Bayesian analysis of retinotopic maps

• Published in: eLife Vol. 7
• Main Authors: Benson, Noah C, Winawer, Jonathan
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 06.12.2018; eLife Sciences Publications, Ltd
• Subjects: Bayesian analysis; Boundaries; Brain mapping; Data processing; Datasets; Functional magnetic resonance imaging; Mapping; Methods; Nervous system; Neuroscience; Neurosciences; NMR; Nuclear magnetic resonance; occipital cortex; Registration; Retina; retinotopic map; retinotopy; Tools and Resources; vision; vision science; Visual cortex; Visual field; visual neuroscience; New York; United States > US; vision science; vision; visual neuroscience; neuroscience; occipital cortex; human; retinotopic map; retinotopy
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.40224

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.