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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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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“e statistically independent and small compared to the signal level41 . Applying Equation 6 on Equations 3 and 4 gives σε =√ σ2 12S|| 2 + σ2 12S⊥ 2 b −2 (7) σμFA =σε√ 27 200 (𝑀𝐷)2 √ε(ε+ 3 5 (𝑀𝐷)2) 3 2 (8) where it was assumed that the powder averaging is done over 12 directions for both parallel and orthogonal acquisit…”
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“The FA values were used since broad areas of white matter FA alteration in ADHD and IGD have been repeatedly observed (40-42).”
MethodDamatac, C. G., Chauvin, R. J., Zwiers, M. P., van Rooij, D., Akkermans, S. E. A., Naaijen, J., Hoekstra, P. J., Hartman, C. A., Oosterlaan, J., Franke, B., et al. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.07.015 · Cited by 17
“Finally, we must note that the physiological interpretation of DWI findings remains somewhat speculative (15,58).”
BackgroundExploratory factor analysis with structured residuals for brain network data
van Kesteren, E., & Kievit, R. (2020) · Network Neuroscience · doi:10.1162/netn_a_00162 · Cited by 22
What cortical areas are responsible for blindsight in hemianopic patients?
Sánchez-López, J., Cardobi, N., Pedersini, C. A., Savazzi, S., & Marzi, C. A. (2020) · Cortex · doi:10.1016/j.cortex.2020.08.007 · Cited by 17
White matter but not grey matter predicts change in reading skills after intervention
Partanen, M., Kim, D. H. C., Rauscher, A., Siegel, L. S., & Giaschi, D. (2020) · Dyslexia · doi:10.1002/dys.1668 · Cited by 20
Møller, C. M., Garza‐Villarreal, E. A., Hansen, N. C., Højlund, A., Bærentsen, K. B., Chakravarty, M. M., & Vuust, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-83135-x · Cited by 19
Multiparametric characterization of white matter alterations in early stage Huntington disease
Adanyeguh, I., Branzoli, F., Delorme, C., Méneret, A., Monin, M., Luton, M.-P., Durr, A., Sabidussi, E., & Mochel, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-92532-1 · Cited by 13
Mishima, R., Isobe, M., Noda, T., Tose, K., Kawabata, M., Noma, S., & Murai, T. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111393 · Cited by 17
White matter brain structure predicts language performance and learning success
Sánchez, S. M., SCHMIDT, H. J., Gallardo, G., Anwander, A., Bräuer, J., Friederici, A. D., & Knösche, T. R. (2022) · Human Brain Mapping · doi:10.1002/hbm.26132 · Cited by 17
Self-citationKocher, M., Gleichgerrcht, E. L., Nesland, T., Rorden, C., Fridriksson, J., Spampinato, M. V., & Bonilha, L. (2015) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00016 · Cited by 17
“The streamline count obtained from probabilistic tractography is considered to be a biophysical representation of the underlying axonal bundles and myelin sheath (Jones et al., 2013).”
MethodSemi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves
Renauld, E., Descoteaux, M., Bernier, M., Garyfallidis, E., & Whittingstall, K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156436 · Cited by 15
van Dalen, J. W., Caan, M. W. A., van Gool, W. A., et al. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9631-5 · Cited by 17
“White matter degradation is considered to be captured by lowered fractional anisotropy (FA) and increased mean diffusivity (MD) within the reconstructed projections (Jones et al. 2013).”
BackgroundWhite matter microstructure in boys with persistent depressive disorder
Vilgis, V., Vance, A. L. A., Cunnington, R., & Silk, T. J. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.06.020 · Cited by 17
White matter architecture in major depression with anxious distress symptoms
Heij, J., Penninx, B. W. J. H., van Velzen, L. S., van Tol, M., van der Wee, N. J. A., Veltman, D. J., & Aghajani, M. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109664 · Cited by 17
Li, J., Cui, H., Liu, Z.-P., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55064-3 · Cited by 17
Early microstructure of white matter associated with infant attention
Dowe, K. N., Planalp, E. M., Dean, D., Alexander, A. L., Davidson, R. J., & Goldsmith, H. H. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100815 · Cited by 18
“…7 May 2019; Received in revised form 13 June 2020; Accepted 1 July 2020 Developmental Cognitive Neuroscience 45 (2020) 100815 Jernigan, 2010) that occurs alongside, and in response to, the development of cognitive and behavioral functions, including attention (Kagan and Herschkowitz, 2005).”
BackgroundHerken, H., Başay, B. K., Büber, A., Başay, Ö., Alaçam, H., Öztürk, Ö., Süren, S., Ay, Ö. İ., Ağladıoğlu, K., Erdal, M. E., et al. (2016) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s104450 · Cited by 17
“46,74,75 As van Ewijk et al mentioned, reduced FA values may be an indication of disrupted WM integrity such as demyelination; in another region with crossing fibers, it may represent neuronal branching.”
Zhang, R., Tam, S.-K. T. S., Wong, N. M. L., Wu, J., Tao, J., Chen, L., Lin, K., & Lee, T. M. C. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102916 · Cited by 16
“, 2010), and white matter integrity deficits generally originate from myelin or axonal damage (Jones et al., 2013).”
Brainstem Diffusion Tensor Tractography and Clinical Applications in Pain
Zhang, Y., & Furst, A. J. (2022) · Frontiers in Pain Research · doi:10.3389/fpain.2022.840328 · Cited by 16
Multi-tract multi-symptom relationships in pediatric concussion
Guberman, G. I., Stojanovski, S., Nishat, E., Ptito, A., Bzdok, D., Wheeler, A. L., & Descoteaux, M. (2022) · eLife · doi:10.7554/elife.70450 · Cited by 18
“We used high- quality multishell dMRI data (Jones et al., 2013), as well as modeling approaches, tractography techniques, and microstructural measures robust to crossing fibers, partial volume effects, and connectivity biases (Girard et al.”
MethodRasmussen, J. M., Thompson, P. M., Gyllenhammer, L. E., Lindsay, K. L., O’Connor, T. G., Koletzko, B. V., Entringer, S., Wadhwa, P. D., & Buß, C. (2022) · Obesity · doi:10.1002/oby.23452 · Cited by 15
“Because DTI models reflect diffusion processes that are nonspecific to the cellular processes that underlie signal variation (43), there are plausible alternative explanations of variation in HTH MD.”
BackgroundMcCormick, E. M., & Kievit, R. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1042-22.2023 · Cited by 18
“We used FA given its widespread use in investigations of aging and WM; however, it should be noted that FA is a complex measure and its exact relation to WM health is not fully understood (Jones et al., 2013).”
MethodNelson, M. C., Royer, J., Lu, W. D., Leppert, I. R., Campbell, J. S. W., Schiavi, S., Jin, H., Tavakol, S., de Wael, R. V., Rodríguez‐Cruces, R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00330 · Cited by 17
“…that structural measures derived using a voxel-wise diffusion tensor model are particularly sensitive to the white matter “ architectural paradigm ” (Jones et al., 2013), these results suggest that white matter features related to fiber orientation and geometry actually diverge with increasing…”
BackgroundMethodInfluentialAlcoholism and sexual dimorphism in the middle longitudinal fascicle: a pilot study
Seitz, J., Sawyer, K. S., Papadimitriou, G., Oscar‐Berman, M., Ng, I. S. H., Kubicki, A., Mouradian, P., Ruiz, S. M., Kubicki, M. R., Harris, G. J., et al. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9579-5 · Cited by 16
Diverging Progression of Network Disruption and Atrophy in Alzheimer’s Disease and Semantic Dementia
Andreotti, J., Dierks, T., Wahlund, L., & Grieder, M. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160571 · Cited by 17
“The supplementary material is available in the electronic version of this article: http://dx.doi.org/ 10.3233/JAD-160571.”
BackgroundInfluentialSepehrband, F., O’Brien, K., & Barth, M. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26608 · Cited by 16
The perisylvian language network and language analytical abilities
Kepinska, O., Lakke, E. A. J. F., Dutton, E. M., Caspers, J., & Schiller, N. O. (2017) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2017.07.003 · Cited by 17
Næss‐Schmidt, E. T., Blicher, J. U., Tietze, A., Rask, C. U., Svendsen, S. W., Schröder, A., Thastum, M. M., Tuborgh, A. H., Frederiksen, O.-V., Østergaard, L., et al. (2017) · Brain Injury · doi:10.1080/02699052.2017.1377352 · Cited by 17
“the interpretation of diffusion MRI may not be specific (43), but a study by Pierpaoli et al.”
BackgroundMethodInfluentialHatton, S. N., Panizzon, M. S., Vuoksimaa, E., Hagler, D. J., Fennema‐Notestine, C., Rinker, D. A., Eyler, L. T., Franz, C. E., Lyons, M. J., Neale, M. C., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24002 · Cited by 15
Roddy, D. W., Roman, E., Rooney, S., Andrews, S., Farrell, C., Doolin, K., Levins, K. J., Tozzi, L., Tierney, P., Barry, D., et al. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2018.00039 · Cited by 16
“Whole brain, HARDI (Jones and Leemans, 2011; Jones et al., 2013) was acquired using a spin-echo echo-planar imaging (SE EPI) pulse sequence (TE = 52 ms, TR = 11,260 ms, flip angle alpha = 90◦), FOV 224 mm, 60 axial slices, 2 mm3 isotropic voxels, b-value = 1500 s mm−2 in 61 non-collinear gradient…”
Clinical application of advanced MR methods in children: points to consider
Wilke, M., Groeschel, S., Lorenzen, A., Rona, S., Schuhmann, M. U., Ernemann, U., & Krägeloh‐Mann, I. (2018) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.658 · Cited by 15
“For dMRI, investing in an appropriate data acquisition scheme and using a nontensor based analysis approach both seem most important in the single-case setting in particular.(5,80,81) Similarly, it should be remembered that the final tractography result in a given patient will always be the result of many, and highly…”
MethodAsymmetric fiber trajectory distribution estimation using streamline differential equation
Feng, Y., & He, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101686 · Cited by 15
Regional neurodegeneration correlates with sleep–wake dysfunction after stroke
Gottlieb, E., Egorova, N., Khlif, M. S., Khan, W., Werden, E., Pase, M. P., Howard, M. E., & Brodtmann, A. G. (2020) · SLEEP · doi:10.1093/sleep/zsaa054 · Cited by 15
Understanding Cluster Headache Using Magnetic Resonance Imaging
Ferraro, S., Nigri, A., Demichelis, G., et al. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00535 · Cited by 16
Hyde, C., Fuelscher, I., Sciberras, E., Efron, D., Anderson, V., & Silk, T. (2020) · Progress in Neuro-psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2020.110125 · Cited by 17
Dikmeer, N., Beşiroğlu, L., Biase, M. A. D., Zalesky, A., Kasal, M. I., Bektaş, A. B., Durmaz, E., Polat, S., Gelal, F. M., & Zorlu, N. (2020) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13241 · Cited by 16
Weeda, M. M., Pruis, I. J., Westerveld, A. S. R., Brouwer, I., Bellenberg, B., Barkhof, F., Vrenken, H., Lukas, C., Schneider, R., & Pouwels, P. J. W. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.575611 · Cited by 16
Toescu, S. M., Hales, P. W., Kaden, E., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa377 · Cited by 16
Aberrant auditory system and its developmental implications for autism
Yu, L., & Wang, S. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1863-6 · Cited by 18
Jossinger, S., Kronfeld-Duenias, V., Zislis, A., Amir, O., & Ben‐Shachar, M. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02210-7 · Cited by 16
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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