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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To improve readability, through this paper we will use the term tractography to refer exclusively to streamline tractography.
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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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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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
- Diffusion MRI
- White matter fascicles
- Automatic classification of white matter tracts
- Computational neuroanatomy