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The white matter query language: a novel approach for describing human white matter anatomy

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Abstract

We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist’s expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.

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Notes

  1. To improve readability, through this paper we will use the term tractography to refer exclusively to streamline tractography.

  2. It has been pointed to us that these procedures have been recently updated to constrained spherical deconvoltion-based tractography approaches in the supplementary material of a recent article by Rojkova et al. (2015). This procedure could be better suited for the dataset and tractography algorithm used in this work. Hence, we think that manual validation against this novel protocol could be an interesting follow-up to this article.

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Acknowledgments

This work has been supported by NIH grants: R01MH074794, R01MH092862, P41RR013218, R01MH097979, P41EB015902, VA Boston Healthcare System, Boston, MA and Swedish Research Council (VR) Grant 2012-3682. Demian Wassermann wishes to thank Dr. Maxime Descoteaux for helpful discussions.

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Authors and Affiliations

  1. Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, 1249 Boylston, 02215, Boston, MA, USA

    Demian Wassermann, Yogesh Rathi, Marek Kubicki & Carl-Fredrik Westin

  2. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, 1249 Boylston, 02215, Boston, MA, USA

    Demian Wassermann, Nikos Makris, Yogesh Rathi, Martha Shenton & Marek Kubicki

  3. Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, 1249 Boylston, 02215, Boston, MA, USA

    Demian Wassermann & Ron Kikinis

  4. Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA, 02129, USA

    Nikos Makris & Marek Kubicki

  5. Athena Project Team, INRIA Sophia Antipolis-Méditerranée, 2004 route des Lucioles, 06902, Sophia Antipolis CEDEX, France

    Demian Wassermann

  6. Department of Psychiatry, VA Boston Healthcare System, Harvard Medical School, Brockton, MA, USA

    Martha Shenton

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  1. Demian Wassermann
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Wassermann, D., Makris, N., Rathi, Y. et al. The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct Funct 221, 4705–4721 (2016). https://doi.org/10.1007/s00429-015-1179-4

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