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Cobundlemap: Consistent 2D Parameterization of Fiber Bundles Across Subjects and Hemispheres

We present CoBundleMAP, a manifold learning based method for jointly parameterizing streamlines from diffusion MRI tractography. CoBundleMAP significantly improves the previously proposed BundleMAP approach by establishing anatomical correspondences not only between different subjects, but also between the left and right hemispheres, by introducing a two-dimensional parameterization, by focusing analysis on a reliable core part of the bundle, and via a novel mechanism for feature extraction. We use CoBundleMAP to analyze hemispheric asymmetries, and demonstrate that it improves accuracy in a gender classification task.