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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The behaviour of DTI indices in the human brain, which is characterized by a high density of crossing fibre that influence DTI measures, is only partially understood and remains speculative.
“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
DTI alterations may stem from a variety of tissue changes including altered crossing fibres, permeability of the axonal membrane, inflammation, or oedema.
“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
DTI metrics are not specific to particular cellular or molecular processes.
“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]
Raw streamline count is not a quantitative measure of connection density.
“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
NOS does not provide a truly quantitative measure of connection strength because of the difference in resolution between the measured dMRI signal and actual axon dimensions.
“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
Many factors, such as cell death, edema, gliosis, inflammation, change in myelination, increase in connectivity of crossing fibers, and increase in extracellular or intracellular water, may cause changes in FA.
“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
These measures reflect a composite of axonal organization, myelin, extracellular water, fiber geometry, and cerebrovascular injury.
“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
WM integrity can be affected by many different parameters including axon packing density, axon caliber, myelination, microglia, inflammation, and tissue architecture.
“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
Higher mean diffusivity is often seen in damaged tissues.
“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
FA changes can reflect one or more microstructural alterations, including Wallerian degeneration or reduced neuronal membrane permeability.
“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
Diffusion-weighted imaging has many limitations and sources of error at each step of the analysis pipeline.
“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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“Although subject to many experimental and statistical limitations 23 and interpretational pitfalls 24 , comparative studies have suggested that representations of long-distance connectivity derived from invasive histological methods and noninvasive imaging of neuroanatomical structure are…”
BackgroundThe connectomics of brain disorders
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BackgroundThe hubs of the human connectome are generally implicated in the anatomy of brain disorders
Crossley, N. A., Mechelli, A., Scott, J., Carletti, F., Fox, P. T., McGuire, P., & Bullmore, E. T. (2014) · Brain · doi:10.1093/brain/awu132 · Cited by 1,166 · see citing works
“Although DTI provides the most direct access to the structural network of the living human brain, it also has limitations (Morris et al., 2008; Jones and Cercignani, 2010; Craddock et al., 2013; Jones et al., 2013).”
BackgroundImaging structural and functional brain development in early childhood
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2018.1 · Cited by 1,013 · see citing works
Imaging structural co-variance between human brain regions
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3465 · Cited by 1,064 · see citing works
PANDA: a pipeline toolbox for analyzing brain diffusion images
Cui, Z., Zhong, S., Xu, P., Yong He, A., & Gong, G. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00042 · Cited by 758 · see citing works
“These issues have been well discussed by a few recent articles (Jones and Cercignani, 2010; Jones et al., 2012).”
BackgroundMethodInfluentialContributions and challenges for network models in cognitive neuroscience
Sporns, O. (2014) · Nature Neuroscience · doi:10.1038/nn.3690 · Cited by 743 · see citing works
“More detailed analyses have employed labeling density 18 or, in the case of diffusion imaging, density of tractography streamlines, connection probabilities or indices of myelination status to estimate connections weights, although anatomical interpretations remain unclear 19 .”
MethodSmith, R. E., Tournier, J., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.092 · Cited by 758 · see citing works
A hitchhiker's guide to diffusion tensor imaging
Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00031 · Cited by 893 · see citing works
“As a final take-home message, WM integrity assumptions must always be made with extreme caution (Beaulieu, 2002; Jones et al., 2012).”
BackgroundSIFT: Spherical-deconvolution informed filtering of tractograms
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752 · see citing works
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743 · see citing works
Reworking the language network
Fedorenko, E., & Thompson-Schill, S. L. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.12.006 · Cited by 615 · see citing works
Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?
Franke, K., & Christian Gaser, B. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00789 · Cited by 615 · see citing works
Diffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · see citing works
Effects of aging on functional and structural brain connectivity
Damoiseaux, J. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.077 · Cited by 557 · see citing works
“These diffusion imaging measures have limitations, as pointed out by (Jones et al., 2013).”
BackgroundSpurious group differences due to head motion in a diffusion MRI study
Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N., & Fischl, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.027 · Cited by 508 · see citing works
“Anisotropy and diffusivity measures derived from DW-MRI are sensitive to several confounding factors, including head motion, partial volume, and fiber crossing effects (Jones and Cercignani, 2010;MetzlerBaddeley et al., 2012; Jones et al., 2013).”
BackgroundVeraart, J., Sijbers, J., Sunaert, S. G., Leemans, A., & Jeurissen, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.028 · Cited by 505 · see citing works
“Recently, it has been suggested to use non-linear tensor estimators rather than its linear alternatives (Jones et al., 2012).”
BackgroundDisconnected aging: Cerebral white matter integrity and age-related differences in cognition
Bennett, I. J., & Madden, D. J. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.11.026 · Cited by 492 · see citing works
“It is important to note, however, that tractography and related DTI indices are measures of water diffusion and thus are indirect measures of structural connectivity (Jones et al., 2013).”
BackgroundGradients of structure–function tethering across neocortex
Vázquez-Rodríguez, B., Suárez, L. E., Markello, R. D., Shafiei, G., Paquola, C., Hagmann, P., van den Heuvel, M. P., Bernhardt, B. C., Spreng, R. N., & Mišić, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903403116 · Cited by 458 · see citing works
“To mitigate concerns about inconsistencies in reconstruction of individual participant connectomes (89, 90), as well as the sensitive dependence of network measures on false positives and false negatives (81), we adopted a group-consensus approach (25, 63, 80).”
Ageing and brain white matter structure in 3,513 UK Biobank participants
Cox, S. R., Ritchie, S. J., Tucker-Drob, E. M., Liewald, D. C., Hagenaars, S. P., Davies, G., Wardlaw, J. M., Gale, C. R., Bastin, M. E., & Deary, I. J. (2016) · Nature Communications · doi:10.1038/ncomms13629 · Cited by 487 · see citing works
“The observed mean decline of FA in older age could be affected by, among other factors 5 , decreases in the density and/or an increase in the dispersion orientation of neurites (dendrites and axons); FA is unable to differentiate between these possibilities 26 .”
Donahue, C. J., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T. E., Dyrby, T. B., Coalson, T., Kennedy, H., Knoblauch, K., Van Essen, D. C., et al. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0493-16.2016 · Cited by 380 · see citing works
“Tractography is an indirect method for inferring connectivity and various methodological confounds introduce noise and/or bias (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013; Van Essen et al., 2014).”
MethodImaging human connectomes at the macroscale
Craddock, R. C., Jbabdi, S., Yan, C.-G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., & Milham, M. P. (2013) · Nature Methods · doi:10.1038/nmeth.2482 · Cited by 419 · see citing works
The human connectome: Origins and challenges
Sporns, O. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.023 · Cited by 434 · see citing works
“This point is particularly important in diffusion imaging and tractography since the appropriate characterization of white matter connectivity and microstructure requires careful application of methods and interpretation (Jones et al., 2013).”
BackgroundInfluentialLifespan maturation and degeneration of human brain white matter
Yeatman, J. D., Wandell, B. A., & Mezer, A. (2014) · Nature Communications · doi:10.1038/ncomms5932 · Cited by 426 · see citing works
“Diffusion measurements are open to many biological interpretations 9,25,26 , and there may be multiple, independent, biological processes active across the lifespan that cannot be separated with diffusion measurements alone.”
Networks of anatomical covariance
Evans, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.054 · Cited by 429 · see citing works
Cooperative and competitive spreading dynamics on the human connectome
Mišić, B., Betzel, R. F., Nematzadeh, A., Goñi, J., Griffa, A., Hagmann, P., Flammini, A., Ahn, Y.-Y., & Sporns, O. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.035 · Cited by 342 · see citing works
“…this approach has several known limitations, including the inability to perfectly resolve crossing fibers and a susceptibility to both false positives and false negatives, resulting in diminished anatomical accuracy (Jones et al., 2013; Thomas et al., 2014), which may be reflected in our results.”
ResultRojkova, K., Volle, E., Urbanski, M., Humbert, F., Dell’Acqua, F., & Thiebaut de Schotten, M. (2016) · Brain Structure and Function · doi:10.1007/s00429-015-1001-3 · Cited by 361 · see citing works
Alves, P. N., Foulon, C., Karolis, V., Bzdok, D., Margulies, D. S., Volle, E., & Thiebaut de Schotten, M. (2019) · Communications Biology · doi:10.1038/s42003-019-0611-3 · Cited by 357 · see citing works
The Human Connectome Project and beyond: Initial applications of 300mT/m gradients
McNab, J. A., Edlow, B. L., Witzel, T., Huang, S. Y., Bhat, H., Heberlein, K., Feiweier, T., Liu, K., Keil, B. R., Cohen‐Adad, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.074 · Cited by 345 · see citing works
“Current tractography methods are plagued by significant levels of false positive and false negative findings (Jones et al., 2013).”
BackgroundTowards quantitative connectivity analysis: reducing tractography biases
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.074 · Cited by 324 · see citing works
Associations between vascular risk factors and brain MRI indices in UK Biobank
Cox, S. R., Lyall, D. M., Ritchie, S. J., Bastin, M. E., Harris, M. A., Buchanan, C. R., Fawns‐Ritchie, C., Barbu, M. C., de Nooij, L., Reus, L. M., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz100 · Cited by 329 · see citing works
Q-space trajectory imaging for multidimensional diffusion MRI of the human brain
Westin, C., Knutsson, H. E., Pasternak, O., Szczepankiewicz, F., Özarslan, E., van Westen, D., Mattisson, C., Bogren, M., O’Donnell, L. J., Kubicki, M. R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.039 · Cited by 329 · see citing works
“…such as cell death, edema, inflammation, demyelination, increase in extracellular or intracellular water and partial volume effects, may cause similar changes in FA (Assaf and Pasternak, 2008; Bihan and Johansen-Berg, 2012; Jones et al., 2013; Vos et al., 2012; O'Donnell and Pasternak, 2015).”
Thalamic structures and associated cognitive functions: Relations with age and aging
Fama, R., & Sullivan, E. V. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.03.008 · Cited by 323 · see citing works
Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment
Mito, R., Raffelt, D., Dhollander, T., Vaughan, D. N., Tournier, J.-D., Salvado, O., Brodtmann, A., Rowe, C. C., Villemagne, V. L., & Connelly, A. (2018) · Brain · doi:10.1093/brain/awx355 · Cited by 309 · see citing works
Studying neuroanatomy using MRI
Lerch, J. P., van der Kouwe, A. J. W., Raznahan, A., Paus, T., Johansen‐Berg, H., Miller, K. L., Smith, S. M., Fischl, B. R., & Sotiropoulos, S. N. (2017) · Nature Neuroscience · doi:10.1038/nn.4501 · Cited by 313 · see citing works
Structural connectomics in brain diseases
Griffa, A., Baumann, P., Thiran, J., & Hagmann, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.056 · Cited by 306 · see citing works
“Alterations of such connectivity indicators should be interpreted with caution, as their biological origins are manifold and cannot be reliably distinguished with diffusion MRI (Jones et al., 2012).”
Garyfallidis, E., Côté, M.-A., Rheault, F., Sidhu, J., Hau, J., Petit, L., Fortin, D., Cunanne, S., & Descoteaux, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.015 · Cited by 279 · see citing works
The Corticocortical Structural Connectivity of the Human Insula
Ghaziri, J., Tucholka, A., Girard, G., Houde, J., Boucher, O., Gilbert, G., Descoteaux, M., Lippé, S., Rainville, P., & Nguyen, D. K. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv308 · Cited by 299 · see citing works
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
Needle, A. R., Lepley, A. S., & Grooms, D. R. (2016) · Sports Medicine · doi:10.1007/s40279-016-0666-y · Cited by 300 · see citing works
“Variations of this technique, such as diffusion imaging and tractography, can additionally provide measures of interconnectivity in the cortex by detecting changes in water diffusion across the brain [118, 119].”
MethodNetwork-Level Structure-Function Relationships in Human Neocortex
Mišić, B., Betzel, R. F., de Reus, M. A., van den Heuvel, M. P., Berman, M. G., McIntosh, A. R., & Sporns, O. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw089 · Cited by 277 · see citing works
“However, diffusion-based tractography has also been shown to be susceptible to both false positives and false negatives (Jones et al. 2013; Thomas etal.2014).”
Connectome sensitivity or specificity: which is more important?
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.035 · Cited by 275 · see citing works
“Does a high streamline count indicate a strong connection comprising many axonal projections, or a highly probable yet weak connection comprising few axons (Jones et al, 2013)?”
Building connectomes using diffusion MRI: why, how and but
Sotiropoulos, S. N., & Zalesky, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3752 · Cited by 290 · see citing works
“For this reason, they are error prone and the results can be more difficult to quantify compared with those of corresponding invasive methods.(25) They also offer substantially lower spatial resolution than chemical tracers and microscopic techniques and they cannot estimate the directionality of connections.”
BackgroundIndividual brain structure and modelling predict seizure propagation
Proix, T., Bartoloméi, F., Guye, M., & Jirsa, V. (2017) · Brain · doi:10.1093/brain/awx004 · Cited by 281 · see citing works
“The number of streamlines may not directly reflect the strength of signal transmission between two areas but simply the probability that a streamline between two regions is found by the tractography algorithm (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013).”
BackgroundDiffusion tensor imaging in Parkinson's disease: Review and meta-analysis
Atkinson-Clément, C., Pinto, S., Eusébio, A., & Coulon, O. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.07.011 · Cited by 270 · see citing works
“Jones et al. (2013) recommended to use at least 30 diffusion directions, in order “to obtain robust estimates of tensor-derived properties”; Ni et al. (2006) only observed differences of FA between 6, 21 and 31 directions for regions with low anisotropy (Ni et al., 2006).”
Network connectivity determines cortical thinning in early Parkinson’s disease progression
Yau, Y. H. C., Zeighami, Y., Baker, T. E., Larcher, K. M.-H., Vainik, U., Dadar, M., Fonov, V., Hagmann, P., Griffa, A., Mišić, B., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-02416-0 · Cited by 271 · see citing works
The effects of SIFT on the reproducibility and biological accuracy of the structural connectome
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.004 · Cited by 266 · see citing works
“This is however contrary to a fundamental limitation of streamlines tractography approaches that is well-established in thefield: streamline count is not a validmarker of axonalfibre count (Jones et al., 2013).”
Evaluation and statistical inference for human connectomes
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259 · see citing works
“It is time consuming, the neural tissue is distorted and shrunk during the histological preparation, and the method can be applied to only a few cubic millimeters of white-matter volume of a single pathway 48 .”
MethodAging of Cerebral White Matter
Liu, H., Yang, Y.-Y., Xia, Y., Zhu, W., Leak, R., Wei, Z., Wang, J.-Y., & Hu, X.-M. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.11.006 · Cited by 249 · see citing works
Delineation of early brain development from fetuses to infants with diffusion MRI and beyond
Ouyang, M., Dubois, J., Yu, Q., Mukherjee, P., & Huang, H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.017 · Cited by 250 · see citing works
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255 · see citing works
“In fact, the structural connectivity between different brain regions is usually quantified by counting the number of recovered pathways or averaging some scalar maps along them [14].”
BackgroundAdolescent impatience decreases with increased frontostriatal connectivity
van den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1423095112 · Cited by 253 · see citing works
Figley, C. R., Uddin, M. N., Wong, K., Kornelsen, J., Puig, J., & Figley, T. D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.799576 · Cited by 253 · see citing works
“Indeed, the complex interactions between these factors and the resulting inability to ascribe apparent diffusion changes to any particular cause, is why (Jones et al., 2013) and others have advocated for researchers to exercise caution when interpreting DTI data and avoid drawing conclusions about microstructural or t…”
BackgroundAssociation of Amyloid Pathology With Myelin Alteration in Preclinical Alzheimer Disease
Dean, D. C., Hurley, S. A., Kecskemeti, S. R., O’Grady, J. P., Canda, C., Davenport-Sis, N. J., Carlsson, C. M., Zetterberg, H., Blennow, K., Asthana, S., et al. (2017) · JAMA Neurology · doi:10.1001/jamaneurol.2016.3232 · Cited by 252 · see citing works
The development of brain network hubs
Oldham, S., & Fornito, A. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.12.005 · Cited by 244 · see citing works
Connectometry: A statistical approach harnessing the analytical potential of the local connectome
Yeh, F.-C. F., Badre, D., & Verstynen, T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.053 · Cited by 249 · see citing works
“Mapping end-to-end connections has been a challenge due to methodological limitations (Jones et al., 2013; Reveley et al., 2015; Thomas et al., 2014), and a reliable and reproducible approach is still under active research.”
BackgroundMessé, A., Rudrauf, D., Benali, H., & Marrelec, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003530 · Cited by 215 · see citing works
Essayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.011 · Cited by 249 · see citing works
“Several indepth review articles are available (Assaf and Pasternak, 2008; Jones et al., 2013; O'Donnell and Westin, 2011), including recent neurosurgery-specific reviews and commentaries (Bi and Chiocca, 2014; Nimsky et al., 2016a), so here we present a brief overview of the most relevant…”
BackgroundFast track to the neocortex: A memory engram in the posterior parietal cortex
Brodt, S., Gais, S., Beck, J., Erb, M., Scheffler, K., & Schönauer, M. (2018) · Science · doi:10.1126/science.aau2528 · Cited by 224 · see citing works
Lasič, S., Szczepankiewicz, F., Eriksson, S., Nilsson, M., & Topgaard, D. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00011 · Cited by 242 · see citing works
“Because fiber orientation dispersion and several other tissue properties are inherently entangled in the echo attenuation of the PGSE experiment, changes in FA are not specific to any particular tissue characteristics [16].”
BackgroundMethodConnectomics: A new paradigm for understanding brain disease
Fornito, A., & Bullmore, E. T. (2014) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2014.02.011 · Cited by 234 · see citing works
Traumatic and nontraumatic spinal cord injury: pathological insights from neuroimaging
Dávid, G., Mohammadi, S., Martin, A. R., Cohen‐Adad, J., Weiskopf, N., Thompson, A. J., & Freund, P. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0270-5 · Cited by 240 · see citing works
Imaging structural and functional brain networks in temporal lobe epilepsy
Bernhardt, B. C., Hong, S., Bernasconi, A., & Bernasconi, N. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00624 · Cited by 232 · see citing works
“While somewhat challenged in regions where different fiber populations intersect (Behrens et al., 2003; Jones et al., 2012), such as the cortical gray matter, tractography can generate consistent results, particularly in deep white matter.”
BackgroundTractometer: Towards validation of tractography pipelines
Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228 · see citing works
“Hence, as mentioned in (Jones et al., 2013), fiber count should be avoided.”
BackgroundStructural connectivity differences in left and right temporal lobe epilepsy
Besson, P., Dinkelacker, V., Valabrègue, R., Thivard, L., Leclerc, X., Baulac, M., Sammler, D., Olivier Colliot, A., Lehéricy, S., Samson, S., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.071 · Cited by 228 · see citing works
“However, we cannot amend the global limitations of tractography in yielding good signal to noise ratio close to the cortical ribbon (Jones et al., 2013).”
BackgroundMethodInfluentialPalesi, F., Rinaldis, A. D., Castellazzi, G., Calamante, F., Muhlert, N., Chard, D., Tournier, J., Magenes, G., D'Angelo, E. U., & Wheeler‐Kingshott, C. A. M. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-13079-8 · Cited by 234 · see citing works
Roalf, D. R., Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K., Gennatas, E. D., Calkins, M. E., Moore, T. M., Hopson, R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.068 · Cited by 199 · see citing works
“…spikes) and susceptibility artifacts (Anderson, 2001; Bastin et al., 1998; Skare et al., 2000), which have come under scrutiny (Heim et al., 2004; Jones et al., 1999, 2013; Lauzon et al., 2013; Owens et al., 2012; Tournier et al., 2011; Yendiki et al., 2014), and are the focus of several new…”
Imaging biomarkers in neurodegeneration: current and future practices
Young, P. N. E., Estarellas, M., Coomans, E. M., Srikrishna, M., Beaumont, H., Maaß, A., Venkataraman, A. V., Lissaman, R., Jiménez, D., Betts, M. J., et al. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00612-7 · Cited by 229 · see citing works
Specificity and robustness of long-distance connections in weighted, interareal connectomes
Betzel, R. F., & Bassett, D. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720186115 · Cited by 214 · see citing works
Learning to Read Improves the Structure of the Arcuate Fasciculus
de Schotten, M. T., Cohen, L., Amemiya, E., Braga, L. W., & Dehaene, S. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs383 · Cited by 222 · see citing works
Multidimensional diffusion MRI
Topgaard, D. (2016) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2016.12.007 · Cited by 218 · see citing works
Neuroimaging in functional neurological disorder: State of the field and research agenda
Perez, D. L., Nicholson, T. R., Asadi-Pooya, A. A., Bègue, I., Butler, M., Carson, A. J., David, A. S., Deeley, Q., Diez, I., Edwards, M. J., et al. (2021) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102623 · Cited by 216 · see citing works
“This procedure requires that images be transformed to a common anatomical space to assure correspondence across subjects, generally using the Statistical Parametric Mapping (SPM) (Ashburner and Friston, 2000) or FMRIB Software Library (FSL) VBM analysis pipelines (Jenkinson et al., 2012).”
MethodAssessment of system dysfunction in the brain through MRI-based connectomics
Filippi, M., van den Heuvel, M. P., Fornito, A., Yong He, A., Pol, H. E. H., Agosta, F., Comı, G., & Rocca, M. A. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70144-3 · Cited by 215 · see citing works
Gadie, A., Shafto, M. A., Leng, Y., Cam-CAN, & Kievit, R. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-014920 · Cited by 216 · see citing works
Rasmussen, J. M., Graham, A. M., Entringer, S., Gilmore, J. H., Styner, M. A., Fair, D. A., Wadhwa, P. D., & Buß, C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.020 · Cited by 211 · see citing works
“…et al., 2017; Dubois et al., 2014) However, given the speculative nature of interpreting microscopic properties from macroscopic DTI observations (Jones et al., 2013), future efforts in animal models aimed at elucidating the microstructural correlates of macroscopic diffusion measures are…”
From Phineas Gage and Monsieur Leborgne to H.M.: Revisiting Disconnection Syndromes
de Schotten, M. T., Dell’Acqua, F., Ratiu, P., Leslie, A., Howells, H., Cabanis, E. A., Iba-Zizen, M. T., Plaisant, O., Simmons, A., Dronkers, N. F., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv173 · Cited by 210 · see citing works
Modelling white matter with spherical deconvolution: How and why?
Dell’Acqua, F., & Tournier, J. (2018) · NMR in Biomedicine · doi:10.1002/nbm.3945 · Cited by 206 · see citing works
Palesi, F., Tournier, J., Calamante, F., Muhlert, N., Castellazzi, G., Chard, D., D'Angelo, E. U., & Wheeler‐Kingshott, C. A. M. G. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0861-2 · Cited by 205 · see citing works
“While recognizing that tractography provides only indirect evidence of anatomical connectivity between regions and cannot distinguish between direct connections and pathways involving synapses (like the cerebello-thalamo-cortical pathway) (Catani et al. 2012; Jones et al. 2013), we aimed to assess the usefulness of tr…”
BackgroundInfluentialLampinen, B., Szczepankiewicz, F., Mårtensson, J., van Westen, D., Sundgren, P. C., & Nilsson, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.053 · Cited by 200 · see citing works
“A similar situation faced users of DTI, who early on tried to interpret the FA parameter as an index of 'white matter integrity' (Jones et al. 2013).”
Influentialvan den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4105-13.2014 · Cited by 178 · see citing works
“For example, numerous structural properties likely contribute to tract likelihood, including axon caliber, axonal density, membrane permeability, and myelination (Jones et al., 2013).”
BackgroundKodiweera, C., Alexander, A. L., Harezlak, J., McAllister, T. W., & Wu, Y.-C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.033 · Cited by 179 · see citing works
“…crossing fibers in the brain white matter is ~90%, which may explain the lower than expected pathologic specificity of DTI metrics in complex white matter areas of the brain (Sullivan and Pfefferbaum 2006, Jones and Cercignani 2010, Tournier et al. 2011, Jeurissen et al. 2013, Jones et al. 2013).”
Developmental changes in brain network hub connectivity in late adolescence
Baker, S. T. E., Lubman, D. I., Yucel, M., Allen, N. B., Whittle, S., Fulcher, B. D., Zalesky, A., & Fornito, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5043-14.2015 · Cited by 168 · see citing works
“…of changes in connectivity measures derived from diffusion MRI, and it is possible that changes in fiber trajectory, organization, density, or other parameters related to axonal integrity may also affect the measures examined in our analysis (Paus, 2010; Fornito et al., 2013; Jones et al., 2013).”
BackgroundMapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Self-citationChang, E. H., Argyelan, M., Aggarwal, M., Chandon, T.-S. S., Karlsgodt, K. H., Mori, S., & Malhotra, A. K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.068 · Cited by 186 · see citing works
“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, 20…”
InfluentialJones, D. K., Alexander, D. C., Bowtell, R. W., Cercignani, M., Dell’Acqua, F., McHugh, D. J., Miller, K. L., Palombo, M., Parker, G. J. M., Rudrapatna, U. S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.047 · Cited by 179 · see citing works
Self-citationAsymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain
Fernandez-Miranda, J., Wang, Y.-B., Pathak, S., Stefaneau, L., Verstynen, T., & Yeh, F.-C. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0751-7 · Cited by 176 · see citing works
“Recent reviews in diffusion fiber tracking (Jones and Cercignani 2010; Jbabdi and Johansen-Berg 2011; Jones et al. 2013) are against using the number of streamlines to derive a quantitative index due to poor reproducibility, and therefore we preferred the volume of streamlines as a more reliable…”
Betzel, R. F., Griffa, A., Hagmann, P., & Mišić, B. (2018) · Network Neuroscience · doi:10.1162/netn_a_00075 · Cited by 165 · see citing works
Finding the Elusive Psychiatric “Lesion” With 21st-Century Neuroanatomy: A Note of Caution
Weinberger, D. R., & Radulescu, E. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15060753 · Cited by 173 · see citing works
White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease
Araque Caballero, M. Á., Suárez-Calvet, M., Duering, M., Franzmeier, N., Benzinger, T., Fagan, A. M., Bateman, R. J., Jack, C. R., Levin, J., Dichgans, M., et al. (2018) · Brain · doi:10.1093/brain/awy229 · Cited by 179 · see citing works
“, 2007), although this is still a matter of intense research (Jones et al., 2013).”
BackgroundInfluentialKhalsa, S., Mayhew, S. D., Chechlacz, M., Bagary, M. S., & Bagshaw, A. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.022 · Cited by 180 · see citing works
“, since there are contributions from several methodological and physiological factors which are not related to the underlying connectivity (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013).”
Gomez, J., Pestilli, F., Witthoft, N., Golarai, G., Liberman, A., Poltoratski, S., Yoon, J. M. D., & Kalanit Grill-spector, A. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.027 · Cited by 181 · see citing works
“The physiological factors underlying the diffusivity properties of our reported fiber tracts are likely diverse (Beaulieu, 2002) Neuron 85, 216–22 and difficult to directly link to microscopic tissue properties (Jones et al., 2013).”
BackgroundInteractions between brain structure and behavior: The corpus callosum and bimanual coordination
Gooijers, J., & Swinnen, S. P. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.03.008 · Cited by 173 · see citing works
Hecht, E. E., Gutman, D. A., Bradley, B. A., Preuss, T. M., & Stout, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.039 · Cited by 175 · see citing works
From Cajal to Connectome and Beyond
Swanson, L. W., & Lichtman, J. W. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-033954 · Cited by 174 · see citing works
Characterizing longitudinal white matter development during early childhood
Dean, D. C., O’Muircheartaigh, J., Dirks, H., Waskiewicz, N., Walker, L., Doernberg, E., Piryatinsky, I., & Deoni, S. C. L. (2015) · Brain Structure and Function · doi:10.1007/s00429-014-0763-3 · Cited by 174 · see citing works
“2001) and are influenced by a broad range of other microstructural changes (Jones et al. 2013).”
The physical and biological basis of quantitative parameters derived from diffusion MRI.
Winston, G. P. (2012) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2223-4292.2012.12.05 · Cited by 174 · see citing works
A review of white matter microstructure alterations of pathways of the reward circuit in depression
Bracht, T., Linden, D. E. J., & Keedwell, P. A. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.06.041 · Cited by 172 · see citing works
“…different sub-compartments of the CB (e.g. the subgenual CB or the parahippocampal cingulum) since white matter microstructure alterations may only be present and detectable in sub-compartments of this large fi bre bundle (Bracht et al., 2015b; Carballedo et al., 2012; Jones et al., 2013a).”
BackgroundMethodPujol, S., Wells, W., Pierpaoli, C., Brun, C., Gee, J., Cheng, G., Vemuri, B., Commowick, O., Prima, S., Stamm, A., et al. (2015) · Journal of Neuroimaging · doi:10.1111/jon.12283 · Cited by 171 · see citing works
Tractography-based Parcellation of the Human Middle Temporal Gyrus
Xu, J., Wang, J., Fan, L., Li, H., Zhang, W., Hu, Q., & Jiang, T. (2015) · Scientific Reports · doi:10.1038/srep18883 · Cited by 172 · see citing works
- 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
- Cited by
- 2,479
- References
- 200
- Citations by year
- 2026: 107 · 2025: 134 · 2024: 122 · 2023: 147 · 2022: 175 · 2021: 233 · 2020: 205 · 2019: 201 · 2018: 188 · 2017: 195 · 2016: 204 · 2015: 176 · 2014: 133 · 2013: 103 · 2012: 9
- FWCI
- 93.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- 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
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