Bayesian Population Receptive Field Modelling

TL;DR

This paper presents a Bayesian framework and software for mapping population receptive fields (pRFs) from fMRI data, allowing for uncertainty estimation, hypothesis testing, and flexible stimulus space modeling.

Contribution

It introduces a probabilistic approach to pRF modeling that estimates parameter uncertainty and compares models using Bayesian evidence, advancing beyond traditional methods.

Findings

Bayesian pRF modeling estimates parameter uncertainty.

Model comparison favors a circular DoG pRF model.

Framework validated with simulations and 7T fMRI data.

Abstract

We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental manipulations enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance / covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which should be taken into account when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain.