Investigating white matter fibre density and morphology using fixel-based analysis. Raffelt, D. A., Tournier, J., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. NeuroImage, 144(Pt A):58–73, January, 2017. doi abstract bibtex 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.
@article{raffelt_investigating_2017,
title = {Investigating white matter fibre density and morphology using fixel-based analysis},
volume = {144},
issn = {1095-9572},
doi = {10.1016/j.neuroimage.2016.09.029},
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.},
language = {eng},
number = {Pt A},
journal = {NeuroImage},
author = {Raffelt, David A. and Tournier, J.-Donald and Smith, Robert E. and Vaughan, David N. and Jackson, Graeme and Ridgway, Gerard R. and Connelly, Alan},
month = jan,
year = {2017},
pmid = {27639350},
pmcid = {PMC5182031},
keywords = {Cross-section, Density, Diffusion, Fibre, Fixel, MRI},
pages = {58--73},
}
Downloads: 0
{"_id":"hRAEjShprH2xNzpiW","bibbaseid":"raffelt-tournier-smith-vaughan-jackson-ridgway-connelly-investigatingwhitematterfibredensityandmorphologyusingfixelbasedanalysis-2017","author_short":["Raffelt, D. A.","Tournier, J.","Smith, R. E.","Vaughan, D. N.","Jackson, G.","Ridgway, G. R.","Connelly, A."],"bibdata":{"bibtype":"article","type":"article","title":"Investigating white matter fibre density and morphology using fixel-based analysis","volume":"144","issn":"1095-9572","doi":"10.1016/j.neuroimage.2016.09.029","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.","language":"eng","number":"Pt A","journal":"NeuroImage","author":[{"propositions":[],"lastnames":["Raffelt"],"firstnames":["David","A."],"suffixes":[]},{"propositions":[],"lastnames":["Tournier"],"firstnames":["J.-Donald"],"suffixes":[]},{"propositions":[],"lastnames":["Smith"],"firstnames":["Robert","E."],"suffixes":[]},{"propositions":[],"lastnames":["Vaughan"],"firstnames":["David","N."],"suffixes":[]},{"propositions":[],"lastnames":["Jackson"],"firstnames":["Graeme"],"suffixes":[]},{"propositions":[],"lastnames":["Ridgway"],"firstnames":["Gerard","R."],"suffixes":[]},{"propositions":[],"lastnames":["Connelly"],"firstnames":["Alan"],"suffixes":[]}],"month":"January","year":"2017","pmid":"27639350","pmcid":"PMC5182031","keywords":"Cross-section, Density, Diffusion, Fibre, Fixel, MRI","pages":"58–73","bibtex":"@article{raffelt_investigating_2017,\n\ttitle = {Investigating white matter fibre density and morphology using fixel-based analysis},\n\tvolume = {144},\n\tissn = {1095-9572},\n\tdoi = {10.1016/j.neuroimage.2016.09.029},\n\tabstract = {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.},\n\tlanguage = {eng},\n\tnumber = {Pt A},\n\tjournal = {NeuroImage},\n\tauthor = {Raffelt, David A. and Tournier, J.-Donald and Smith, Robert E. and Vaughan, David N. and Jackson, Graeme and Ridgway, Gerard R. and Connelly, Alan},\n\tmonth = jan,\n\tyear = {2017},\n\tpmid = {27639350},\n\tpmcid = {PMC5182031},\n\tkeywords = {Cross-section, Density, Diffusion, Fibre, Fixel, MRI},\n\tpages = {58--73},\n}\n\n\n\n\n\n\n\n\n\n\n\n","author_short":["Raffelt, D. A.","Tournier, J.","Smith, R. E.","Vaughan, D. N.","Jackson, G.","Ridgway, G. R.","Connelly, A."],"key":"raffelt_investigating_2017","id":"raffelt_investigating_2017","bibbaseid":"raffelt-tournier-smith-vaughan-jackson-ridgway-connelly-investigatingwhitematterfibredensityandmorphologyusingfixelbasedanalysis-2017","role":"author","urls":{},"keyword":["Cross-section","Density","Diffusion","Fibre","Fixel","MRI"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/lconcha","dataSources":["vCGHbq7YMoL4xbgTv"],"keywords":["cross-section","density","diffusion","fibre","fixel","mri"],"search_terms":["investigating","white","matter","fibre","density","morphology","using","fixel","based","analysis","raffelt","tournier","smith","vaughan","jackson","ridgway","connelly"],"title":"Investigating white matter fibre density and morphology using fixel-based analysis","year":2017}