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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Modelling the laminar connectome of the human brain
Shamir, I., Tomer, O., Krupnik, R., & Assaf, Y. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02513-x · Cited by 8
“…for white matter connectivity analysis, use of the number of streamlines as network edges is innately limited by the lack of additional streamline features, such as axon myelination, length, size, and density (Jones et al. 2013; De Santis et al. 2014; Maier-Hein et al. 2017; Calamante 2019).”
BackgroundAlm, K. H., Soldan, A., Pettigrew, C. A., Faria, A. V., Hou, X., Lu, H., Moghekar, A., Mori, S., Albert, M., & Bakker, A. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.951076 · Cited by 10
FiberNeat: Unsupervised White Matter Tract Filtering
Chandio, B. Q., Chattopadhyay, T., Owens‐Walton, C., Reina, J. E. V., Nabulsi, L., Thomopoulos, S. I., Garyfallidis, E., & Thompson, P. M. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9870877 · Cited by 10
“Tractography algorithms often generate streamlines that are false positives or anatomically implausible, such as streamlines that loop, that have sharp curves and angles, that terminate prematurely in white matter, or that connect anatomically implausible regions of the brain [3], [4].”
BackgroundTarumi, T., Fukuie, M., Yamabe, T., Kimura, R., Zhu, D. C., Byun, K. O., Maeda, S., & Sugawara, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1042426 · Cited by 10
“, membrane permeability) properties of axons (Jones et al., 2013).”
Voxel-based analysis of diffusion tensor imaging in patients with mesial temporal lobe epilepsy
Sanches, P., Fujisao, E. K., Braga, A. M. D. S., et al. (2017) · Epilepsy Research · doi:10.1016/j.eplepsyres.2017.03.004 · Cited by 10
“Decreasing axonal density, increasing axonal caliber nd reducing the degree of myelination should all lead to increased D and reduced FA (Jones et al., 2013).”
Charvet, C. J., Palani, A., Kabaria, P., & Takahashi, E. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz054 · Cited by 9
“For instance, diffusion MR tractography has difficulty resolving the precise terminations of pathways within the gray matter (Jones et al. 2013), which is especially challenging when comparing brains differing in size and structure.”
BackgroundMeissner, T. W., Genç, E., Mädler, B., & Weigelt, S. (2021) · Cortex · doi:10.1016/j.cortex.2020.12.016 · Cited by 10
Castellanos, F. X., Cortese, S., & Proal, E. (2013) · Current topics in behavioral neurosciences · doi:10.1007/7854_2013_244 · Cited by 13
“However, as elaborated below, we must resist the temptation to over-interpret diffusion data as directly revealing structural connectivity (Jones et al. 2013).”
BackgroundResultInfluentialYung, A. C., Mattucci, S., Bohnet, B., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.067 · Cited by 10
Collegiate athlete brain data for white matter mapping and network neuroscience
Caron, B. A., Stuck, R., McPherson, B., Bullock, D. N., Kitchell, L. M., Faskowitz, J., Kellar, D., Cheng, H., Newman, S. D., Port, N., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-00823-z · Cited by 11
“To quantify the SNR in the preprocessed, acpc-aligned dMRI data, the workflow provided by Dipy to map SNR in the corpus callosum was used 57,77,78 implemented as brainlife.app.120.”
MethodLanguage and agingPreprint
Peelle, J. E. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/xp29u · Cited by 12
Sustaining wakefulness: Brainstem connectivity in human consciousnessPreprint
Edlow, B. L., Olchanyi, M., Freeman, H. J., Li, J., Maffei, C., Snider, S. B., Zöllei, L., Iglesias, J. E., Augustinack, J. C., Bodien, Y. G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.13.548265 · Cited by 10
Surgent, O., Dean, D. C., Alexander, A. L., Dadalko, O. I., Guerrero-Gonzalez, J., Taylor, D., Skaletski, E., & Travers, B. G. (2021) · Brain Communications · doi:10.1093/braincomms/fcab112 · Cited by 9
Zhao, P., Gu, Y., Feng, W., Xia, X., Tian, X., Du, Y., & Li, X. (2022) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2105129 · Cited by 8
Chary, K., Manninen, E., Claessens, J., Ramírez-Manzanares, A., Gröhn, O. H. J., & Sierra, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-29010-3 · Cited by 10
The right uncinate fasciculus supports verbal short-term memory in aphasia
Olivé, G., Peñaloza, C., Vaquero, L., Laine, M., Martin, N., & Rodríguez‐Fornells, A. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02628-9 · Cited by 9
Serpa, M. H., Doshi, J. J., Joaquim, H. P. G., Vieira, É. L. M., Erus, G., Chaim-Avancini, T. M., Cavallet, M., Guglielmi, L. G., Sallet, P. C., Talib, L. L., et al. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.05.005 · Cited by 10
Lewis, L., Corcoran, M., Cho, K. I. K., Kwak, Y., Hayes, R. A., Larsen, B., & Jalbrzikowski, M. (2023) · Schizophrenia · doi:10.1038/s41537-023-00411-7 · Cited by 9
Grand, Q. L., Tsuchida, A., Koch, A., Imtiaz, M. A., Aziz, N. A., Vigneron, C., Zago, L., Lathrop, M. G., Dubrac, A., Couffinhal, T., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02604-7 · Cited by 16
Interoceptive brain network mechanisms of mindfulness-based training in healthy adolescents
Tymofiyeva, O., Sipes, B. S., Luks, T. L., Hamlat, E. J., Samson, T. E., Hoffmann, T. J., Glidden, D. V., Jakary, A., Li, Y., Ngan, T., et al. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1410319 · Cited by 13
Depersonalization-Derealization Disorder: Etiological Mechanism, Diagnosis and Management
Wilkhoo, H. S., Islam, A. W., Reji, F., Sanghvi, L., Potdar, R., & Solanki, S. (2024) · Discoveries · doi:10.15190/d.2024.09 · Cited by 9
McWhinney, S. R., Tremblay, A., Chevalier, T. M., Lim, V. K., & Newman, A. J. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0451 · Cited by 9
“DTI allows for in vivo evaluation of restrictions on water molecule displacement imposed by microstructure (i.e., cell membranes, myelin sheaths, and microtubules) (Jones et al., 2012), which is commonly interpreted as a proxy for tract integrity (Kraus et al., 2007; Mad-den et al., 2011).”
Microstructural changes in the brain after long‐term post‐concussion symptoms: A randomized trial
Næss‐Schmidt, E. T., Blicher, J. U., Thastum, M. M., Rask, C. U., Svendsen, S. W., Schröder, A., Tuborgh, A. H., Østergaard, L., Sangill, R., Lund, T. E., et al. (2020) · Journal of Neuroscience Research · doi:10.1002/jnr.24773 · Cited by 9
“Inversion recovery-based cerebrospinal fluid suppression was also employed for both mean kurtosis tensor and diffusion tensor imaging to reduce partial volume effects (Jones et al., 2013).”
MethodZhao, G., Zhang, C., Zhan, Y., & He, L. (2022) · Disease Markers · doi:10.1155/2022/2623179 · Cited by 9
RetractedFiber Ball white matter modeling reveals microstructural alterations in healthy brain aging
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Altered myelination in youth born with congenital heart disease
Easson, K., Gilbert, G., Rohlicek, C. V., Saint‐Martin, C., Descoteaux, M., Deoni, S., & Brossard‐Racine, M. (2022) · Human Brain Mapping · doi:10.1002/hbm.25866 · Cited by 9
“Neurite orientation dispersion and density imaging, an advanced multi-compartment diffusion modeling technique, has recently been used to uncover reductions in the neurite density index in infants (Karmacharya et al., 2018) and youth (Easson et al., 2020) with CHD.”
BackgroundMethodNing, Y., Song, N., Zhu, H., Zheng, S., Jia, Y., Yin, D., Li, K., & Jia, H. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.04.127 · Cited by 9
Increased structural connectivity in high schizotypy
Messaritaki, E., Foley, S. F., Barawi, K., Ettinger, U., & Jones, D. K. (2022) · Network Neuroscience · doi:10.1162/netn_a_00279 · Cited by 8
Self-citationTous, C., Jodoin, A., Grabs, D., Houten, E. E. W. V., & Bureau, N. J. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28424 · Cited by 9
Structural connectivity and its association with social connectedness in early adolescence
Driver, C., Moore, L., Mohamed, A. Z., Boyes, A., Sacks, D. D., Mills, L., McLoughlin, L. T., Lagopoulos, J., & Hermens, D. F. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.114259 · Cited by 9
White matter microstructure is associated with the precision of visual working memory
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Brain volumes and white matter diffusion across the adult lifespan in temporal lobe epilepsy
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The role of brain white matter in depression resilience and response to sleep interventions
Bresser, T., Leerssen, J., Hölsken, S., Groote, I. R., Foster‐Dingley, J. C., van den Heuvel, M. P., & Someren, E. J. W. V. (2023) · Brain Communications · doi:10.1093/braincomms/fcad210 · Cited by 8
“However, DTI proxy measures alone are insufficient to draw conclusions about biological changes that drive the measured changes or differences in WM diffusion.(60,66)”
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“Importantly, tractography results are variable across and within tractography methods [107] and inferences about underlying white matter structure should be interpreted with caution [108].”
BackgroundBridging functional and anatomical neural connectivity through cluster synchronization
Baruzzi, V., Lodi, M., Sorrentino, F., & Storace, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-49746-2 · Cited by 9
“This finding is in agreement with other studies that have questioned the reliability of using connectivity matrices describing fiber density as a quantitative indication of connection weights 61–63 .”
Asaridou, S. S., Cler, G. J., Wiedemann, A. M., Krishnan, S., Smith, H. J., Willis, H. E., Healy, M. P., & Watkins, K. E. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00142 · Cited by 11
“FA is sensitive to white matter microstructural properties, including myelin content; however, differences in FA do not necessarily translate to differences in myelination (Jones et al., 2013).”
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Advances in Neuroimaging for Neurodegenerative Disease
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Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes
Liang, X., Yeh, C., Connelly, A., & Calamante, F. (2018) · Brain Topography · doi:10.1007/s10548-018-0661-8 · Cited by 9
“Furthermore, quantification of structural connectomes based on streamlines tractograms is known to be problematic (Jones et al. 2013).”
BackgroundWidespread white matter DTI alterations in mesial temporal sclerosis independent of disease side
Corrêa, D. G., Pereira, M. L., Zimmermann, N., Doring, T. M., Ventura, N., Rêgo, C. C. D. S., de Souza, J. M., Alves‐Leon, S. V., & Gasparetto, E. L. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.08.013 · Cited by 9
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White Matter Pathology in Schizophrenia
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Chen, Y., Zhang, Z.-K., Yong He, A., & Zhou, C. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa060 · Cited by 11
“…due to the need for noninvasiveness and the limitations of dMRI, such as fiber crossing and missed tracing for short fibers, it has been quite challenging to access human brain high-resolution structural connectome thus far (Mori et al. 2002; Dauguet et al. 2007; Jones 2010; Jones et al. 2013).”
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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