Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

F1000Res. 2019 Sep 24;8:1681. doi: 10.12688/f1000research.20496.2. eCollection 2019.

Abstract

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

Keywords: functional MRI; population receptive fields; replicability; site comparison.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain Mapping*
  • Female
  • Humans
  • Magnetic Resonance Imaging*
  • Neuroimaging
  • Visual Cortex*

Grant support

This work was supported by a European Research Council Starting Grant (310829, WMOSPOTWU) and start-up funding from the Faculty of Medical and Health Sciences at the University of Auckland.