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Mark M. Schira, Alex R. Wade, Leonoid L. Kontsevich, Christopher W. Tyler; Geometric and metric properties of visual areas V1 and V2 in humans. Journal of Vision 2005;5(8):897. doi: 10.1167/5.8.897.
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INTRODUCTION: Before the human cortex was measured by fMRI, Schwartz (1980, Biological Cybernetics) proposed a set of complex-logarithmic mapping functions to match the actually measured structure of visual cortex for primates and cats, and proposed a model for human visual cortex. Since 1980, the size, shape and cortical magnification functions of early visual areas have been measured in humans with fMRI using retinotopic mapping procedures. As it is well known, the relationship between visual field eccentricity and cortical distance from the fovea can be described approximately by a log function. The function describing the increase in width of each visual area with eccentricity is not known, however. The complete mapping between visual space and cortex is a combination of these two functions.
METHODS: We have collected retinotopic mapping data on 8 subjects using standard fMRI procedures. We are using the atlas fitting functions from the VISTA-toolbox (Dougherty et al. 2003) to define iso-eccentricity lines on the flattened cortices of these subjects. We can then measure the length of these lines along the 3D-surface of the reconstructed cortical manifold.
RESULTS: We find a substantial increase in V1 width with eccentricity. In our subjects, V1 width continuously increased by a factor of between two and four from 1° to 16° eccentricity.
CONCLUSIONS: The combined measurements of eccentricity magnification functions and width magnification functions can be used to calculate the amount and isotropy of cortical area devoted to visual space at any eccentricity, which may be compared with theoretical treatments of the mapping of visual space to cortex. These results are inconsistent with the mapping function log(z+a) with an estimated a, proposed by Schwartz (1993 in Visual Science and Engineering). We will also provide data to rigorous constrain more elaborated models such as the conformal dipole mapping scheme of Balasubramanian et al. (2002 Neural Networks).
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