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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“that water diffusion anisotropy is not exclusively defined by myelin integrity but also may be affected by axonal pathology.(45,46) In addition, it was also suggested that MD and RD could be better metrics differentiating white matter condition in control and cuprizonetreated animals than FA.”
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“In WM, for example, very different configurations of axon density, size and myelination may result in the same FA and MD (e.g., Jones et al., 2013).”
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“However, the specificity of using DTI to quantify neuroinflammation is mixed given that several measures (e.g., fractional anisotropy, mean diffusivity) may also be impacted by demyelination and membrane permeability (Jones et al. 2013).”
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“Four sub-segments (11-14) were assigned to the ICRAN.”
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“These measures, however, are inappropriate for capturing the total length of the axonal projections within a brain region (46, 47).”
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“Therefore, the combination of both sensitive and speci fi c dMRI metrics expands our inferences to allow for in-depth phenotyping of white matter microstructure.”
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“…of diffusion is determined by microstructural elements that may hinder diffusion in any direction, such as density, permeability, and integrity of axonal and myelin membranes, activation of glia, microvasculature, and enlargement or tortuosity of extracellular spaces ( Jones et al., 2013).”
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“Notably, the number of reconstructed fibers is not a true measurement of axon count (Jones et al. 2013).”
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“Microstructural order is used to refer to the degree of diffusivity and anisotropy with regards to the tissue structure, as factors such as axon diameter, packing density and membrane permeability affect diffusion metrics without being necessarily a correlate of structural integrity (41).”
MethodMailleux, L., Simón-Martínez, C., Radwan, A., et al. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02070-1 · Cited by 21
Functional hemispherectomy: postoperative motor state and correlation to preoperative DTI
Nelles, M., Urbach, H., Sassen, R., Schöne-Bake, J., Tschampa, H. J., Träber, F., Delev, D., Elger, C. E., Jurcoane, A., & Hattingen, E. (2015) · Neuroradiology · doi:10.1007/s00234-015-1564-y · Cited by 20
“FA reflects the degree of diffusion directionality and is related to axonal density and the degree of myelination [17, 19], whereas the tracked streamlines and corresponding voxels may reflect a component of connectivity between brain regions [19].”
BackgroundKuhn, A. W., Zuckerman, S. L., Solomon, G. S., Casson, I. R., & Viano, D. C. (2016) · Sports Health A Multidisciplinary Approach · doi:10.1177/1941738116674006 · Cited by 22
“FA provides an index of white matter structure and integrity by measuring directionality of water motion.(29,49) FA is sensitive to the acute phases of mTBI and has utility in studying the intermediateand long-term structural effects of RBT.”
BackgroundBolzenius, J. D., Velez, C., Lewis, J., Bigler, E., Wade, B. S. C., Cooper, D. B., Kennedy, J., Reid, M., Ritter, J., York, G., et al. (2018) · The journal of head trauma rehabilitation · doi:10.1097/htr.0000000000000378 · Cited by 20
Network Connectivity Determines Cortical Thinning In Early Parkinson’s Disease ProgressionPreprint
Yau, Y., Zeighami, Y., Baker, T. E., Larcher, K., Vainik, U., Dadar, M., Fonov, V., Hagmann, P., Griffa, A., Mišić, B., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/147611 · Cited by 24
Repeatability Analysis of Global and Local Metrics of Brain Structural Networks
Andreotti, J., Jann, K., Melie‐García, L., Giezendanner, S., Dierks, T., & Federspiel, A. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0202 · Cited by 21
“This characteristic helps determining spurious connections, but it may be misleading in the interpretation of the weights arising from tractography results (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013).”
The impact of long-term abacus training on modular properties of functional brain network
Xie, Y., Weng, J., Wang, C., Xu, T., Peng, X., & Chen, F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.057 · Cited by 20
Topological principles and developmental algorithms might refine diffusion tractography
Innocenti, G. M., Dyrby, T. B., Girard, G., St‐Onge, E., Thiran, J., Daducci, A., & Descoteaux, M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1759-1 · Cited by 20
“…In spite of the several astute algorithms proposed to “clean” diffusion tractography (Sommer et al. 2016; Schurr et al. 2018 and references therein) the method still suffers from a number of drawbacks (Jones and Cercignani 2010; Jones et al. 2012; Daducci et al. 2016; Maier-Hein et al. 2017).”
MethodSuprano, I., Kocevar, G., Stamile, C., Hannoun, S., Fourneret, P., Revol, O., Nusbaum, F., & Sappey‐Marinier, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-76528-x · Cited by 25
Fiber-specific white matter alterations in early-stage tremor-dominant Parkinson’s disease
Andica, C., Kamagata, K., Saito, Y., Uchida, W., Fujita, S., Hagiwara, A., Akashi, T., Wada, A., Ogawa, T., Hatano, T., et al. (2021) · npj Parkinson's Disease · doi:10.1038/s41531-021-00197-4 · Cited by 21
Zhao, H., Cheng, J., Liu, T., Jiang, J., Koch, F. C., Sachdev, P. S., Basser, P. J., Wen, W. X., & Initiative, A. D. N. (2021) · Human Brain Mapping · doi:10.1002/hbm.25628 · Cited by 19
Early white matter connectivity and plasticity in post stroke aphasia recovery
Schevenels, K., Gerrits, R., Lemmens, R., Smedt, B. D., Zink, I., & Vandermosten, M. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103271 · Cited by 22
Li, Q., Zhai, L., Jiang, Q., Qin, W., Li, Q., Yin, X., & Guo, M. (2015) · Neuroscience Letters · doi:10.1016/j.neulet.2015.04.027 · Cited by 21
Tang, J., Zhong, S., Chen, Y., Chen, K., Zhang, J., Gong, G., Fleisher, A., Yong He, A., & Zhang, Z. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2015.67 · Cited by 19
Understanding Neuronal Architecture in Obesity through Analysis of White Matter Connection Strength
Riederer, J., Shott, M. E., DeGuzman, M. C., Pryor, T. L., & Frank, G. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00271 · Cited by 20
“By measuring the diffusion of water molecules within the brain, FA, RD and MD can be calculated, which allows for examination of axonal microstructure and can provide information on axonal ordering, axonal density and degree of myelination (Jones et al., 2013).”
BackgroundInfluentialSpader, H. S., Dean, D., LaFrance, W. C., Jr., Raukar, N. P., Cosgrove, G. R., Eyerly‐Webb, S. A., Ellermeier, A., Correia, S., Deoni, S., & Rogg, J. M. (2018) · Journal of neurosurgery · doi:10.3171/2017.12.jns171597 · Cited by 20
The Connectivity Fingerprint of the Human Frontal Cortex, Subthalamic Nucleus, and Striatum
Isaacs, B. R., Forstmann, B. U., Temel, Y., & Keuken, M. C. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2018.00060 · Cited by 24
Allostatic load and disordered white matter microstructure in overweight adults
Ottino‐González, J., Jurado, M. Á., García‐García, I., Segura, B., Marqués‐Iturria, I., Palacios, M. J. S., Tor, E., Prats-Soteras, X., Caldú, X., Junqué, C., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-34219-8 · Cited by 20
White matter connectivity reductions in the pre‐clinical continuum of psychosis: A connectome study
Oestreich, L., Randeniya, R., & Garrido, M. I. (2018) · Human Brain Mapping · doi:10.1002/hbm.24392 · Cited by 18
Sierk, A., Manthey, A., Brakemeier, E., Walter, H., & Daniels, J. K. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00274-x · Cited by 24
Islam, M. R., Luo, R., Valaris, S., Haley, E. B., Takase, H., Chen, Y. I., Dickerson, B. C., Schön, K., Arai, K., Nguyen, C. T., et al. (2019) · Brain Plasticity · doi:10.3233/bpl-190090 · Cited by 21
“Generally, this approach is an accepted way to investigate/characterize large fiber bundles, although the literature on humans also includes substantial misinterpretation, as since myelination only provides indirect interpretation, and some studies present alternate interpretations [16, 17].”
BackgroundYang, J., Jiang, X., Wei, S., et al. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.05.030 · Cited by 20
Enhancing the estimation of fiber orientation distributions using convolutional neural networks
Lucena, O., Vos, S. B., Vakharia, V. N., Duncan, J. S., Ashkan, K., Sparks, R. E., & Sebastien Ourselin, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104643 · Cited by 23
Joly, O., Rousié, D., Jissendi, P., Rousié, M., & Frankó, E. (2014) · European Spine Journal · doi:10.1007/s00586-014-3435-3 · Cited by 21
Multivariate characterization of white matter heterogeneity in autism spectrum disorder
Dean, D., LANGE, N. D., Travers, B. G., Prigge, M. B. D., Matsunami, N., Kellett, K. A., Freeman, A., Kane, K. L., Adluru, N., Tromp, D., et al. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.01.002 · Cited by 23
“sensitive to underlying changes of white matter, these parameters lack specificity to underlying microstructural changes (Alexander et al., 2011; Beaulieu, 2002; Jones et al., 2013).”
Uncovering a Role for the Dorsal Hippocampal Commissure in Recognition Memory
Postans, M., Parker, G. D., Lundell, H., Ptito, M., Hamandi, K., Gray, W., Aggleton, J. P., Dyrby, T. B., Jones, D. K., & Winter, M. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz143 · Cited by 21
“Individual voxels can therefore contain multiple crossing fiber populations (Jeurissen et al. 2013; Jones et al. 2013).”
BackgroundSelf-citationDiffusion MRI tractography filtering techniques change the topology of structural connectomes
Frigo, M., Deslauriers-Gauthier, S., Parker, D., Aziz Ould Ismail, A., John Kim, J., Verma, R., & Deriche, R. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc29b · Cited by 19
Ammitzbøll, C., Dyrby, T. B., Börnsen, L., Schreiber, K., Ratzer, R., Romme Christensen, J., Iversen, P., Magyari, M., Lundell, H., Jensen, P. E. H., et al. (2023) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2023.104854 · Cited by 20
Guberman, G. I., Houde, J.-C., Ptito, A., Gagnon, I., & Descoteaux, M. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-02002-8 · Cited by 17
The structure of hippocampal circuitry relates to rapid category learning in humans
Schlichting, M. L., Gumus, M., Zhu, T., & Mack, M. L. (2021) · Hippocampus · doi:10.1002/hipo.23382 · Cited by 20
“Such metrics rely on estimates of diffusion tensors which are poor models of complex local fibres: they cannot represent more than one independent orientation nor account for crossing fibres (Tournier et al., 2012; Jeurissen et al., 2013; Jones et al., 2013; Riffert et al., 2014).”
BackgroundBenchmarking functional connectivity by the structure and geometry of the human brain
Liu, Z.-Q., Betzel, R. F., & Mišić, B. (2022) · Network Neuroscience · doi:10.1162/netn_a_00236 · Cited by 20
Haykal, S., Invernizzi, A., Carvalho, J., et al. (2022) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7495 · Cited by 19
“changes in axonal density, myelination, axonal orientation, and membrane permeability.(19) Higher order biophysical models of DWI have been recently developed to provide more specific and”
MethodFiber-specific structural properties relate to reading skills in children and adolescents
Meisler, S. L., & Gabrieli, J. D. E. (2022) · eLife · doi:10.7554/elife.82088 · Cited by 19
Trofimova, O., Latypova, A., Domenicantonio, G. D., Lutti, A., de Lange, A. G., Kliegel, M., Stringhini, S., Marques-Vidal, P. M., Vaucher, J., Vollenweider, P., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04741-1 · Cited by 21
Karayumak, S. C., Lyall, A. E., Biase, M. A. D., Seitz‐Holland, J., Zhang, F., Kelly, S., Elad, D., Pearlson, G. D., Tamminga, C. A., Sweeney, J. A., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02068-1 · Cited by 19
“However, it is important to acknowledge that the dMRI signal is influenced by a variety of underlying biological constructs, ranging from inherent physiological differences such as myelination, axonal packing, and crossing fibers, to more pathological features like edema, demyelination, or axonal degeneration [6, 7].”
BackgroundLin, C., Knoop, L., Frigerio, I., Bol, J. G. J. M., Rozemüller, A. J. M., Berendse, H. W., Pouwels, P. J. W., van de Berg, W. D. J., & Jonkman, L. E. (2023) · Movement Disorders · doi:10.1002/mds.29510 · Cited by 20
“The interpretation of increased FA remains challenging.(85) Whereas a decrease in FA is often attributed to demyelination, loss of axons, and/or interrupted connections, an increased FA at early stages of disease is often attributed to compensatory mechanisms from increased fiber density by axonal hypertrophy.”
BackgroundBrain MRI detects early-stage alterations and disease progression in Friedreich ataxia
Adanyeguh, I. M., Joers, J. M., Deelchand, D. K., Hutter, D., Eberly, L. E., Guo, B., Iltis, I., Bushara, K. O., Henry, P., & Lenglet, C. (2023) · Brain Communications · doi:10.1093/braincomms/fcad196 · Cited by 18
“However, these changes cannot be attributed to a specific biological mechanism due to limitations in the interpretation of DTI metrics, 24 mostly due to the presence of crossing-fibres.”
Ghanbari, Y., Smith, A. R., Schultz, R. T., & Verma, R. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.006 · Cited by 19
“It is debatable which technique is appropriate in more accurately measuring the brain anatomical connectivity in DTI (Descoteaux et al., 2009), and how DTI fiber tracts should be interpreted (Assaf and Pasternak, 2008; Jones et al., 2013).”
BackgroundKaller, C. P., Reisert, M., Katzev, M., Umarova, R. M., Mader, I., Hennig, J., Weiller, C., & Köstering, L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht276 · Cited by 20
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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