Journal article
PLoS ONE, 2014
For the possibility of internships or lab visits, please contact Mario Senden via mario.senden@maastrichtuniversity.nl
Department of Cognitive Neuroscience
Maastricht University
Oxfordlaan 55
6229EV Maastricht
Department of Cognitive Neuroscience
Maastricht University
Oxfordlaan 55
6229EV Maastricht
APA
Click to copy
Senden, M., Reithler, J., Gijsen, S., & Goebel, R. (2014). Evaluating Population Receptive Field Estimation Frameworks in Terms of Robustness and Reproducibility. PLoS ONE.
Chicago/Turabian
Click to copy
Senden, M., J. Reithler, Sven Gijsen, and R. Goebel. “Evaluating Population Receptive Field Estimation Frameworks in Terms of Robustness and Reproducibility.” PLoS ONE (2014).
MLA
Click to copy
Senden, M., et al. “Evaluating Population Receptive Field Estimation Frameworks in Terms of Robustness and Reproducibility.” PLoS ONE, 2014.
BibTeX Click to copy
@article{m2014a,
title = {Evaluating Population Receptive Field Estimation Frameworks in Terms of Robustness and Reproducibility},
year = {2014},
journal = {PLoS ONE},
author = {Senden, M. and Reithler, J. and Gijsen, Sven and Goebel, R.}
}
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.