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

TL;DR

This paper introduces the White Matter Query Language, a new intuitive and automated method for describing, labeling, and analyzing human white matter tracts in diffusion MRI data, facilitating large-scale neuroanatomical studies.

Contribution

It presents a novel query language with near-English syntax for defining and automatically extracting white matter tracts, improving accuracy and efficiency over manual methods.

Findings

Accurately labels white matter tracts automatically

Creates a white matter atlas from 77 subjects

Detects tract changes associated with schizophrenia

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.