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Noah C Benson, Omar H Butt, Sandeep Jain, David H Brainard, Geoffrey K Aguirre; Cortical surface structure predicts extrastriate retinotopic function. Journal of Vision 2013;13(9):271. https://doi.org/10.1167/13.9.271.
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© ARVO (1962-2015); The Authors (2016-present)
We have shown that surface topology can predict retinotopic organization in area V1 (Benson et al., 2012). Here we extend this approach to extrastriate cortex (V2, V3) by creating an anatomical registration space in which a model of visual area organization may be fit to retinotopic mapping (RM) data. Sweeping-bar RM (10° eccentricity) was conducted in 19 subjects (548 TRs total, 3mm voxels). Anatomical images were mapped to FreeSurfer’s fsaverage pseudo-hemisphere and then flattened. Cortical flattening produces geometric distortions. To apply the banded, double-sech retinotopic model proposed by Schira et al. (2010), we created a registration of the aggregate RM data (across subject, left and right hemisphere) to the Schira model using a spring simulation. Vertices were treated as point masses connected by ideal springs. The springs drove each vertex to minimize the difference between its retinotopic mapping value and the corresponding position in the Schira model while preserving the local topography of the cortical surface. The Schira model was then fit to RM data within the resulting registration space. Schira model parameters for RH and LH data were virtually identical, suggesting that RH and LH can be treated as a single hemisphere in the registration space. The median absolute error across V1, V2, and V3 for all subjects and vertices was 12.6°/0.9° of polar angle/eccentricity for the left and 18.8°/0.9° of polar angle/eccentricity for the right hemisphere. The same errors, calculated using a leave-one-out paradigm in which function was predicted from only the anatomy of the target hemisphere, were 31.0°/1.1° of polar angle/eccentricity (LH), and 29.4°/1.2° of polar angle/eccentricity (RH). Our registration-based method combines anatomy and functional data to predict the retinotopic organization of extrastriate visual cortex. Using the method, we show that the retinotopic organization of extrastriate cortex may be well predicted by cortical surface topology alone.
Meeting abstract presented at VSS 2013
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