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]
Citations
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952
SENSE: Sensitivity encoding for fast MRI
Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5 · Cited by 6,303
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970
Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellús̆, V., Wang, J., Kiefer, B., & Haase, A. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10171 · Cited by 5,311
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,628
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912
Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo
Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656
Flagged first, then by citations
9 of the 200 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Le Bihan, 1985, In vivo magnetic resonance imaging of diffusion I, C. R. Acad. Sci. II, 301, 1109
- Tournier, 2009, How many diffusion gradient directions are required for HARDI?, Proc. Int. Soc. Magn. Reson. Med., 17, 358
Cited by
Sorted by citations
Items 901–1,000 of 2,327
Working Memory Training Effects on White Matter Integrity in Young and Older Adults
Dziemian, S., Appenzeller, S., von Bastian, C. C., Jäncke, L., & Langer, N. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.605213 · Cited by 26
“The latter has often been referred to as the “crossing fiber problem” in literature (Alexander and Seunarine, 2010; Tournier, 2010; Jones et al., 2013).”
BackgroundMallas, E., Carletti, F., Chaddock, C. A., Woolley, J., Picchioni, M., Shergill, S. S., Kane, F., Allin, M. P. G., Barker, G. J., & Prata, D. P. (2016) · PeerJ · doi:10.7717/peerj.1570 · Cited by 27
Hermesdorf, M., Berger, K., Szentkirályi, A., Schwindt, W., Dannlowski, U., & Wersching, H. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.01.013 · Cited by 25
“MRI-based diffusion tensor imaging allows a precise assessment of white matter microstructure, quantified as fractional anisotropy (FA), which reflects a variety of white matter microstructural properties such as myelination and packing density (Jones et al. 2013).”
White matter microstructure variations contribute to neurological soft signs in healthy adults
Hirjak, D., Thomann, P. A., Wolf, R. C., Kubera, K. M., Goch, C. J., Hering, J., & Maier-Hein, K. H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23609 · Cited by 27
“The MD value refers to the extent of water diffusion and might indicate inflammation, extracellular edema, tissue necrosis, or changes of liquid viscosity [Jones et al., 2013; Nagahara et al., 2014].”
BackgroundDesign of optimal nonlinear network controllers for Alzheimer's disease
Sanchez-Rodriguez, L. M., Iturria‐Medina, Y., Baines, E. A., Mallo, S. C., Dousty, M., Sotero, R. C., & Initiative, O. B. O. T. A. D. N. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006136 · Cited by 26
Ressel, V., van Hedel, H. J. A., Scheer, I., & Tuura, R. (2018) · European Radiology Experimental · doi:10.1186/s41747-018-0066-1 · Cited by 27
Brett, B. L., Wu, Y., Mustafi, S. M., et al. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2019.01345 · Cited by 27
Koops, E. A., Haykal, S., & van Dijk, P. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2996-20.2021 · Cited by 26
“In voxels with multiple and crossing fiber bundles (i.e., complex fiber populations), DTI cannot readily assign differences in white matter to a specific pathway (Douaud et al., 2011; Jones et al., 2013; Mito et al., 2018).”
BackgroundOttino‐González, J., Uhlmann, A., Hahn, S., Cao, Z., Cupertino, R. B., Schwab, N., Allgaier, N. A., Nelly Alia-klein, A., Ekhtiari, H., Fouché, J., et al. (2021) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2021.109185 · Cited by 27
Analyses of microstructural variation in the human striatum using non-negative matrix factorization
Robert, C. M., Patel, R., Blostein, N., Steele, C. C., & Chakravarty, M. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118744 · Cited by 23
DiBella, E. V. R., Sharma, A., Richards, L., Prabhakaran, V., Majersik, J. J., & HashemizadehKolowri, S. K. (2022) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7414 · Cited by 25
Ferris, J. K., Greeley, B., Vavasour, I. M., Kraeutner, S. N., Rinat, S., Ramirez, J., Black, S. E., & Boyd, L. A. (2022) · Brain Communications · doi:10.1093/braincomms/fcac142 · Cited by 27
“However, while changes to DTI microstructure are often interpreted as reflecting demyelination, FA and MD are also impacted by multiple factors beyond damage to myelinated axons, such as underlying fibre architecture, the presence of glial cells, and changes in local fluid.(24,25) This is an important caveat in interp…”
BackgroundLindner, P., Savić, I., Sitnikov, R., Budhiraja, M., Liu, Y., Jokinen, J., Tiihonen, J., & Hodgins, S. (2016) · Translational Psychiatry · doi:10.1038/tp.2015.216 · Cited by 28
Farquharson, S., Tournier, J., Calamante, F., Mandelstam, S. A., Burgess, R., Schneider, M. E., Berkovic, S. F., Scheffer, I. E., Jackson, G. D., & Connelly, A. (2016) · Radiology · doi:10.1148/radiol.2016150852 · Cited by 27
White matter microstructure in children with autistic traits
Blanken, L. M. E., Muetzel, R. L., Jaddoe, V. W. V., Verhulst, F. C., van der Lugt, A., Tiemeier, H. W., & White, T. J. H. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.03.015 · Cited by 25
Microstructural anatomical differences between bilinguals and monolinguals
Singh, N. C., Rajan, A., Malagi, A., Ramanujan, K., Canini, M., Rosa, P. A. D., Raghunathan, P., Weekes, B., & Abutalebi, J. (2017) · Bilingualism Language and Cognition · doi:10.1017/s1366728917000438 · Cited by 30
Dennis, E. L., Caeyenberghs, K., Asarnow, R. F., Babikian, T., Bartnik‐Olson, B., Bigler, E. D., Figaji, A., Giza, C. C., Goodrich‐Hunsaker, N. J., Hodges, C. B., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00363-x · Cited by 26
Lyall, D. M., Harris, S. E., Bastin, M. E., Maniega, S. M., Murray, C. L., Lutz, M. W., Saunders, A. M., Roses, A. D., Hernández, M. D. C. V., Royle, N. A., et al. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.89 · Cited by 26
Increased white matter metabolic rates in autism spectrum disorder and schizophrenia
Mitelman, S. A., Buchsbaum, M. S., Young, D. S., Haznedar, M. M., Hollander, E., Shihabuddin, L., Hazlett, E. A., & Bralet, M. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9785-9 · Cited by 26
Rojas-Vite, G., Coronado‐Leija, R., Narvaez, O., Ramírez-Manzanares, A., Marroquin, J. L., Noguez-Imm, R., Aranda, M. L., Scherrer, B., Larriva-Sahd, J. A., & Concha, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116013 · Cited by 25
Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique
Barrick, T. R., Spilling, C. A., Ingo, C., Madigan, J. B., Isaacs, J. D., Rich, P. M., Jones, T. L., Magin, R. L., Hall, M. G., & Howe, F. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116606 · Cited by 26
The role of the pre-commissural fornix in episodic autobiographical memory and simulation
Williams, A. N., Ridgeway, S., Postans, M., Graham, K. S., Lawrence, A. D., & Hodgetts, C. J. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107457 · Cited by 26
Yon, M., Martins, J. P. D. A., Bao, Q., Budde, M. D., Frydman, L., & Topgaard, D. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4355 · Cited by 23
Giampiccolo, D., Howells, H., Bährend, I., Schneider, H. U., Raffa, G., Rosenstock, T., Vergani, F., Vajkoczy, P., & Picht, T. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa158 · Cited by 25
“While tensor-based approaches are commonly recommended for sequences with low b -values (Jones et al. , 2013), a number of recent studies have indicated that spherical deconvolution techniques may also be used and provide fewer false negatives (Farquharson et al. , 2013; Calamuneri et al. , 2018).”
Kimpton, J., Batallé, D., Barnett, M., Hughes, E., Chew, A. T. M., Falconer, S., Tournier, J., Alexander, D. C., Zhang, H. G., Edwards, A. D., et al. (2020) · Neuroradiology · doi:10.1007/s00234-020-02584-9 · Cited by 24
Aberrant cortico-thalamic structural connectivity in premature-born adults
Menegaux, A., Meng, C., Bäuml, J., Berndt, M. T., Hedderich, D. M., Schmitz‐Koep, B., Schneider, S. C., Nuttall, R., Zimmermann, J., Daamen, M., et al. (2021) · Cortex · doi:10.1016/j.cortex.2021.04.009 · Cited by 26
Dissociation of structural and functional connectomic coherence in glioma patients
Jütten, K., Weninger, L., Mainz, V., Gauggel, S., Binkofski, F., Wiesmann, M., Merhof, D., Clusmann, H., & Na, C.-H. (2021) · Scientific Reports · doi:10.1038/s41598-021-95932-5 · Cited by 27
Hardi, F. A., Goetschius, L. G., Peckins, M. K., Jeanne Brooks-gunn, B., McLanahan, S., McLoyd, V. C., Lopez‐Duran, N. L., Mitchell, C. M., Hyde, L. W., & Monk, C. S. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01801 · Cited by 27
The structural connectome in ADHD
Bu, X., Cao, M., Huang, X., & Yong He, A. (2021) · Psychoradiology · doi:10.1093/psyrad/kkab021 · Cited by 25
“However, some researchers later argued that FA changes should not be simply interpreted as changes in microstructural integrity because FA can be naturally low when there is a large axon diameter, low packing density, or fiber crossing (Jones et al. , 2013).”
BackgroundSoldan, A., Alfini, A. J., Pettigrew, C. A., Faria, A. V., Hou, X., Lim, C., Lu, H., Spira, A. P., Zipunnikov, V., & Albert, M. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.04.006 · Cited by 31
Moody, J. F., Dean, D., Kecskemeti, S. R., Blennow, K., Zetterberg, H., Kollmorgen, G., Suridjan, I., Wild, N., Carlsson, C. M., Johnson, S. C., et al. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12381 · Cited by 24
“26 Immunoassays were performed on either a cobas e 601 analyzer (Elecsys β-Amyloid (1-42) CSF, β-Amyloid (1-40) CSF, Elecsys Phospho-Tau (181P) CSF) or a cobas e 411 analyzer (NfL, neurogranin, α-synuclein, GFAP, sTREM2, and YKL-40).”
MethodFusion of white and gray matter geometry: A framework for investigating brain development
Savadjiev, P., Yogesh Rathi, A., Sylvain Bouix, A., Smith, A. R., Schultz, R. T., Verma, R., & Westin, C. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.013 · Cited by 27
Stuttering treatment and brain research in adults: A still unfolding relationship
Ingham, R. J., Ingham, J. C., Euler, H. Α., & Neumann, K. (2017) · Journal of Fluency Disorders · doi:10.1016/j.jfludis.2017.02.003 · Cited by 26
Whitford, T. J., Oestreich, L., Ford, J. M., Roach, B. J., Loewy, R. L., Stuart, B. K., & Mathalon, D. H. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx144 · Cited by 27
Shah, C., Liu, J., Lv, P., Sun, H., Xiao, Y., Liu, J., Zhao, Y., Zhang, W., Li, Y., Gong, Q., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00318 · Cited by 27
“, 2013), such tractographymethod, should be used cautiously in order to resolve crossing fiber bundles (Tournier et al., 2011; Jones et al., 2013).”
MethodMu, J., Chen, T., Li, P., Ding, D., Ma, X., Zhang, M., & Liu, J.-X. (2018) · Human Brain Mapping · doi:10.1002/hbm.24321 · Cited by 21
Physical activity is associated with left corticospinal tract microstructure in bipolar depression
Bracht, T., Steinau, S., Federspiel, A., Schneider, C., Wiest, R., & Walther, S. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.09.033 · Cited by 26
“axons, higher axon density or from higher density of glia cells such as oligodendrocytes (Jones et al., 2013).”
BackgroundMethodInfluentialDalamagkas, K., Tsintou, M., Yogesh Rathi, A., O’Donnell, L. J., Pasternak, O., Gong, X., Zhu, A., Savadjiev, P., Papadimitriou, G., Kubicki, M. R., et al. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-018-0006-y · Cited by 27
“In addition, false positive or false negative results have been reported in tractography (Farquharson et al. 2013; Jones et al. 2013; O’Donnell and Pasternak 2015; O’Donnell and Westin 2011).”
Matthews, L. G., Smyser, C. D., Cherkerzian, S., Alexopoulos, D. K., Kenley, J. K., Tuuli, M. G., Nelson, D. M., & Inder, T. E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219596 · Cited by 26
Seiger, R., Gryglewski, G., Klöbl, M., Kautzky, A., Godbersen, G. M., Rischka, L., Vanicek, T., Hienert, M., Unterholzner, J., Silberbauer, L., et al. (2021) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyab008 · Cited by 29
High Angular Resolution Diffusion Imaging
Farquharson, S., & Tournier, J. (2015) · Springer eBooks · doi:10.1007/978-1-4939-3118-7_20 · Cited by 27
Editorial: Beyond diffusion tensor imaging
Fernandez-Miranda, J. C. (2013) · Journal of neurosurgery · doi:10.3171/2012.10.jns121800 · Cited by 26
Easson, K., Rohlicek, C. V., Houde, J.-C., Gilbert, G., Saint-Martin, C., Fontes, K., Majnemer, A., Marelli, A., Wintermark, P., Descoteaux, M., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116255 · Cited by 25
Liebrand, L. C., Natarajan, S. J., Caan, M. W. A., Schuurman, P. R., van den Munckhof, P., de Kwaasteniet, B., Luigjes, J., Bergfeld, I. O., Denys, D., & van Wingen, G. A. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102363 · Cited by 24
McPhee, G. M., Downey, L. A., Wesnes, K. A., & Stough, C. K. K. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.638109 · Cited by 27
“Unfortunately, complex tissue architecture such as crossing, or bending fibers that deviate from the parallel, coherent fibers assumed in the DTI model, can make interpretation of DTI outcomes challenging (Jones et al., 2013).”
BackgroundTymofiyeva, O., Ziv, E., Barkovich, A. J., Hess, C. P., & Xu, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096196 · Cited by 25
“Various approaches to weighting network connection have been used in other studies, streamline count being the most common [17].”
BackgroundMethodInfluentialMourão, L. F., Friel, K. M., Sheppard, J. J., et al. (2017) · Dysphagia · doi:10.1007/s00455-017-9816-0 · Cited by 25
Schumacher, L. V., Reisert, M., Nitschke, K., Egger, K. E., Urbach, H., Hennig, J., Weiller, C., & Kaller, C. P. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.086 · Cited by 25
Hao, X., Yin, L., Tian, J., et al. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00245 · Cited by 25
“FA is a common DTI parameter that measures the degree of voxel diffusion (Jones et al., 2013; Ferris et al., 2017).”
BackgroundDenier, N., Walther, S., Schneider, C., Federspiel, A., Wiest, R., & Bracht, T. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.008 · Cited by 23
The Superior Colliculus and Amygdala Support Evaluation of Face Trait in Blindsight
Ajina, S., Pollard, M. I., & Bridge, H. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00769 · Cited by 25
Gatto, R. G. (2020) · Journal of Integrative Neuroscience · doi:10.31083/j.jin.2020.03.165 · Cited by 27
“Therefore, the careful consideration of such pitfalls will avoid misconceptions of the dMRI capabilities (Jones et al., 2013).”
BackgroundWhite Matter-Based Structural Brain Network of Major Depression
Chen, T., Chen, Z., & Gong, Q. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-981-33-6044-0_3 · Cited by 25
Age affects white matter microstructure and episodic memory across the older adult lifespan
Merenstein, J. L., Corrada, M. M., Kawas, C. H., & Bennett, I. J. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.06.021 · Cited by 25
Greșiță, A., Ruscu, M. A., Hermann, D. M., et al. (2021) · GeroScience · doi:10.1007/s11357-021-00432-z · Cited by 25
The relationship between white matter microstructure and self-perceived cognitive decline
Archer, D. B., Moore, E. E., Pamidimukkala, U., Shashikumar, N., Pechman, K. R., Blennow, K., Zetterberg, H., Landman, B. A., Hohman, T. J., Jefferson, A. L., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102794 · Cited by 26
“Importantly, these tracts are traditionally associated with executive function, (Stuss and Alexander, 2000) information processing speed, (Kochunov et al., 2010) and memory, (Fujie et al., 2008) so damage in these tracts may contribute to SCD through these cognitive domains.”
BackgroundSchiavi, S., Lu, P., Weigel, M., Lutti, A., Jones, D. K., Kappos, L., Granziera, C., & Daducci, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118922 · Cited by 24
Self-citationBoccard, S., Fernandes, H. M., Jbabdi, S., van Hartevelt, T. J., Kringelbach, M. L., Quaghebeur, G., Moir, L., Mancebo, V. P., Pereira, E. A. C., FitzGerald, J. J., et al. (2015) · World Neurosurgery · doi:10.1016/j.wneu.2015.08.065 · Cited by 26
Whalley, H. C., Dimitrova, R., Sprooten, E., Dauvermann, M. R., Romaniuk, L., Duff, B. J., Watson, A. R., Moorhead, B., Bastin, M. E., Semple, S. I. K., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130900 · Cited by 26
“Hence we are unable to interpret findings as providing evidence of a particular cellular pathology [74].”
BackgroundJohnson, M. A., Diaz, M. T., & Madden, D. J. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0822-9 · Cited by 24
Graph Theoretic Analysis of Human Brain Networks
Fornito, A. (2016) · Neuromethods · doi:10.1007/978-1-4939-5611-1_10 · Cited by 26
Role of Brain Structure in Predicting Adherence to a Physical Activity Regimen
Gujral, S., McAuley, E., Oberlin, L. E., Kramer, A. F., & Erickson, K. I. (2017) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000526 · Cited by 27
“FA is a commonly used measure of white matter derived from DTI and represents overall anisotropy within a voxel (45).”
Hales, P. W., Smith, V., Dhanoa-Hayre, D., O’Hare, P., Mankad, K., D’Arco, F., Cooper, J., Kaur, R., Phipps, K. P., Bowman, R. J. C., et al. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.10.010 · Cited by 25
Chen, X., Zhao, Y., Zhong, S., Cui, Z., Li, J., Gong, G., Dong, Q., & Nan, Y. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1608-2 · Cited by 25
Cortical parcellation based on structural connectivity: A case for generative models
Tittgemeyer, M., Rigoux, L., & Knösche, T. R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.077 · Cited by 24
“In other words, structural connectivity may determine to a considerable extent the functional organisation of the cortex (Barbas, 2015; Jbabdi and Behrens, 2012; Kaas, 2002; Passingham et al., 2002; Rockland, 2002).”
BackgroundSelf-citationFacial nerve tractography: A new tool for the detection of perineural spread in parotid cancers
Rouchy, R.-C., Attyé, A., Médici, M., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5318-1 · Cited by 25
“These results could be explained because PNS mainly impact on the cellular compartment while the degree of anisotropy is influenced by many factors such as axon diameter, inter-axon spacing, membrane permeability, myelination and the coherence of axon orientations [32].”
BackgroundMesocorticolimbic Connectivity and Volumetric Alterations in DCC Mutation Carriers
Vosberg, D. E., Zhang, Y., Menegaux, A., Chalupa, A., Manitt, C., Zehntner, S., Eng, C., DeDuck, K., Allard, D., Durand, F., et al. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.3251-17.2018 · Cited by 25
“First, there are general limitations of diffusion MRI tractography to infer fiber pathways, including indirect measurement of axonal orientations and the challenge of resolving crossing fibers (Jones et al., 2013; Jbabdi et al., 2015).”
BackgroundDistinct neural circuits support incentivized inhibition
Leong, J. K., MacNiven, K. H., Samanez-Larkin, G. R., & Knutson, B. D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.055 · Cited by 26
“Both FA and MD can vary as a function of fascicle density, axon diameter, and myelination (Jones et al., 2013; Chang et al., 2017), but while greater FA typically indexes increased tract coherence, greater MD instead indexes decreased tract coherence.”
Fukushima, M., & Sporns, O. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006497 · Cited by 25
Fil, J. E., Joung, S., Hayes, C. A., & Dilger, R. N. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.649536 · Cited by 25
Lázaro, L., Calvo, A., Ortiz, A. G., Ortiz, A. E., Morer, Á., Moreno, E., Calvo, R. M., & Bargalló, N. (2014) · Depression and Anxiety · doi:10.1002/da.22330 · Cited by 27
“In addition, separate quantification of AD (along the axon) and RD (perpendicular to main axis) yields more specific information about tissue properties, indicating the myelin and axonal changes that underlie the changes observed in FA.”
BackgroundHansen, B., & Jespersen, S. N. (2016) · Scientific Data · doi:10.1038/sdata.2016.72 · Cited by 24
Bracht, T., Viher, P. V., Stegmayer, K., Strik, W. K., Federspiel, A., Wiest, R., & Walther, S. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102044 · Cited by 23
Material hardship, prefrontal cortex–amygdala structure, and internalizing symptoms in children
Lichtin, R. D., Merz, E. C., He, X., Desai, P. M., Simon, K. R., Melvin, S. A., Maskus, E. A., & Noble, K. G. (2020) · Developmental Psychobiology · doi:10.1002/dev.22020 · Cited by 26
Prasad, J. D., van de Looij, Y., Gunn, K. C., Ranchhod, S. M., White, P. B., Berry, M. J., Bennet, L., Sizonenko, S. V., Gunn, A. J., & Dean, J. M. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.12.006 · Cited by 24
Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts
Baraković, M., Girard, G., Schiavi, S., Romascano, D., Descoteaux, M., Granziera, C., Jones, D. K., Innocenti, G. M., Thiran, J., & Daducci, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.646034 · Cited by 25
Self-citationCorticospinal Tract Microstructure Predicts Distal Arm Motor Improvements in Chronic Stroke
Kim, B., Schweighofer, N., Haldar, J. P., Leahy, R. M., & Winstein, C. J. (2021) · Neurology Report · doi:10.1097/npt.0000000000000363 · Cited by 23
Koirala, N., Perdue, M. V., Su, X., Grigorenko, E. L., & Landi, N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118426 · Cited by 25
Kelly, C. E., Cheong, J. L. Y., Molloy, C. S., Anderson, P. J., Lee, K. J., Burnett, A., Connelly, A., Doyle, L. W., Thompson, D. K., & for the Victorian Infant Collaborative Study Group (2014) · PLoS ONE · doi:10.1371/journal.pone.0093188 · Cited by 24
“The lll, lH and MD are thought to be sensitive to changes in one or more properties of white matter microstructure, including white matter fiber density, fiber diameter, myelination or membrane permeability [32].”
BackgroundInfluentialKamiya, K., Kamagata, K., Miyajima, M., Nakajima, M., Hori, M., Tsuruta, K., Mori, H., Kunimatsu, A., Arai, H., Aoki, S., et al. (2016) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2015-0093 · Cited by 24
Altered microstructure rather than morphology in the corpus callosum after lower limb amputation
Li, Z., Li, C., Fan, L., Jiang, G., Wu, J., Jiang, T., Yin, X., & Wang, J. (2017) · Scientific Reports · doi:10.1038/srep44780 · Cited by 24
Long‐term white matter tract reorganization following prolonged febrile seizures
Pujar, S. S., Seunarine, K. K., Martinos, M. M., Neville, B. G. R., Scott, R. C., Chin, R., & Clark, C. A. (2017) · Epilepsia · doi:10.1111/epi.13724 · Cited by 26
White matter deficits in schizophrenia are global and don’t progress with age
Kanaan, R., Picchioni, M., McDonald, C., Shergill, S. S., & McGuire, P. K. (2017) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867417700729 · Cited by 24
“…and packing, membrane permeability, the ‘partial volume’ effect of adjacent structures and the crossing of tracts within a voxel of measurement (Jones et al., 2013), the two greatest Australian & New Zealand Journal of Psychiatry, 51(10) Australian & New Zealand Journal of Psychiatry,…”
BackgroundJordan, K., Amirbekian, B., Keshavan, A., & Henry, R. G. (2017) · Journal of Neuroimaging · doi:10.1111/jon.12467 · Cited by 24
Walker, S. E., Wood, T., Cash, D., Mesquita, M. S., Williams, S., & Sandi, C. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14061 · Cited by 25
Quantifying nerve decussation abnormalities in the optic chiasm
Puźniak, R. J., Ahmadi, K., Kaufmann, J., Gouws, A. D., Morland, A. B., Pestilli, F., & Hoffmann, M. B. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102055 · Cited by 26
Sánchez, S. M., Duarte-Abritta, B., Abulafia, C., Pino, G. D., Bocaccio, H., Castro, M. N., Sevlever, G. E., Fonzo, G. A., Nemeroff, C. B., Gustafson, D., et al. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2019.12.019 · Cited by 27
Schönknecht, P., Anwander, A., Petzold, F., Schindler, S., Knösche, T. R., Möller, H. E., Hegerl, U., Turner, R., & Geyer, S. (2013) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-012-0389-5 · Cited by 24
Self-citationImaging of the superficial white matter in health and disease
Dyken, P. C. V., KHAN, A. R., & Palaniyappan, L. K. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00221 · Cited by 24
“This signal can be used to model the microstructure, inferring what structure would have led to the observed signal ( Alexander et al., 2019; Bihan & Iima, 2015; Jones et al., 2013).”
BackgroundMethodDiffusion MRI with machine learning
Karimi, D., & Warfield, S. K. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00353 · Cited by 23
“The challenges and pitfalls of analyzing the dMRI data have been discussed in dedicated publications [9], [15], [24].”
Markett, S. A., de Reus, M. A., Reuter, M., Montag, C., Weber, B., Schoene‐Bake, J., & van den Heuvel, M. P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.005 · Cited by 25
“FA is sensitive to axon myelination, but also to axon diameter or packing density, making a direct interpretation of potential biological under-pinnings speculative (Song et al., 2002; Takahashi et al., 2002; Jones et al., 2013).”
Edde, M., Dilharreguy, B., Theaud, G., Chanraud, S., Helmer, C., Dartigues, J., Amieva, H., Michèle Allard, A., Descoteaux, M., & Catheline, G. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02121-7 · Cited by 20
Naze, S., Proix, T., Atasoy, S., & Kozloski, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117364 · Cited by 23
“However, connectome reconstruction methods generally suffer from many caveats that hinder the accurate and reproducible reconstruction of connectomes [38, 41].”
The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure
Boshkovski, T., Kocarev, L., Cohen‐Adad, J., Mišić, B., Lehéricy, S., Stikov, N., & Mancini, M. (2021) · Network Neuroscience · doi:10.1162/netn_a_00179 · Cited by 22
White Matter Integrity Underlies the Physical-Cognitive Correlations in Subjective Cognitive Decline
Wei, Y., Hsu, C. H., Huang, W., Chen, Y.-L., Lin, C., Chen, C., Lin, C., Shyu, Y., & Lin, C. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.700764 · Cited by 23
“Although DTI has been widely used to study brain white matter, its capacity of evaluating white matter microstructure was limited and must be interpreted with caution (Jones et al., 2013).”
BackgroundLiu, G., Zheng, W., Liu, H., Guo, M., Ma, L., Hu, W., Ke, M., Sun, Y., Zhang, J., & Zhang, Z. (2022) · Human Brain Mapping · doi:10.1002/hbm.25873 · Cited by 23
Structural Connectivity in Down Syndrome and Alzheimer’s Disease
Saini, F., Dell’Acqua, F., & Strydom, A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.908413 · Cited by 22
Brain myelination at 7 months of age predicts later language development
Corrigan, N. M., Yarnykh, V. L., Huber, E., Zhao, T. C., & Kuhl, P. K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119641 · Cited by 25
“, 2017) which are sensitive to a variety of tissue properties but cannot be used to make inferences specific to myelination alone (Jones et al., 2013).”
Orientation dependence of R2 relaxation in the newborn brain
Bartels, L., Doucette, J., Birkl, C. R., Zhang, Y., Weber, A. M., & Rauscher, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119702 · Cited by 23
Graph Theory Measures and Their Application to Neurosurgical Eloquence
Tanglay, O., Dadario, N. B., Chong, E. H. N., Tang, S. J., Young, I. M., & Sughrue, M. E. (2023) · Cancers · doi:10.3390/cancers15020556 · Cited by 24
“Streamlines, however, do not directly correspond to axons and are confounded by factors unrelated to connectivity such as myelination, density, and axonal number within a region [15,16].”
Background
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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