Crystallinity characterization of white matter in the human brain

TL;DR

This paper introduces a novel approach using materials science tools to characterize white matter microstructure in the human brain, revealing new insights into its heterogeneity and potential as a biomarker.

Contribution

It applies crystallinity analysis and bond-orientational order parameters to brain imaging data, providing multi-scale characterization of white matter microstructure.

Findings

Crystallinity varies across brain regions and correlates with anatomical features.

Crystallinity is highly reliable and varies between individuals.

The method identifies fiber crossings and fascicles effectively.

Abstract

White matter microstructure underpins cognition and function in the human brain through the facilitation of neuronal communication, and the non-invasive characterization of this structure remains a research frontier in the neuroscience community. Efforts to assess white matter microstructure, however, are hampered by the sheer amount of information needed for characterization. Current techniques within neuroimaging deal with this problem by representing white matter features with single scalars that are often not easy to interpret. Here, we address these issues by introducing tools from materials science for the characterization of white matter microstructure. We investigate structure on a mesoscopic scale by analyzing its homogeneity and determining which regions of the brain are structurally homogeneous, or "crystalline" in the context of materials science. We find that crystallinity provides novel information and varies across the brain along interpretable lines of anatomical difference, with highest homogeneity in regions adjacent to the corpus callosum. Furthermore, crystallinity is highly reliable, and yet also varies between individuals, making it a potentially useful biomarker to examine individual differences in white matter along various dimensions. We also parcellate white matter into "crystal grains," or contiguous sets of voxels of high structural similarity, and find overlap with other white matter parcellations. Finally, we characterize the shapes of local white matter signatures through another tool from materials science - bond-orientational order parameters - and locate fiber crossings and fascicles with this new technique. Our results provide new means of assessing white matter microstructure on multiple length scales, and open new avenues of future inquiry. We hope that this work fosters future productive dialogue between the fields of soft matter and neuroscience.