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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“1D), however, current state-of-the-art SDE methods cannot resolve their differential contributions (De Santis et al., 2014; Henriques et al., 2019, 2015; Jones et al., 2013).”
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“Stricto sensu probabilistic tractography estimates the mathematical confidence of a white matter pathway for a given dataset (Jones 2008, 2010; Jones et al. 2013) and not structural connectivity strength.”
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“Rician noise is characterized by a rectified "noise floor" (Jones and Basser, 2004), and is especially problematic when signal averaging is performed (Eichner et al., 2015; Jones et al., 2013), as in the SMT approach.”
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“…and van Zijl, 2002, Behrens et al., 2003), or to derive measures characterizing the state of the tissue, which can in turn be used to detect changes and differences related to, for example, development, aging, disease, learning, and cognitive performance (for an overview, see Jones et al., 2013).”
InfluentialSelf-citationWhite-matter structure in the right hemisphere predicts Mandarin Chinese learning success
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“…likely reflect a number of different tract-specific properties, including fiber diameter, fiber density, membrane permeability, and myelination (Beaulieu, 2002; Jones, Kn€osche, & Turner, 2012), which can be associated with higher AD and/or lower RD (Song et al., 2002; Takahashi et al., 2002).”
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Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants
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“On the one hand, since DTI measures are highly sensitive (e.g., see reviews (Jones et al., 2013) and (Deprez et al., 2013)), it is likely that the increased FA and decreased diffusivities found in this study are indeed a representation of brain maturation.”
BackgroundWidespread Effects of Alcohol on White Matter Microstructure
Fortier, C. B., Leritz, E. C., Salat, D. H., Lindemer, E., Maksimovskiy, A. L., Shepel, J., Williams, V. J., Venne, J. R., Milberg, W., & McGlinchey, R. E. (2014) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.12568 · Cited by 72
“The relative contribution of specific axonal components (e.g., membrane, cytoskeleton, myelin) and architectural characteristics to tensor-based scalar measures of FA in WM tissue continues to be debated (see Jones et al., 2013).”
BackgroundBracht, T., Schnell, S., Federspiel, A., Razavi, N., Horn, H., Strik, W. K., Wiest, R., Dierks, T., Müller, T., & Walther, S. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.12.004 · Cited by 73
How to produce personality neuroscience research with high statistical power and low additional cost
Mar, R. A., Spreng, R. N., & DeYoung, C. G. (2013) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-013-0202-6 · Cited by 78
van Hemmen, J., Saris, I. M. J., Cohen‐Kettenis, P. T., Veltman, D. J., Pouwels, P. J. W., & Bakker, J. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw156 · Cited by 74
“Even though AD and RD provide more specific information about FA, the exact physiological correlates of these measures are still under investigation (Jones et al. 2013).”
BackgroundNetwork-Based Asymmetry of the Human Auditory System
Mišić, B., Betzel, R. F., Griffa, A., de Reus, M. A., He, Y., Zuo, X., van den Heuvel, M. P., Hagmann, P., Sporns, O., & Zatorre, R. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy101 · Cited by 70
Sex differences in the IQ-white matter microstructure relationship: A DTI study
Dunst, B., Benedek, M., Koschutnig, K., Jauk, E., & Neubauer, A. C. (2014) · Brain and Cognition · doi:10.1016/j.bandc.2014.08.006 · Cited by 73
Pannek, K., Boyd, R. N., Fiori, S., Guzzetta, A., & Rose, S. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.05.018 · Cited by 71
Garcés, P., Pineda‐Pardo, J. A., Canuet, L., Aurtenetxe, S., López, M. E., Marcos, A., Yus, M., Luque, M. L., del Pozo, F., Sancho, M., et al. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.09.004 · Cited by 69
“While FC between the anterior cingulate and the posterior part of the DMN was reduced in MCI, no differences could be seen with SC, although this may be attributed to limitations in the tractography technique (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013).”
Disruption of brain white matter microstructure in women with anorexia nervosa
Via, E., Zalesky, A., Sánchez, I., Forcano, L., Harrison, B. J., Pujol, J., Fernández‐Aranda, F., Menchón, J. M., Soriano‐Mas, C., Cardoner, N., et al. (2014) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.130135 · Cited by 72
Vasung, L., Raguž, M., Kostović, I., & Takahashi, E. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00348 · Cited by 74
“…spatial resolution that makes it difficult to determine more detailed angular information as well as to detect laminar and columnar origins of tractography pathways [for details, see (Jones et al., 2013)], lead to challenges in the evaluation of functional significance of specific fiber pathways.”
BackgroundInfluentialRodríguez‐Cruces, R., & Concha, L. (2015) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2223-4292.2015.02.06 · Cited by 73
“While it is certainly possible that some white matter fascicles undergo plastic changes following surgery, the paradoxical improvement of FA values in regions of known fiber crossings highlights the inability of the tensor model to fully capture information in regions of complex architectural configurations, and the i…”
BackgroundInfluentialWhite matter injury and neurodevelopmental disabilities: A cross-disease (dis)connection
Cainelli, E., Arrigoni, F., & Vedovelli, L. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101845 · Cited by 74
Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project
Henriques, R. N., Correia, M., Marrale, M., Huber, E., Kruper, J., Koudoro, S., Yeatman, J. D., Garyfallidis, E., & Rokem, A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.675433 · Cited by 68
“However, in many important cases, it is known to be systematically biased (Jones et al., 2013; De Santis et al., 2014; Henriques et al., 2015).”
BackgroundDavis, S. W., & Cabeza, R. E. L. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0464-15.2015 · Cited by 70
“We focused on FA values, as the number of tract streamlines within a tract group may be more susceptible to study-specific variations in image acquisition, voxel size, or tractography algorithm (Jones et al., 2013).”
MethodImpact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach
Barrio-Arranz, G., de Luis-Garcı́a, R., Tristán‐Vega, A., Martín‐Fernández, M., & Aja‐Fernández, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137905 · Cited by 70
Andrews, D. S., Lee, J. K., Solomon, M., Rogers, S. J., Amaral, D. G., & Nordahl, C. W. (2019) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-019-9291-z · Cited by 71
“Caution must be taken in interpreting altered anisotropic diffusion properties as directly reflecting increased or decreased connectivity in ASD [78].”
BackgroundMeier, J. M., Perdikis, D., Blickensdörfer, A., Stefanovski, L., Liu, Q., Maith, O., Dinkelbach, H. Ü., Baladron, J., Hamker, F. H., & Ritter, P. (2022) · Experimental Neurology · doi:10.1016/j.expneurol.2022.114111 · Cited by 73
White matter integrity as a marker for cognitive plasticity in aging
de Lange, A. G., Bråthen, A. C. S., Grydeland, H., Sexton, C. E., Johansen‐Berg, H., Andersson, J., Rohani, D. A., Nyberg, L. H., Fjell, A. M., & Walhovd, K. B. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.07.007 · Cited by 77
“The signal may not only be modulated by axon density, membrane permeability, and myelination but also by how axons are laid out within the voxel as the gradient is applied along a given axis (Jones et al., 2013).”
Double diffusion encoding MRI for the clinic
Yang, G., Tian, Q., Leuze, C. W., Wintermark, M., & McNab, J. A. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27043 · Cited by 67
Tullo, S., Patel, R., Devenyi, G. A., Salaciak, A., Bedford, S. A., Farzin, S., Wlodarski, N., Tardif, C. L., Breitner, J. C. S., & Chakravarty, M. M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24771 · Cited by 63
“Thus, DTI-based descriptors of the white matter diffusion properties do not specifically reflect myelin content, and are likely better suited to examine the state of the white matter (Arshad et al., 2016; Jones et al., 2013).”
BackgroundTakeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Hashizume, H., Sassa, Y., Kotozaki, Y., Miyauchi, C. M., Yokoyama, R., Iizuka, K., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.058 · Cited by 71
“At the macro-level, most MRI measures have interrelations (Hugenschmidt et al., 2008; Takeuchi et al., 2014a) and macro-level MRI measures are suggested to reflect diverse micro-level brain processes (e.g., Jones et al., 2013).”
Heterogeneity in Brain Microstructural Development Following Preterm Birth
Dimitrova, R., Pietsch, M., Christiaens, D., Ciarrusta, J., Wolfers, T., Batallé, D., Hughes, E., Hutter, J. M., Cordero‐Grande, L., Price, A. N., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa069 · Cited by 68
“Despite providing sensitivity to developmental and pathological changes in brain microstructure (Mori et al. 2002; Dean et al. 2013), the tensor model lacks specificity regarding the biological substrate driving variation (Jones et al. 2013).”
Bilingualism modulates the white matter structure of language-related pathways
Hämäläinen, S., Sairanen, V., Leminen, A., & Lehtonen, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.081 · Cited by 75
“Factors that can modulate the FA include, but are not limited to, the degree of axonal myelination, axonal packing density, membrane permeability, the organizational coherence of the axons, and measurement technical details (Jones et al., 2013; Sairanen et al., 2017).”
Mapping White Matter Microstructure in the One Month Human Brain
Dean, D., Planalp, E. M., Wooten, W., Adluru, N., Kecskemeti, S., Frye, C. J., Schmidt, C. K., Schmidt, N. L., Styner, M. A., Goldsmith, H. H., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-09915-6 · Cited by 68
A framework for multi-component analysis of diffusion MRI data over the neonatal period
Pietsch, M., Christiaens, D., Hutter, J. M., Cordero‐Grande, L., Price, A. N., Hughes, E., Edwards, A. D., Hajnal, J. V., Counsell, S. J., & Tournier, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.060 · Cited by 68
“Neonatal atlases of subjects aged 38 weeks or older using T 1 or T 2 -weighted images are reported in (Kazemi et al., 2007; Noorizadeh et al., 2013; Ghadimi et al., 2017; Alexander et al., 2017; Shi et al., 2011, 2014; Zhang et al., 2016a, 2016b; Li et al., 2015; Oishi et al., 2011; Gousias et al.,…”
MethodVuoksimaa, E., Panizzon, M. S., Hagler Jr, D. J., Hatton, S. N., Fennema‐Notestine, C., Rinker, D., Eyler, L. T., Franz, C. E., Lyons, M. J., Neale, M. C., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23502 · Cited by 58
White Matter Microstructure and Cognitive Function in Young Women With Polycystic Ovary Syndrome
Rees, A., Udiawar, M., Berlot, R., Jones, D. K., & O'Sullivan, M. J. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-2318 · Cited by 72
“These reports illustrate that there is no simple relationship between diffusion measures and better or worse white matter “integrity” (32).”
BackgroundSelf-citationA population-based atlas of the human pyramidal tract in 410 healthy participants
Chenot, Q., Tzourio‐Mazoyer, N., Rheault, F., Descoteaux, M., Crivello, F., Zago, L., Mellet, E., Jobard, G., Joliot, M., Mazoyer, B. M., et al. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1798-7 · Cited by 67
Detecting microstructural deviations in individuals with deep diffusion MRI tractometry
Chamberland, M., Genc, S., Tax, C. M. W., Shastin, D., Koller, K., Raven, E. P., Cunningham, A. C., Doherty, J. L., van den Bree, M. B. M., Parker, G. D., et al. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00126-8 · Cited by 67
Self-citationDisentangling the effects of Alzheimer’s and small vessel disease on white matter fibre tracts
Dewenter, A., Jacob, M. A., Cai, M., Gesierich, B. G., Hager, P., Kopczak, A., Biel, D., Ewers, M., Tuladhar, A. M., de Leeuw, F., et al. (2022) · Brain · doi:10.1093/brain/awac265 · Cited by 72
“16 Histology studies show that the brain’s white matter architecture is highly complex, with up to 98% of the white matter consisting of multiple fibres with crossing fibre orientations.”
Santis, S. D., Barazany, D., Jones, D. K., & Assaf, Y. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25644 · Cited by 66
“Diffusion tensor MRI (1) allows estimation of biomarkers that reflect largely axonal properties, but are also modulated by the myelin content (2), failing to resolve the intrinsic duality of WM (3,4).”
BackgroundSelf-citationSparse Solution of Fiber Orientation Distribution Function by Diffusion Decomposition
Yeh, F.-C. F., & Tseng, W. I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075747 · Cited by 67
Wright, D. K., Liu, S., van der Poel, C., McDonald, S. J., Brady, R. D., Taylor, L., Yang, L., Gardner, A. J., Ordidge, R. J., O'Brien, T. J., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw254 · Cited by 70
“DWI methods, which are sensitive to axonal injury (Pierpaoli et al. 1996; Le Bihan et al. 2001; Jones et al. 2013), identified progressive CST damage in rats given a TBI.”
BackgroundTymofiyeva, O., Connolly, C. G., Ho, T. C., Sacchet, M. D., Henje, E., LeWinn, K. Z., Xu, D., & Yang, T. T. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.09.013 · Cited by 67
“A large and growing body of research implicates the ventromedial PFC and dorsolateral PFC (DLPFC) (which includes portions of MFG) as key neural substrates underlying adult depression (Koenigs and Grafman, 2009).”
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White matter abnormalities and impaired attention abilities in children born very preterm
Murray, A. L., Thompson, D. K., Pascoe, L., Leemans, A., Inder, T. E., Doyle, L. W., Anderson, J. F. I., & Anderson, P. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.044 · Cited by 70
Modular‐level alterations of structure–function coupling in schizophrenia connectome
Sun, Y., Dai, Z., Li, J., Collinson, S. L., & Sim, K. (2016) · Human Brain Mapping · doi:10.1002/hbm.23501 · Cited by 65
“The choice of the most accurate representation of the underlying neurobiological connectivity [Jones et al., 2013] and the optimal template resolution for patient studies remains an open question.”
BackgroundMethodCoad, B. M., Postans, M., Hodgetts, C. J., Muhlert, N., Graham, K. S., & Lawrence, A. D. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.11.006 · Cited by 75
Motion sickness susceptibility fluctuates through the menstrual cycle.
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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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