population receptive field model of the magnetoencephalography response

et al. 2021

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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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“…Also, our approach is not constrained by cancellation effects of opposite facing dipoles. On the contrary, our approach can be used to investigate the effect of source cancellation on sensor responses by simulating different temporal patterns in visual cortex (Kupers, Benson, & Winawer, 2020).…”

Section: Relationship To Reconstructing Cortical Retinotopy From Meg Sensor Responsesmentioning

confidence: 99%

A Population Receptive Field Model of the Magnetoencephalography Response

Kupers

Edadan

Benson

et al. 2020

Preprint

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1AbstractComputational 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, indicate 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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A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex

Cited by 7 publications

References 116 publications

Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG

Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG

A population receptive field model of the magnetoencephalography response

A Population Receptive Field Model of the Magnetoencephalography Response

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