The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy

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 10 association and 15 projection tracts per hemisphere, along with 7 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.

Brain Structure and Function, Springer Verlag, 2016

Cited by in corpus (11)

• TractSeg - Fast and accurate white matter tract segmentation
• Combined tract segmentation and orientation mapping for bundle-specific tractography
• Tractography and machine learning: Current state and open challenges
• Filtering in tractography using autoencoders (FINTA)
• DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
• FIESTA: Autoencoders for accurate fiber segmentation in tractography
• Reducing variability in along-tract analysis with diffusion profile realignment
• Parallel optimization of fiber bundle segmentation for massive tractography datasets
• Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering
• Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation
• FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI