Zuiderbaan, W. | Search bioRxiv

bioRxiv2021

The influence of objecthood on the representation of natural images in the visual cortex

Neurons in early visual cortex are not only sensitive to the image elements in their receptive field but also to the context determining whether the elements are part of an object or background. We here assessed the effect of objecthood in natural images on neuronal activity in early visual cortex, with fMRI in humans and electrophysiology in monkeys. We report that boundaries and interiors of objects elicit more activity than the background. Boundary effects occur remarkably early, implying that visual cortical neurons are tuned to features characterizing object boundaries in natural images. When a new image is presented the influence of the object interiors on neuronal activity occurs during a late phase of neuronal response and earlier when eye movements shift the image representation, implying that object representations are remapped across eye-movements. Our results reveal how object perception shapes the representation of natural images in early visual cortex.

neuroscience↗

bioRxiv2020

A Population Receptive Field Model of the Magnetoencephalography Response

1Computational 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 subjects 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.

neuroscience↗