Simulating Stimulus-Specific Variability in Population Receptive Field Mapping
Poster No:
2077
Submission Type:
Abstract Submission
Authors:
David Linhardt1, Siddharth Mittal2, Michael Woletz1, Christian Windischberger2
Institutions:
1Medical University of Vienna, Vienna, Austria, 2Medical University of Vienna, Vienna, Vienna
First Author:
Co-Author(s):
Introduction:
Population receptive field (pRF) modeling quantifies the organizational properties of visual cortex areas by mapping locations in the visual field to corresponding regions in the cortex. While this method is powerful and reproducible (Linhardt et al., 2022), measurement parameters and analysis choices can significantly influence the results. One crucial factor in pRF is the selection of the visual stimulus variant, as this determines the detection efficiency. Until now, large-scale simulations of pRF parameters have been challenging due to high computational demands. Our lab recently developed GEM-pRF, a fast pRF analysis software, using a mathematical framework which allows for GPU acceleration (Mittal et al., 2024). GEM-pRF is capable of processing thousands of pRF time courses simultaneously. In this work, we utilize these capabilities to simulate the variability of ground-truth time courses for two commonly used stimuli (moving bar and wedge/ring) and compare the results to experimental findings (Linhardt et al., 2021).
Methods:
We simulated ground-truth positions evenly distributed across the visual field (on a 51×51 grid ranging from -9° to 9° visual angle) with pRF sizes s dependent on voxel eccentricity r, following the formula: s(r) = 0.5 + 0.1r. Gaussian white noise was added to the simulated time courses. For each ground-truth position and noisy time course, we estimated pRF parameters (x, y, s) and analyzed the distribution of the estimated parameters for each ground-truth position. To evaluate parameter estimation accuracy, we calculated root-mean-square (RMS) errors across different locations in the visual field.
Results:
Error distributions revealed systematic patterns across the visual field (Figure 1). Both wedge/ring (Figure 1, first row) and bar (Figure 1, first row) stimuli exhibited lower errors near the fovea and increased errors toward stimulus boundaries, likely due to reduced encoding power when pRFs extend beyond the stimulus edge. The third row of Figure 1 shows the differences in RMS error between the two stimuli. In the foveal visual field (<2°), the bar stimulus exhibited slightly higher location errors, whereas in more peripheral regions (2–5°), the wedge stimulus showed higher errors. Outside the 5° visual angle the results show inconsistencies, which likely can be attributed to the proximity to the stimulus edge. These findings align with previous empirical results (Linhardt et al., 2021), where the wedge/ring stimulus yielded higher variance explained in the foveal region, while the bar stimulus outperformed in peripheral areas. A higher variance explained is also associated with reduced inaccuracies in fitting, reflected in lower RMS errors. Similarly, pRF size parameters followed this trend, with higher RMS errors for the bar stimulus in the central 5° and a reversal of this pattern in the periphery. These results confirm that each stimulus type offers distinct advantages for different regions of the visual field.
Conclusions:
These simulation results provide an objective method for comparing visual stimulation paradigms in pRF mapping by quantifying parameter variability across the visual field. This approach offers valuable insights for optimizing experimental designs in visual neuroscience research and for understanding the inherent limitations of different stimulus configurations.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Perception, Attention and Motor Behavior:
Perception: Visual 1
Keywords:
FUNCTIONAL MRI
Vision
Other - pRF modeling, retinotopy
1|2Indicates the priority used for review
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Provide references using APA citation style.
Linhardt, D., Pawloff, M., Woletz, M., Hummer, A., Tik, M., Vasileiadi, M., Ritter, M., Lerma-Usabiaga, G., Schmidt-Erfurth, U., & Windischberger, C. (2022). Intrasession and Intersession Reproducibility of Artificial Scotoma pRF Mapping Results at Ultra-High Fields. Eneuro, 9(5), ENEURO.0087-22.2022. https://doi.org/10.1523/eneuro.0087-22.2022
Mittal, S., Woletz, M., Linhardt, D., & Windischberger, C. (2024). A novel approach for population-receptive field mapping using high-performance computing. Journal of Vision, 24(10), 536. https://doi.org/10.1167/jov.24.10.536