Extremely fast pRF mapping for real-time applications

Salil Bhat,Michael Lührs,Rainer Goebel,Mario Senden

doi:10.1016/j.neuroimage.2021.118671

Salil Bhat, Michael Lührs + Show 2 more

Open Access

https://doi.org/10.1016/j.neuroimage.2021.118671

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Abstract

Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is built upon hashed-Gaussian encoding of the stimulus, which tremendously reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.

Highlights

The retinotopic organization of the human visual cortex has intrigued neuroscientists ever since the beginning of the nineteenth century when visual field maps were first discovered in soldiers suffering from occipital wounds (Holmes, 1918; Inouye, 1909);
We propose a fast approach for receptive field mapping and population receptive field (pRF) parameter estimation that is suitable for real-time applications
We evaluated our approach on simulated as well as real empirical data in terms of computational times, correlations between predicted and acquired blood-oxygenlevel dependent (BOLD) signals, fidelity of estimated receptive field shapes and parameters and the suitability of estimated pRF shapes for projecting cortical activity back into the visual field

Summary

Introduction

The retinotopic organization of the human visual cortex has intrigued neuroscientists ever since the beginning of the nineteenth century when visual field maps were first discovered in soldiers suffering from occipital wounds (Holmes, 1918; Inouye, 1909);. Dumoulin and Wandell (2008) spearheaded the population receptive field (pRF) mapping approach which provided an expandable, parametric, model of receptive fields. This allowed researchers to study additional properties of receptive fields and their topography as well as relationships between receptive field properties. The pRF approach has, for instance, enabled researchers to understand the relationship between eccentricity and the size of receptive fields along the visual hierarchy (Dumoulin and Wandell 2008; Amano et al, 2009; Harvey and Dumoulin 2011; Silva et al, 2018), to investigate neural plasticity and visual development from childhood to adulthood (Dekker et al, 2019; Gomez et al, 2018) and to study the dynamic changes of receptive fields in response to attention (Klein et al, 2014; Kay et al, 2015; Vo et al, 2017; Es et al, 2018). Receptive fields can be inverted to provide a decoding model for reconstructing perceived, as well as imagined, visual stimuli (Thirion et al, 2006; Senden et al, 2019 )

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