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.
- Jonathan Winawer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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.
- Mark Schira, University of Wollongong, Australia
- Received: July 18, 2018
- Accepted: November 29, 2018
- Accepted Manuscript published: December 6, 2018 (version 1)
© 2018, Benson & Winawer
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