qPRF: A system to accelerate population receptive field decoding

Abstract

Patterns of BOLD response can be decoded using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). The time cost of evaluating the PRF model is high, often requiring days to decode BOLD signals for a small cohort of subjects. We introduce the qPRF, an efficient method for decoding that reduced the computation time by a factor of 1436 when compared to another widely available PRF decoder (Kay, Winawer, Mezer and Wandell, 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen, Smith, Barch, Behrens, Yacoub and Ugurbil, 2013). With a specially designed data structure and an efficient search algorithm, the qPRF optimizes the five PRF model parameters according to a least-squares criterion. To verify the accuracy of the qPRF solutions, we compared them to those provided by Benson, Jamison, Arcaro, Vu, Glasser, Coalson, Van Essen, Yacoub, Ugurbil, Winawer and Kay (2018). Both hemispheres of the 181 subjects in the HCP data set (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were decoded by qPRF in 15.2 hours on an ordinary CPU. The absolute difference in R2 reported by Benson et al. and achieved by the qPRF was negligible, with a median of 0.39% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 99.7% of vertices. The qPRF may facilitate the development and computation of more elaborate models based on the PRF framework, as well as the exploration of novel clinical applications. HighlightsO_LIWe describe a novel software system, qPRF, which can perform population receptive field (PRF) decoding of BOLD fMRI at speeds about 1400 times faster than the conventional systems designed for PRF decoding. C_LIO_LIWe show that qPRF yields estimates of PRF model parameters that, in terms of goodness-of-fit, are equivalent to estimates derived using the conventional systems. C_LIO_LIAn efficient similarity-based search strategy, underlies the accelerated computations of qPRF, supported by a specially designed data structure wherein tens of millions of pre-computed prediction curves are stored. C_LI