1. A population receptive field model of the magnetoencephalography response
- Author
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Akhil Edadan, Noah C. Benson, Jonathan Winawer, Maartje C. de Jong, Serge O. Dumoulin, Wietske Zuiderbaan, Eline R Kupers, Cognitive Psychology, and Spinoza Centre for Neuroimaging
- Subjects
Adult ,Male ,Computer science ,Cognitive Neuroscience ,Population ,Neurosciences. Biological psychiatry. Neuropsychiatry ,Electroencephalography ,Stimulus (physiology) ,Article ,Neuroimaging ,medicine ,Humans ,Computer Simulation ,Cortical surface ,education ,Electrocorticography ,Evoked Potentials ,education.field_of_study ,medicine.diagnostic_test ,business.industry ,Magnetoencephalography ,Pattern recognition ,Neurophysiology ,Middle Aged ,Magnetic Resonance Imaging ,Cortex (botany) ,Visual field ,Neurology ,Receptive field ,Research Design ,Female ,Artificial intelligence ,Visual Fields ,business ,Functional magnetic resonance imaging ,RC321-571 - Abstract
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, 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.
- Published
- 2021