Bayesian analysis of retinotopic maps

Abstract
Data availability
Article and author information

Abstract

Human visual cortex is organized into multiple retinotopic maps. Characterizing the arrangement of these maps on the cortical surface is essential to many visual neuroscience studies. Typically, maps are obtained by voxel-wise analysis of fMRI data. This method, while useful, maps only a portion of the visual field and is limited by measurement noise and subjective assessment of boundaries. We developed a novel Bayesian mapping approach which combines observation-a subject's retinotopic measurements from small amounts of fMRI time-with a prior-a learned retinotopic atlas. This process automatically draws areal boundaries, corrects discontinuities in the measured maps, and predicts validation data more accurately than an atlas alone or independent datasets alone. This new method can be used to improve the accuracy of retinotopic mapping, to analyze large fMRI datasets automatically, and to quantify differences in map properties as a function of health, development and natural variation between individuals.

Data availability

All data generated or analyzed in this study have been made public on an Open Science Foundation website: https://osf.io/knb5g/Preprocessed MRI data as well as analyses and source code for reproducing figures and performing additional analyses can be found on the Open Science Foundation website https://osf.io/knb5g/.Performing Bayesian inference using your own retinotopic maps.To perform Bayesian inference on a FreeSurfer subject, one can use the neuropythy Python library (https://github.com/noahbenson/neuropythy). For convenience, this library has also been packaged into a Docker container that is freely available on Docker Hub (https://hub.docker.com/r/nben/neuropythy).The following command will provide an explanation of how to use the Docker:> docker run -it --rm nben/neuropythy:v0.5.0 register_retinotopy --helpDetailed instructions on how to use the tools documented in this paper are included in the Open Science Foundation website mentioned above.

The following data sets were generated

1. Benson NC
2. Winawer J
(2018) Bayesian Models of Human Retinotopic Organization
Open Science Framework, osf.io/knb5g/.

https://osf.io/knb5g/

Article and author information

Author details

Noah C Benson

Department of Psychology, New York University, New York, United States

For correspondence
nben@nyu.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-2365-8265
Jonathan Winawer

Department of Psychology, New York University, New York, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7475-5586

Funding

National Eye Institute (R01 EY027401)

Jonathan Winawer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: This study was conducted with the approval of the New York University Institutional Review Board (IRB-FY2016-363) and in accordance with the Declaration of Helsinki. Informed consent was obtained for all subjects.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.