Probabilistic Maps of Visual Topography in Human Cortex

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 25; no. 10; pp. 3911 - 3931
• Main Authors: Wang, Liang, Mruczek, Ryan E.B., Arcaro, Michael J., Kastner, Sabine
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.10.2015
• Subjects: Atlases as Topic; Brain Mapping - methods; Female; Humans; Image Processing, Computer-Assisted - methods; Likelihood Functions; Magnetic Resonance Imaging; Male; Probability; Reproducibility of Results; Visual Cortex - physiology; Visual Perception - physiology; mapping; cortical organization; atlas; functional magnetic resonance imaging; topography
• ISSN: 1047-3211; 1460-2199; 1460-2199
• DOI: 10.1093/cercor/bhu277

The human visual system contains an array of topographically organized regions. Identifying these regions in individual subjects is a powerful approach to group-level statistical analysis, but this is not always feasible. We addressed this limitation by generating probabilistic maps of visual topographic areas in 2 standardized spaces suitable for use with adult human brains. Using standard fMRI paradigms, we identified 25 topographic maps in a large population of individual subjects (N = 53) and transformed them into either a surface- or volume-based standardized space. Here, we provide a quantitative characterization of the inter-subject variability within and across visual regions, including the likelihood that a given point would be classified as a part of any region (full probability map) and the most probable region for any given point (maximum probability map). By evaluating the topographic organization across the whole of visual cortex, we provide new information about the organization of individual visual field maps and large-scale biases in visual field coverage. Finally, we validate each atlas for use with independent subjects. Overall, the probabilistic atlases quantify the variability of topographic representations in human cortex and provide a useful reference for comparing data across studies that can be transformed into these standard spaces.