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Title: Investigating white matter fibre density and morphology using fixel-based analysis
Austin Authors: Raffelt, David A;Tournier, Jacques-Donald;Smith, Robert E;Vaughan, David N;Jackson, Graeme D ;Ridgway, Gerard R;Connelly, Alan
Affiliation: Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
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
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
Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
Issue Date: 1-Jan-2017
Date: 2016-09-14
Publication information: NeuroImage 2017; 144(Pt A): 58-73
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.
DOI: 10.1016/j.neuroimage.2016.09.029
Journal: NeuroImage
PubMed URL:
Type: Journal Article
Subjects: Diffusion
Magnetic Resonance Imaging
Appears in Collections:Journal articles

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