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Neuroimage. 2017 Jan 1;144(Pt A):58-73. doi: 10.1016/j.neuroimage.2016.09.029. Epub 2016 Sep 14.

Investigating white matter fibre density and morphology using fixel-based analysis.

Author information

1
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia. Electronic address: david.raffelt@florey.edu.au.
2
Department of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK.
3
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.
4
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Neurology, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia.
5
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia.
6
FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK.

Abstract

Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.

KEYWORDS:

Cross-section; Density; Diffusion; Fibre; Fixel; MRI

PMID:
27639350
PMCID:
PMC5182031
DOI:
10.1016/j.neuroimage.2016.09.029
[Indexed for MEDLINE]
Free PMC Article

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