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
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 2,301–2,327 of 2,327
Chen, Y., Lü, P., Hu, Q., Zhao, Z., & Xu, B. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1946629
Budd, A. S., Solar, K. G., Gee, M., Nelles, K., Mankasingh, B., Ismail, Z., Smith, E. E., Martyna, M. R., Ba, F., Kalra, S., et al. (2026) · Hippocampus · doi:10.1002/hipo.70127
“With respect to the third and final point, FA is an indirect measure of underlying tissue orientation; further evidence is needed to determine the cause of any changes (Jones et al. 2013).”
Anand, S., Yeh, F.-C. F., & Venkadesh, S. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122228
“In practice, many studies prioritize diffusion tensor–derived scalars such as FA and MD, which are among the most established metrics in the literature (Figley et al., 2021; Jones et al., 2013; Nir et al., 2017).”
Cote, S. E., Ruitenberg, M. F. L., van der Geest, J. N., Frost, N. A., King, J. B., Duff, K. M., & Koppelmans, V. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100405
“Additional aspects of white matter microstructure include myelination, axon consistency, and diameter (Jones et al., 2013).”
Neuroplasticity after traumatic brain injury: mechanisms and clinical implications
O’Connor, M., Sankar, S., Sharma, S., Alghazi, D., Chundru, V., Al-Alem, Z. Y. M. Z., Lukin, R., & Banjo, E. (2026) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-026-04624-4
Huang, L., Cheng, P., Liu, G., Xiang, Y., Hong, Z., & Le, W. (2026) · MedComm · doi:10.1002/mco2.71017
“DTI is affected by axon diameter and density, myelin content, fiber orientation coherence, extracellular water, and other glial cells [ 55 ].”
Kang, J. C. (2026) · Brain and Behavior · doi:10.1002/brb3.71751
“…were accompanied by higher RD with unchanged AD (Table 3 )—a pattern often linked to lower myelination (Song et al. 2002 ), but equally attributable here to greater fiber dispersion or partial-volume contributions in these crossing-fiber-rich pathways (Jeurissen et al. 2013 ; Jones et al. 2013 ).”
Martiadis, V., Scognamiglio, P., Raffone, F., Olivola, M., Iniziato, V., Barlattani, T., Pacitti, F., & Berardis, D. D. (2026) · Brain Sciences · doi:10.3390/brainsci16101020
Tan, R., Lu, W., Yang, A., 杜俊清, Xu, X., Zhao, X., Qiu, M., & Tian, Y. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1966652
“FA reflects the directionality of water diffusion within a voxel and can be affected by changes in axon diameter, axon density, crossing fibers, inflammation, and edema—all of which can change with learning-related activity independent of any change in myelin (Jones et al., 2013).”
Myelin-Weighted Core Network Connectivity Supports Trait-Level Future ThinkingPreprint
Reed, L. S., Messaritaki, E., Hedge, C., Hodgetts, C. J., McNabb, C. B., Jones, D. K., & Lawrence, A. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.29.754335
Self-citationPruckner, P., Mito, R., Vaughan, D. N., Schilling, K. G., Morgan, V. L., Englot, D. J., & Smith, R. E. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1414
D'Mello, R. J., Shorey‐Kendrick, L. E., Karpf, J. A., et al. (2026) · Neurobiology of Disease · doi:10.1016/j.nbd.2026.107640
Multimodal convergence is needed before inferring neuroaxonal injury in migraine
Zhang, Y., & Jiang, J. (2026) · The Journal of Headache and Pain · doi:10.1186/s10194-026-02533-6
Karimi, E., Sabet, A. S., Terplan, M., & Parsaei, M. (2026) · Neurotoxicology and Teratology · doi:10.1016/j.ntt.2026.107723
Long-term Brain Health among Females with New Onset Hypertensive Disorders of Pregnancy
Kaur, A., Fonseca, A. A., Behlouli, H., Rajah, M. N., & Pilote, L. (2026) · AJOG Global Reports · doi:10.1016/j.xagr.2026.100704
Li, Y., Gao, H., Lin, L., & Xiong, M. (2026) · Brain Sciences · doi:10.3390/brainsci16101076
Zhou, X., Chen, Y., Luo, Y., et al. (2026) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2026.112163
Yin, D., Luo, C., Hu, J., & Bi, S.-Z. (2026) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2026.1954740
The small giant actor in the pathogenesis of neurodegenerative diseases: Microtubules
Gündoğdu, A. A., Gücüyener, N., & Özbabalık, D. (2026) · Metabolic Brain Disease · doi:10.1007/s11011-026-02001-9
“It cannot directly measure micro - tubule structure, tubulin post-translational modifications, or microtubule dynamics in living patients (Jones et al. 2013 ).”
Jin, X., Kim, H., Jung, H., CUI, Y., Singh, S. S., Seo, H. G., Kim, S. E., Lee, H. W., Farr, T. D., Wardlaw, J. M., et al. (2026) · medRxiv · doi:10.64898/2026.10.15.26365689
Characterizing post-stroke changes in brain microstructure using the mean apparent propagator model
Vega, R. J., Richerson, W. T., Krucoff, M. O., et al. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.104076
Gambarte, L., Wendt, J., Hippen, R., et al. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122306
Experience-Dependent Neuroglial Plasticity in Autism Spectrum Disorder
Mota, F. S. B., Oliveira, M. V., Pinto‐Junior, V. R., Clemente, J. C. M., Freire, J. V. F. F. E., Oliveira, J. V. M., Leite, Á. J. M., Nascimento, K. S., Cavada, B. S., & Osterne, V. J. S. (2026) · Neuroglia · doi:10.3390/neuroglia7040040
Niu, C., Chen, P., Xiong, C., Jiang, M., Wang, S., Zhang, C., He, H., & Si, J. (2026) · Research Square · doi:10.21203/rs.3.rs-11133556/v1
Brain wiring and neuronal dynamics : advances in MR imaging of focal epilepsy
Besseling, R. M. H. (2014) · Research Publications (Maastricht University) · doi:10.26481/dis.20140404rb
Bradstreet, L. E. (2022) · Digital Archive @ GSU · doi:10.57709/6416202
Teeuw, J. (2014) · Utrecht University Repository (Utrecht University)
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
