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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Atlas of human brain imaging-derived phenotypes and disease risk
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“Despite its strengths, DTI remains constrained by susceptibility to motion artifacts, partial volume effects, and simplified model assumptions that may not fully capture the complexity of neural fiber architecture [91].”
Advances in diffusion MRI in frontotemporal dementia
Ferraro, P. M., & Spinelli, E. G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-23812-3.00009-6
Advances in diffusion MRI in multiple sclerosis
Martínez‐Heras, E., Solana, E., Vivó, F., Calvi, A., Pareto, D., Prados, F., & Llufriú, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-23812-3.00008-4
Almeida, M., Zekelman, L., Lan, Z., Rushmore, J., Karayumak, S. C., Zhang, F., Pieper, S., Makris, N., Golby, A. J., Yogesh Rathi, A., et al. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03148-y
“Similarly, while NoS is widely used, its precise biological basis remains unclear (Jones et al. 2013 ; Zhang et al. 2022 ).”
BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure
Dong, Z., Lin, Y., Fang, J., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36839-3_14
Decoding everyday levels of musical training from subcortical white-matter architecture
Lumaca, M., Pearce, M. T., Keller, P. E., Vuust, P., Brattico, E., Baggio, G., Hat, K., Heggli, O. A., & Sandberg, K. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1325
“…due to tractography reconstruction biases, while fractional anisotropy is difficult to interpret in regions with crossing fibers, which are predominant throughout the brain (~90% of white-matter voxels) ( Jeurissen et al., 2013; Jones et al., 2013; Maier-Hein et al., 2017; Yeh et al., 2016).”
Wen, J., Lan, G., Fu, Y., Jiang, W., Xu, B., Zhang, J., & Initiative, A. D. N. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02139-9
“After FDR correction, the p -value was 0.05 FA [41].”
InfluentialSheng, X.-X., Xu, L., Xu, D., Yuan, D., Xie, W., Ge, J., Zhao, Y., Yang, C., & Zhigang, M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354301
“…compared to punctate and early confluent WMHs. Fiber tracing revealed that some strip-shaped WMHs with inconsistent principal axes and maximum diffusivity directions still had fibers following their principal axes, possibly due to crossed fiber bundles masking the maximum eigenvalue direction [26].”
Li, M., Yuan, X., Zhang, C., Li, R., Wang, B., & Li, K. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111855
Multimodel Diffusion MRI Signatures in Atypical Parkinsonian Disorders
Tian, Y.-Q., Ali, F., Machulda, M., Lowe, V., Josephs, K., & Whitwell, J. (2026) · Human Brain Mapping · doi:10.1002/hbm.70616 · Cited by 1
Wang, Z., Xie, T., Wang, Y., Jia, X., & Wang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1829931
Escudero, A. A., Scerri, K., Sammut, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.30.741684
“Streamline-based reconstructions are susceptible to false-positive pathways and require cautious anatomical interpretation (Jones et al., 2013; O’Donnell and Pasternak, 2015; Maier-Hein et al., 2017).”
Quantifying visual pathway overlap to identify training-related microstructural change in hemianopiaPreprint
Browne, M., Millington-Truby, R. S., Bridge, H., Sahraie, A., & Ajina, S. (2026) · bioRxiv · doi:10.64898/2026.07.28.740991
“Diffusion MRI (dMRI) combined with tractography provides the principal non-invasive method for reconstructing white matter pathways in vivo and assessing their structural properties (Catani et al., 2012; Johansen-Berg, 2010; Jones et al., 2013).”
Diffusion MRI of gray matter microstructural profiles in healthy aging
Merenstein, J. L. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106896
Microstructural Features Linking White Matter and Mathematics in Adolescence
Geeraert, B., Kunimoto, K., Lebel, R. M., & Lebel, C. A. (2026) · ASN NEURO · doi:10.1080/17590914.2026.2714165
“…matter tissue, and are biased by extraneous factors including complex fiber architectures (crossing, fan-ning, etc), partial voluming, and processing considerations (Beaulieu, 2002; H € uppi & Dubois, 2006; Jeurissen et al., 2013; Jones et al., 2013; Lebel & Deoni, 2018; Yoshida et al., 2013).”
A reproducible low-dimensional architecture of human white-matter connectivityPreprint
Tomasi, D. G., Elliott, B., Sarlls, J. E., & Volkow, N. D. (2026) · Research Square · doi:10.21203/rs.3.rs-10637290/v1
Zheng, M., Zhao, H., Liu, Y., et al. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1870430
Podwalski, P., Dawidowski, B., Franczak, Ł., Tyburski, E. M., Misiak, B., Sagan, L. M., & Samochowiec, J. (2026) · Science Progress · doi:10.1177/00368504261484783
“6,49 A schematic overview of the main WM pathways discussed in this review and their potential links with emotional regulation, cognitive control, and interpersonal functioning is presented in Figure 2.”
Contelles-Marco, E., Sanchez-Palomares, P., Gómez-Sánchez, R. M., Chover‐Sierra, E., Navas-Echazarreta, N., Pozo‐Herce, P. D., Czapla, M., Juárez‐Vela, R., Martínez‐Sabater, A., & Martínez-Pascual, R. M. (2026) · Women · doi:10.3390/women6030056
Optimizing Brachial Plexus Diffusion Tensor Imaging
Shim, E., Ham, S., Kim, B. H., Kang, W. Y., Kang, C. H., Ahn, K., Lee, K.-C., Choi, I. Y., Kim, I. S., & Choi, H. (2026) · Investigative Radiology · doi:10.1097/rli.0000000000001284
Zavaliangos‐Petropulu, A., Pfeiffer, P., Crawford, M., Norris, V., Espinoza, R. T., Wu, P., Kruse, J. L., Abbott, C., McClintock, S. M., Sheline, Y. I., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10683316/v1
“The basis of interpreting increases in MD or free water has been partly inherited from neurodegenerative and aging research, in which diffusion metrics are treated as indices of tissue health: lower FA or NDI as reduced "WM integrity," and higher MD or FISO as a marker of pathology[63–66].”
Kovacs, B. Z., Poortman, S. R., Clement, E. A. V. D. P., van Haren, N. E. M., & van Houtum, L. A. E. M. (2026) · JAACAP Open · doi:10.1016/j.jaacop.2026.09.001
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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