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J Am Stat Assoc. 2016;111(515):1050-1060. doi: 10.1080/01621459.2016.1164050. Epub 2016 Oct 18.

Parametrization of white matter manifold-like structures using principal surfaces.

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Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205.
Department of Neurophysics, Max Planck Institute, Leipzig, Germany, 04103.
Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, MD 20892.
Department of Radiology and Imaging Sciences, National Institute of Health, Bethesda, MD 20892.


In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.


Corpus Callosum; Principal Curves and Surfaces; Thin Plate Splines

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