PT  - JOURNAL ARTICLE
AU  - Salil Bhat
AU  - Michael Lührs
AU  - Rainer Goebel
AU  - Mario Senden
TI  - Extremely Fast pRF Mapping for Real-Time Applications
AID  - 10.1101/2021.03.24.436795
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.03.24.436795
4099  - http://biorxiv.org/content/early/2021/03/25/2021.03.24.436795.short
4100  - http://biorxiv.org/content/early/2021/03/25/2021.03.24.436795.full
AB  - 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 build upon hashed-Gaussian encoding of the stimulus, which significantly 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.Competing Interest StatementThe authors have declared no competing interest.