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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.
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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]
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“It may also reflect contributions from other pathological processes, such as axonal loss or reduced fiber coherence [40–42].”
Manelis, A., Hu, H., Satz, S., Iyengar, S., & Swartz, H. A. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119574 · Cited by 2
You, Y., Ward, J., Ho, F. K. W., Lyall, D. M., & Hastie, C. E. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf350 · Cited by 2
“25 NODDI provides estimates of neurite density, diffusion of water molecules in the free-water component and neurite orientation dispersion using ICVF, ISOVF and OD, respectively.”
Lou, J., Tian, X., Sun, Y., Xian, J., Lei, W., Wang, M., & Liu, B. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03687-8 · Cited by 5
“Moreover, technical factors such as limited sensitivity of diffusion MRI tractography and residual noise in estimating fi ne-grained connectivity gradients may further attenuate observed effects [76, 77].”
Fabrizio-Stover, E. M., Dias, J. W., McClaskey, C. M., & Harris, K. C. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.10.007 · Cited by 5
“DKI metrics for white matter microstructure (FA) are associated with myelin, but can be influenced by other factors (e.g., fiber dispersion, inflammation) (Beaulieu, 2002; Takahashi et al., 2002; Jones et al., 2013; Jiang et al., 2021).”
Detailed connectomic cluster resource for white matter mapping from ultra-high-field diffusion MRI
Yip, H., He, Y., Xie, Y. S., Zhang, F., & Wu, Y. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121704 · Cited by 2
Dindarian, S., Chaganti, J. R., Rafiei, N., & Faro, S. H. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag004 · Cited by 2
“Motion sensitivity and preprocessing variability remain challenges but can be mitigated through harmonized acquisition and multimodal integration (Assaf and Pasternak, 2008 ; Jones et al. , 2013 ).”
The white matter of Aha! moments
Salvi, C., Luchini, S. A., Pestilli, F., Hanekamp, S., Hope, T., Parrish, T. B., Beeman, M., & Grafman, J. H. (2026) · BMC Psychology · doi:10.1186/s40359-025-03593-0 · 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
“…step in our ability to quantify changes to the brain’s white matter connections ( Basser et al., 1994), known limitations to the technique’s modeling assumptions and biological interpretations ( Jones et al., 2013) have led to major improvements in DWI acquisition and modeling approaches.”
Facca, M., Felice, A. D., & Bertoldo, A. (2026) · Communications Biology · doi:10.1038/s42003-025-09444-3 · Cited by 2
Caiani, G., Arrigoni, E., Pisoni, A., & Fiocchi, S. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1749851 · Cited by 1
“Yet this measure is highly sensitive to acquisition parameters, seeding strategies, and algorithmic biases, and does not reliably reflect underlying axonal density or cross-sectional area (Jones, 2010; Jones et al., 2013).”
Asturias, A., Saez-Calveras, N., Yu, J., Feltrin, F. S., Jha, M. K., Sharma, V. D., Maldjian, J. A., O'Suillebhain, P., & Shah, B. (2026) · American Journal of Roentgenology · doi:10.2214/ajr.25.34168 · Cited by 2
Avram, M., Menegaux, A., Müller, F., Zaczek, H., Korda, A. I., Rogg, H. V., Becker, A. M., Ley, L., Liechti, M. E., & Borgwardt, S. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102791 · Cited by 2
Determining the Brainstem’s Role in Loss of Independent Joint Control in Chronic Stroke
Owen, M. (2017) · doi:10.21985/n2bx8z · Cited by 2
Features of white matter development in very preterm children from infancy to late childhood
Gilchrist, C. P., & Thompson, D. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817986-4.00029-8 · Cited by 2
Xu, X., Jin, Y., Pan, N., Cao, M., Jin, J., Ma, J., Fan, X., Tan, S., Song, X., & Li, X. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.851669 · Cited by 2
Kievit, R., & Simpson-Kent, I. L. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.010 · Cited by 2
Chung, S., Fieremans, E., Rath, J. F., & Lui, Y. W. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823036-7.00026-8 · Cited by 2
Afandiev, R., Zakharova, N., Pogosbekyan, E., Potapov, A. A., & Pronin, I. (2021) · Radiology - Practice · doi:10.52560/2713-0118-2022-1-77-90 · Cited by 2
Wende, T., Hoffmann, A., Fehrenbach, M. K., Sander, C., Kasper, J., Meyer, H., Arlt, F., Scherlach, C., Meixensberger, J., Hoffmann, K., et al. (2022) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2106149 · Cited by 2
Diffusion Measures of Subcortical Structures Using High-Field MRI
Baek, H. (2023) · Brain Sciences · doi:10.3390/brainsci13030391 · Cited by 2
“Use of higher field MRI (7T) has shown that it is possible to obtain higher spatial resolution than what is offered at 3T MRI with acceptable SNR, demonstrating the potential for improvements in diffusion data [5].”
BackgroundCivier, O., Sourty, M., & Calamante, F. (2023) · Scientific Reports · doi:10.1038/s41598-022-17213-z · Cited by 3
A tutorial on discourse analysis in healthy and pathological ageing
Suárez, A. V. (2023) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12919 · Cited by 2
Sone, D. (2023) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s402378 · Cited by 2
“Therefore, understanding the structure and organization of the brain’s white matter network is essential for gaining insights into the brain’s functional architecture.(8,9)”
BackgroundExamining post-concussion white matter change in a pediatric sample
Takagi, M., Ball, G., Babl, F. E., Anderson, N., Chen, J., Clarke, C., Davis, G. A., Hearps, S. J. C., Pascouau, R., Cheng, N., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103486 · Cited by 2
Lan, X., Zhu, X., Bai, W., Liu, H., Wang, H., Dun, W., Guo, C., Zhang, M., & Niu, X. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16154 · Cited by 3
“However, establishing the relationships between DTI-derived parameter changes and specific microstructural alterations remains challenging (Alba-Ferrara & de Erausquin, 2013; Jones et al., 2013).”
BackgroundSantis, S. D., Selim, M. K., & Canals, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-32483-3_27 · Cited by 3
Kanazawa, Y., Ikemitsu, N., Kinjo, Y., Harada, M., Hayashi, H., Taniguchi, Y., Ito, K. R., Bito, Y., Matsumoto, Y., & Haga, A. (2023) · BJR|Open · doi:10.1093/bjro/tzad003 · Cited by 2
The hindbrain and cortico-reticular pathway in adolescent idiopathic scoliosis
Soh, R. C. C., Chen, B., Hartono, S., Lee, M. S., Lee, W., Lim, S. L., Gan, J., Maréchal, B., Chan, L. L., & Lo, Y. L. (2024) · Clinical Radiology · doi:10.1016/j.crad.2024.01.027 · Cited by 2
Quach, M., Ali, I. Y., Shultz, S. R., Casillas-Espinosa, P. M., Hudson, M. R., Jones, N. C., Silva, J. C., Yamakawa, G. R., Braine, E. L., Immonen, R. J., et al. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5142 · Cited by 2
Midrigan-Ciochina, L., Vodacek, K. P., Sewell, C. D., & Corina, D. P. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00144 · Cited by 2
“Lastly, the traditional methods largely based on diffusion tensor imaging (DTI; Basser & Pierpaoli, 2011) suffer from several limitations and have been largely criticized in recent years (e.g., Farquharson & Tournier, 2016; Jones et al., 2013).”
BackgroundMethodAntidepressant-related microstructural changes in the external capsule
Kerpel, A., Davenport, E. M., Proskovec, A. L., Xi, Y., Berry, J. D., Yetkin, Z., Maldjian, J. A., & Yu, F. F. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00891-w · Cited by 2
“Hence, FA is insufficient for characterizing changes in the tissue microstructure (Alexander et al., 2007; Jones et al., 2013; Tournier et al., 2011; Yoo et al., 2007).”
Intrinsic functional and structural network organization in the macaque insula
Charbonneau, J. A., Raven, E. P., Katsumi, Y., Santistevan, A. C., Taylor, C., & Bliss‐Moreau, E. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00261 · Cited by 2
“FA is calculated from the eigenvalues of the diffusion tensor, and representative of fiber architecture, including packing of cellular structures and their distribution and orientation within tissue ( Jones et al., 2013).”
MethodProgressive remodeling of structural networks following surgery for operculo-insular epilepsy
Obaïd, S., Guberman, G. I., St‐Onge, E., Campbell, E., Edde, M., Lamsam, L. A., Bouthillier, A., Weil, A. G., Daducci, A., Rheault, F., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1400601 · Cited by 3
“The CW is not only less influenced by variations in the morphology of bundles and by the selection of tracking criteria (43, 50, 78), but also better reflects the underlying axonal microarchitecture (46, 47).”
BackgroundCurrent challenges and opportunities for tractography
Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., Schilling, K. G., Petit, L., Dell’Acqua, F., Leemans, A., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00037-9 · Cited by 2
Deterministic fiber tractography
Leemans, A., Dell’Acqua, F., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00029-x · Cited by 2
Sutkus, L. T., Sommer, K. M., Li, Z., Sutton, B. P., Donovan, S. M., & Dilger, R. N. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1484497 · Cited by 2
“Specifically, DTI provides an estimate of fiber orientation rather than a precise measurement and parameter values can be highly influenced by crossing fibers (Jeurissen et al., 2013; Jones et al., 2013).”
InfluentialAccelerated diffusion tensor imaging with self-supervision and fine-tuning
Martin, P., Martin, D., Altbach, M. I., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-96459-9 · Cited by 2
Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI
Cicimen, A. G., Tregidgo, H. F. J., Figini, M., Messaritaki, E., McNabb, C. B., Palombo, M., Evans, J., Cercignani, M., Jones, D. K., & Alexander, D. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86920-4_10 · Cited by 2
“This problem is not easily rectifiable on models that only use dMRI scans as input as obtaining a sufficient Signal-to-Noise Ratio (SNR) at high resolution is a challenging task, especially for dMRI models which require several volumes to be acquired [11].”
BackgroundSelf-citationMahemuti, Z., Andica, C., Kamagata, K., Takabayashi, K., Uchida, W., Guo, S., Arai, T., Tabata, H., Naito, H., Tamura, Y., et al. (2025) · Brain and Behavior · doi:10.1002/brb3.70526 · Cited by 2
“Although pathological conditions cause changes in various neuronalstructures,suchasthemyelinsheath,axonaldispersion, density, diameter, and membrane permeability (Jones et al. 2013), the DTI model represents the diffusion behaviors related to these pathologies with a single tensor per voxel.”
Navigation in brain networks is possible using only local measures
Hocking, A., Vogelsang, L., Jiang, F., Le, H., Morgan, K. J., Parsons, N., Poudel, G. R., Shelyag, S., & Ugon, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-04347-z · Cited by 2
Yeung, H. W., Buchanan, C. R., Moodie, J. E., Deary, I. J., Tucker–Drob, E. M., Bastin, M. E., Whalley, H. C., Smith, K., & Cox, S. R. (2025) · Human Brain Mapping · doi:10.1002/hbm.70314 · Cited by 2
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
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