Data from: Evaluating population receptive field estimation frameworks in terms of robustness and reproducibility

Within vision research retinotopic mapping and the more general receptive
field estimation approach constitute not only an active field of research
in itself but also underlie a plethora of interesting applications. This
necessitates not only good estimation of population receptive fields
(pRFs) but also that these receptive fields are consistent across time
rather than dynamically changing. It is therefore of interest to maximize
the accuracy with which population receptive fields can be estimated in a
functional magnetic resonance imaging (fMRI) setting. This, in turn,
requires an adequate estimation framework providing the data for
population receptive field mapping. More specifically, adequate decisions
with regard to stimulus choice and mode of presentation need to be made.
Additionally, it needs to be evaluated whether the stimulation protocol
should entail mean luminance periods and whether it is advantageous to
average the blood oxygenation level dependent (BOLD) signal across
stimulus cycles or not. By systematically studying the effects of these
decisions on pRF estimates in an empirical as well as simulation setting
we come to the conclusion that a bar stimulus presented at random
positions and interspersed with mean luminance periods is generally most
favorable. Finally, using this optimal estimation framework we furthermore
tested the assumption of temporal consistency of population receptive
fields. We show that the estimation of pRFs from two temporally separated
sessions leads to highly similar pRF parameters.