Garikoitz Lerma-Usabiaga^1,2 (), Chris I. Baker³, Noah C. Benson⁴, Tessa Dekker^5,6, Serge O. Dumoulin^7,8,9,10, Justin Gardner¹¹, Kalanit Grill-Spector¹¹, Kendrick Kay¹², Tomas Knapen^7,9, Eline R. Kupers^11,12, Fernanda Lenita Ribeiro^13,14, D. Samuel Schwarzkopf^15,16, Brian Wandell¹¹, Jonathan Winawer¹⁷, Christian Windischberger¹⁸, David Linhardt¹⁸; ¹BCBL. Basque Center on Cognition, Brain and Language. Spain, ²Ikerbasque, Basque Foundation for Science. Spain, ³Laboratory of Brain & Cognition, National Institute of Mental Health, Bethesda, MD, USA, ⁴eScience Institute, University of Washington, Seattle, Washington, USA, ⁵Institute of Ophthalmology, University College London, United Kingdom, ⁶Experimental Psychology, University College London, United Kingdom, ⁷Spinoza Centre for Neuroimaging, Netherlands, ⁸Netherlands Institute for Neuroscience, Royal Netherlands Academy of Sciences, Netherlands, ⁹Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Netherlands, ¹⁰Experimental Psychology, Utrecht University, Netherlands, ¹¹Department of Psychology & Wu Tsai Neurosciences Institute, Stanford University, CA, USA, ¹²Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, USA, ¹³School of Electrical Engineering and Computer Science, The University of Queensland; Brisbane QLD, Australia, ¹⁴Department of Medicine, Justus-Liebig University Giessen; Giessen, Hessen, Germany, ¹⁵School of Optometry & Vision Science, University of Auckland, New Zealand, ¹⁶Experimental Psychology, University College London, United Kingdom, ¹⁷Department of Psychology & Center for Neural Science, New York University, NY, USA, ¹⁸High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna. Austria

Population receptive field (pRF) modeling is a quantitative technique for characterizing neural responses to visual stimuli. It is most commonly used to measure the spatial sensitivity (position and spatial extent in the visual field) of neural populations. Recent advances have significantly expanded its applications and accessibility: pRF solutions across many voxels can now used by deep neural networks to automate segmentation of visual areas; some pRF models are now used to estimate temporal or spatiotemporal tuning; and pRFs are used in clinical applications to track patient disease progression and monitor treatment. These developments make pRF modeling more powerful and accessible than ever before, but also highlight the need for collaborative efforts to establish standards and quality checks, and to share expertise. In this work, we introduce an open collaborative initiative concerning methods for pRF modeling. The goals of the initiative are to examine key aspects of pRF analysis, from data acquisition to model fitting, and suggest how methodological choices influence pRF estimates. We identify challenges to common implementation and interpretation and propose solutions based on collective experience. For non-pRF users, we aim to provide accessible explanations of the method, along with suggested guidelines and potential applications. For current pRF users, we discuss practical challenges that may arise during implementation and suggest strategies to address these issues. For method developers, we highlight areas that need improvement and propose possible directions for future research and validation of models. We will present our initiative's current progress, discuss our planned publication, and invite interested practitioners to collaborate with the group.

Acknowledgements: Due to character limits, funding acknowledgments will appear on the poster