From evoked potentials to cortical currents: Resolving V1 and V2 components using retinotopy constrained source estimation without fMRI

Inverso, Samuel A.; Goh, Xin-Lin; Henriksson, Linda; Vanni, Simo; James, Andrew

doi:10.1002/hbm.23128

Cited by 9 publications

(12 citation statements)

References 79 publications

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“…Thus far, we focused on the 10 Hz steady-state spectral amplitudes and ignored the corresponding 10 Hz phases . This phase component can vary across epochs and MEG sensors due to processing delays in the visual system and depend on stimulus features, such as contrast (Shapley & Victor, 1978) and eccentricity (Jeffreys, 1971; Burkitt, Silberstein, Cadusch, & Wood, 2000; Ales, Yates, & Norcia, 2013; Inverso, Goh, Henriksson, Vanni, & James, 2016). Because our stimulus was a bar sweeping in different directions across the visual field and likely activated both early and later visual areas which differ in response timing, we expected variability in the 10 Hz phases across MEG sensors.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

A Population Receptive Field Model of the Magnetoencephalography Response

Kupers

Edadan

Benson

et al. 2020

Preprint

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“…This method uses visual field maps to guide constraints on the number of solutions when inverse modeling the sources. For example, one can create a correlation matrix that only includes visual field areas to constrain the possible solutions (Hagler et al, 2009; Hagler & Dale, 2013; Hagler, 2014; Cottereau, Ales, & Norcia, 2015) or apply an exhaustive search to define neighboring sources for every stimulus location (Ales, Carney, & Klein, 2010; Inverso, Goh, Henriksson, Vanni, & James, 2016). This approach has been shown to increase source localization accuracy, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…None of these assumptions holds exactly in the brain. For example, there are differences in EEG sensor response timing and amplitudes for checkerboard stimuli that vary in eccentricity (Jeffreys, 1971; Ales et al, 2013; Inverso et al, 2016), and higher level visual areas beyond V1-V3 can also show steady state responses to contrast-reversing stimuli (Ales, Farzin, Rossion, & Norcia, 2012). The steady state response to contrast-reversing patterns may also differ in amplitude between visual areas, as suggested by fMRI-constrained source localization of EEG signals (Di Russo et al, 2005; Di Russo et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Section: Encoding Model Assumptionsmentioning

confidence: 99%

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

Kupers

Benson

Winawer

2020

Preprint

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Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.

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“…A challenge to the analysis of MEG responses to even elementary (low-level) visual stimuli is that the cortical visual areas show substantial inter-individual variability in their size and position relative to anatomical landmarks, especially sulci (Amunts et al, 2000;Van Essen and Dierker, 2007), inducing great variability in the waveforms and spatial patterns of the evoked electromagnetic responses (Ales et al, 2010;Inverso et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Cortical dynamics of saccade-target selection during free-viewing of natural scenes

Henriksson

Ölander

Hari

2016

Preprint

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Acknowledgements:We thank Mia Illman for help in MEG measurements and Veli-Matti Saarinen for help in eye-tracking measurements. The annotations of the stimulus images were created using the Object Labeling Tool from Derek Hoiem, University of Illinois. ABSTRACTNatural visual behaviour entails explorative eye movements, saccades, that bring different parts of a visual scene into the central vision. The neural processes guiding the selection of saccade targets are still largely unknown. Therefore, in this study, we tracked with magnetoencephalography (MEG) cortical dynamics of viewers who were freely exploring novel natural scenes. Overall, the viewers were largely consistent in their gaze behaviour, especially if the scene contained any persons. We took a fresh approach to relate the eyegaze data to the MEG signals by characterizing dynamic cortical representations by means of representational distance matrices. Specifically, we compared the representational distances between the stimuli in the evoked MEG responses with predictions based (1) on the low-level visual similarity of the stimuli (as visually more similar stimuli evoke more similar responses in early visual areas) and (2) on the eye-gaze data. At 50-75 ms after the scene onset, the similarity of the occipital MEG patterns correlated with the low-level visual similarity of the scenes, and already at 75-100 ms the visual features attracting the first saccades predicted the similarity of the right parieto-occipital MEG responses. Thereafter, at 100-125 ms, the landing positions of the upcoming saccades explained MEG responses. These results indicate that MEG signals contain signatures of the rapid processing of natural visual scenes as well as of the initiation of the first saccades, with the processing of the saccade target preceding the processing of the landing position of the upcoming saccade.Humans naturally make eye movements to bring different parts of a visual scene to the fovea where our visual acuity is the best. Tracking of eye gaze can reveal how we make inferences about the content of a scene by looking at different objects, or which visual cues automatically attract our attention and gaze. The brain dynamics governing natural gaze behaviour is still largely unknown. Here we suggest a novel approach to relate eyetracking results with brain activity, as measured with magnetoencephalography (MEG), and demonstrate signatures of natural gaze behaviour in the MEG data already before the eye movements occur.

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From evoked potentials to cortical currents: Resolving V1 and V2 components using retinotopy constrained source estimation without fMRI

Cited by 9 publications

References 79 publications

A Population Receptive Field Model of the Magnetoencephalography Response

A Population Receptive Field Model of the Magnetoencephalography Response

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

Cortical dynamics of saccade-target selection during free-viewing of natural scenes

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