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.
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“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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“The decrease in FA could reflect demyelination, larger axon diameters, lower packing density, increased membrane permeability and/or axons less coherently organized within the voxel (Jones et al., 2013).”
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“…between monosynaptic and polysynaptic connections (Johansen-Berg and Rushworth, 2009), and (4) should not be used in isolation (i.e., without supporting data or hypotheses) to draw conclusions about the degree of myelination, fiber/axon counts or “white matter integrity” (Jones et al., 2013).”
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“…of the tissue properties known to influence FA (e.g., crossing fibers, myelination, axonal count, and axonal densities) can have counteracting effects on FA, but may still be relevant for promoting efficient and rapid transmission of neural information for reading processes (Jones et al., 2013).”
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“However, equating FA with an index of white matter integrity is an oversimplified interpretation (Jones et al. 2013), and contrasting effects of training on FA are also apparent in previous studies, with some showing higher FA in functionally relevant brain regions in relation to training or to…”
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“These markers are sensitive to myelination, axonal degeneration, local fiber count and orientation [100], as well as changes in extracellular space which could be more indicative of neuroinflammation [44].”
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“Nevertheless, in the core of all the above resources is tractography which has known limitations (Jones, 2010; Jones et al., 2013; Maier-Hein et al., 2017), hence the information generated by regionconnect constitutes only estimates of the average healthy young adult brain connectivity and not the…”
Hoppenbrouwers, M., Vandermosten, M., & Boets, B. (2014) · Research in Autism · doi:10.1016/j.rasd.2013.12.018 · Cited by 42
Jeong, J., Tiwari, V. N., Behen, M. E., Chugani, H. T., & Chugani, D. C. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00110 · Cited by 42
“Thus, the false estimates of PC efferent related streamlines may reflect uncertain change in geometry such as curvature, density, and length, suggesting that SC may be suboptimal to measure the degree of probable connection in PC efferent fibers (Jones et al., 2013).”
A comprehensive tractography study of patients with bipolar disorder and their unaffected siblings
Sprooten, E., Barrett, J., McKay, D., Knowles, E. E. M., Mathias, S. R., Winkler, A. M., Brumbaugh, M. S., Landau, S., Cyr, L., Kochunov, P. V., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23253 · Cited by 39
“The b-matrix of the diffusion gradients was rotated using the same transformation, as recommended by [Jones et al., 2013].”
MethodGil, R., Khabipova, D., Zwiers, M. P., et al. (2016) · NMR in Biomedicine · doi:10.1002/nbm.3616 · Cited by 39
Emerging Magnetic Resonance Imaging Techniques and Analysis Methods in Amyotrophic Lateral Sclerosis
Barritt, A. W., Gabel, M. C., Cercignani, M., & Leigh, P. N. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.01065 · Cited by 39
“Moreover, a reduction in FA signifies changes in both neurite density and orientation dispersion without distinguishing their individual contributions (28, 29).”
BackgroundOishi, H., Takemura, H., Aoki, S., Fujita, I., & Amano, K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804741115 · Cited by 39
Gaudio, S., Carducci, F., Piervincenzi, C., Olivo, G., & Schiöth, H. B. (2019) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.180121 · Cited by 41
“31 Different approaches can be used to analyze DTI data, such as regionofinterest and wholebrain analyses.”
MethodCrystallized and fluid intelligence are predicted by microstructure of specific white‐matter tracts
Góngora, D., Vega‐Hernández, M., Jahanshahi, M., Valdes-Sosa, P. A., Bringas-Vega, M. L., & CHBMP (2019) · Human Brain Mapping · doi:10.1002/hbm.24848 · Cited by 38
“This apparently counterintuitive result may be viewed as we stated in the introduction, due to the fact that FA is a highly complex measure that must be interpreted with caution (Jones et al., 2013).”
BackgroundMethodMartín‐Noguerol, T., Barousse, R., Amrhein, T. J., Royuela‐del‐Val, J., Montesinos, P., & Luna, A. (2020) · Radiographics · doi:10.1148/rg.2020190058 · Cited by 40
Abnormalities in the white matter tracts in patients with Parkinson disease and psychosis
Lenka, A., Ingalhalikar, M., Shah, A., Saini, J. S., Arumugham, S. S., Hegde, S., George, L., Yadav, R., & Pal, P. K. (2020) · Neurology · doi:10.1212/wnl.0000000000009363 · Cited by 39
MRI-based biomarkers of accelerated aging and dementia risk in midlife: how close are we?
Elliott, M. L. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101075 · Cited by 39
Zolkefley, M. K. I., Firwana, Y. M. S., Hatta, H. Z. M., Rowbin, C., Nassir, C. M. N. C. M., Hanafi, M. H., Abdullah, M. S., & Mustapha, M. (2021) · Journal of Physical Therapy Science · doi:10.1589/jpts.33.75 · Cited by 39
Kato, S., Hagiwara, A., Yokoyama, K., Andica, C., Tomizawa, Y., Hoshino, Y., Uchida, W., Nishimura, Y., Fujita, S., Kamagata, K., et al. (2022) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2022.120205 · Cited by 39
Evidence that the frontal pole has a significant role in the pathophysiology of schizophrenia
Snelleksz, M., Rossell, S. L., Gibbons, A., Nithianantharajah, J., & Dean, B. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114850 · Cited by 41
Quentin, R., Chanes, L., Migliaccio, R., Valabrègue, R., & Valero‐Cabré, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.083 · Cited by 39
“Indeed, the FA value depends on a large set of structural and physiological variables, like the number, diameter and myelination level of axons, but is also influenced by the intra-voxel orientational dispersion (Jones et al., 2012).”
Fronto-parietal network supports context-dependent speech comprehension
Smirnov, D., Glerean, E., Lahnakoski, J. M., Salmi, J., Jääskeläinen, I. P., Sams, M. E., & Nummenmaa, L. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.09.007 · Cited by 38
Warbrick, T., Rosenberg, J., & Shah, N. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.075 · Cited by 41
White matter microstructure is associated with language in children born very preterm
Mürner-Lavanchy, I. M., Kelly, C. E., Reidy, N., Doyle, L. W., Lee, K. J., Inder, T., Thompson, D. K., Morgan, A. T., & Anderson, P. J. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.09.020 · Cited by 38
Brain white matter microstructure in deficit and non-deficit subtypes of schizophrenia
Spalletta, G., Rossi, P. D., Piras, F., Iorio, M., Dacquino, C., Scanu, F., Girardi, P., Caltagirone, C., Kirkpatrick, B. W., & Chiapponi, C. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.12.006 · Cited by 38
“However, in contrast to the diffusivity indices, FA proved not to be a very specific index, reflecting the tridimensional architecture of WM fibers or their integrity, so the interpretation of its biological correlates is far from being clear andmay indicate a composite series of events (Jones et al., 2013).”
BackgroundPostans, M., Hodgetts, C. J., Mundy, M. E., Jones, D. K., Lawrence, A. D., & Graham, K. S. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0026-14.2014 · Cited by 38
“Because FA and MD are both affected by multiple axonal properties, including myelination, density, diameter, and configuration, it is not yet possible to attribute differences between our FA/MD findings to a single white-matter subcomponent (Jones et al., 2013).”
BackgroundSelf-citationNæss‐Schmidt, E. T., Blicher, J. U., Eskildsen, S. F., Tietze, A., Hansen, B., Stubbs, P. W., Jespersen, S. N., Østergaard, L., & Nielsen, J. F. (2017) · Brain Injury · doi:10.1080/02699052.2016.1229034 · Cited by 39
“3 mm(3); FOV= 96 × 96mm(2); 38 slices; duration = 3 min 45 s) [20], inversion recovery based CSF suppression was employed to reduce partial volume effects [23].”
MethodMenegaux, A., Meng, C., Neitzel, J., Bäuml, J., Müller, H. J., Bartmann, P., Dieter Wolke, C., Wohlschläger, A. M., Finke, K., & Sorg, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.017 · Cited by 38
Childhood Brain Tumors: a Systematic Review of the Structural Neuroimaging Literature
Ailion, A. S., Hortman, K., & King, T. Z. (2017) · Neuropsychology Review · doi:10.1007/s11065-017-9352-6 · Cited by 38
“Probabilistic methods are vulnerable to factors that are unrelated to actual fiber connectivity, and deterministic methods have been found to be more accurate at reconstructing tract connectivity (Jbabdi and Johansen-Berg 2011; Jones et al. 2013).”
BackgroundMethodStandardization of Small Animal Imaging—Current Status and Future Prospects
Mannheim, J. G., Kara, F., Doorduin, J., Fuchs, K., Reischl, G., Liang, S., Verhoye, M., Gremse, F., Mezzanotte, L., & Huisman, M. C. (2017) · Molecular Imaging and Biology · doi:10.1007/s11307-017-1126-2 · Cited by 45
Cavelti, M., Homan, S., Federspiel, A., Walther, S., Stegmayer, K., Giezendanner, S., Laimböck, K., Dierks, T., Strik, W. K., Horn, H., et al. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.05.011 · Cited by 38
Subtle white matter alterations in schizophrenia identified with a new measure of fiber density
Stämpfli, P., Sommer, S., Manoliu, A., Burrer, A., Schmidt, A., Herdener, M., Seifritz, E., Kaiser, S., & Kirschner, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-40070-2 · Cited by 40
Alm, K. H., & Bakker, A. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181210 · Cited by 38
“Diffusion-weighted MR imaging (DW-MRI) enables the quantification of microstructural properties of white matter pathways, including myelination, axonal diameter, axonal density, and membrane permeability [13].”
Fimbria-Fornix Volume Is Associated With Spatial Memory and Olfactory Identification in Humans
Dahmani, L., Courcot, B., Near, J., Patel, R., Amaral, R. S. C., Chakravarty, M. M., & Bohbot, V. D. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00087 · Cited by 37
Ma, X., Uǧurbil, K., & Wu, X. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116852 · Cited by 40
“…and/or diffusion weightings will lead to decreased SNR, rendering the quantitative analysis of diffusion unreliable (Dietrich et al., 2001; Huang et al., 2004; Jones and Basser, 2004; Lori et al., 2002) and making the interpretation of the quantitative results difficult (Jones et al., 2013).”
Volumetric and Diffusion Abnormalities in Subcortical Nuclei of Older Adults With Cognitive Frailty
Wan, M., Xia, R., Lin, H., Qiu, P., He, J., Ye, Y., Tao, J., Chen, L., & Zheng, G. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00202 · Cited by 38
Leuze, C. W., Goubran, M., Baraković, M., Aswendt, M., Tian, Q., Hsueh, B., Crow, A., Weber, E. M. M., Steinberg, G. K., Zeineh, M., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117692 · Cited by 37
“Unfortunately, the accuracy of the voxel-wise diffusion orientation, as well as the subsequent fiber tracking, strongly depend on several different factors, including the quality and quantity of the data, efficacy of the preprocessing steps, adequacy of the model, ambiguity in intra-voxel crossings, and the choice of…”
BackgroundMertse, N., Denier, N., Walther, S., Breit, S., Grosskurth, E. D., Federspiel, A., Wiest, R., & Bracht, T. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.01.035 · Cited by 38
Bock, A. S., & Fine, I. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00971 · Cited by 41
“HUMAN NEUROSCIENCE REVIEW ARTICLE published: 17 December 2014 doi: 10.3389/fnhum.2014.00971 Anatomical and functional plasticity in early blind individuals and the mixture of experts architecture”
BackgroundMethodInfluentialNeuroimaging-Based Phenotyping of the Autism Spectrum
Bernhardt, B. C., Martino, A. D., Valk, S. L., & Wallace, G. L. (2016) · Current topics in behavioral neurosciences · doi:10.1007/7854_2016_438 · Cited by 37
White matter structural connectivity and episodic memory in early childhood
Ngo, C. T., Alm, K. H., Metoki, A., Hampton, W. H., Riggins, T., Newcombe, N. S., & Olson, I. R. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.11.001 · Cited by 43
“FA depends on several factors including axonal packing, membrane thickness, myelination, and crossing fibers (Beaulieu, 2002; Jones et al., 2013).”
BackgroundInhibitory Control and the Structural Parcelation of the Right Inferior Frontal Gyrus
Bøen, R., Raud, L., & Huster, R. J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.787079 · Cited by 37
Microstructural brain abnormalities in Huntington's disease: A two‐year follow‐up
Odish, O. F. F., Leemans, A., Reijntjes, R. H. A. M., van den Bogaard, S. J. A., Dumas, E. M., Wolterbeek, R., Tax, C. M. W., Kuijf, H. J., Vincken, K. L., van der Grond, J., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22756 · Cited by 37
Whalley, H. C., Nickson, T., Pope, M., Nicol, K., Romaniuk, L., Bastin, M. E., Semple, S. I. K., McIntosh, A. M., & Hall, J. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.01.016 · Cited by 39
“We are unable therefore to interpret fi ndings as providing evidence of a particular cellular pathology (Jones et al., 2013).”
Leading non‐Gaussian corrections for diffusion orientation distribution function
Jensen, J. H., Helpern, J. A., & Tabesh, A. (2013) · NMR in Biomedicine · doi:10.1002/nbm.3053 · Cited by 39
Mailleux, L., Klingels, K., Fiori, S., Simón-Martínez, C., Demaerel, P., Locus, M., Fosseprez, E., Boyd, R. N., Guzzetta, A., Ortibus, E., et al. (2017) · European Journal of Paediatric Neurology · doi:10.1016/j.ejpn.2017.05.006 · Cited by 37
Reliability of White Matter Microstructural Changes in HIV Infection: Meta-Analysis and Confirmation
O’Connor, E. E., Jaillard, A. S., Renard, F., & Zeffiro, T. A. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5229 · Cited by 38
“with related SNR variation, might bias measurements of diffusion measures, particularly in higher magnetic fields.(62,63) Nevertheless, meta-regression of acquisition parameters across studies did”
BackgroundStrain, J. F., Burdo, T. H., Song, S., Sun, P., El-Ghazzawy, O., Nelson, B., Westerhaus, E. T., Baker, L., Vaida, F., & Ances, B. M. (2017) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000001513 · Cited by 37
“This model depicts axonal projections but can be insensitive to subtle patterns of diffusion that reflect underlying pathological changes due to persistent inflammation (Jones et al., 2013).”
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
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