Topological Receptive Field Model for Human Retinotopic Mapping

TL;DR

This paper introduces a topological receptive field model that enhances the decoding of retinotopic maps from fMRI data by enforcing topological constraints, leading to more accurate and biologically plausible visual cortex mappings.

Contribution

The novel tRF model incorporates topological conditions into retinotopic mapping, improving the accuracy of fMRI-based visual cortex representations.

Findings

tRF reduces topological violations in retinotopic maps

tRF improves model explaining power

tRF produces biologically plausible maps

Abstract

The mapping between visual inputs on the retina and neuronal activations in the visual cortex, i.e., retinotopic map, is an essential topic in vision science and neuroscience. Human retinotopic maps can be revealed by analyzing the functional magnetic resonance imaging (fMRI) signal responses to designed visual stimuli in vivo. Neurophysiology studies summarized that visual areas are topological (i.e., nearby neurons have receptive fields at nearby locations in the image). However, conventional fMRI-based analyses frequently generate non-topological results because they process fMRI signals on a voxel-wise basis, without considering the neighbor relations on the surface. Here we propose a topological receptive field (tRF) model which imposes the topological condition when decoding retinotopic fMRI signals. More specifically, we parametrized the cortical surface to a unit disk, characterized the topological condition by tRF, and employed an efficient scheme to solve the tRF model. We tested our framework on both synthetic and human fMRI data. Experimental results showed that the tRF model could remove the topological violations, improve model explaining power, and generate biologically plausible retinotopic maps. The proposed framework is general and can be applied to other sensory maps.