GEM-pRF: GPU-empowered mapping of population receptive fields for large-scale fMRI analysis.

Abstract

Population receptive field (pRF) mapping is a fundamental technique for understanding retinotopic organisation of the human visual system. Since its introduction in 2008, however, its scalability has been severely hindered by the computational bottleneck of iterative parameter refinement. Current state-of-the-art implementations either sacrifice precision for speed or rely on slow iterative parameter updates, limiting their applicability to large-scale datasets. Here, we present a novel mathematical reformulation of the General Linear Model (GLM), wrapped in a GPU-Empowered Mapping of population Receptive Fields (GEM-pRF) software implementation. By orthogonalizing the design matrix, our approach enables the direct and fast computation of the objective function's derivatives, which are used to eliminate the iterative refinement process. This approach dramatically accelerates pRF estimation with high accuracy. Validation using empirical and simulated data confirms GEM-pRF's accuracy, and benchmarking against established tools demonstrates a reduction in computation time of almost two orders of magnitude. With its modular and extensible design, GEM-pRF provides a critical advancement for large-scale fMRI retinotopic mapping. Furthermore, our reformulated GLM approach in combination with GPU-based implementation offers a broadly applicable solution that may extend beyond visual neuroscience, accelerating computational modelling across various domains in neuroimaging and beyond.