Model-driven parameterization of the cortical surface for localization and inter-subject matching

In this paper we present a generic and organized model of cortical folding, and a way to implement this model on any given cortical surface. This results in a model-driven parameterization, providing an anatomically meaningful coordinate system for cortical localization, and implicitly defining inter-subject surface matching without any deformation of surfaces. We present our cortical folding model and show how it naturally defines a parameterization of the cortex. The mapping of the model to any given cortical surface is detailed, leading to an anatomically invariant coordinate system. The process is evaluated on real data in terms of both anatomical and functional localization, and shows improved performance compared to a traditional volume-based normalization. It is fully automatic and available with the BrainVISA software platform.

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