Bayesian population receptive field modelling

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...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2018-10, Vol.180 (Pt A), p.173-187
Main Authors: Zeidman, Peter, Silson, Edward Harry, Schwarzkopf, Dietrich Samuel, Baker, Chris Ian, Penny, Will
Format: Article
Language:English
Subjects:
Bayesian
Bayesian analysis
Brain
Brain mapping
Free energy
Functional magnetic resonance imaging
Hypothesis testing
Mapping
Mathematical models
Modelling
Noise
Population receptive field
pRF
Receptive field
Retina
Retinotopy
Scaling
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Summary: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 stimuli 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 log 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 is taken into account by the Bayesian methods we describe when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7 T 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. •We introduce a Bayesian toolbox for population receptive field (pRF) mapping.•Neuronal and haemodynamic parameters are estimated per voxel or per region.•Hypotheses can be tested by comparing pRF models based on their evidence.•The uncertainty over parameters (such as pRF size) is estimated and visualised.•We establish face validity using simulations and test-rest reliability with 7 T fMRI.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2017.09.008