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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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“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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Cites
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MethodVoxel-Based Morphometry—The Methods
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MethodPermutation inference for the general linear model
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.060 · Cited by 3,563 · cited 1×
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MethodSelf-citationProbabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471
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BackgroundMethodInfluentialTraining induces changes in white-matter architecture
Scholz, J., Klein, M. C., Behrens, T. E. J., & Johansen-Berg, H. (2009) · Nature Neuroscience · doi:10.1038/nn.2412 · Cited by 1,508 · cited 1×
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BackgroundJeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A., & Sijbers, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.061 · Cited by 1,484 · cited 1×
“(Jeurissen et al., 2014)) instead of tissue segmentations from T1-weighted images.”
BackgroundSelf-citationDiffusion tensor imaging of white matter tract evolution over the lifespan
Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., & Beaulieu, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.094 · Cited by 1,100 · cited 1×
“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.”
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Items 401–500 of 604
Timing of selective basal ganglia white matter loss in Huntington’s diseasePreprint
Zeun, P., McColgan, P., Dhollander, T., Gregory, S., Johnson, E., Papoutsi, M., Nair, A., Scahill, R., Rees, G., & Tabrizi, S. (2021) · bioRxiv · doi:10.1101/2021.02.17.431568 · Cited by 4
Naeyaert, M., Aelterman, J., Audekerke, J. V., Golkov, V., Cremers, D., Pižurica, A., Sijbers, J., & Verhoye, M. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28520 · Cited by 4
Notes on “A cautionary note on the use of SIFT in pathological connectomes”
Smith, R., Calamante, F., & Connelly, A. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28266 · Cited by 4
Self-citationChildhood conduct problems are associated with reduced white matter fibre density and morphology
Burley, D. T., Genc, S., & Silk, T. (2020) · bioRxiv · doi:10.1101/2020.05.29.123364 · Cited by 4
“In contrast, fixel-based analysis (FBA) (Raffelt et al., 2012; Raffelt et al., 2017) is a recently developed advanced diffusion MRI analysis”
BackgroundMethodInfluentialGagnon, H., Simmonite, M., Cassady, K., Chamberlain, J. D., Freiburger, E., Lalwani, P., Kelley, S., Foerster, B., Park, D. C., Petrou, M., et al. (2018) · doi:10.1101/466516 · Cited by 4
Chen, Y.-N., Hoffmann, J., Syeda, W., Mito, R., & Moffat, B. A. (2026) · NeuroImage: Clinical · doi:10.1016/j.nicl.2026.104024 · Cited by 3
“In addition to DKI, other advanced dMRI techniques include fixel-based analysis (FBA) (Raffelt et al., 2012; Raffelt et al., 2017), which enables characterisation of specific fibre populations even within crossing-fibre regions, and microstructural compartment models (e.g., NODDI) that can provide…”
Scott, A., Lo, A., Xia, Y., Thompson, K., Bodimeade, H., Fazlollahi, A., Weber, N., Curley, C., Hunt, S., Morris, K., et al. (2026) · British Journal of Haematology · doi:10.1111/bjh.70386 · Cited by 3
Chu, T.-T., Xiong, F., Gao, F., Tian, X.-B., & Tu, Y. (2026) · The Journal of Headache and Pain · doi:10.1186/s10194-026-02306-1 · Cited by 3
“Using constrained spherical deconvolution (CSD) to resolve multiple fibre populations (“fixels”) within a voxel, FBA enables fibre-specific inference even in crossing-fibre regions [18, 19].”
Utility of Harmonisation for Fixel‐Based Metrics in Travelling Subjects and Alzheimer's Disease Data
Zou, R., Kamagata, K., Mito, R., Takabayashi, K., Andica, C., Uchida, W., Guo, S., Kitagawa, T., Fujita, S., Uematsu, A., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70408 · Cited by 3
Yoon, C. D., & Dean, D. C. (2025) · Autism Research · doi:10.1002/aur.70122 · Cited by 3
“One study used an in-house pipeline that combined both tools (TiDi-Fused; Guerrero-Gonzalez et al. 2022), while two studies followed a recommended fixel-based analysis pipeline (Dhollander et al. 2021; Raffelt et al. 2017) using both tools (Kirkovski et al. 2020, 2023”
Weakening of Subcortical and Strengthening of Cortical Visual Pathways Across Early Adolescence
Sangchooli, A., Rowe, E., Smith, R. E., Dumontheil, I., & Garrido, M. I. (2026) · Human Brain Mapping · doi:10.1002/hbm.70626 · Cited by 1
“…global intensity normalization, and brain mask derivation, exploiting information from multi-tissue decomposition of the diffusion signal (Jeurissen et al. 2014; Raffelt et al. 2017; Dhollander et al. 2021), with the brain mask derivation component performed using SynthStrip (Hoopes et al. 2022).”
Self-citationLu, Y.-Q., Liu, Y.-S., Zhou, X., Wu, H., Zhou, Y., Hu, L.-Y., Zhang, H., Long, Y., Pang, C., Xi, J., et al. (2025) · Journal of Alzheimer's Disease · doi:10.1177/13872877251372953 · Cited by 3
“21,22 FBA produces metrics like fi ber cross section (FC), fi ber density (FD), and a combined measure of fi ber density and cross section (FDC), which can provide changes in fi ber structure and offer a visual assessment of microstructural alterations.”
Matyi, M. A., Radhakrishnan, H., Olm, C. A., Phillips, J. S., Cook, P. A., Rhodes, E., Gee, J. C., Irwin, D. J., McMillan, C. T., & Massimo, L. M. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103853 · Cited by 6
“FODs were intensity-normalized using mtnormalize (Raffelt et al., 2017).”
Abbo, N., Mitchell, T., Tonekaboni, S. H., Anagnostou, E., Andrade, B. F., Thorpe, K., & Beal, D. S. (2025) · Pilot and Feasibility Studies · doi:10.1186/s40814-025-01650-4 · Cited by 3
“MRtrix3 software will be used to process diffusion imaging data to determine changes in white matter tracts [76].”
Manelis, A., Hu, H., Satz, S., Iyengar, S., & Swartz, H. A. (2025) · medRxiv · doi:10.1101/2025.02.19.25322569 · Cited by 3
“Joint bias field correction and global intensity normalization of the multi-tissue compartment parameters were performed using the mtnormalise command for all subjects (Dhollander et al., 2021; Raffelt et al., 2017).”
InfluentialTremblay, S. A., Spreng, R., Wearn, A. R., Alasmar, Z., Pirhadi, A., Tardif, C., Chakravarty, M. M., Villeneuve, S., Leppert, I., Carbonell, F., et al. (2024) · bioRxiv · doi:10.1101/2024.08.21.608995 · Cited by 3
Volumetric and diffusion MRI abnormalities associated with dysarthria in multiple sclerosis
Kenyon, K., Strik, M., Noffs, G., Morgan, A. T., Kolbe, S., Harding, I. H., Vogel, A., Boonstra, F., & van der Walt, A. (2024) · Brain Communications · doi:10.1093/braincomms/fcae177 · Cited by 3
“46 FBA has recently been used to assess white matter damage in multiple sclerosis.”
MethodInfluentialPretzel, P., Wilke, M., Tournier, J., Goelz, R., Lidzba, K., Hauser, T.-K., & Groeschel, S. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1241387 · Cited by 3
“Given the nature of fixel metrics, these findings can be interpreted to reflect reduced capacity to relay information (12) within these tracts.”
BackgroundMethodSelf-citationNeuroimaging and plasma marker evidence for white matter macrostructure loss in Parkinson’s diseasePreprint
Zarkali, A., Hannaway, N., McColgan, P., Heslegrave, A., Veleva, E., Laban, R., Zetterberg, H., Lees, A. J., Fox, N. C., & Weil, R. (2023) · bioRxiv · doi:10.1101/2023.09.22.558937 · Cited by 3
Zhao, J., Jing, B., Liu, J., Chen, F., Wu, Y., & Li, H. (2023) · Chinese Medical Journal · doi:10.1097/cm9.0000000000002829 · Cited by 3
“Finally, FDC, the product of FD and FC, provides a more robust measure of axonal integrity as it combines information from both metrics.[22] We extracted the mean FD, FC, and FDC values for fixels along each tract of interest in the template space for all tracts and all subjects.”
BackgroundMethodInfluentialFiber-specific white matter alterations in Parkinson's disease patients with freezing of gait.
Zhou, W.-Y., He, J.-Z., Zhang, C., Pan, Y., Sang, T., & Qiu, X. (2023) · Brain Research · doi:10.1016/j.brainres.2023.148440 · Cited by 3
Melis, M., Blommaert, J., Radwan, A., Smeets, A., der Gucht, K. V., Deprez, S., & Sunaert, S. G. (2023) · Mindfulness · doi:10.1007/s12671-023-02140-5 · Cited by 3
Advanced Diffusion MRI for prediction of Stroke Recovery
Hui, E. S. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28523 · Cited by 3
“The FD of each subject is thus estimated from the amplitude of the subject’s fixel that has the closest location and orientation to that of the template in the same voxel.(24) The cross section of fiber bundle (FC) is then calculated using the determinant of the Jacobian of the nonlinear spatial warp that maps from th…”
BackgroundMethodInfluentialAhmadi, K., Pereira, J., van Westen, D., Pasternak, O., Zhang, F., Nilsson, M., Stomrud, E., Spotorno, N., & Hansson, O. (2023) · medRxiv · doi:10.1101/2023.02.17.23286094 · Cited by 3
“FBA was performed according to the recommended pipeline of Mrtrix3 (Raffelt et al., 2017; Tournier et al., 2019).”
MethodInfluentialKang, X.-J., Yoon, B., Shah, S., & Adamson, M. (2023) · Neurosciences research · doi:10.1016/j.neures.2023.01.009 · Cited by 3
Kim, W., & Kim, M. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205162119 · Cited by 11
Laethem, D. V., de Steen, F. V., Kos, D., Naeyaert, M., Schuerbeek, P. V., D’haeseleer, M., D'hooghe, M. B., Schependom, J. V., & Nagels, G. (2022) · Trials · doi:10.1186/s13063-022-06697-9 · Cited by 3
“Fibre cross-section (FC) and apparent fibre density (FD) will be investigated in white matter through a fixel-based hARDI analysis [57].”
MethodModelArray: a memory-efficient R package for statistical analysis of fixel dataPreprint
Zhao, C.-Y., Tapera, T. M., Bagautdinova, J., Bourque, J., Covitz, S., Gur, R., Gur, R., Larsen, B., Mehta, K., Meisler, S., et al. (2022) · bioRxiv · doi:10.1101/2022.07.12.499631 · Cited by 3
Gooijers, J., Luca, A. D., Adab, H. Z., Leemans, A., Roebroeck, A. F., & Swinnen, S. P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118433 · Cited by 4
Christiaen, E., Goossens, M., Descamps, B., Delbeke, J., Wadman, W. J., Vonck, K. E. J., Boon, P., Raedt, R., & Vanhove, C. (2021) · Brain Connectivity · doi:10.1089/brain.2021.0026 · Cited by 4
“In addition, this model does not take into account that many voxels contain crossing or kissing fibers, leading to diffusion metrics that may not be fiber-specific and even erroneous (Jeurissen et al., 2014; Raffelt et al., 2017).”
InfluentialSavard, M., Pascoal, T., Dhollander, T., Iturria-Medina, Y., Vitali, P., Therriault, J., Mathotaarachchi, S., Benedet, A., Gauthier, S., & Rosa-Neto, P. (2020) · bioRxiv · doi:10.1101/2020.05.01.064576 · Cited by 3
“Fixel-based metrics We used the fixel-based analysis framework (D. A. Raffelt et al., 2017; D. Raffelt, Tournier, Rose, et al., 2012) to compute the fiber density (FD) and the fiber cross-section (FC) at the fixel level (Figure 1b).”
BackgroundMethodFixel-Based Analysis of White Matter in Temporal Lobe Epilepsy
Feshki, M., Parham, E., & Soltanin-Zadeh, H. (2018) · Iranian Conference on Biomedical Engineering · doi:10.1109/icbme.2018.8703504 · Cited by 3
“The recently introduced parameters of Fiber Density (FD) and Fiber Cross-section (FC) have provided a basis for introducing the Fiber Density and Cross-section (FDC) parameter, which is evaluated according to micro and macro variations of fibers [11].”
BackgroundMethodInfluentialSommer, S., Kozerke, S., Seifritz, E., & Stämpfli, P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00729 · Cited by 4
“Numerous models based on diffusion weighted imaging have been proposed to estimate parameters related to the restricted, intra-axonal compartment, commonly referred to as fiber density (Calamante et al., 2015; Raffelt et al., 2016).”
BackgroundDisrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis
Al-Amin, M. M., Grandjean, J., Klohs, J., & Kim, J. (2020) · doi:10.1101/2020.04.27.064634 · Cited by 2
Chang, J., Lipscombe, H., Lv, J., Mccombe, P., Henderson, R. D., Ngo, S., Steyn, F., & Shaw, T. B. (2026) · BMC Medicine · doi:10.1186/s12916-026-04763-6 · Cited by 2
“Fixel fibre density and cross-section metrics for each participant were generated with MRTrix [33].”
A Direct Auditory Subcortical Route to the Amygdala Associated with Fear in Humans
Kosteletou-Kassotaki, E., Cinca-Tomás, M. T., Varriano, F., Soria, G., Prats-Galino, A., & Domínguez-Borràs, J. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.1431-25.2026 · Cited by 2
Mito, R., Genc, S., Halim, J. A., Yang, J. Y., Tournier, J.-D., Kean, M., Kokkinos, C., McIntyre, R., Biase, M. D. D., Smith, R. E., et al. (2026) · Imaging neuroscience · doi:10.1162/imag.a.1153 · Cited by 2
“…psychopathology ( Grazioplene et al., 2022; Kristensen et al., 2023, 2024; Lyon et al., 2019), and neurological disorders ( Mito et al., 2022, 2024; Raffelt et al., 2017; Vaughan et al., 2017), as well as in healthy development ( Genc et al., 2018; Genc, Malpas, et al., 2020) and aging ( Kelley et…”
InfluentialSelf-citationWhite matter alterations in crossing fibres following traumatic brain injury
Jia, Y., Bourke, N., Mallas, E.-J., Caeyenberghs, K., Simoni, S. D., Jenkins, P. O., Duque, J. F. D., Sharp, D. J., & Parker, T. D. (2026) · Brain Communications · doi:10.1093/braincomms/fcag042 · Cited by 2
“6,7 Crossing fibres of this type are found in over 90% of WM voxels, making this a significant challenge for the assessment of TAI for a number of reasons.”
Influentialsf-pediatric: A robust and age-adaptable end-to-end pipeline for pediatric diffusion MRIPreprint
Gagnon, A., Boré, A., Caron, A. V., Edde, M., Thoumyre, S., Lepage, J., Talati, A., Posner, J., Ouellet, A., Brunet, M. A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.19.700454 · Cited by 1
“To evaluate the quality of the tractogram WM coverage, we compute the tract density image from the tractogram and evaluate the overlap with the WM mask using the DICE score.”
Diencephalic integrity explains aspects of hippocampal amnesia
Argyropoulos, G. P. D., Aggleton, J. P., & Butler, C. (2026) · Cerebral Cortex · doi:10.1093/cercor/bhag069 · Cited by 2
“As in recent studies ( Zarkali et al. 2020; Andica et al. 2021), we selected fiber density and cross-section (FDC) as the measure of interest, since it reflects both microstructural and macrostructural changes, representing a tract’s overall ability to relay information ( Raffelt et al. 2017).”
Fixel-based analysis reveals detailed white matter changes in semantic dementia
Mandelli, M., Cobigo, Y., Perretti, I., Leichter, D., Alba, C., Bogley, R., Wellman, N., Ramkrishnan, S., Miller, Z., Miller, B. L., et al. (2026) · Brain Structure and Function · doi:10.1007/s00429-025-03064-7 · Cited by 2
“To overcome these limitations, Fixel-based analysis (FBA) has been introduced as an anatomically precise framework for studying WM alterations (Raffelt et al. 2015, 2017).”
InfluentialZhu, H., Zhu, S., Zhao, M.-X., Zhu, Z.-Y., Shao, Y., Lu, X., Liu, T., Zhu, H.-G., Shu, N., Lin, H., et al. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70623 · Cited by 2
Tranfa, M., Pieperhoff, L., Pontillo, G., Luckett, E. S., Collij, L. E., Oliveira, T. G., Tesi, N., Vilor‐Tejedor, N., Altmann, A., Roccatagliata, L., et al. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01888-3 · Cited by 2
Burnett, J., Cobden, A. L., Burmester, A., Akhlaghi, H., D, J. F. D., & Caeyenberghs, K. (2025) · Human Brain Mapping · doi:10.1002/hbm.70394 · Cited by 2
Multilevel white matter degeneration associated cognitive dysfunction in mild cognitive impairment
Chen, Y., Liu, T.-T., Cao, L.-Z., Wang, L., Funahashi, S., Tang, X.-Y., Wu, J.-L., Meng, W., Zhang, J., & Yan, T.-Y. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03179-7 · Cited by 2
Kala, D., Prysiazhniuk, Y., Michal, L., Šanda, J., Holubová, Z., Kudr, M., Krýsl, D., Maulisová, A., Jiránková, K., Marusič, P., et al. (2025) · Epilepsia Open · doi:10.1002/epi4.70109 · Cited by 2
“47,48 All analyses were conducted using default parameters as described in the respective original publications.”
Yu, Y., Fu, J., Wang, M.-H., Zhou, R.-H., Shan, C., Sun, X.-Y., & Feng, Y.-J. (2025) · Journal of Neuroimaging · doi:10.1111/jon.70072 · Cited by 2
“FDC, calculated as the product of FD and FC, integrates both microscopic and macroscopic structural properties, providing a comprehensive measure of a fiber tract’s capacity to transmit information by reflecting changes in both axonal density and bundle morphology [24].”
InfluentialNo Support for White Matter Alterations in Adults With Dyslexia: A Fixel-Based Diffusion MRI Study
Verhelst, H., Gerrits, R., & Karlsson, E. M. (2025) · Neurobiology of Language · doi:10.1162/nol.a.17 · Cited by 2
“Since FDC is proportional to axonal volume, it serves as an indirect marker of the biophysical properties of white matter tracts, including action potential conduction velocity, which is influenced by factors such as axonal diameter and myelination (Raffelt et al., 2017; Waxman, 1980).”
InfluentialBeyer, F., Kleine, L., Zülke, A., Luppa, M., Mildner, T., Gensichen, J., Frese, T., Czock, D., Wiese, B., König, H., et al. (2025) · NeuroImage: Clinical · doi:10.1016/j.nicl.2025.103796 · Cited by 2
“Fixel based analysis (FBA) was implemented using MRtrix3 following the recommended pipeline (Raffelt et al., 2017).”
Billaud, C., & Yu, J.-H. (2025) · Network Neuroscience · doi:10.1162/netn_a_00461 · Cited by 2
“Because fixel-based analyses (FBAs) improve upon conventional DTI within-voxel averaging, they can identify WM tracts more accurately (Dhollander et al., 2021; Jeurissen et al., 2013; Raffelt et al., 2017).”
InfluentialNewman, B. T., Horn, J. D. V., & Druzgal, T. J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70403 · Cited by 2
Le, H., Bonthrone, A., Uus, A., Fenchel, D., Lautarescu, A., Dimitrakopoulou, K., Edwards, A., Hajnal, J., Counsell, S., Cordero-Grande, L., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03252-3 · Cited by 2
Self-citationWei, J.-H., Liu, Z.-H., Su, H.-H., She, Y., Wang, J., Liu, Y., Zhong, Y.-K., Mo, L.-J., Tan, C.-H., Liu, X., et al. (2025) · Neurological Sciences · doi:10.1007/s10072-024-07951-5 · Cited by 2
“FD is therefore a measure of microsrtuctural WM changes and may be linked to axon degeneration [12, 20].”
InfluentialWhite Matter Tract Crossing and Bottleneck Regions in the Fetal Brain
Calixto, C., Soldatelli, M. D., Li, B., Vasung, L., Jaimes, C., Gholipour, A., Warfield, S., & Karimi, D. (2025) · Human Brain Mapping · doi:10.1002/hbm.70132 · Cited by 2
“To follow a consistent terminology, we adopt the nomenclature proposed in the relevant recent papers (Zhang et al. 2022; Maier-Hein et al. 2017; Schilling et al. 2022; Raffelt et al. 2017).”
White matter differences between younger and older adults revealed by fixel-based analysis
de la Cruz, F., Schumann, A., Rieger, K., Güllmar, D., Reichenbach, J. R., & Bär, K. (2024) · Aging Brain · doi:10.1016/j.nbas.2024.100132 · Cited by 1
Peterson, B. S., Delavari, S., Sadik, J., Ersland, L., Elgen, I., Sawardekar, S., Bansal, R., & Aukland, S. M. (2024) · Journal of Child Psychology and Psychiatry and Allied Disciplines · doi:10.1111/jcpp.14069 · Cited by 2
“…of preterm birth have employed recently developed, so-called ‘fixel-based’metrics that aim to characterize morphological characteristics of fibre tracts reconstructed from estimated diffusion tensors, while accounting for crossing fibre geometries within each white matter voxel (Raffelt et al., 2017).”
Objective Determination of Site-of-Lesion in Auditory Neuropathy
Zanin, J., & Rance, G. (2024) · Ear and Hearing · doi:10.1097/aud.0000000000001589 · Cited by 2
“The FBA was performed using MRtrix3Tissue and followed the steps recommended by Raffelt et al. (2017) and described in detail in Zanin et al. (2020).”
Qi, Y., Song, L., Liu, X., Xu, B., Yang, W., Li, M., Li, M., Zhu, Z., Liu, W., Yang, Z., et al. (2024) · Clinical Kidney Journal · doi:10.1093/ckj/sfae286 · Cited by 2
“The fibre density and cross-section ( FDC) metric, a product of FD and FC, disre-gards microstructural or macrostructural specificity and quantifies the total effect on fibre properties [20 ].”
InfluentialAndrews, L., Keller, S. S., Ratcliffe, C., Osman-Farah, J., Shepherd, H., Bhojak, M., & Macerollo, A. (2024) · Tremor and Other Hyperkinetic Movements · doi:10.5334/tohm.904 · Cited by 2
Matthews, T. E., Lumaca, M., Witek, M. A. G., Penhune, V. B., & Vuust, P. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02836-x · Cited by 4
“Combining these measures provides a compre - hensive index of intra-axonal volume which can be inter - preted in terms of information transfer capacity (Dhollander et al. 2021 ; Raffelt et al. 2017a , b).”
InfluentialWhite matter organisation of sensorimotor tracts is associated with motor imagery in childhood
Mukherjee, M., Hyde, C., Barhoun, P., Bianco, K. M., Singh, M., Waugh, J., Silk, T., Lum, J., Caeyenberghs, K., Williams, J., et al. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02813-4 · Cited by 2
“Following intensity normalization (Raffelt et al. 2017 ), a population template specific to the study was generated using the FOD maps from all 22 participants.”
BackgroundMethodResultInfluentialMidrigan-Ciochina, L., Vodacek, K. P., Sewell, C. D., & Corina, D. P. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00144 · Cited by 2
“The FD measure is confounded by the changes in axonal density, axonal diameter, and membrane permeability, which all influence the FD metric (for additional details, see Raffelt et al., 2017).”
BackgroundMethodInfluentialKim, W., & Kim, M. J. (2024) · Psychological Medicine · doi:10.1017/s0033291724000473 · Cited by 3
“…brain voxels (Tustison et al., 2010), and multi-tissue constrained spherical deconvolution was conducted to estimate multiple fiber orientations in each voxel (Jeurissen, Tournier, Dhollander, Connelly, & Sijbers, 2014), followed by calculation of fiber orientation density (Raffelt et al., 2017).”
MethodFibre-specific white matter changes in anorexia nervosa.
de la Cruz, F., Schumann, A., Rieger, K., Giuliano, M. D., & Bär, K. (2023) · Psychiatry research. Neuroimaging · doi:10.1016/j.pscychresns.2023.111736 · Cited by 2
“In turn, FDC can serve as an indicator for the overall quantity of axons within the fibre tract, taking into account variations in both FD and FC.”
InfluentialShao, Y., Li, L., Peng, W.-P., Lu, W.-Z., & Wang, Y. (2023) · Die Radiologie · doi:10.1007/s00117-023-01192-x · Cited by 2
Polk, S. E., Kleemeyer, M. M., Bodammer, N., Misgeld, C., Porst, J., Wolfarth, B., Kühn, S., Lindenberger, U., Düzel, S., & Wenger, E. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100155 · Cited by 4
“In order to make up for this shortcoming, newer metrics have been developed using “ fixels, ” which represent specific fiber bundles within a voxel (Raffelt et al., 2017).”
Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolutionPreprint
Callaghan, R., Alexander, D., Palombo, M., & Zhang, H. (2021) · ORCA Online Research @Cardiff (Cardiff University) · doi:10.48550/arxiv.2103.08237 · Cited by 2
Characterizing white matter alterations in drug-naïve de novo Parkinson’s disease with diffusion MRI
Xiao, Y.-M., Peters, T., & Khan, A. R. (2021) · bioRxiv · doi:10.1101/2021.02.13.431081 · Cited by 2
Orije, J., Cardon, E., Hamaide, J., Jonckers, E., Darras, V., Verhoye, M., & der Linden, A. V. (2021) · bioRxiv · doi:10.1101/2021.01.21.427111 · Cited by 2
“5.5.2 Fixel based analysis For calculation of the fiber-based metrics, we followed the preprocessing steps as defined in (Raffelt et al., 2017).”
BackgroundMethodInfluentialHausmann, A. C., Querbach, S. K., Rubbert, C., Schnitzler, A., Caspers, J., & Hartmann, C. (2026) · Scientific Reports · doi:10.1038/s41598-026-65470-z · Cited by 1
“…approaches, including constrained spherical deconvolution and multi-fixel models, may further extend the present findings by disentangling fiber-specific alterations within regions of complex crossing architecture 36,54 , thereby complementing the compartment-specific information provided by NODDI.”
Multimodel Diffusion MRI Signatures in Atypical Parkinsonian Disorders
Tian, Y.-Q., Ali, F., Machulda, M., Lowe, V., Josephs, K., & Whitwell, J. (2026) · Human Brain Mapping · doi:10.1002/hbm.70616 · Cited by 1
Zanoni, S., Lv, J.-L., Naismith, S., Smith, R., & Calamante, F. (2026) · Research Square · doi:10.21203/rs.3.rs-10123464/v1 · Cited by 1
“…and evaluation of the information provided by the novel SBA method, the same data were also analysed using Fixel-Based Analysis (FBA) 13 , a well-established and widely used framework for detecting changes in WM metrics, both in the presence and absence of clinical conditions 4,25,81–84 .”
Self-citationKala, D., Abragimovich, T., Janča, R., Profantová, N., Koblížek, M., Kalina, A., Kudr, M., Prysiazhniuk, Y., Michal, L., Šanda, J., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-57292-w · Cited by 1
“Pre-processing of diffusion data Diffusion-weighted data were pre-processed using MRtrix3 48 and FSL frameworks.”
Multimodal imaging reveals resilient memory networks in carriers of pathogenic ARID1B variants
Fabre, A., Aljabali, K., Boisgontier, J., Fillon, L., Guida, L., Saitovitch, A., Rio, M., Amiel, J., Michot, C., Dangouloff-Ros, V., et al. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04090-7 · Cited by 1
“FBA was performed according to the recommended pipeline of MRtrix3 [11, 12].”
Tian, Y., & Whitwell, J. L. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121984 · Cited by 2
Manifold lateralisation and variability in the language connectome at 7T
Dulyan, L., Bortolami, C., Chacón, E. G., Beyh, A., de Schotten, M. T., & Forkel, S. (2026) · Research Square · doi:10.21203/rs.3.rs-9267400/v1 · Cited by 1
“Recent methodological advances have demonstrated that high-angular resolution and fixel-based analyses can capture fine-grained tract profiles and individual variability 20,21 .”
Transhemispheric optic pathway degeneration following unilateral post-geniculate lesions
Halbertsma, H. N., Haykal, S., Willis, H., Bridge, H., Jansonius, N., & Cornelissen, F. W. (2026) · Brain Communications · doi:10.1093/braincomms/fcag023 · Cited by 1
“To account for potential confounding effects of intracranial volume on the fibre-bundle cross-section measures, 29,38 we included z -scored, sex-adjusted, intracranial volume residuals (see Supplementary Methods—Intracranial volume correction) as an additional covariate.”
Yeh, C.-H., Chen, J.-R., Chao, Y.-P., Wu, C.-T., Chou, T.-L., Gau, S., Ni, H., & Lin, H.-Y. (2026) · NeuroImage: Clinical · doi:10.1016/j.nicl.2026.103948 · Cited by 1
“These subject-level means were then entered into MRtrix3 ′ s population_template routine (Raffelt et al., 2017) to build a group template that served as the common spatial reference for all longitudinal analyses.”
Wang, A. P., Green, M., Humburg, P., Moseley, G., McAuley, J. H., & Rae, C. D. (2025) · European Journal of Pain · doi:10.1002/ejp.70202 · Cited by 1
“FBA avoids errors relating to diffusion in voxels containing multiple crossing fibres (Farquharson et al. 2013).”
InfluentialPatterns of Structural Disconnection Driving Proprioceptive Deficits in Chronic Stroke
Kaeja, M., Gajiyeva, L., Iturria-Medina, Y., Villringer, A., Sehm, B., & Steele, C. (2025) · Stroke · doi:10.1161/strokeaha.125.052266 · Cited by 1
Guerra, R. B., Peñate-Castro, W., Díaz-Velis, E. F., Pérez, C. S., Alfonso, S. B., Hernández, M. H., Fleta, J. L. H., & Molina, J. M. (2025) · Brain Science · doi:10.3390/brainsci15111155 · Cited by 1
“Reconstructions with implausible trajectories or motion artifacts were repeated following the quality control procedures described in recent reviews of diffusion MRI [34,35].”
Fixel-Based White Matter Correlates of Sentence Comprehension in Post-Stroke Aphasia
Fang, D.-X., Ji, X.-T., Li, H.-Z., Xu, S., Yang, Y.-L., Zhan, J.-Y., Kong, A. P.-H., & Hu, R. (2025) · Brain Science · doi:10.3390/brainsci15101039 · Cited by 1
“Subsequent clearance of debris leads to fiber tract atrophy and reduced FC [41].”
InfluentialAnterograde degeneration along the visual pathway following optic nerve injury: a review
Chapelle, A.-C. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1623798 · Cited by 1
“To address this issue, a more advanced method called fixel-based analysis (FBA) has been developed (20).”
Fixel-Based Analysis Reveals Detailed White Matter Changes in Semantic DementiaPreprint
Mandelli, M., Cobigo, Y., Perretti, I., Leichter, D., Alba, C., Bogley, R., Wellman, N., Ramkrishnan, S., Miller, Z., Miller, B. L., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6874132/v1 · Cited by 1
“To overcome these limitations, Fixel-based analysis (FBA) has been introduced as an anatomically precise framework for studying WM alterations (Raffelt et al. 2015, 2017).”
InfluentialGramegna, L., Zoli, M., Sighinolfi, G., Carrozzi, A., Vornetti, G., Cantoni, E., Guaraldi, F., Asioli, S., Tonon, C., Manners, D., et al. (2025) · NeuroImage: Clinical · doi:10.1016/j.nicl.2025.103827 · Cited by 1
Fixel-based evidence for preserved white matter asymmetry in human situs inversus totalis
Karlsson, E. M., Verhelst, H., & Vingerhoets, G. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02931-7 · Cited by 1
“To this end, FD was computed as the integral of the FOD lobe corresponding to each fixel and FC was computed based on the warps that were generated during registration to the FOD group template (Raffelt et al. 2017).”
InfluentialSigurðardottir, J. N., Tournier, J., Batallé, D., Bonthrone, A. F., Christiaens, D., Hutter, J. M., Cordero‐Grande, L., Chew, A. T. M., Nosarti, C., Hajnal, J. V., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.28.640038 · Cited by 1
“This approach was selected to overcome biases in axonal micro and macrostructure quantification due to crossing and “kissing” fibres within the same voxel (Tournier et al., 2007; Jeurissen et al., 2014; Raffelt et al., 2017a), characteristic of the diencephalic WM.”
Self-citationHabibi, A. T., Alaya, I. B., Tensaouti, F., Baudou, É., Arribarat, G., Pollidoro, L., Péran, P., Chaix, Y., Labidi, S., & Laprie, A. (2025) · Journal of Neuroimaging · doi:10.1111/jon.70007 · Cited by 1
“This metric is derived from registration-generated warps, as explained by Raffelt et al. [30]); -”
Tang, Z., Wang, X.-Y., Cabezas, M., D'Souza, A., Barnett, M., Calamante, F., Cai, W., & Wang, C.-Y. (2024) · ACM Multimedia Asia · doi:10.1145/3696409.3700288 · Cited by 1
“Fibre bundle elements within a voxel (fixel) is a specialized element representing the smallest discrete components of a fibre bundle, which can be derived from FOD for each specific voxel [18, 19].”
Janko, M., Santaniello, S. D., Brockmann, C., Wolf, M. N., Grauhan, N. F., Schöffling, V., Dimova, V., Ponto, K. A., Hoffmann, E. M., Kleinekofort, W., et al. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16490 · Cited by 1
“The FBA framework (Raffelt et al., 2017) provides advanced derivable DWI metrics which fulfil the requirements for quantitative analysis.”
Kang, X.-J., Yoon, B. C., Grossner, E., & Adamson, M. M. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09681-7 · Cited by 1
“…deconvolution (Tournier et al., 2007), a probabilistic streamlines algorithm for fiber tractography (Tournier et al., 2013), fixel-based analysis (FBA) of apparent fiber density and fiber cross-section (Raffelt et al., 2017), and quantitative structural connectivity analysis (Smith et al., 2012).”
Duanmu, X.-J., Wen, J.-Q., Qin, J.-Y., Tan, S.-J., Wu, C.-Q., Yuan, W.-J., Zheng, Q.-S., Zhou, C., Wu, H.-T., Chen, J.-W., et al. (2024) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2024.106559 · Cited by 1
Newman, B., Jacokes, Z., Venkadesh, S., Webb, S., Kleinhans, N., McPartland, J. C., Druzgal, T., Pelphrey, K., & Horn, J. V. V. (2023) · bioRxiv · doi:10.1101/2023.07.23.550166 · Cited by 1
Individual fixel-based white matter abnormalities in the epilepsiesPreprint
Mito, R., Pedersen, M., Pardoe, H., Parker, D., Smith, R., Cameron, J., Scheffer, I., Berkovic, S., Vaughan, D., & Jackson, G. D. (2023) · medRxiv · doi:10.1101/2023.03.16.23287290 · Cited by 1
Self-citationMore than the sum of its parts: disrupted core-periphery of multiplex networks in multiple sclerosis
Pontillo, G., Prados, F., Wink, A., Kanber, B., Bisecco, A., Broeders, T., Brunetti, A., Cagol, A., Calabrese, M., Castellaro, M., et al. (2022) · medRxiv · doi:10.1101/2022.12.17.22283623 · Cited by 1
Newman, B. T., Patrie, J., & Druzgal, T. (2022) · bioRxiv · doi:10.1101/2022.11.23.517748 · Cited by 1
White matter structural bases for predictive tapping synchronization
García-Saldivar, P., de León, C., Concha, L., & Merchant, H. (2022) · bioRxiv · doi:10.1101/2022.09.05.506691 · Cited by 1
“cross-section, and fiber density cross-section; FD, FC, and FDC, respectively) (Raffelt et al., 2017).”
MethodInfluentialAutomated quantification of brain connectivity in Alzheimer's disease using ClusterMetric.
Wang, J.-Q., Wen, C., Li, J., Chen, J., & Feng, Y.-J. (2022) · Neuroscience Letters · doi:10.1016/j.neulet.2022.136724 · Cited by 1
“Expansion or contraction in the perpendicular plane reflected the differences in the number of axons [18].”
InfluentialWhite Matter Microstructure in Obsessive-Compulsive Disorder: More Methods, More Answers.
van den Heuvel, O. A., & Vriend, C. (2021) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.06.007 · Cited by 1
Chou, I. W. Y., Manelis, A., Swartz, H. A., Leung, O., Phillips, M. L., So, S. F., Chu, C. W., Lu, H., Lam, L. C. W., & Mak, A. D. P. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121957 · Cited by 1
Metadata
- 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
- 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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