The DOI matches its Crossref record; 191 of the 200 references it cites were found in a registry or an index.
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Abstract
Diffusion-weighted MRI (DW-MRI) has been increasingly used in imaging neuroscience over the last decade. An early form of this technique, diffusion tensor imaging (DTI) was rapidly implemented by major MRI scanner companies as a scanner selling point. Due to the ease of use of such implementations, and the plausibility of some of their results, DTI was leapt on by imaging neuroscientists who saw it as a powerful and unique new tool for exploring the structural connectivity of human brain. However, DTI is a rather approximate technique, and its results have frequently been given implausible interpretations that have escaped proper critique and have appeared misleadingly in journals of high reputation. In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“We caution that the behaviour of the DTI indices in the human brain, which is characterized by a high density of crossing fibre that influence DTI measures (Jeurissen et al., 2013), is only partially understood and remains speculative (Jones et al., 2013).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Jones et al., 2013
In the citing paper
“DTI alterations may stem from a variety of tissue changes including altered crossing fibres, permeability of the axonal membrane, inflammation, or oedema (Jones et al., 2013).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Jones et al., 2013
In the citing paper
“However, DTI metrics are not specific to particular cellular or molecular processes.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [16]
In the citing paper
“This limitation is often expressed as the mantra “streamline count is not quantitative.””
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Jones et al., 2013
In the citing paper
“However, because of the difference in resolution between the measured dMRI signal and actual axon dimensions (as described in Section 3.3: “Connection density biases”), many studies have emphasized that NOS does not provide a truly quantitative measure of connection strength (Jones, 2010; Jones et al., 2013; Sotiropoulos and Zalesky, 2019; Yeh et al., 2020).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Jones et al., 2013
In the citing paper
“For example, many factors (e.g., cell death, edema, gliosis, inflammation, change in myelination, increase in connectivity of crossing fibers, increase in extracellular or intracellular water, etc.) may cause changes in FA (Assaf and Pasternak, 2008; Jones et al., 2013; Le Bihan and Johansen-Berg, 2012).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Jones et al., 2013
In the citing paper
“However, these measures reflect a composite of axonal organization, myelin, extracellular water, fiber geometry, and cerebrovascular injury (Beck et al., 2021; Jones et al., 2013).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Jones et al., 2013
In the citing paper
“This has led to considerable debate, since the inception of DTI, over the biological significance of these DTI-derived measures since WM integrity can be affected by many different parameters including axon packing density, axon caliber, myelination, microglia, inflammation, and tissue architecture (Mori and Zhang, 2006; Le Bihan and Johansen-Berg, 2012; Jones et al., 2013).”
From The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains (2017) · cites this paper as Jones et al., 2013
In the citing paper
“higher values corresponding to higher diffusivity, which is often observed in damaged tissues (Jones et al., 2013)”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Jones et al., 2013
In the citing paper
“Changes in FA signal one or many microstructural alterations, such as Wallerian degeneration (Pierpaoli et al., 2001; Thomalla et al., 2004) or decreased neuronal membrane permeability (Jones, KnöSche, & Turner, 2013).”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Jones, KnöSche, & Turner, 2013
In the citing paper
“Moreover, diffusion-weighted imaging has many limitations and sources of error at each step of the analysis pipeline that must be considered when interpreting results236, 237.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [237]
Citations
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970
Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellús̆, V., Wang, J., Kiefer, B., & Haase, A. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10171 · Cited by 5,311
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,628
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912
Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo
Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656
Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999) · Annals of Neurology · doi:10.1002/1531-8249(199902)45:2 · Cited by 3,513
Flagged first, then by citations
9 of the 200 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Le Bihan, 1985, In vivo magnetic resonance imaging of diffusion I, C. R. Acad. Sci. II, 301, 1109
- Tournier, 2009, How many diffusion gradient directions are required for HARDI?, Proc. Int. Soc. Magn. Reson. Med., 17, 358
Cited by
Sorted by citations
Items 1,701–1,800 of 2,327
Zhang, Y., Chen, H., Qi, R., Ke, J., Xu, Q., Zhong, Y., Wu, Y., Guo, Y., Lu, G., & Chen, F. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00755-1 · Cited by 5
Oka, N., Sakoh, M., Hirayama, M., Niiyama, M., & GJEDDE, A. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-29557-1 · Cited by 6
Caporale, A. S., Nezzo, M., Trani, M. G. D., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13050860 · Cited by 5
“For lesions in the PZ, SNR was approximately 60 in the b0-image, and it decreased up to 20, for b = 2500 s/mm2 (Figure 1a), whereas lesions in the CG presented a lower SNR (Figure 1b), still above the tolerance threshold for DWI up to the highest b-value [30].”
BackgroundGaughan, C., Nasa, A., de Román, E. M., Cullinane, D. P., Kelly, L. J., Riaz, S., Brady, C., Browne, C., Sooknarine, V., Mosley, O., et al. (2023) · The Cerebellum · doi:10.1007/s12311-023-01579-5 · Cited by 5
“Whole brain, high angular diffusion imaging (HARDI) [47, 48] was acquired using a spin-echo echo-planar imaging pulse sequence (TE = 52 ms, TR = 11,260 ms, flip angle alpha = 90°), FOV 224 mm, 60 axial slices, 2 mm 3 isotropic voxels, b-value = 1500 s mm −2 in 61 non-collinear gradient directions.”
MethodDaducci, A., Schiavi, S., Christiaens, D., Smith, R. E., & Alexander, D. C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00014-8 · Cited by 7
Graph-Theoretical Analysis of Brain Networks
Sporns, O. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00341-9 · Cited by 5
Ameliorating Spinal Cord Injury in an Animal Model With Mechanical Tissue Resuscitation
Zheng, Z., Morykwas, M. J., Tatter, S. B., Gordon, S., McGee, M. P., Green, H., & Argenta, L. C. (2015) · Neurosurgery · doi:10.1227/neu.0000000000001063 · Cited by 5
Losnegård, A., Lundervold, A., & Hodneland, E. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00013 · Cited by 4
Grigorian, A., McKetton, L., & Schneider, K. A. (2016) · Journal of Visualized Experiments · doi:10.3791/53759 · Cited by 6
What can the topology of white matter structural networks tell us about mild cognitive impairment?
Berlot, R., & O'Sullivan, M. J. (2017) · Future Neurology · doi:10.2217/fnl-2016-0022 · Cited by 5
Magnetic Resonance Imaging in Huntington’s Disease
Gregory, S., Scahill, R. I., Rees, G. E., & Tabrizi, S. J. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7825-0_16 · Cited by 5
“Diffusion Weighted Imaging Diffusion Weighted Imaging (DWI) investigates white matter microstructure within brain tissue by examining the effect of tissue organisation on water movement [14-16].”
BackgroundSamsir, M. S., Zakaria, R., Razak, S. A., Ismail, M. S., Rahim, M. Z. A., Lin, C., Osman, N. M. F. N., Asri, M. A., & Ahmad, A. H. (2018) · Malaysian Journal of Medical Sciences · doi:10.21315/mjms2018.25.5.7 · Cited by 5
Linking spherical mean diffusion weighted signal with intra-axonal volume fraction
Li, H., Chow, H.-M., Chugani, D., & Chugani, H. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.11.006 · Cited by 23
“For this reason, the interpretation of FA as white matter integrity has been questioned [7].”
BackgroundAltered white matter structure in auditory tracts following early monocular enucleation
Wong, N. A., Rafique, S. A., Moro, S. S., Kelly, K. R., & Steeves, J. K. E. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102006 · Cited by 5
“Increased RD can also indicate disorganization due to the growth of new axons (Jones et al., 2013; O'Brien and Olavarria, 1994; Olavarria and Malach, 1987).”
BackgroundInfluentialNotes 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
Crystallinity characterization of white matter in the human brain
Teich, E., Cieslak, M., Giesbrecht, B., Vettel, J., Grafton, S. T., Satterthwaite, T., & Bassett, D. (2020) · New Journal of Physics · doi:10.1088/1367-2630/ac1286 · Cited by 5
Tracking the visual system—from the optic chiasm to primary visual cortex
Puźniak, R. J., Prabhakaran, G. T., Buentjen, L., Schmitt, F. C., & Hoffmann, M. B. (2020) · Clinical Epileptology · doi:10.1007/s10309-020-00384-y · Cited by 5
Cerebral White Matter Mediation of Age-Related Differences in Picture Naming Across Adulthood
Troutman, S. B. W., Madden, D. J., & Diaz, M. T. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00065 · Cited by 5
“Diffusion tensor imaging (DTI) provides information regarding the rate and directionality of molecular water motion at the voxel level (Basser, 1995; Jones et al., 2013; Pierpaoli et al., 1996; A. W. Song et al., 1996; S.-K. Song et al., 2003, 2005).”
BackgroundInfluentialAbnormal white matter structure in hoarding disorder
Mizobe, T., Ikari, K., Tomiyama, H., Murayama, K., Kato, K., Hasuzawa, S., Togao, O., Hiwatashi, A., & Nakao, T. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.01.031 · Cited by 5
Liu, X., Geiger, M., Zhou, C., & Hildebrandt, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-17850-4 · Cited by 5
Blood pressure changes impact corticospinal integrity and downstream gait and balance control
Coon, E. A., Castillo, A. M., Lesnick, T. G., Raghavan, S., Mielke, M. M., Reid, R. I., Windham, B. G., Petersen, R. C., Jack, C. R., Graff‐Radford, J., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.08.011 · Cited by 5
Gagnon, A., Descoteaux, M., Bocti, C., & Takser, L. (2022) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2022.111568 · Cited by 5
Sugimoto, H., Sekiguchi, T., & Otake, M. (2022) · Social Neuroscience · doi:10.1080/17470919.2023.2166580 · Cited by 5
“…of FA changes is limited by the variety of modulating factors, such as myelination, axonal diameter, and number of axons (Beaulieu et al., 2014; Curran et al., 2016; Jones et al., 2013; Roberts et al., 2013; Zatorre et al., 2012), this index is generally used as an indicator of WM health.”
BackgroundTax, C. M. W., Genc, S., MacIver, C., Nilsson, M., Wardle, M., Szczepankiewicz, F., Jones, D. K., & Peall, K. J. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103419 · Cited by 4
“…weightings (b ≤ 1500 s/mm 2 ) whose metrics, such as fractional anisotropy ( FA ) and mean diffusivity ( MD ), provide only limited understanding of the underlying tissue properties, (Jones et al., 2013) making definitive attribution of imaging to specific microstructural compartments challenging.”
BackgroundSelf-citationHermesdorf, M., Wulms, N., Maceski, A. M., Leppert, D., Benkert, P., Wiendl, H., Kühle, J., & Berger, K. (2023) · GeroScience · doi:10.1007/s11357-023-00846-x · Cited by 5
Sensitivity of diffusion MRI to perilesional reactive astrogliosis in focal ischemia
Weber, R. A., Chan, C. H., Nie, X., Maggioncalda, E. C., Valiulis, G., Lauer, A., Hui, E. S. K., Jensen, J. H., & Adkins, D. L. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3717 · Cited by 5
“com/journ understood.(4,5) In response to injury, a key process in brain tissue is reactive astrogliosis, which involves hypertrophy of astrocytes, alterations in astrocyte gene expression and astrocyte proliferation.”
BackgroundAttention Deficit/Hyperactivity Disorder
Cortese, S., & Castellanos, F. X. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398270-4.00004-5 · Cited by 5
White Matter and Social CognitionPreprint
Wang, Y., Metoki, A., Alm, K. H., & Olson, I. R. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/179473 · Cited by 5
“The ability to delineate WM fasciculi oriented in different directions improves as the b-value increases, but higher b-values (e.g. >3000) come at a cost of lower signal-to-noise ratio (Jones et al., 2013).”
BackgroundMethodInfluentialMito, R., Dhollander, T., Xia, Y., Raffelt, D. A., Salvado, O., Churilov, L. P., Rowe, C. C., Brodtmann, A. G., Villemagne, V. L., & Connelly, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/623124 · Cited by 5
“, most white matter voxels) (Jeurissen et al., 2013; Jones et al., 2013), and changes to DTI metrics may be difficult to interpret in brain injury (Budde et al.”
BackgroundMethodInfluentialSparse multiway decomposition for analysis and modeling of diffusion imaging and tractographyPreprint
Caiafa, C. F., & Pestilli, F. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07170 · Cited by 5
“Beyond the great excitement brought about by all these technologies, much work is necessary to develop approaches for evaluation and validation of results (Catani et al, 2012; Jones et al, 2013; Sporns et al, 2005).”
The developing hippocampus: Microstructural evolution through childhood and adolescencePreprint
Karat, B. G., Genc, S., Raven, E. P., Palombo, M., KHAN, A. R., & Jones, D. K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.19.608590 · Cited by 5
Self-citationMaternal Cardiovascular Health in Early Pregnancy and Childhood Brain Structure
Silva, C. C. V., Santos, S., Muetzel, R. L., Vernooij, M. W., van Rijn, B. B., Jaddoe, V. W. V., & Marroun, H. E. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.026133 · Cited by 4
Paitel, E. R., Pettigrew, C. A., Callow, D. D., Moghekar, A., Miller, M. I., Faria, A. V., Oishi, K., Albert, M., & Soldan, A. (2025) · Human Brain Mapping · doi:10.1002/hbm.70398 · Cited by 4
“Generally, higher fractional anisotropy (FA) and lower mean diffusivity (MD) are considered to reflect better WM microstructure integrity (Bennett and Madden 2014; Madden et al. 2012), though the biological interpretability of FA and MD is limited (Jones et al. 2013).”
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
“Reductions in FA are often reported in DTI studies, and interpreted as disruptions to “ white matter integrity ” (Jones et al., 2013; O'Donnell and Pasternak, 2015).”
Soliman, R. K., Tax, C. M. W., Elfetoh, N. A., Zaitoun, M., & Khedr, E. M. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.10.004 · Cited by 4
Su, H., Yan, S., Zhu, H., Liu, Y., Zhang, G., Peng, X., Zhang, S., Li, Y., & Zhu, W. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1334508 · Cited by 4
Deferm, W., Tang, T., Moerkerke, M., Daniëls, N., Steyaert, J. G., Alaerts, K., Ortibus, E., Naulaers, G., & Boets, B. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103580 · Cited by 4
“The reduction in FA along the middle part of the right SLF I and anterior part of the left SLF II likely indicates decreased axonal myelination or less densely packed axons in these regions for the VPT group (Beaulieu, 2002; Curran et al., 2016; Jones et al., 2013).”
BackgroundVisuospatial working memory of abacus trained and untrained children
Ansah, E. E., Nyarko, N. Y., Kumador, D., Owusu-Bempah, J., & Mahama, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325525 · Cited by 4
“Several studies indicate that Arithmetic Mental Calculation (AMC) promotes arithmetic literacy and improves cognitive abilities [40,41].”
Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence
Bogusz, F., Pieciak, T., Afzali, M., & Pizzolato, M. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.05.007 · Cited by 4
Lissaman, R., Lancaster, T. M., Parker, G. D., Graham, K. S., Lawrence, A. D., & Hodgetts, C. J. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100126 · Cited by 4
HIV peripheral neuropathy‐related degeneration of white matter tracts to sensorimotor cortex
Timtim, S., Simmons, A. N., Hays, C. C., Strigo, I. A., Sorg, S. F., Ellis, R. J., & Keltner, J. R. (2022) · Journal of NeuroVirology · doi:10.1007/s13365-022-01051-w · Cited by 4
Advances in magnetic resonance imaging
Wilson, H., de Natale, E. R., & Politis, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821651-4.00009-9 · Cited by 4
Fountain, C., Ghuman, H., Paldino, M. J., Tamber, M. S., Panigrahy, A., & Modo, M. (2023) · Diagnostics · doi:10.3390/diagnostics13091529 · Cited by 4
Ingvaldsen, S. H., Jørgensen, A. P. M., Grøtting, A., Sand, T., Eikenes, L., Haberg, A. K., Indredavik, M. S., Lydersen, S., Austeng, D., Morken, T. S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52836-4 · Cited by 4
Variability in white matter structure relates to hallucination proneness
Johnson, J. F., Schwartze, M., Belyk, M., Pinheiro, A. P., & Kotz, S. A. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103643 · Cited by 4
“Moreover, these detectable changes to diffusion are susceptible to the contribution of multiple physiological variations such as in myelination, axon density, or membrane permeability (Jones et al., 2013).”
BackgroundXu, Q., Chen, Y., Miller, S., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120857 · Cited by 4
“Fiber tractography using diffusion tensor imaging (DTI) has been applied to visualize WM tracts for clinical diagnosis and surgical planning [8-10].”
BackgroundDiff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model
Zhang, L., He, J., Li, W., Liang, D., & Zhu, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3523532 · Cited by 4
“…motion is modeled within a voxel using a three-dimensional ellipsoidal tensor, and by solving for diffusion features, a series of quantitative DTI maps can be obtained, such as Fractional Anisotropy (FA), Mean Diffusivity (MD), Color FA, Axial Diffusivity (AD), and Radial Diffusivity (RD) [3].”
Neuroimaging advances in neurocognitive disorders among HIV-infected individuals
Wang, H., Jiu, X., Wang, Z., & Zhang, Y. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1479183 · Cited by 4
Fan, W., Cheng, J., Li, C., Yang, J., Wu, R., Zou, J., & Wang, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86920-4_6 · Cited by 4
Smullen, D., Bagshaw, A. P., Shalev, L., Tsafrir, S., Kolodny, T., & Mevorach, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-02326-y · Cited by 4
“However, the anatomy which underpins AD and thus the implication of reduced AD is debated (53).”
Gao, Z., Xie, X., Liu, F., Xu, T., Zhang, N., Zhang, X., Li, J., Kong, Y., Lv, D., & Wu, T. (2025) · Journal of Pain Research · doi:10.2147/jpr.s512581 · Cited by 4
“FA values, as a microstructural marker of WM, reflect the degree of organization of nerve fibers within WM. 35 Decreased FA values typically indicate damage or degeneration of nerve fibers within WM tracts, which may lead to reduced efficiency in neural signal transmission and subsequently affect the communication and…”
A multi-compartment model for pathological connectomes
Bosticardo, S., Battocchio, M., Schiavi, S., Zalesky, A., Granziera, C., & Daducci, A. (2025) · Network Neuroscience · doi:10.1162/netn.a.30 · Cited by 4
“…follow the orientation of water molecules estimated by diffusion-weighted magnetic resonance imaging (dMRI) without making assumptions about the underlying axonal density or other biological features of interest (Jones, Knösche, & Turner, 2013; Smith et al., 2022; Sotiropoulos & Zalesky, 2019).”
Wenz, H., Al‐Zghloul, M., Hart, E., et al. (2016) · World Neurosurgery · doi:10.1016/j.wneu.2016.05.085 · Cited by 4
Diffusion Tensor Imaging (DTI) and Tractography
Rüber, T., Elger, C. E., & Weber, B. (2016) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-642-35923-1_22 · Cited by 4
Sommer, S., Kozerke, S., Seifritz, E., & Stämpfli, P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00729 · Cited by 4
“Two excellent reviews considering tractography pitfalls can be found in Jbabdi and Johansen-Berg (2011), Jones (2010), and Jones et al. (2012).”
BackgroundComparison of NODDI and spherical mean signal for measuring intra-neurite volume fraction
Li, H., Nikam, R. M., Kandula, V. V. R., Chow, H. M., & Choudhary, A. K. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.11.021 · Cited by 4
White matter integrity in young medication-naïve bipolar II depressed adults
Mak, A. D. P., Leung, O. N. W., Chou, I. W. Y., Wong, S., Chu, C. W., Yeung, D., So, S. H., Ma, S. L., Lam, L. C. W., Leung, C. M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-81355-9 · Cited by 4
Hsu, C. H., Huang, C., Tsai, S., Chen, L., Li, H., Lo, C. Z., & Lin, C. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0961 · Cited by 4
“Although DTI can be acquired and has been widely used to study brain WM, its capacity to capture the microstructure was limited (Jones et al., 2013).”
Tang-Wright, K., Smith, J. E. T., Bridge, H., Miller, K. L., Dyrby, T. B., Ahmed, B., Reislev, N. L., Sallet, J., Parker, A. J., & Krug, K. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab364 · Cited by 4
“Diffusion-weighted magnetic resonance imaging (dMRI) is a noninvasive, indirect measure of structural brain connectivity based on the most probable diffusion patterns of water moleculeswithfundamentalstatisticalandmethodologicallim-itations (Basser et al. 2000; Jones 2010; Jones et al. 2013).”
InfluentialModular community structure of the face network supports face recognition
Levakov, G., Sporns, O., & Avidan, G. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab458 · Cited by 4
Kawagoe, T. (2022) · Life · doi:10.3390/life12030416 · Cited by 6
“, stressing that the DTI values are just a marker, not a direct measure, of SC [60].”
BackgroundChien, Y., Chen, Y.-J., Tseng, W., Hsu, Y., Wu, C., Tseng, W. I., & Gau, S. S. (2022) · Autism · doi:10.1177/13623613221125620 · Cited by 4
“Notably, decreased FA can be attributable to altered microstructural properties, including myelination, axon density, thickness, membrane permeability, and fiber coherence (Jones et al., 2013).”
BackgroundWard, I. L., Raven, E. P., de la Rosa, S., Jones, D. K., Teufel, C., & Hagen, E. V. D. (2023) · Human Brain Mapping · doi:10.1002/hbm.26211 · Cited by 3
Self-citationFunctional MRI but not white matter fibre dissection identifies language dominance
Busaidi, A. A., Gangemi, E., Wastling, S. J., Berg, A. S. V. D., Mancini, L., & Yousry, T. A. (2023) · European Radiology · doi:10.1007/s00330-023-09838-z · Cited by 4
“We chose volume and HMOA as metrics as they are less affected by tract morphology compared with FA [53, 52–56], and their values are less ambiguous than the number of streamlines and relative fiber density (RFD) [54].”
MethodInfluentialLanders, M. J. F., Rutten, G.-J. M., Baene, W. D., Gehring, K., Sitskoorn, M. M., & Butterbrod, E. (2023) · Cortex · doi:10.1016/j.cortex.2023.05.019 · Cited by 4
Role of deep brain stimulation (DBS) in addiction disorders
Iqbal, J., Mansour, M. N. M., Saboor, H. A., Suyambu, J., Lak, M. A., Zeeshan, M. H., Hafeez, M. H., Arain, M. A., Mehmood, M., Mehmood, D., et al. (2023) · Surgical Neurology International · doi:10.25259/sni_662_2023 · Cited by 4
“[53] e relationship between substance abuse and white matter microstructure varies across different substances:”
BackgroundIdentification and Connectomic Profiling of Concussion Using Bayesian Machine Learning
Hacker, B. J., Imms, P. E., Dharani, A. M., Zhu, J., Chowdhury, N. F., Chaudhari, N. N., & Andrei Irimia, A. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2023.0509 · Cited by 4
“First-order statistics (i.e., mean, standard deviation) of cognitive scores were calculated for injured participants and HCs in SPSS version 28.”
Freire, I., Lopes, T., Afonso, S., & Pereira, D. J. (2024) · Neuroradiology · doi:10.1007/s00234-024-03362-7 · Cited by 6
“This can lead to false negatives, if we do not correctly map existing tracts and, for example, tracking may stop prematurely; or false positives , if we map tracts that do not exist and tracking may switch to an unrelated neighboring tract [7, 15, 16].”
Network Modeling of Epilepsy Using Structural and Functional MRI
Caciagli, L., Bernhardt, B. C., Bernasconi, A., & Bernasconi, N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316257951.009 · Cited by 3
Stutheit-Zhao, E. Y., Vavasour, I. M., Zakeri, M., Borich, M. R., Laule, C., Rauscher, A., Traboulsee, A. L., Li, D. K. B., Boyd, L. A., & MacKay, A. L. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00141 · Cited by 4
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
Baro, V., Sartori, L., Caliri, S. L., et al. (2023) · Brain Sciences · doi:10.3390/brainsci13020234 · Cited by 4
Ontology-based approach for in vivo human connectomics: the medial Brodmann area 6 case study
Moreau, T., & Bernard Gibaud, A. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00009 · Cited by 3
“It is worth saying that tractography results should be interpreted with care (Jones et al., 2013).”
BackgroundDiffusion tensor MR microscopy of tissues with low diffusional anisotropy
Bajd, F., Mattea, C., Stapf, S., et al. (2016) · Radiology and Oncology · doi:10.1515/raon-2016-0018 · Cited by 4
A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation
Ye, C., & Prince, J. L. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.09.003 · Cited by 4
“Instead, the connectivity patterns provide a qualitative evaluation of connectivity (Jones et al., 2013).”
BackgroundMethodInfluentialTesta, C. M., Evangelisti, S., Popeo, M., Zanigni, S., Gramegna, L. L., Fantazzini, P., Tonon, C., Manners, D. N., & Lodi, R. (2016) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-016-0600-1 · Cited by 4
A structural motor network correlates with motor function and not impairment post stroke
Peters, S., Wadden, K. P., Hayward, K. S., Neva, J. L., Auriat, A. M., & Boyd, L. A. (2017) · Neuroscience Letters · doi:10.1016/j.neulet.2017.08.036 · Cited by 4
The robust and independent nature of structural STS asymmetries
Bain, J. S., Filo, S., & Mezer, A. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01952-3 · Cited by 4
Inward versus reward: white matter pathways in extraversion
Leshem, R., Paoletti, P., Piervincenzi, C., Carducci, F., Mallio, C. A., Errante, Y., Quattrocchi, C. C., & Ben‐Soussan, T. D. (2019) · Personality Neuroscience · doi:10.1017/pen.2019.6 · Cited by 4
“The relationship between AD and RD (degree of anisotropy) can be expressed by FA (Jones et al., 2013).”
BackgroundVinçon-Leite, A., Saitovitch, A., Lemaître, H., Rechtman, E., Fillon, L., Grévent, D., Calmon, R., Brunelle, F., Boddaert, N., & Zilbovicius, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-63273-4 · Cited by 4
Magnetic resonance imaging in Huntington's disease
Belogianni, C., Wilson, H., de Natale, E. R., & Politis, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821651-4.00001-4 · Cited by 4
A population-based atlas of the human pyramidal tract in 410 healthy participantsPreprint
Chenot, Q., Tzourio‐Mazoyer, N., Rheault, F., Descoteaux, M., Crivello, F., Zago, L., Mellet, E., Jobard, G., Joliot, M., Mazoyer, B. M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/251108 · Cited by 4
Meissner, T. W., Genç, E., Mädler, B., & Weigelt, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/v9tkz · Cited by 4
Shafiei, G., Bazinet, V., Dadar, M., Manera, A. L., Collins, D. L., Dagher, A., Borroni, B., Sánchez‐Valle, R., Moreno, F., Laforce, R. J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.24.457538 · Cited by 4
Structure-Function Relationships in Brain-Injured Patients: A Scoping ReviewPreprint
Parsons, N., Hughes, M. E., Poudel, G. R., D, J. F. D., & Caeyenberghs, K. (2020) · MetArXiv (OSF Preprints) · doi:10.31222/osf.io/wnhye · Cited by 4
Molecular and connectomic vulnerability shape cross-disorder cortical abnormalitiesPreprint
Hansen, J. Y., Shafiei, G., Vogel, J. W., Smart, K. A., Bearden, C. E., Hoogman, M., Franke, B., van Rooij, D., Buitelaar, J. K., McDonald, C. R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.21.476409 · Cited by 4
“Second, despite the fact that structural connectomes were reconstructed from high resolution diffusion spectrum imaging, diffusion tractography is still prone to false-positives and false-negatives [58, 68, 132].”
BackgroundEthical and legal implications of the methodological crisis in neuroimagingPreprint
Kellmeyer, P. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/2gehg · Cited by 4
Human white matter myelination rate slows down at birthPreprint
Grotheer, M., Bloom, D., Kruper, J., Richie-Halford, A., Zika, S., González, V. A. A., Yeatman, J. D., Kalanit Grill-spector, A., & Rokem, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.02.530800 · Cited by 4
“Instead, they reflect various changes to the white matter including the diameter, spacing, and orientation of fibers along with myelination (29).”
BackgroundMetric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia
Ding, J., Thye, M. D., Edmondson-Stait, A. J., Szaflarski, J. P., & Mirman, D. (2024) · Brain Communications · doi:10.1093/braincomms/fcae313 · Cited by 4
“17,18 However, practical limitations, such as cost and clinical constraints, mean that often DWI data are not collected and only low-resolution structural MRI or CT images are available.”
Lasič, S., Just, N., Nilsson, M., Szczepankiewicz, F., Budde, M. D., & Lundell, H. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.35 · Cited by 3
“One approach to increase specificity is to employ increasingly more sophisticated biophysical models ( Novikov, Kiselev, et al., 2018; Olesen et al., 2022; Palombo et al., 2020), which often rely on fragile assumptions ( Jelescu et al., 2020; Jones et al., 2013; Lampinen et al., 2023).”
Kangarani‐Farahani, M., & Zwicker, J. G. (2025) · Neuroimage Reports · doi:10.1016/j.ynirp.2025.100272 · Cited by 3
“…organization; (2) mean diffusivity (MD), the average amount of water diffusion independent of directionality in each voxel; (3) axial diffusivity (AD), water diffusivity along the tract; and (4) radial diffusivity (RD), water diffusivity perpendicular to the tract (Jones et al., 2013).”
Fiber orientation‐dependent T1 angular features in human white matter at 1.5 T, 3 T, and 7 T
Kauppinen, R. A., Paasonen, E., Thotland, J., Könönen, M., Pisharady, P. K., Lenglet, C., Hakumäki, J. M., Gröhn, O. H. J., & Garwood, M. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70009 · Cited by 3
“35,36 To minimize this issue, the FA cutoff was set at 0.5, above which the crossing fiber issue is less prominent.”
InfluentialYang, W., Niu, J., Xiong, Y., Wang, X., Li, J., Wang, Z., Song, D., & Chen, B. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112455 · Cited by 3
Frontotemporal dementia characterization using neurite orientation dispersion and density imaging
Pisano, S., Basaia, S., Agosta, F., Canu, E., Spinelli, E. G., Cecchetti, G., Ghirelli, A., Sibilla, E., Magnani, G., Caso, F., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf442 · Cited by 6
“13 To overcome some of these issues, a multi-shell diffusion MRI technique called neurite orientation dispersion and density imaging (NODDI) has been introduced.”
Jacobs, B., Olivé, G., Rico, I., Rodríguez‐Fornells, A., & Peñaloza, C. (2026) · Cortex · doi:10.1016/j.cortex.2025.12.005 · Cited by 3
Motor learning induces myelin-related white matter changes revealed by MRI-based in vivo histology
Aye, N., Kaufmann, J., Heinze, H., Düzel, E., Ziegler, G., Taubert, M., & Lehmann, N. (2026) · Communications Biology · doi:10.1038/s42003-026-09712-w · Cited by 3
“This acquisition provides suf fi cient diffusion weighting to probe water pools exhibiting restricted diffusion (e.g., within neurites) while reducing signal contributions from faster diffusing extracellular water compartments 12,15 .”
White matter predictors of cerebellar tDCS treatment effects in aphasia rehabilitation
Johnson, M. A., Keser, Z., Lammers, B., Sydnor, M. J., Murter, J. L., Sadil, P. S., Zhang, Y., Desmond, J. E., Hillis, A. E., Lindquist, M. A., et al. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1659337 · Cited by 3
Karmakar, D. K., Badhe, P., Mhatre, P. P., Shrivastava, S., Sultan, M., Shankar, G., Tekriwal, K., & Moharkar, S. (2023) · Cureus · doi:10.7759/cureus.47811 · Cited by 4
Skandalakis, G. P., Linn, W., Yeh, F.-C. F., Kazim, S. F., Komaitis, S., Neromyliotis, E., Dimopoulos, D., Drosos, E., Hadjipanayis, C. G., Kongkham, P. N., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26771 · Cited by 3
Verschuur, A. S., Tax, C. M. W., Boomsma, M. F., Carlson, H. L., van Wezel‐Meijler, G., King, R., Leemans, A., & Leijser, L. M. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1416672 · Cited by 3
“While experimenting, it is crucial to bear in mind that cleaner tracts, more fi ber crossings, a higher streamline count, or longer streamlines do not inherently equate to more accurate results or “ higher connectivity ” (46).”
Background
Metadata
- Authors
- Jones, D. K., Knösche, T. R., & Turner, R.
- Affiliations
- Cardiff University (GB)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Corresponding author
- Jones, Derek K.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 73
- Pages
- 239-254
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2117987496
- Semantic Scholar
- 18c1f13bf2af5a9efb0126b7bd4683fa1b786532
- Cited by
- 2,402
- References
- 200
- Citations by year
- 2026: 140 · 2025: 134 · 2024: 121 · 2023: 150 · 2022: 183 · 2021: 225 · 2020: 209 · 2019: 203 · 2018: 189 · 2017: 201 · 2016: 213 · 2015: 182 · 2014: 135 · 2013: 105
- FWCI
- 92.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust (096646/Z/11/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
- diffusion-weighted imaging, diffusion tensor imaging, white matter integrity, fiber count, structural connectivity
- MeSH terms
- Brain, Brain Mapping, Cell Count, Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Nerve Fibers, Neural Pathways, Models, Statistical, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Signal-To-Noise Ratio
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
