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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InfluentialBoukadi, M., Marcotte, K., Bedetti, C., Houde, J., Désautels, A., Deslauriers‐Gauthier, S., Chapleau, M., Boré, A., Descoteaux, M., & Brambati, S. M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.01055 · Cited by 34
Applying microstructural models to understand the role of white matter in cognitive development
Huber, E., Henriques, R. N., Owen, J. P., Rokem, A., & Yeatman, J. D. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100624 · Cited by 34
“For example, developmental variation in fractional anisotropy (FA) can reflect differences in axonal packing density, caliber, myelination, or spatial coherence, as well as changes in the number, size, and branching of glial cells (Alexander et al., 2007; Basser and Pierpaoli, 1996; De Santis et al., 2014; Jeurissen e…”
Rogers, J. C., González-Madruga, K., Kohls, G., Baker, R., Clanton, R. L., Pauli, R., Birch, P., Chowdhury, A., Kirchner, M., Andersson, J., et al. (2019) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2019.02.019 · Cited by 36
From ideas to action: The prefrontal–premotor connections that shape motor behavior
Grafton, S. T., & Volz, L. J. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804281-6.00013-6 · Cited by 35
Fibre-specific laterality of white matter in left and right language dominant people
Verhelst, H., Dhollander, T., Gerrits, R., & Vingerhoets, G. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117812 · Cited by 33
Pamir, Z., Bauer, C. M., Bailin, E. S., Bex, P. J., Somers, D. C., & Merabet, L. B. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102821 · Cited by 39
Meinert, S., Nowack, N., Grotegerd, D., Repple, J., Winter, N. R., Abheiden, I., Enneking, V., Lemke, H., Waltemate, L., Stein, F., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01330-8 · Cited by 34
Satzer, D. I., Mahavadi, A. K., Lacy, M. A., Grant, J. E., & Warnke, P. C. (2021) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2021-327730 · Cited by 34
Multi-Fiber Tractography Visualizations for Diffusion MRI Data
Vos, S. B., Viergever, M. A., & Leemans, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081453 · Cited by 34
“With a growing number of diffusion MRI researchers opting for these more advanced diffusion MRI methods over conventional DTI, especially for tractography purposes, it is becoming increasingly important to fully utilize the potential offered by these approaches [22].”
BackgroundDiffusion tensor imaging and white matter abnormalities in patients with disorders of consciousness
Cavaliere, C., Aiello, M., Perri, C. D., Fernández‐Espejo, D., Owen, A. M., & Soddu, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01028 · Cited by 34
“Fiber orientation is obtained using either the diffusion orientation density function (dODF) or the fiber orientation density function (fODF) (Jones et al., 2013).”
BackgroundMethodMoura, L. M., Kempton, M. J., Barker, G. J., Salum, G. A., Gadelha, A., Pan, P. M., Hoexter, M. Q., Aquilla, M. A. G. D., Picon, F. A., Anés, M., et al. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.12.021 · Cited by 33
“Multiple hypotheses have been elaborated in order to explain this thinning: pruning of synapses, axons, and dendrites[1], a consequence of partial volume effects[20] such as myelination[1,20] or enlargement of axonal caliber[8,21].”
Altered White Matter Microstructure in Adolescents and Adults with Bulimia Nervosa
He, X., Stefan, M., Terranova, K., STEINGLASS, J. E., & Marsh, R. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.354 · Cited by 35
“For example, the acquisition of at least 30 gradient directions and isotropic voxels is now recommended (Jones et al, 2013), and newer parameters may reduce the crossing fiber issue (Jbabdi et al, 2010).”
BackgroundAltered structural connectivity is related to attention deficit/hyperactivity subtypes: A DTI study
Ercan, E. S., Süren, S., Bacanlı, A., Yazıcı, K. U., Çallı, C., Ardıç, Ü. A., Jafari, D. A., Kosova, B., Özyurt, O., Aydın, C., et al. (2016) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2016.04.002 · Cited by 34
Saito, Y., Kubicki, M. R., Koerte, I. K., Otsuka, T., Yogesh Rathi, A., Pasternak, O., Sylvain Bouix, A., Eckbo, R., Kikinis, Z., von Hohenberg, C. C., et al. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9685-z · Cited by 33
“…Trace differences as support for the dysmyelination theory of schizophrenia (Kubicki et al. 2007), it is worth noting that the diffusion is not only sensitive to myelin changes, but also depends on the axonal packing, axonal size, extracellular water content and tract geometry (Jones et al. 2013).”
BackgroundSun, Y., Li, J., Suckling, J., & Feng, L. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00361 · Cited by 36
“However, this fiber tracking method may become hindered in correctly tracing fiber streamlines when the directional information at some point along the tract is not univocal (i.e., fiber crossing) (Jones et al., 2013).”
Ajina, S., & Bridge, H. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.01.007 · Cited by 34
White matter network alterations in patients with depersonalization/derealization disorder
Sierk, A., Daniels, J. K., Manthey, A., Kok, J. G., Leemans, A., Gaebler, M., Lamke, J.-P., Kruschwitz, J., & Walter, H. (2018) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.170110 · Cited by 36
Introduction to Diffusion Tensor Imaging
Emsell, L., Hecke, W. V., & Tournier, J. (2015) · Springer eBooks · doi:10.1007/978-1-4939-3118-7_2 · Cited by 34
Teubner-Rhodes, S. E., Vaden, K. I., Cute, S. L., Yeatman, J. D., Dougherty, R. F., & Eckert, M. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4342-15.2016 · Cited by 30
“These measures fundamentally reflect local restrictions on the rate (MD) and direction (FA) of diffusion, so several different combinations of structural features could give rise to identical values of these metrics (Bennett et al., 2010; Jones et al., 2013).”
BackgroundPreserved white matter microstructure in young patients with anorexia nervosa?
Pfuhl, G., King, J. A., Geisler, D., Roschinski, B., Ritschel, F., Seidel, M., Bernardoni, F., Müller, D. K., White, T. J. H., Roessner, V., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23296 · Cited by 33
“…motion of water molecules to estimate diffusion rates in three-dimensional space and is highly sensitive to subtle changes in WM integrity since water molecules show the fastest unidirectional diffusion along highly directional myelinated WM tracts [Jones et al., 2013; Le Bihan, 2003].”
BackgroundCerebello-cerebral connectivity in the developing brain
Pieterman, K. J., Batallé, D., Dudink, J., Tournier, J., Hughes, E., Barnett, M., Benders, M. J. N. L., Edwards, A. D., Hoebeek, F. E., & Counsell, S. J. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1296-8 · Cited by 36
“We are aware of limitations of assessing streamline count (Jones et al. 2013) and so chose to assess our results as a percentage of streamlines in the CTC and CPC pathways.”
MethodAugustijn, M. J. C. M., Deconinck, F. J. A., D’Hondt, E., Acker, L. V., Guchtenaere, A. D., Lenoir, M., & Caeyenberghs, K. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9760-5 · Cited by 33
Ye, Q., & Bai, F. (2018) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000080 · Cited by 33
“81 Functional alterations in fMRI also lack specificity.”
BackgroundWhat Can Computational Models Contribute to Neuroimaging Data Analytics?
Popovych, O. V., Manos, T., Hoffstaedter, F., & Eickhoff, S. B. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00068 · Cited by 33
The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?
Dávid, S., Heemskerk, A. M., Corrivetti, F., de Schotten, M. T., Sarubbo, S., Corsini, F., BENEDICTIS, A. D., Petit, L., Viergever, M. A., Jones, D. K., et al. (2019) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2019.00024 · Cited by 32
“Although dMRI based tractography is a promising technique, there are many well-known pitfalls and limitations in acquiring and analyzing dMRI data (Jones and Cercignani, 2010; Jones et al., 2013b; O’Donnell and Pasternak, 2015).”
MethodSelf-citationSafai, A., Prasad, S., Chougule, T., Saini, J. S., Pal, P. K., & Ingalhalikar, M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24878 · Cited by 34
Schiavi, S., Petracca, M., Battocchio, M., Mendili, M. M. E., Paduri, S., Fleysher, L., Inglese, M., & Daducci, A. (2020) · Human Brain Mapping · doi:10.1002/hbm.24989 · Cited by 34
“Second, by giving the possibility to compare more ‘quantitative’ metrics rather than measures derived from the nonquantitative streamline number (Jones et al., 2013), COMMIT offers the possibility to assess differences in network properties beyond changes driven by density discrepancy.”
BackgroundMethodIndirect structural disconnection-symptom mapping
Sperber, C., Griffis, J. C., & Kasties, V. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02559-x · Cited by 32
Accelerated corpus callosum development in prematurity predicts improved outcome
Thompson, D. K., Lee, K. J., van Bijnen, L., Leemans, A., Pascoe, L., Scratch, S. E., Cheong, J. L. Y., Egan, G. F., Inder, T. E., Doyle, L. W., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22874 · Cited by 33
“Furthermore, future studies may benefit from more sensitive and specific measures of the WM microstructure than tensor-derived diffusion parameters, such as those obtained from composite hindered and restricted model of diffusion, or diffusion kurtosis imaging [Jones et al., 2013].”
BackgroundInfluentialValidation of Network Communicability Metrics for the Analysis of Brain Structural Networks
Andreotti, J., Jann, K., Melie‐García, L., Giezendanner, S., Abela, E., Wiest, R., Dierks, T., & Federspiel, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115503 · Cited by 33
Pediatric traumatic brain injury: Language outcomes and their relationship to the arcuate fasciculus
Liegeois, F. J., Mahony, K. E., Connelly, A., Pigdon, L., Tournier, J., & Morgan, A. T. (2013) · Brain and Language · doi:10.1016/j.bandl.2013.05.003 · Cited by 32
Cazzoli, D., & Chechlacz, M. (2016) · Cortex · doi:10.1016/j.cortex.2016.06.015 · Cited by 33
Sener, S., Hecke, W. V., Feyen, B. F. E., der Steen, G. V., Pullens, P., van den Hauwe, L., Menovsky, T., Parizel, P. M., Jorens, P. G., & Maas, A. I. R. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001325 · Cited by 33
“The exact biological and microstructural underpinnings of DTI are not yet fully understood, and interpretation of DTI analysis is complex.(19) Nevertheless, FA—characterizing the directional nonuniformity of water diffusion—is sensitive to microstructural changes in white matter integrity, and the decline in FA can be…”
BackgroundFiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis
Kumar, K., Desrosiers, C., Kaleem Siddiqi, A., Olivier Colliot, A., & Toews, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.083 · Cited by 37
Shen, K., Goulas, A., Grayson, D. S., Eusebio, J., Gati, J. S., Menon, R. S., McIntosh, A. R., & Everling, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.018 · Cited by 36
Liu, Z.-Q., Shafiei, G., Baillet, S., & Mišić, B. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120276 · Cited by 32
Connectome reorganization associated with temporal lobe pathology and its surgical resection
Larivière, S., Park, B., Royer, J., DeKraker, J., Ngo, A., Sahlas, E., Chen, J., Rodríguez‐Cruces, R., Weng, Y., Frauscher, B., et al. (2024) · Brain · doi:10.1093/brain/awae141 · Cited by 32
Subgenual Cingulum Microstructure Supports Control of Emotional Conflict
Keedwell, P. A., Doidge, A. N., Meyer, M., Lawrence, N., Lawrence, A. D., & Jones, D. K. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw030 · Cited by 34
Self-citationAltered Brain Wiring in Parkinson's Disease: A Structural Connectome-Based Analysis
Shah, A., Lenka, A., Saini, J. S., Wagle, S. A., Naduthota, R. M., Yadav, R., Pal, P. K., & Ingalhalikar, M. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0506 · Cited by 32
“First, our diffusion protocol did not involve high angular resolution and, therefore, may not guarantee a robust diffusion model (Jones et al., 2013).”
Advances in computational and statistical diffusion MRI
O’Donnell, L. J., Daducci, A., Wassermann, D., & Lenglet, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3805 · Cited by 32
“TherearestillmanyopenchallengesindMRI,includingnon-specificityoftraditionalDTI-derivedmeasurements, 124 , 125 uncertain anatomical accuracy of dMRI tractography 126 - 128 and a general lack of ground truth, which is partially addressed using synthetic or physical fiber phantoms.”
BackgroundValue of diffusion kurtosis imaging in assessing low‐grade gliomas
Goryawala, M., Heros, D. O., Komotar, R. J., Sheriff, S., Lavi, E. S., & Maudsley, A. A. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26012 · Cited by 34
Lam, T. K., Binns, M. A., Honjo, K., DAWSON, D. R., Roß, B., Stuss, D. T., Black, S. E., Chen, J. J., Fujioka, T., & Chen, J. J. (2018) · Scientific Reports · doi:10.1038/s41598-018-27541-8 · Cited by 32
Increased Peripheral Interleukin 10 Relate to White Matter Integrity in Schizophrenia
Fu, G., Zhang, W., Dai, J., Liu, J., Li, F. Y., Wu, D., Xiao, Y., Shah, C., Sweeney, J. A., Wu, M. C., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00052 · Cited by 33
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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