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

      research-article
      a , * , b , c , a , a , d , e , a , d , f , g , h , a , d , e
      Neuroimage
      Academic Press
      AFD, apparent fibre density, CHARMED, composite hindered and restricted model of diffusion, CUSP-MFM, cube and sphere multi-fascicle model, DWI, diffusion-weighted imaging, FA, fractional anisotropy, FC, fibre-bundle cross-section, FD, fibre density, FDC, fibre density & cross-section, Fixel, A specific fibre population within a voxel, FBA, fixel-based analysis., FBM, fixel-based morphometry, FOD, fibre orientation distribution, FWE, family-wise error, FWHM, full width at half maximum, MRI, magnetic resonance imaging, SNR, signal-to-noise ratio, SPM, statistical parametric mapping, TBM, tensor based morphometry, VBA, voxel-based analysis., Diffusion, MRI, Fixel, Fibre, Density, Cross-section

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          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.

          Graphical abstract

          Highlights

          • A fixel is defined as a specific fibre population within a voxel.

          • We describe a comprehensive approach to fixel-based analysis (FBA) of white matter.

          • A novel method to investigate fibre bundle morphology (cross-section) is presented.

          • We compare fibre density, cross-section and a combined measure in a clinical cohort.

          • The three different analyses give unique yet complementary information.

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          Most cited references41

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          Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.

          During rest, multiple cortical brain regions are functionally linked forming resting-state networks. This high level of functional connectivity within resting-state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting-state functional magnetic resonance imaging time-series with diffusion tensor imaging scans. Nine consistently found functionally linked resting-state networks were retrieved from the resting-state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting-state networks. Our results show that well-known anatomical white matter tracts interconnect at least eight of the nine commonly found resting-state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal-frontal networks. Our results suggest that the functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.
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            About "axial" and "radial" diffusivities.

            This article presents the potential problems arising from the use of "axial" and "radial" diffusivities, derived from the eigenvalues of the diffusion tensor, and their interpretation in terms of the underlying biophysical properties, such as myelin and axonal density. Simulated and in vivo data are shown. The simulations demonstrate that a change in "radial" diffusivity can cause a fictitious change in "axial" diffusivity and vice versa in voxels characterized by crossing fibers. The in vivo data compare the direction of the principle eigenvector in four different subjects, two healthy and two affected by multiple sclerosis, and show that the angle, alpha, between the principal eigenvectors of corresponding voxels of registered datasets is greater than 45 degrees in areas of low anisotropy, severe pathology, and partial volume. Also, there are areas of white matter pathology where the "radial" diffusivity is 10% greater than that of the corresponding normal tissue and where the direction of the principal eigenvector is altered by more than 45 degrees compared to the healthy case. This should strongly discourage researchers from interpreting changes of the "axial" and "radial" diffusivities on the basis of the underlying tissue structure, unless accompanied by a thorough investigation of their mathematical and geometrical properties in each dataset studied. (c) 2009 Wiley-Liss, Inc.
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              Methodological considerations on tract-based spatial statistics (TBSS).

              Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.
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                Author and article information

                Contributors
                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                01 January 2017
                01 January 2017
                : 144
                : Pt A
                : 58-73
                Affiliations
                [a ]Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
                [b ]Department of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
                [c ]Centre for the Developing Brain, King's College London, London, UK
                [d ]Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
                [e ]Department of Neurology, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
                [f ]Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
                [g ]FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
                [h ]Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
                Author notes
                [* ]Correspondence to: Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, Victoria 3084, Australia.Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre245 Burgundy StreetHeidelbergVictoria3084Australia david.raffelt@ 123456florey.edu.au
                Article
                S1053-8119(16)30494-3
                10.1016/j.neuroimage.2016.09.029
                5182031
                27639350
                e6518a76-0027-4bb3-a83a-3937c765514b
                © 2016 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 8 January 2016
                : 13 September 2016
                Categories
                Article

                Neurosciences
                afd, apparent fibre density,charmed, composite hindered and restricted model of diffusion,cusp-mfm, cube and sphere multi-fascicle model,dwi, diffusion-weighted imaging,fa, fractional anisotropy,fc, fibre-bundle cross-section,fd, fibre density,fdc, fibre density & cross-section,fixel, a specific fibre population within a voxel,fba, fixel-based analysis.,fbm, fixel-based morphometry,fod, fibre orientation distribution,fwe, family-wise error,fwhm, full width at half maximum,mri, magnetic resonance imaging,snr, signal-to-noise ratio,spm, statistical parametric mapping,tbm, tensor based morphometry,vba, voxel-based analysis.,diffusion,mri,fixel,fibre,density,cross-section

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