Biology subjects

Woletz, M.

Publications and source records attributed to Woletz, M..

3 recordsLinked to original sources

bioRxiv2026

Computational simulations reproduce in vivo population receptive field mapping fMRI results

Population receptive field (pRF) mapping is widely used to characterize retinotopic organization based on functional magnetic resonance imaging (fMRI) data. Despite its broad adoption, the factors governing intra- and inter-subject variability in pRF estimates remain incompletely understood, limiting the ability to evaluate and optimize visual stimulation paradigms prior to data collection. Here, we investigate whether large-scale simulations can reproduce in vivo run-to-run variability patterns observed in pRF mapping and provide mechanistic insight into their origins. With GEMSim-pRF, our newly proposed computational framework for large-scale simulation and estimation of pRF responses, we generated millions of synthetic fMRI time courses across a wide range of receptive field parameters and noise conditions. We analyzed the variability of pRF estimation results derived from simulations and compared them with in vivo data from the publicly available NYU Retinotopy Dataset. Here we show that our simulation results matched the characteristic eccentricity-dependent variability observed in empirical pRF estimates. These findings show that key variability patterns observed in empirical pRF mapping can be successfully reproduced in large-scale simulations, establishing simulation-based analysis as a practical approach for understanding, predicting, evaluating and ultimately improving the behaviour of retinotopic mapping paradigms before empirical data collection.

neuroscience↗

bioRxiv2025

GEM-pRF: GPU-Empowered Mapping of Population Receptive Fields for Large-Scale fMRI Analysis

Population receptive field (pRF) mapping is a fundamental technique for understanding retinotopic organization 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 functions derivatives, which are used to eliminate the iterative refinement process. This approach dramatically accelerates pRF estimation while maintaining full accuracy. Validation using empirical and simulated data confirms GEM-pRFs accuracy, and benchmarking against established tools demonstrates an order-of-magnitude reduction in computation time. 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 offer a broadly applicable solution that may extend beyond visual neuroscience, accelerating computational modelling across various domains in neuroimaging and beyond.

neuroscience↗

bioRxiv2024

Biases in volumetric versus surface analyses in population receptive field mapping

Population receptive field (pRF) mapping is a quantitative fMRI analysis method that links visual field positions with specific locations in the visual cortex. A common preprocessing step in pRF analyses involves projecting volumetric fMRI data onto the cortical surface, often leading to upsampling of the data. This process may introduce biases in the resulting pRF parameters. To investigate this, we present CON-pRF, a fully containerized pipeline for pRF mapping analysis that is designed to maximize reproducibility in pRF mapping studies. Using this pipeline, we compared pRF maps generated from original volumetric with those from upsampled surface data. Our results show substantial increases in pRF coverage in the central visual field of upsampled data sets. These effects were consistent across early visual cortex areas V1-3. Further analysis indicates that this bias is primarily driven by the non-linear relationship between cortical distance and visual field eccentricity, known as cortical magnification. Our results demonstrate that reproducible analysis pipelines enable the detection of potential biases introduced by varying processing steps, particularly when comparing across differently processed datasets. Key PointsO_LISpatial upsampling increases pRF coverage in the fovea due to enhanced CNR and cortical magnification. C_LIO_LIThe study highlights the need for careful consideration of data processing steps. C_LIO_LICON-pRF is a containerized pipeline for enhanced reproducibility in pRF analysis. C_LI

neuroscience↗