Investigating white matter fibre density and morphology using fixel-based analysis.
Open accessAbstract
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.
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Fixel-based analysis approaches have been proposed to characterize a specific fiber population within a voxel (i.e., a "fixel").
“recent fixel-based analysis approaches have been proposed to characterize a specific fiber population within a voxel (i.e., a “fixel”) (Raffelt et al., 2017).”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Raffelt et al., 2017
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Why do axons differ in caliber?
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“…Basser, 2005; Barazany et al., 2009; Jespersen et al., 2007; Lu et al., 2006; Raffelt et al., 2012b; Stanisz et al., 1997; Zhang et al., 2012), and that the exchange of water between the intra-axonal and extra-axonal space is negligible on the timescale of a diffusion MRI experiment (Quirk et…”
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“…white matter fibre density and morphology using fixel-based analysis David A. Raffelt a,n, J.-Donald Tournier b,c, Robert E. Smith a, David N. Vaughan a,d,e, Graeme Jackson a,d,f, Gerard R. Ridgway g,h, Alan Connelly a,d,e a Florey Institute of Neuroscience and Mental Health, Melbourne,…”
MethodPGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study
Drobnjak, I., Zhang, H., Ianuş, A., Kaden, E., & Alexander, D. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25631 · Cited by 151 · cited 1× · see citing works
“However, most white matter voxels are known to contain crossing fibres (Jeurissen et al., 2012), and voxel-averaged measures such as FA are not fibre-specific (or even erroneous) in such regions, which confounds interpretation of apparent differences (Douaud et al., 2011; Pierpaoli et al., 2001;…”
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“It is therefore reasonable to consider that the intra-axonal volume (of axons within a given fixel) is a quantity related to the white matter's local ‘ability to relay information’.”
BackgroundMethodInfluentialWhite Matter Changes Associated with Antipsychotic Treatment in First-Episode Psychosis
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“…correlations (Johansen-Berg, 2010), genetics (Meyer-Lindenberg, 2009), structural plasticity (Scholz et al., 2009), treatment response (Szeszko et al., 2014) and neuroscientific efforts to relate structural and functional connectivity (Calamante et al., 2013; Stephan et al., 2009; Van…”
BackgroundThe CONNECT project: Combining macro- and micro-structure
Assaf, Y., Alexander, D., Jones, D., Bizzi, A., Behrens, T. E., Clark, C., Cohen, Y., Dyrby, T., Hüppi, P., Knoesche, T., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.055 · Cited by 135 · see citing works
Reorientation of fiber orientation distributions using apodized point spread functions
Raffelt, D., Tournier, J.-D., Crozier, S., Connelly, A., & Salvado, O. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.23058 · Cited by 132 · cited 26× · see citing works
“While FODs benefit from high angular resolution and high b-value DWI data, FOD registration is best performed using a relatively low spherical harmonic degree of 4 (Raffelt et al., 2011).”
BackgroundMethodInfluentialSelf-citationValidity of modulation and optimal settings for advanced voxel-based morphometry
Raduà, J., Canales-Rodríguez, E. J., Pomarol-Clotet, E., & Salvador, R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.084 · Cited by 122 · cited 1× · see citing works
“…note that the combined FDC analysis may not always be more sensitive if the effect of interest is predominantly in either FD or FC, since combining FD and FC also combines the variance from each source (see also the argument that modulation in VBM can increase variability, Radua et al. (2014)).”
BackgroundNeural connectivity as an intermediate phenotype: Brain networks under genetic control
Meyer-Lindenberg, A. (2009) · Human Brain Mapping · doi:10.1002/hbm.20639 · Cited by 117 · cited 1× · see citing works
“…(Mills and Tamnes, 2014), aging (Lebel et al., 2012), individual differences and brain-behaviour correlations (Johansen-Berg, 2010), genetics (Meyer-Lindenberg, 2009), structural plasticity (Scholz et al., 2009), treatment response (Szeszko et al., 2014) and neuroscientific efforts to relate…”
BackgroundNeuroimaging of motor neuron diseases
Kassubek, J., Ludolph, A., & Müller, H.-P. (2012) · Therapeutic Advances in Neurological Disorders · doi:10.1177/1756285612437562 · Cited by 94 · cited 1×
“…Corr Melbou E-m journal homepage: www.elsevier.com/locate/neuroimage Investigating white matter fibre density and morphology using fixel-based analysis David A. Raffelt a,n, J.-Donald Tournier b,c, Robert E. Smith a, David N. Vaughan a,d,e, Graeme Jackson a,d,f, Gerard R. Ridgway g,h, Alan…”
MethodGroeschel, S., Tournier, J., Northam, G. B., Baldeweg, T., Wyatt, J. S., Vollmer, B., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.034 · Cited by 93 · see citing works
Self-citationEvaluating the Accuracy of Diffusion MRI Models in White Matter
Rokem, A., Yeatman, J. D., Pestilli, F., Kay, K. N., Mezer, A., van der Walt, S., & Wandell, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123272 · Cited by 91 · cited 1× · see citing works
“1 Previous publications have used the word ‘fibre’ (Assaf and Basser, 2005), ‘fascicle’ (Rokem et al., 2015; Scherrer and Warfield, 2012) or ‘fibre population’ (Behrens et al.”
Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI
Scherrer, B., & Warfield, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048232 · Cited by 86 · cited 2× · see citing works
“However, we note that the proposed fixel-based analysis framework can be used to investigate other measures of interest, such as fixel-specific diffusivity measures (Scherrer et al., 2016; Scherrer and Warfield, 2012), or relaxometry (De Santis et al.”
MethodAxonal Damage in Multiple Sclerosis
Haines, J., Inglese, M., & Casaccia, P. (2011) · The Mount Sinai journal of medicine, New York · doi:10.1002/msj.20246 · Cited by 85 · cited 4× · see citing works
“Furthermore, many neurological disorders (including Motor Neurone Disease (Kassubek et al., 2012), Multiple Sclerosis (Haines et al., 2011), Epilepsy (Otte et al., 2012), and Alzheimer's disease (Radanovic et al., 2013)) involve reduction or disruption of brain ‘connectivity’ due to pathological…”
BackgroundMethodInfluentialGee, J. (1999) · Pattern Recognition · doi:10.1016/s0031-3203(98)00093-4 · Cited by 85 · cited 5×
“To investigate white matter morphology, many previous studies have used VBM (Mechelli et al., 2005), which can be thought of as a TBM analysis weighted/masked by the tissue segmentation (note this is only true for modern VBM methods where images are registered as accurately as possible and modulated by the Jacobian de…”
MethodInfluentialRadanovic, M., Pereira, F., Stella, F., Aprahamian, I., Ferreira, L. K., Forlenza, O., & Busatto, G. (2013) · Expert Review of Neurotherapeutics · doi:10.1586/ern.13.45 · Cited by 80 · cited 3× · see citing works
“Contents lists available at ScienceDirect NeuroImage http://d 1053-81 Abbre restricte DWI, di section; populat metry; width a ratio; SP voxel-b n Corr Melbou E-m journal homepage: www.elsevier.com/locate/neuroimage Investigating white matter fibre density and morphology using fixel-based analysis David A. Raffelt a,n,…”
BackgroundMethodInfluentialRiffert, T. W., Schreiber, J., Anwander, A., & Knösche, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.015 · Cited by 74 · see citing works
Zhang, H., Awatea, S. P., Das, S. R., Woo, J., Melhem, E., Gee, J., & Yushkevich, P. (2010) · Medical Image Anal. · doi:10.1016/j.media.2010.05.002 · Cited by 69 · cited 1× · see citing works
“In related work, Zhang et al. (2010) also proposed a combined analysis of microscopic measures with macroscopic morphology.”
BackgroundThe effect of template selection on diffusion tensor voxel-based analysis results
van Hecke, W., Leemans, A., Sage, C., Emsell, L., Veraart, J., Sijbers, J., Sunaert, S., & Parizel, P. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.005 · Cited by 67 · cited 3× · see citing works
“In vivo methods to quantify white matter changes that alter connectivity are also of interest in relation to psychiatric disorders (Kubicki under the CC BY license (http://creativecommons.org/licenses/by/4.0/). et al., 2007), development (Mills and Tamnes, 2014), aging (Lebel et al., 2012), individual differences and…”
BackgroundInfluentialT1 relaxometry of crossing fibres in the human brain
Santis, S. D., Assaf, Y., Jeurissen, B., Jones, D., & Roebroeck, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.037 · Cited by 55 · see citing works
Santis, S. D., Assaf, Y., Evans, C. J., & Jones, D. (2014) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24717 · Cited by 53 · see citing works
Calamante, F., Masterton, R., Tournier, J.-D., Smith, R., Willats, L., Raffelt, D., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.054 · Cited by 51 · cited 1× · see citing works
“, 2014) and neuroscientific efforts to relate structural and functional connectivity (Calamante et al., 2013; Stephan et al., 2009; Van Den Heuvel et al., 2009).”
BackgroundSelf-citationMohammadi, S., Keller, S., Glauche, V., Kugel, H., Jansen, A., Hutton, C., Flöel, A., & Deppe, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036851 · Cited by 41 · see citing works
Kernel regression estimation of fiber orientation mixtures in diffusion MRI
Cabeen, R. P., Bastin, M. E., & Laidlaw, D. H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.061 · Cited by 37 · cited 2× · see citing works
“…2014), apparent fibre density (Dell’acqua et al., 2010; Raffelt et al., 2012b), neurite density (Jespersen et al., 2010; Zhang et al., 2012), intra-axonal volume fraction (Panagiotaki et al., 2012) fibre volume fraction (Cabeen et al., 2015), fascicle fraction of occupancy (Scherrer et al., 2016)).”
BackgroundCorrection: Evaluating the Accuracy of Diffusion MRI Models in White Matter
Rokem, A., Yeatman, J. D., Pestilli, F., Kay, K. N., Mezer, A., van der Walt, S., & Wandell, B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0139150 · Cited by 19 · cited 1×
“1 Previous publications have used the word ‘fibre’ (Assaf and Basser, 2005), ‘fascicle’ (Rokem et al., 2015; Scherrer and Warfield, 2012) or ‘fibre population’ (Behrens et al., 2007; Raffelt et al., 2012b) to refer to a specific population of fibres within a single voxel.”
MethodFiber density estimation from single q-shell diffusion imaging by tensor divergence
Reisert, M., Mader, I., Umarova, R., Maier, S., van Elst, L. V., & Kiselev, V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.032 · Cited by 19 · cited 1× · see citing works
“…2005; Barazany et al., 2009; Jespersen et al., 2007; Lu et al., 2006; Raffelt et al., 2012b; Stanisz et al., 1997; Zhang et al., 2012), and that the exchange of water between the intra-axonal and extra-axonal space is negligible on the timescale of a diffusion MRI experiment (Quirk et al., 2003).”
BackgroundRibeiro, L. C. D. Q., & Ribeiro, M. G. (2017) · doi:10.1007/978-3-319-51899-2_3 · Cited by 18 · cited 2×
“…known to contain crossing fibres (Jeurissen et al., 2012), and voxel-averaged measures such as FA are not fibre-specific (or even erroneous) in such regions, which confounds interpretation of apparent differences (Douaud et al., 2011; Pierpaoli et al., 2001; Wheeler-Kingshott and Cercignani, 2009).”
BackgroundTensor-Based Morphometry of Fibrous Structures with Application to Human Brain White Matter
Zhang, H., Yushkevich, P., Rueckert, D., & Gee, J. (2009) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-642-04271-3_57 · Cited by 6 · cited 8× · see citing works
“(3) (derived independently in Raffelt et al. (2010)) is mathematically equivalent to the method for computing the change in fibre cross-section in Zhang et al. (2009).”
BackgroundMethodInfluential
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Research works printed but not linked: 3
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Sorted by citations
MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · bioRxiv · doi:10.1101/551739 · Cited by 3,043
Self-citationMRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974 · see citing works
Self-citationGorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005209 · Cited by 311 · see citing works
Self-citationFibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment
Mito, R., Raffelt, D., Dhollander, T., Vaughan, D. N., Tournier, J.-D., Salvado, O., Brodtmann, A., Rowe, C. C., Villemagne, V. L., & Connelly, A. (2018) · Brain · doi:10.1093/brain/awx355 · Cited by 309 · see citing works
“…for both microstructural changes in density (with the fibre density metric), and macrostructural, morphometric changes (with the fibre bundle cross-section metric) enables a more comprehensive analysis of total intra-axonal volume changes within a fibre pathway (FDC metric) (Raffelt et al., 2017).”
BackgroundMethodInfluentialSelf-citationFixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities
Dhollander, T., Clemente, A., Singh, M., Boonstra, F., Civier, O., Duque, J. D., Egorova, N., Enticott, P., Fuelscher, I., Gajamange, S., et al. (2020) · NeuroImage · doi:10.31219/osf.io/zu8fv · Cited by 271
Self-citationFigley, C. R., Uddin, M. N., Wong, K., Kornelsen, J., Puig, J., & Figley, T. D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.799576 · Cited by 253 · see citing works
Modelling white matter with spherical deconvolution: How and why?
Dell'Acqua, F., & Tournier, J.-D. (2018) · NMR in Biomedicine · doi:10.1002/nbm.3945 · Cited by 185 · see citing works
“This is equivalent to comparing the partial volume fractions of fibre populations, allowing for the identification of white matter differences along specific tracts across subjects.(90,91) An equivalent index based on a slightly different interpretation of the fODF is the hindrance modulated orientational anisotropy (…”
MethodSelf-citationCousineau, M., Jodoin, P.-M., Garyfallidis, E., Côté, M.-A., Morency, F. C., Rozanski, V. E., Grand'maison, M., Bedell, B. J., & Descoteaux, M. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.07.020 · Cited by 158 · see citing works
“Raffelt et al. (2012, 2017) NuFO Number of fiber orientations Number of local maxima of the fODF.”
BackgroundChen, Y., Wang, Y., Song, Z., Fan, Y., Gao, T., & Tang, X. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101911 · Cited by 152 · see citing works
Differential Tractography as a Track-Based Biomarker for Neuronal InjuryPreprint
Yeh, F.-C., Zaydan, I. M., Suski, V., Lacomis, D., Richardson, R. M., Maroon, J., & Barrios-Martinez, J. V. (2019) · bioRxiv · doi:10.1101/576025 · Cited by 150 · see citing works
Autosomal dominant cerebellar ataxias: Imaging biomarkers with high effect sizes
Adanyeguh, I., Perlbarg, V., Henry, P.-G., Rinaldi, D., Petit, E., Valabrègue, R., Brice, A., Durr, A., & Mochel, F. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.06.011 · Cited by 133 · see citing works
“The processing steps have previously been fully detailed (Raffelt et al., 2015; 2017) and were performed using the MRtrix software version 0.3.15.”
MethodInfluentialSchilling, K. G., Tax, C., Rheault, F., Landman, B., Anderson, A., Descoteaux, M., & Petit, L. (2021) · bioRxiv · doi:10.1002/hbm.25697 · Cited by 117 · see citing works
“referred to as fixels [29], and are parameterized by the mean”
What’s new and what’s next in diffusion MRI preprocessing
Tax, C., Bastiani, M., Veraart, J., Garyfallidis, E., & Irfanoglu, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118830 · Cited by 107 · see citing works
“…matrices originating from the tensor-based registration techniques have been extensively used to analyze different pathologies and those originating from FOD based registration led to analysis approaches for fiber bundle morphology with fiber bundle elements (FIXEL) ( Raffelt et al., 2017 ).”
BackgroundSydnor, V., Cieslak, M., Duprat, R., Deluisi, J. A., Flounders, M. W., Long, H., Scully, M., Balderston, N., Sheline, Y., Bassett, D., et al. (2021) · bioRxiv · doi:10.1126/sciadv.abn5803 · Cited by 107 · see citing works
Babaeeghazvini, P., Rueda‐Delgado, L. M., Gooijers, J., Swinnen, S. P., & Daffertshofer, A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.721206 · Cited by 106 · see citing works
Determinants and Biomarkers of Progression Independent of Relapses in Multiple Sclerosis
Calabrese, M., Preziosa, P., Scalfari, A., Colato, E., Marastoni, D., Absinta, M., Battaglini, M., Stefano, N. D., Filippo, M. D., Hametner, S., et al. (2024) · Annals of Neurology · doi:10.1002/ana.26913 · Cited by 110 · see citing works
Impact of b‐value on estimates of apparent fibre density
Genc, S., Tax, C., Raven, E. P., Chamberland, M., Parker, G., & Jones, D. (2020) · bioRxiv · doi:10.1002/hbm.24964 · Cited by 98 · see citing works
Macro- and microstructural changes in cosmonauts’ brains after long-duration spaceflight
Jillings, S., van Ombergen, A., Tomilovskaya, E., Rumshiskaya, A., Litvinova, L., Nosikova, I., Pechenkova, E., Rukavishnikov, I., Kozlovskaya, I., Manko, O., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz9488 · Cited by 92 · see citing works
Quantitative streamlines tractography: methods and inter-subject normalisation
Smith, R., Raffelt, D., Tournier, J.-D., & Connelly, A. (2020) · doi:10.31219/osf.io/c67kn · Cited by 92
Self-citationBundle‐specific tractography with incorporated anatomical and orientational priors
Rheault, F., St-Onge, E., Sidhu, J., Maier-Hein, K. H., Tzourio-mazoyer, N., Petit, L., & Descoteaux, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.018 · Cited by 87 · see citing works
“The E-FOD should not be used to compute FOD metrics such as the AFD (Raffelt et al., 2017) or even NuFO (Tournier et al.”
Diffusion-Weighted Imaging: Recent Advances and Applications.
Martínez-Heras, E., Grussu, F., Prados, F., Solana, E., & Llufriu, S. (2021) · Seminars in ultrasound, CT, and MR · doi:10.1053/j.sult.2021.07.006 · Cited by 86 · see citing works
Effect of Particle Size and Surface Charge on Nanoparticles Diffusion in the Brain White Matter
Yuan, T., Gao, L., Zhan, W.-B., & Dini, D. (2022) · Pharmaceutical Research · doi:10.1007/s11095-022-03222-0 · Cited by 84 · see citing works
“White matter (WM) acts as a relay station and transmits messages between different parts within the central nervous system [19].”
BackgroundGenc, S., Smith, R. E., Malpas, C. B., Anderson, V., Nicholson, J., Efron, D., Sciberras, E., Seal, M., & Silk, T. (2018) · bioRxiv · doi:10.1016/j.neuroimage.2018.08.043 · Cited by 84 · see citing works
Self-citationReduced White Matter Fiber Density in Autism Spectrum Disorder
Dimond, D., Schuetze, M., Smith, R. E., Dhollander, T., Cho, I. Y. K., Vinette, S., Eycke, K. D. T., Lebel, C., McCrimmon, A., Dewey, D., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy348 · Cited by 77 · see citing works
Self-citationAction potential propagation and synchronisation in myelinated axons
Schmidt, H., & Knösche, T. (2019) · bioRxiv · doi:10.1371/journal.pcbi.1007004 · Cited by 75 · see citing works
New approaches to studying early brain development in Down syndrome
Baburamani, A. A., Patkee, P. A., Arichi, T., & Rutherford, M. A. (2019) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.14260 · Cited by 74 · see citing works
“More complex models of voxel-wise diffusion such as a fixel-based analysis (fixel refers to a fibre population in a given voxel) can also provide measures of white matter fibre density, fibre cross-sectional area, and the fibre orientation distribution.(105) Other techniques, such as the neurite orientation distributi…”
MethodSchilling, K. G., Tax, C., Rheault, F., Hansen, C. B., Yang, Q., Yeh, F.-C., Cai, L. Y., Anderson, A., & Landman, B. (2021) · bioRxiv · doi:10.1016/j.neuroimage.2021.118451 · Cited by 73 · see citing works
The structural connectome in traumatic brain injury: A meta-analysis of graph metrics
Imms, P., Clemente, A., Cook, M., D'Souza, W., Wilson, P., Jones, D., & Caeyenberghs, K. (2019) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.002 · Cited by 73 · see citing works
“Cognitive deficits (e.g., slow processing speed and poor concentration), motor control deficits (e.g., poor manual dexterity, balance deficits), and behavioural problems (e.g., impulsivity) are particularly common (Rabinowitz & Levin, 2014; Rossi & Sullivan, 1996).”
BackgroundSchroyen, G., Blommaert, J., Weehaeghe, D. V., Sleurs, C., Vandenbulcke, M., Dedoncker, N., Hatse, S., Goris, A., Koole, M., Smeets, A., et al. (2021) · Cancers · doi:10.3390/cancers13164198 · Cited by 70 · see citing works
“This reduction is likely to reflect impaired ability to transfer information across brain regions, potentially indicating impaired WM integrity [37] and thereby possibly contributing to cognitive impairments seen in chemotherapy-treated patients.”
BackgroundMethodPervasive White Matter Fiber Degeneration in Ischemic Stroke
Egorova, N., Dhollander, T., Khlif, M., Khan, W., Werden, E., & Brodtmann, A. (2020) · Stroke · doi:10.1161/strokeaha.119.028143 · Cited by 69 · see citing works
“These diffusion tensor imaging metrics are nonspecific voxel-averaged measures that do not account for complex microstructural fiber geometry and are known to lead to erroneous interpretations in locations where fibers are crossing.(15) In the current study, we examined white matter degeneration in a cohort of partici…”
BackgroundInfluentialDisentangling the effects of Alzheimer’s and small vessel disease on white matter fibre tracts
Dewenter, A., Jacob, M. A., Cai, M., Gesierich, B., Hager, P., Kopczak, A., Biel, D., Ewers, M., Tuladhar, A., de Leeuw, F., et al. (2022) · Brain : a journal of neurology · doi:10.1093/brain/awac265 · Cited by 67 · see citing works
“19 Using this framework, one can simultaneously derive tract-specific measures of fibre density and fibre-bundle cross-section.”
Andrews, D., Lee, J. K., Harvey, D., Waizbard-Bartov, E., Solomon, M., Rogers, S., Nordahl, C., & Amaral, D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.10.013 · Cited by 67 · see citing works
Fiber-specific white matter reductions in Parkinson hallucinations and visual dysfunction
Zarkali, A., McColgan, P., Leyland, L.-A., Lees, A., Rees, G., & Weil, R. (2020) · Neurology · doi:10.1212/wnl.0000000000009014 · Cited by 67 · see citing works
Early childhood development of white matter fiber density and morphology
Dimond, D., Rohr, C. S., Smith, R. E., Dhollander, T., Cho, I. Y. K., Lebel, C., Dewey, D., Connelly, A., & Bray, S. (2019) · bioRxiv · doi:10.1101/624171 · Cited by 67
Self-citationReview of Diffusion MRI Studies in Chronic White Matter Diseases
Raja, R., Rosenberg, G., & Caprihan, A. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.12.007 · Cited by 67 · see citing works
“Moreover, fixel based analysis [99] which computes metrics on more than one fiber population present within a voxel is potentially another significant method for predictions in SVD.”
MethodWhite matter injury and neurodevelopmental disabilities: a cross-disease (dis)connection.
Cainelli, E., Arrigoni, F., & Vedovelli, L. (2020) · Progress in neurobiology · doi:10.1016/j.pneurobio.2020.101845 · Cited by 64 · see citing works
Histological and MRI markers of white matter damage in focal epilepsy.
Deleo, F., Thom, M., Concha, L., Bernasconi, A., Bernhardt, B., & Bernasconi, N. (2018) · Epilepsy Research · doi:10.1016/j.eplepsyres.2017.11.010 · Cited by 63 · see citing works
“analysis, a diffusion weighted MRI reconstruction technique which combines the measurements of fiber cross-sectional area, a measure of morphology, with microstructural information of fiber density (Raffelt et al., 2017), thereby providindg a sensitive marker of intra-axonal volume (Raffelt et al.”
A framework for multi-component analysis of diffusion MRI data over the neonatal period
Pietsch, M., Christiaens, D., Hutter, J., Cordero-Grande, L., Price, A., Hughes, E., Edwards, A., Hajnal, J., Counsell, S., & Tournier, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.060 · Cited by 62 · see citing works
Self-citationPannek, K., Fripp, J., George, J., Fiori, S., Colditz, P., Boyd, R., & Rose, S. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.01.003 · Cited by 60 · see citing works
Diffusion-based tractography atlas of the human acoustic radiation
Maffei, C., Sarubbo, S., & Jovicich, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-40666-8 · Cited by 62 · see citing works
Zarkali, A., McColgan, P., Leyland, L.-A., Lees, A., & Weil, R. (2020) · bioRxiv · doi:10.1002/mds.28477 · Cited by 58 · see citing works
Sex is a defining feature of neuroimaging phenotypes in major brain disorders
Salminen, L. E., Tubi, M., Bright, J. K., Thomopoulos, S., Wieand, A., & Thompson, P. (2020) · Human Brain Mapping · doi:10.1002/hbm.25438 · Cited by 58 · see citing works
Recent advances in diffusion neuroimaging: applications in the developing preterm brain
Pecheva, D., Kelly, C., Kimpton, J., Bonthrone, A., Batalle, D., Zhang, H., & Counsell, S. (2018) · F1000Research · doi:10.12688/f1000research.15073.1 · Cited by 57 · see citing works
A longitudinal fixel-based analysis of white matter alterations in patients with Parkinson's disease
Rau, Y.-A., Wang, S.-M., Tournier, J.-D., Lin, S.-H., Lu, C.-S., Weng, Y., Chen, Y.-L., Ng, S., Yu, S.-W., Wu, Y.-M., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.102098 · Cited by 55 · see citing works
“The detection by FBA might provide additional information related to white matter changes than conventional voxel-based analysis (Raffelt et al., 2017) and may shed more light to the pathology of PD—based on previous studies in the field.”
InfluentialSelf-citationFibre-specific white matter changes in multiple sclerosis patients with optic neuritis
Gajamange, S., Raffelt, D., Dhollander, T., Lui, E. H., van der Walt, A., Kilpatrick, T., Fielding, J., Connelly, A., & Kolbe, S. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.09.027 · Cited by 55 · see citing works
Self-citationZamani, A., Walker, A., Rollo, B., Ayers, K., Farah, R., O'Brien, T., & Wright, D. K. (2022) · Translational Neurodegeneration · doi:10.1186/s40035-022-00291-4 · Cited by 54 · see citing works
“As the name suggests, changes in the combined fibre density and cross-section measure reflect both a macroscopic change to the white matter bundle and a microstructural change due to increased or decreased axon fibre population [15, 38].”
BackgroundMethodMicrostructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans
Radhakrishnan, H., Stark, S. M., & Stark, C. E. L. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00094 · Cited by 52 · see citing works
Genc, S., Malpas, C. B., Ball, G., Silk, T., & Seal, M. (2017) · Brain Structure and Function · doi:10.1007/s00429-018-1658-5 · Cited by 52 · see citing works
“Higher AFD with older developmental age might therefore reflect bigger axon diameter (Raffelt et al. 2017; Genc et al. 2017b), however this would need to be verified by imaging developmental populations using a high gradient system (McNab et al.”
BackgroundMethodAndica, C., Kamagata, K., Kirino, E., Uchida, W., Irie, R., Murata, S., & Aoki, S. (2021) · Molecular Autism · doi:10.1186/s13229-021-00456-4 · Cited by 51 · see citing works
“Using diffusion kurtosis imaging (DKI) [58] and fixel-based analysis (FBA) [59], reduced axonal density, marked by decreased axonal water fraction, has also been suggested within the CC and long-range association tracts in adults with autism [4, 49].”
MethodCalamuneri, A., Arrigo, A., Mormina, E., Milardi, D., Cacciola, A., Chillemi, G., Marino, S., Gaeta, M., & Quartarone, A. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00716 · Cited by 51 · see citing works
“AFD has been recently successfully applied in normal as well as pathological conditions (27, 28).”
BackgroundMethodChoy, S., Bagarinao, E., Watanabe, H., Ho, E. T. W., Maesawa, S., Mori, D., Hara, K., Kawabata, K., Yoneyama, N., Ohdake, R., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25008 · Cited by 49 · see citing works
“The acquired DWI data were processed using MRtrix3 (version 3.0, http://www.mrtrix.org) following the recommended FBA (Raffelt et al., 2017) preprocessing pipeline.”
MethodInfluentialCharacteristics of Neural Network Changes in Normal Aging and Early Dementia
Watanabe, H., Bagarinao, E., Maesawa, S., Hara, K., Kawabata, K., Ogura, A., Ohdake, R., Shima, S., Mizutani, Y., Ueda, A., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.747359 · Cited by 48 · see citing works
Brain microstructural changes and fatigue after COVID-19
Bispo, D. D. D. C., Brandão, P. R. D. P., Pereira, D., Maluf, F., Dias, B. A., Paranhos, H. R., von Glehn, F., de Oliveira, A. C. D., Regattieri, N., Silva, L., et al. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.1029302 · Cited by 48 · see citing works
Tunç, B., Yankowitz, L., Parker, D., Alappatt, J. A., Pandey, J., Schultz, R., & Verma, R. (2019) · Molecular Autism · doi:10.1186/s13229-019-0301-5 · Cited by 47 · see citing works
Sun, D.-L., Haswell, C., Morey, R., & Bellis, M. D. (2018) · Development and Psychopathology · doi:10.1017/s0954579418000093 · Cited by 46 · see citing works
“, 2008); (c) structural connectivity networks based on diffusion tensor imaging (DTI) of fiber tracts (Hofer & Frahm, 2006; Raffelt et al., 2017); and (d) structural connectivity networks delineated by between subject intraregional correlations in measures of cortical thickness or gray matter volume (Gong, He, Chen, &…”
BackgroundGrazioplene, R. G., Bearden, C., Subotnik, K., Ventura, J., Haut, K. M., Nuechterlein, K., & Cannon, T. D. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.02.015 · Cited by 46 · see citing works
“While FA captures information about overall relative diffusion patterns in a voxel, studies in both phantom and human data show that fixel-based analysis can approximate both the density and macrostructural cross-section of distinct fiber bundles traversing a voxel (Raffelt et al., 2015, 2016).”
BackgroundMethodStructural network differences in chronic musculoskeletal pain: Beyond fractional anisotropy
Bishop, J. H., Shpaner, M., Kubicki, A., Clements, S., Watts, R., & Naylor, M. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.021 · Cited by 48 · see citing works
“Fixel-based analysis was then implemented as described in-depth by Raffelt and colleagues (Raffelt et al., 2017).”
Dou, X., Yao, H.-X., Feng, F., Wang, P., Zhou, B., Jin, D., Yang, Z.-Y., Li, J., Zhao, C., Wang, L., et al. (2020) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.2139/ssrn.3349212 · Cited by 45
Arun, A. H., Connelly, A., Smith, R., & Calamante, F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117505 · Cited by 44 · see citing works
Self-citationTract‐specific atrophy in focal epilepsy: Disease, genetics, or seizures?
Vaughan, D., Raffelt, D., Curwood, E., Tsai, M.-H., Tournier, J.-D., Connelly, A., & Jackson, G. (2017) · Annals of Neurology · doi:10.1002/ana.24848 · Cited by 44 · see citing works
“Multiplying FD and FC at each fixel gives a combined measure of the fiber density and cross-sectional area (FDC).(26)”
MethodInfluentialSelf-citationLindsey, H., Wilde, E., Caeyenberghs, K., & Dennis, E. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01296 · Cited by 43 · see citing works
“Fixel-based measures, such as fiber cross-section (FC), a measurement of fixel diameter, can identify tracts that are affected by regions with crossing fibers, overcoming this inherent limitation of the diffusion tensor model (128).”
BackgroundSubtle white matter alterations in schizophrenia identified with a new measure of fiber density
Stämpfli, P., Sommer, S., Manoliu, A., Burrer, A., Schmidt, A., Herdener, M., Seifritz, E., Kaiser, S., & Kirschner, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-40070-2 · Cited by 42 · see citing works
Aging, brain plasticity, and motor learning.
Gooijers, J., Pauwels, L., Hehl, M., Seer, C., Cuypers, K., & Swinnen, S. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102569 · Cited by 40 · see citing works
Jünemann, K., Marie, D., Worschech, F., Scholz, D. S., Grouiller, F., Kliegel, M., van de Ville, D., James, C. E., Krüger, T., Altenmüller, E., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.817889 · Cited by 40 · see citing works
White matter tract signatures of fiber density and morphology in ADHD.
Fuelscher, I., Hyde, C., Anderson, V., & Silk, T. (2021) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2021.02.015 · Cited by 40 · see citing works
Zhang, J., Zhong, S.-C., Zhou, L., Yu, Y.-M., Tan, X., Wu, M., Sun, P., Zhang, W., Li, J.-B., Cheng, R.-D., et al. (2021) · Neuropsychology Review · doi:10.1007/s11065-021-09482-8 · Cited by 40 · see citing works
White matter alterations at pubertal onset
Genc, S., Seal, M., Dhollander, T., Malpas, C., Hazell, P., & Silk, T. (2017) · bioRxiv · doi:10.1016/j.neuroimage.2017.05.017 · Cited by 40 · see citing works
“Fixel-based analysis Fixel-based analysis (FBA) was performed in accordance with a recommended pipeline (Raffelt et al., 2017).”
Diffusion Imaging Reveals Sex Differences in the White Matter Following Sports-Related Concussion.
Wright, D. K., Symons, G. F., O’Brien, W., McDonald, S., Zamani, A., Major, B., Chen, Z.-B., Costello, D. M., Brady, R., Sun, M.-J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab095 · Cited by 39 · see citing works
Lyon, M., Welton, T., Varda, A., Maller, J., Broadhouse, K., Korgaonkar, M., Koslow, S., Williams, L. M., Gordon, E., Rush, A., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101668 · Cited by 39 · see citing works
“In this paper we evaluate fixel-based analysis (FBA) (Raffelt et al., 2017), a new technique for the analysis of diffusion imaging (dMRI), using data from the international Study to Predict Optimized Treatment in Depression (iSPOT-D).”
MethodLiu, T.-T., Gao, F.-S., Zheng, W., You, Y., Zhao, Z.-Y., Lv, Y., Chen, W., Zhang, H.-X., Ji, C., & Wu, D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118465 · Cited by 37 · see citing works
“Fixel- ased analysis (FBA) is a recently developed model-free approach to dentify crossing fibres with each fibre component in a voxel called a fixel ” ( Raffelt et al., 2017 ; Tournier et al., 2004 ) and provides mi- rostructural measurements including the fibre density (FD) and fibre ross-section…”
Zhang, F., Wu, W., Ning, L., McAnulty, G., Waber, D., Gagoski, B., Sarill, K., Hamoda, H., Song, Y., Cai, W. (., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.006 · Cited by 37 · see citing works
“Fixel-based analysis (FBA) that takes account of multiple fibers within a voxel has been proposed recently to enable enhanced whole-brain statistics (Raffelt et al., 2015, 2017), showing the potential of incorporating information from multiple fiber populations into WM analyses.”
Autism spectrum disorders: Neuroimaging findings from systematic reviews
Pua, E. P. K., Bowden, S. C., & Seal, M. L. (2017) · Research in Autism Spectrum Disorders · doi:10.1016/j.rasd.2016.11.005 · Cited by 37 · see citing works
Carandini, T., Mancini, M., Bogdan, I., Rae, C., Barritt, A. W., Sethi, A., Harrison, N., Rashid, W., Scarpini, E., Galimberti, D., et al. (2021) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102587 · Cited by 36 · see citing works
“multiplied by each fixel to obtain FDC values (FDC = FDxFC) (Raffelt et al., 2017).”
BackgroundMethodInfluentialCerebellar structural connectivity and contributions to cognition in frontotemporal dementias.
Chen, Y., Landín-Romero, R., Kumfor, F., Irish, M., Hodges, J., & Piguet, O. (2020) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2020.04.013 · Cited by 35 · see citing works
Adab, H., Chalavi, S., Monteiro, T. S., Gooijers, J., Dhollander, T., Mantini, D., & Swinnen, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116530 · Cited by 35 · see citing works
“The FDC metric was used in these analyses since it is a measure of total intra-axonal volume occupied by white matter fibers and can be denoted as the marker of total capacity of a particular white matter tract for information transmission (Raffelt et al., 2017), i.”
InfluentialFixel-Based Analysis of Visual Pathway White Matter in Primary Open-Angle Glaucoma.
Haykal, S., Ćurčić-Blake, B., Jansonius, N., & Cornelissen, F. (2019) · Investigative Ophthalmology and Visual Science · doi:10.1167/iovs.19-27447 · Cited by 35 · see citing works
“FD can be used to probe WM microstructural changes in a fiber-specific manner, with a decrease in FD indicating a loss of axons.(45,46) FC quantifies gross morphologic (or macrostructural) WM changes by measuring fiber bundle cross-sectional area in a plane perpendicular to fixel orientation.”
BackgroundMethodInfluentialReview and Perspective on Brain Bases of Tinnitus
Husain, F., & Khan, R. A. (2023) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-023-00914-1 · Cited by 34 · see citing works
“DWI also lends itself to more sophisticated analysis techniques, such as fixel-based analysis (FBA), which helps account for multiple fibers within a voxel by reporting metrics such as fiber density and fiber-bundle cross section [30].”
Huang, P.-Y., & Zhang, M.-M. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00905-x · Cited by 34 · see citing works
“Furthermore, problems such as fiber crossing [107] and water contamination [108] can lead to substantiate biases.”
BackgroundPark, Y., Joers, J., Guo, B., Hutter, D., Bushara, K., Adanyeguh, I., Eberly, L., Öz, G., & Lenglet, C. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00411 · Cited by 34 · see citing works
“WM FOD metrics were estimated using MRtrix3 (29) with multi-tissue constrained spherical deconvolution (CSD), accounting for WM and gray matter (GM) compartments (42).”
BackgroundMethodEvaluating spatiotemporal microstructural alterations following diffuse traumatic brain injury
Mohamed, A. Z., Corrigan, F., Collins-Praino, L., Plummer, S. L., Soni, N., & Nasrallah, F. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.102136 · Cited by 34 · see citing works
Toschi, N., Gisbert, R. A., Passamonti, L., Canals, S., & Santis, S. D. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.11.014 · Cited by 34 · see citing works
“While other similar multi-compartment models are available (Fieremans et al, 2011; Zhang et al, 2012; Raffelt et al., 2017), we used CHARMED as our main hypothesis, informed by previous work (De Santis et al.”
MethodPrinciples of temporal association cortex organisation as revealed by connectivity gradients
Freches, G. B., Haak, K., Bryant, K. L., Schurz, M., Beckmann, C., & Mars, R. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02047-0 · Cited by 33 · see citing works
Horbruegger, M., Loewe, K., Kaufmann, J., Wagner, M., Schippling, S., Pawlitzki, M., & Schoenfeld, M. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101740 · Cited by 33 · see citing works
Zarkali, A., McColgan, P., Leyland, L. A., Lees, A. J., & Weil, R. S. (2022) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2021-326630 · Cited by 32 · see citing works
Applications of advanced diffusion MRI in early brain development: a comprehensive review
DiPiero, M. A., Rodrigues, P. G., Gromala, A., & Dean, D. C. I. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02605-8 · Cited by 32 · see citing works
Mitchell, T., Wilkes, B. J., Archer, D. B., Chu, W. T., Coombes, S. A., Lai, S., Mcfarland, N. R., Okun, M., Black, M. L., Herschel, E., et al. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103022 · Cited by 32 · see citing works
“Fixel-based analysis is a novel diffusion MRI technique that combines voxel-based analysis with tract-based analysis to address the problem of crossing fibers within a voxel, which can result in misleading outcomes in voxels with a high number of crossing fibers (Raffelt et al., 2017).”
InfluentialGenc, S., Malpas, C. B., Gulenc, A., Sciberras, E., Efron, D., Silk, T., & Seal, M. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100853 · Cited by 32 · see citing works
Li, Y., Guo, T., Guan, X.-J., Gao, T., Sheng, W.-S., Zhou, C., Wu, J.-J., Xuan, M., Gu, Q., Zhang, M.-M., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102355 · Cited by 32 · see citing works
“Moreover, to ensure that the data are centered around zero and normally distributed, we calculated the log-FC instead of the FC for comparison (Raffelt et al., 2017).”
MethodResultInfluentialGirls’ pubertal development is associated with white matter microstructure in late adolescence
Chahal, R., Vilgis, V., Grimm, K. J., Hipwell, A., Forbes, E. E., Keenan, K., & Guyer, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.050 · Cited by 32 · see citing works
The developing brain structural and functional connectome fingerprint
Ciarrusta, J., Christiaens, D., Fitzgibbon, S., Dimitrova, R., Hutter, J., Hughes, E., Duff, E., Price, A., Cordero-Grande, L., Tournier, J.-D., et al. (2021) · bioRxiv · doi:10.1016/j.dcn.2022.101117 · Cited by 31 · see citing works
Self-citationRoy, M., Fortier, M., Rheault, F., Edde, M., Croteau, É., Castellano, C., Langlois, F., St-Pierre, V., Cuenoud, B., Bocti, C., et al. (2021) · medRxiv · doi:10.1002/trc2.12217 · Cited by 31 · see citing works
Linking Cortical and Connectional Pathology in Schizophrenia.
Biase, M. D. D., Cropley, V., Cocchi, L., Fornito, A., Calamante, F., Ganella, E. P., Pantelis, C., & Zalesky, A. (2018) · Schizophrenia bulletin · doi:10.1093/schbul/sby121 · Cited by 31 · see citing works
Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
Jeurissen, B., & Szczepankiewicz, F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118717 · Cited by 30 · see citing works
Kelley, S., Plass, J., Bender, A. R., & Polk, T. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab056 · Cited by 30 · see citing works
“However, in white matter voxels with crossing fibers one cannot know whether decreases in FA reflect reduced signal from the largest fiber population, increased signal from orthogonal populations, or other combinations of intra-voxel influences (Riffert et al. 2014).”
BackgroundMethodResultInfluentialChahal, R., Kirshenbaum, J., Ho, T., Mastrovito, D., & Gotlib, I. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100899 · Cited by 30 · see citing works
“Further, FDC has been shown to be "more sensitive to certain pathologies and more directly interpretable" than are voxel-averaged quantitative measures (e.g., fractional anisotropy) (page 58, Raffelt et al., 2017).”
BackgroundMethodInfluentialKelly, C. E., Thompson, D. K., Genc, S., Chen, J., Yang, J., Adamson, C., Beare, R., Seal, M., Doyle, L., Cheong, J., et al. (2020) · medRxiv · doi:10.1101/2020.04.01.20049585 · Cited by 30
“is strongly attenuated, leaving the remaining signal localised to the intra-axonal water compartment, and allowing us to make fibre-specific inferences using FBA.(8,35) The observed lower FD in VP children compared with FT children suggests VP children have lower axonal densities within fibre tracts, which could refle…”
InfluentialMaternal depressive symptoms, neonatal white matter, and toddler social-emotional development
Lautarescu, A., Bonthrone, A. F., Pietsch, M., Batalle, D., Cordero-Grande, L., Tournier, J.-D., Christiaens, D., Hajnal, J. V., Chew, A., Falconer, S., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02073-y · Cited by 29 · see citing works
“FBA provides information regarding white matter structure for fibre bundles within a voxel through estimates of microstructure via local fibre density (FD), and morphometry via the fibre cross-section (FC) metric; their product, the fibre density and cross section (FDC), serves as a proxy of a fibre bundle’s capacity…”
BackgroundSelf-citationMapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI
Karat, B. G., DeKraker, J., Hussain, U., Köhler, S., & KHAN, A. R. (2023) · Human Brain Mapping · doi:10.1002/hbm.26461 · Cited by 28 · see citing works
Cognitive Training in Young Patients With Traumatic Brain Injury: A Fixel-Based Analysis
Verhelst, H., Giraldo, D. L., Linden, C. V., Vingerhoets, G., Jeurissen, B., & Caeyenberghs, K. (2019) · Neurorehabilitation and Neural Repair · doi:10.1177/1545968319868720 · Cited by 29 · see citing works
Hyde, C., Fuelscher, I., Enticott, P., Jones, D., Farquharson, S., Silk, T., Williams, J., & Caeyenberghs, K. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.101625 · Cited by 29 · see citing works
- Authors
- Raffelt, D. A., Tournier, J.-D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A.
- Affiliations
- Florey Institute of Neuroscience and Mental Health (AU)
- King's College London (GB)
- The University of Melbourne (AU)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Raffelt, David A.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 144
- Issue
- Pt A
- Pages
- 58-73
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5182031
- OpenAlex
- W2520744072
- Cited by
- 620
- References
- 91
- Citations by year
- 2026: 74 · 2025: 79 · 2024: 78 · 2023: 76 · 2022: 84 · 2021: 91 · 2020: 71 · 2019: 30 · 2018: 26 · 2017: 9 · 2016: 2
- FWCI
- 19.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- National Health and Medical Research Council (NHMRC)
- Victorian Government’s Operational Infrastructure
- UK Medical Research Council (MR/J014257/2)
- Wellcome Trust (091593/Z/10/Z)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- fixel-based analysis, crossing fibers, axon number
- MeSH terms
- Humans, Nerve Fibers, Myelinated, Diffusion Magnetic Resonance Imaging, White Matter
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