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Brain-Imaging

pRF mapping

The Population Receptive Field (pRF) approach is a nice example of how recently, computational modeling of fMRI BOLD responses has become the way to go about analyzing your data. The method and model fit not only into the neuroimaging literature, but extend the idea of the receptive field from single-cell electrophysiology into fMRI. Related paper; Population receptive field estimates in human visual cortex, Dumoulin & Wandell, 2008, and Visual Field Maps in Human Cortex, Brewer, Dumoulin & Wandell, 2007, and Compressive spatial summation in human visual cortex, Kendrick & Jonathan, 2013

Abstract

For most human behaviour we rely heavily on visual input. Whether it is navigating our environment reading, or for social interactions. Understanding how the brain encodes visual information therefore is incredibly valuable to neuroscientific research. Encoding models have been on the rise in recent years, especially ones that aim to map receptive fields on brain space. With our report we aim to do two things. First, we aim to replicate the findings of previous studies on population receptive field mapping of the visual areas V1, V2 and V3 in terms of size, polar angle and eccentricity. Second, we try to expand previous findings by including other regions of interest starting with areas that have been already linked to visual processing. However, we are also conducting a whole brain analysis to find retinotopic areas in the human cortex which have not previously been linked to vision. Generally, our findings replicate previous results. On top of that, our findings also mostly seem to hold true for later visual processing areas. Our whole brain analysis found that certain areas in the parietal and frontal lobes also explain a large amount of the variance of our data. They can, therefore, be termed retinotopic. Using HRF fitting also caused more voxels to surpass the explained variance threshold we set, by increasing the fit of our model. No meaningful differences were obtained when we used a non-linear model to fit our data. This, however, might have been due to our somewhat superficial fitting procedure.