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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“In more general terms, the lack of specificity of the underlying biophysical model carries the risk of overinterpreting inferences made on the basis of diffusion-weighted data (Jones et al., 2013).”
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“In this work, the diffusion MRI analysis was based on DTI, which is not adequate to characterize complex fibre configurations, such as crossing and kissing fibres (Tournier et al 2011, Jones et al 2013).”
MethodBracht, T., Jones, D. K., Bells, S., Walther, S., Drakesmith, M., & Linden, D. E. J. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1183-3 · Cited by 33
“For instance, a cross-sectional study found that higher PA is associated with better performance in executive functioning in children (van der Niet et al. 2014).”
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“Although the ideal number of directions is debatable for diffusion MR tractography, previous analyses suggest a minimum of 30 directions is necessary for accurate tract reconstruction (Jones et al., 2013).”
Damme, K. S. F., Young, C. B., & Nusslock, R. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx017 · Cited by 35
“DWI can also be used to calculate tractography, which estimates white matter tracts that connect gray matter areas and the strength of these connections (Jones et al., 2013).”
MethodHaynes, W. I. A., Clair, A.-H., Fernandez-Vidal, S., Gholipour, B., Morgiève, M., & Mallet, L. (2018) · European Psychiatry · doi:10.1016/j.eurpsy.2018.01.005 · Cited by 29
Opportunities and Challenges for Psychiatry in the Connectomic Era
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“In practice, this requirement is not fulfilled and can never be fully fulfilled due to technical limitations and the indirect estimation of the fODF (Jones et al., 2013).”
InfluentialSelf-citationAydın, S. G., Kurtcan, S., Alkan, A., Güler, S., Filiz, M., Yılmaz, T. F., Sahin, T. U., & Aralaşmak, A. (2016) · Clinical Imaging · doi:10.1016/j.clinimag.2016.06.013 · Cited by 31
White Matter Microstructure Improves Stroke Risk Prediction in the General Population
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“Unfortunately, the complex relationship between white matter and diffusion tensor measurements means that observed differences cannot directly be attributed to particular changes in tissue architecture.(30) Interestingly, loss of white matter microstructural integrity has also been studied within the realm of the disc…”
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“Diffusion-weighted imaging (DWI) is a neuroimaging method that assays the random movement of water molecules to reconstruct the structure of white matter (WM) fibers (Behrens and Johansen-Berg, 2009; Jones et al., 2013; Soares et al., 2013).”
BackgroundFour in vivo g‐ratio‐weighted imaging methods: Comparability and repeatability at the group level
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“However, FA is also sensitive to reorganization of white matter fibers including changes in axon diameter, axonal density, and/or membrane permeability (Beaulieu, 2002; Jones and Cercignani, 2010; Jones et al., 2013; Takahashi et al., 2002).”
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“Nonetheless, the fundamental limitations of the DTI model have been well documented in seminal works (Farquharson and Tournier, 2016; Farquharson et al., 2013; Jeurissen et al., 2013; Jones, 2008; Jones et al., 2013; Tournier et al., 2011).”
BackgroundMethodSelf-citationGatto, R. G., Amin, M., Finkielsztein, A., Weissmann, C., Barrett, T., Lamoutte, C., Uchitel, O., Sumagin, R., Mareci, T. H., & Magin, R. L. (2019) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.1080/21678421.2019.1620285 · Cited by 31
“we propose that the combination of all the above models will overcome its individual limitations leading to a more accurate representation of the complex microstructural architecture changes occurring during the progression of the disease (43,44) (Table 1).”
BackgroundMesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus
Ly, M. J., Foley, L. M., Manivannan, A., Hitchens, T. K., Richardson, R. M., & Modo, M. (2020) · Human Brain Mapping · doi:10.1002/hbm.25119 · Cited by 31
“An SNR >10 has been suggested to be adequate for diffusion MRI-based tractography with some diffusion encoding directions (Descoteaux, Deriche, Knosche, & Anwander, 2009), although others indicate that an SNR >3 is sufficient (Jones, Knosche, & Turner, 2013).”
Kim, B. R., Kwon, H., Chun, M. Y., Park, K. D., Lim, S. M., Jeong, J. H., & Kim, G. H. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.549983 · Cited by 35
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“…whole-brain level in humans ( Vértes and Bullmore, 2015 ), connectivity between brain regions can be inferred via diffusion tensor imaging (DTI) ( Jones et al., 2013 ; Jones and Leemans, 2011 ), as myelinated axonal connections, or functional magnetic resonance imaging (fMRI) ( Smith et al.,…”
Addressing the Path-Length-Dependency Confound in White Matter Tract Segmentation
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“These issues hamper the inference of any robust form of true anatomical connectivity (in terms of parallel associated axons) from these connection-confidence maps ([13], [14]).”
BackgroundCortico-cortical communication dynamics
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“In humans, the neuroanatomical network structure is typically inferred from variants of diffusion tensor imaging and tractography techniques (see Jones et al., 2013 for a sober evaluation).”
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“Lower FA could reflect multiple white matter alterations, such as damage to the axon or myelin membrane, reduced axonal coherence, or reduced axonal density.(61) The uncinate fasciculus connects temporal areas with the orbitofrontal cortex, which allows 6 of 10 COUSIJN ET AL.”
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“The difference does not necessarily indicate axonal loss or neurodegeneration, so one must avoid overinterpreting DTI results.(53) Seven studies have used diffusion imaging to study ET patients, comparing them with normal controls.”
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High-speed tensor tomography: iterative reconstruction tensor tomography (IRTT) algorithm
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“Also, since diffusion is only a proxy for assessing microstructure, structural interpretation may be ambiguous (Jones et al., 2013), resulting in a constant need for validation (Hubbard & Parker, 2009; Dell’Acqua & Catani, 2012).”
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“Data at such strong diffusion weighting are generally characterized by degraded SNR (Descoteaux et al., 2011; Jones et al., 2013), but allows to resolve crossing fibers with higher angular resolution as compared to lower diffusion weightings.”
Afzali, M., Nilsson, M., Palombo, M., & Jones, D. K. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118183 · Cited by 32
Self-citationHigh resolution diffusion tensor imaging of the hippocampus across the healthy lifespan
Solar, K. G., Treit, S., & Beaulieu, C. (2021) · Hippocampus · doi:10.1002/hipo.23388 · Cited by 30
“DTI cannot disentangle crossing-fibers and calculates artificially low FA in areas where 2 or more fiber bundles overlap, therefore the development of a new fiber bundle to make a crossing area would result in lower FA values in a given region (Jones et al., 2013).”
BackgroundYeung, H. W., Stolicyn, A., Buchanan, C. R., Tucker–Drob, E. M., Bastin, M. E., Luz, S. F., McIntosh, A. M., Whalley, H. C., Cox, S. R., & Smith, K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26182 · Cited by 27
“…results might be achieved with network construction approaches other than those applied here. dMRI itself only enables noisy and indirect measurement of water molecule diffusion, and thus faithful reconstruction of brain connectivity remains challenging (Buchanan et al., 2020; Jones et al., 2013).”
BackgroundMultimodal neuroimaging computing: the workflows, methods, and platforms
Liu, S., Cai, W., Liu, S., Zhang, F., FULHAM, M. J., Feng, D. D., Pujol, S., & Kikinis, R. (2015) · Brain Informatics · doi:10.1007/s40708-015-0020-4 · Cited by 30
“Jones et al. [98] recently provided a set of guidelines which define the good practice in dMRI computing.”
BackgroundOptimization of white matter fiber tractography with diffusional kurtosis imaging
Glenn, G. R., Helpern, J. A., Tabesh, A., & Jensen, J. H. (2015) · NMR in Biomedicine · doi:10.1002/nbm.3374 · Cited by 31
“We note that biological interpretation of TD is problematic (27).”
BackgroundMethodMicrostructural differences in the thalamus and thalamic radiations in the congenitally deaf
Lyness, R. C., Alvarez, I., Sereno, M. I., & MacSweeney, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.077 · Cited by 32
“Tractography with DW-MRI involves reconstructing continuous long range trajectories from voxel-wise estimates of the fibre orientation (Jones et al., 2013).”
BackgroundMethodvan Ewijk, H., Groenman, A. P., Zwiers, M. P., Heslenfeld, D. J., Faraone, S. V., Hartman, C. A., Luman, M., Greven, C. U., Hoekstra, P. J., Franke, B., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22695 · Cited by 30
“While FA and MD are overall sensitive to a wide range of tissue properties, neither one is sensitive to one specific tissue property [Jones et al., 2013].”
BackgroundMelbourne, A., Eaton-Rosen, Z., Orasanu, E., Price, D., Bainbridge, A. P., Cardoso, M. J., Kendall, G. S., Robertson, N. J., Marlow, N., & Sebastien Ourselin, A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23188 · Cited by 30
“We adopt this pragmatic definition of what is meant by the results of a tractography algorithm to avoid biological interpretation fallacies when using notions of fibre connectivity and fibre integrity [Jones et al., 2013].”
MethodHirata, F. D. C. C., Sato, J. R., Vieira, G., Lucato, L. T., Leite, C. C., Bor‐Seng‐Shu, E., Pastorello, B., Otaduy, M. C. G., Chaim, K. T., Campanholo, K. R., et al. (2016) · European Radiology · doi:10.1007/s00330-016-4611-0 · Cited by 32
Neuroanatomical correlates of childhood apraxia of speech: A connectomic approach
Fiori, S., Guzzetta, A., Mitra, J., Pannek, K., Pasquariello, R., Cipriani, P., Tosetti, M., Cioni, G., Rose, S., & Chilosi, A. M. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.11.003 · Cited by 36
“Reduction of FA reflects alteredwhitemattermicrostructure, as the consequence of several factors including axon diameter (Takahashi et al., 2002), lower packing density (Takahashi et al., 2002) or increased membrane permeability (Jones et al., 2013).”
Bezgin, G., Solodkin, A., Bakker, R., Ritter, P., & McIntosh, A. R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23506 · Cited by 30
“Therefore, the number of found tracks does neither quantify the strength of a connection nor the probability of the existence of a connection but merely reflect the chance that a streamline could propagate along the specific pathway of the tracks [Jones et al., 2013].”
BackgroundChamberland, M., Girard, G., Bernier, M., Fortin, D., Descoteaux, M., & Whittingstall, K. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0539 · Cited by 30
“…the numerous confounding factors (e.g., false positives, seeding method, stopping criteria, label size, etc.) (Bastiani et al., 2012; Colon-Perez et al., 2015; Girard et al., 2014; Jones et al., 2013; Maier-Hein et al., 2016; Zalesky et al., 2016) that are typically insignificant in FC analysis.”
Patients with chronic bipolar disorder exhibit widespread increases in extracellular free water
Tuozzo, C., Lyall, A. E., Pasternak, O., James, A. C., Crow, T. J., & Kubicki, M. R. (2017) · Bipolar Disorders · doi:10.1111/bdi.12588 · Cited by 30
Innocenti, G. M., Caminiti, R., Rouiller, E. M., Knott, G. W., Dyrby, T. B., Descoteaux, M., & Thiran, J. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx363 · Cited by 30
“We are not aware of comparable work in CNS axons.”
BackgroundInfluentialKarahanoğlu, F. I., Baran, B., Nguyen, Q. T. H., Meskaldji, D. E., Yendiki, A., Vangel, M. G., Santangelo, S. L., & Manoach, D. S. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.06.002 · Cited by 32
“coherence of axon orientation (Beaulieu, 2002; Jones et al., 2013) it is not possible to definitively ascribe group differences in development to a particular white matter property.”
BackgroundBrain structural connectivity correlates with fluid intelligence in children: A DTI graph analysis
Kocevar, G., Suprano, I., Stamile, C., Hannoun, S., Fourneret, P., Revol, O., Nusbaum, F., & Sappey‐Marinier, D. (2018) · Intelligence · doi:10.1016/j.intell.2018.12.003 · Cited by 32
Javadi, S. A. H., Arya, N., Mario, G., et al. (2017) · Neurosurgery · doi:10.1093/neuros/nyx083 · Cited by 30
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Novel insights into axon diameter and myelin content in late childhood and adolescence
Genc, S., Raven, E. P., Drakesmith, M., Blakemore, S., & Jones, D. K. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac515 · Cited by 31
“The underlying microstructural attributes that contribute to these differences in FA (e.g. myelination, axon density, axon diameter, axonal orientation dispersion [OD]) can be further explored using alternative methods (Jones et al. 2013).”
MethodSelf-citationChaim, T. M., Zhang, T., Zanetti, M. V., da Silva, M. A., Louzã, M. R., Doshi, J. J., Serpa, M. H., Duran, F., Caetano, S. C., Davatzikos, C. A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110199 · Cited by 29
“and, thus, are sensitive to the degree of myelination, density and organization of WM tracts, all of which may reflect maturational processes [22,42,43].”
BackgroundLocal termination pattern analysis: a tool for comparing white matter morphology
Cieslak, M., & Grafton, S. T. (2013) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9254-z · Cited by 27
“tensor model (see (Jones et al. 2013) for an instructive”
InfluentialSchweren, L., Hartman, C. A., Zwiers, M. P., Heslenfeld, D. J., Franke, B., Oosterlaan, J., Buitelaar, J. K., & Hoekstra, P. J. (2016) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2016.02.007 · Cited by 31
“It remains speculative, however, which neurodevelopmental or neuropathological processes underlie such alterations in FA and MD (Jones et al., 2013).”
Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder
Dean, D., Travers, B. G., Adluru, N., Tromp, D., Destiche, D. J., Samsin, D., Prigge, M. B. D., Zielinski, B. A., Fletcher, P. T., Anderson, J. S., et al. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0385 · Cited by 30
“While these are sensitive to the underlying white matter microstructure, a broad range of microstructural changes can influence these parameters (Alexander et al., 2007b; Beaulieu, 2002; Jones et al., 2013).”
Dinkelacker, V., Dupont, S., & Samson, S. (2016) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2016.08.028 · Cited by 29
“Tractography and connectome analysis are nowadays a mainstream technology, with clinical implications for presurgical planning [59,60], while methodological constraints have to be respected [61,62].”
for the VMCI-Tuscany study group, Mascalchi, M., Salvadori, E., Toschi, N., Giannelli, M., Orsolini, S., Ciulli, S., Ginestroni, A., Poggesi, A., Giorgio, A., et al. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9873-5 · Cited by 29
The Temporal Dynamics of Brain Plasticity in Aging
de Lange, A. G., Bråthen, A. C. S., Rohani, D. A., Fjell, A. M., & Walhovd, K. B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy003 · Cited by 33
“Although DTI may be sensitive to underlying cellular changes of large enough volumetric contribution (Sagi et al. 2012; Fields 2015), the signal is also in fl uenced by how axons are laid out within the voxel, as the gradients are applied along given axes (Jones et al. 2013).”
BackgroundRegional brain volumes, microstructure and neurodevelopment in moderate–late preterm children
Kelly, C. E., Thompson, D. K., Spittle, A. J., Chen, J., Seal, M. L., Anderson, P. J., Doyle, L. W., & Cheong, J. L. Y. (2020) · Archives of Disease in Childhood Fetal & Neonatal · doi:10.1136/archdischild-2019-317941 · Cited by 29
Rodrigues, B., Coelho, A., Portugal‐Nunes, C., Magalhães, R. J. D. S., Moreira, P. S., Castanho, T. C., Amorim, L., Marques, P., Soares, J. M., Sousa, N., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00786 · Cited by 33
Kreilkamp, B. A. K., McKavanagh, A., Alonazi, B., Bryant, L., Das, K., Wieshmann, U. C., Marson, A. G., Taylor, P. N., & Keller, S. S. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102564 · Cited by 27
“Furthermore, this measure may depend on tractography algorithm, curvature, length, width and myelination and may not be considered a good indicator for fiber count (Jones et al. 2013).”
BackgroundZheng, H., Bergamino, M., Ford, B. N., Kuplicki, R., Yeh, F.-C. F., Bodurka, J., Burrows, K., Tulsa 1000 Investigators, Aupperle, R. L., Bodurka, J., et al. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-00971-1 · Cited by 29
Amemiya, K., Naito, E., & Takemura, H. (2021) · Cortex · doi:10.1016/j.cortex.2021.02.027 · Cited by 32
Microstructural Periventricular White Matter Injury in Post-hemorrhagic Ventricular Dilatation
Isaacs, A. M., Neil, J. J., McAllister, J. P., Dahiya, S., Castañeyra-Ruiz, L., Merisaari, H., Botteron, H. E., Alexopoulos, D., George, A., Sun, P., et al. (2021) · Neurology · doi:10.1212/wnl.0000000000013080 · Cited by 30
“Critically, DTI is not capable of resolving diffusivities of crossing fibers,(12) quantifying axonal density, or accurately assessing myelin integrity in the mixed presence of myelinated, dysmyelinated, and unmyelinated axons.(12,13) Further, neuroinflammation, a hallmark of PHH pathophysiology, is associated with mul…”
BackgroundIncreased microstructural white matter correlations in left, but not right, temporal lobe epilepsy
Pustina, D., Doucet, G. E., Sperling, M. R., Sharan, A. D., & Tracy, J. I. (2014) · Human Brain Mapping · doi:10.1002/hbm.22614 · Cited by 30
Conti, E., Calderoni, S., Gaglianese, A., Pannek, K., Mazzotti, S., Rose, S., Scelfo, D., Tosetti, M., Muratori, F., Cioni, G., et al. (2015) · Autism Research · doi:10.1002/aur.1533 · Cited by 29
Diffusion Tensor Imaging to Map Brain Microstructural Changes in CADASIL
Mascalchi, M., Pantoni, L., Giannelli, M., Valenti, R., Bianchi, A., Pracucci, G., Orsolini, S., Ciulli, S., Tessa, C., Poggesi, A., et al. (2016) · Journal of Neuroimaging · doi:10.1111/jon.12374 · Cited by 33
Hawco, C. S., Voineskos, A. N., Radhu, N., Rotenberg, D. J., Ameis, S. H., Backhouse, F. A., Semeralul, M. O., & Daskalakis, Z. J. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9657-8 · Cited by 28
He, X., Steinberg, E., Stefan, M., Fontaine, M., Simpson, H. B., & Marsh, R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23883 · Cited by 28
“Second, the DTI data were collected from 2012 to 2014; recent advances suggest that future studies should acquire at least thirty gradient directions and isotropic voxels (Jones et al., 2013).”
MethodAndersson, G., Orädd, G., Sultan, F. R., & Novikov, L. N. (2018) · Scientific Reports · doi:10.1038/s41598-018-30961-1 · Cited by 30
Takemura, H., Ogawa, S., Mezer, A., Horiguchi, H., Miyazaki, A., Matsumoto, K., Shikishima, K., Nakano, T., & Masuda, Y. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101826 · Cited by 29
Altered fronto-striatal fiber topography and connectivity in obsessive-compulsive disorder
Nakamae, T., Sakai, Y., Abe, Y., Nishida, S., Fukui, K., Yamada, K., Kubota, M., Denys, D., & Narumoto, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112075 · Cited by 28
“Higher structural connectivity by increasing axonal density and the degree of myelination would lead to reduced RD and increased FA [21].”
BackgroundInfluentialKim, E., Figueiredo, I., Simmons, C., Randall, K., Gonzalez, L. R., Wood, T., Ranieri, B., Sureda-Gibert, P., Howes, O. D., Pariante, C. M., et al. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.07.010 · Cited by 28
van Duinkerken, E., IJzerman, R. G., Klein, M., Moll, A. C., Snoek, F. J., Scheltens, P., Pouwels, P. J. W., Barkhof, F., Diamant, M., & Tijms, B. M. (2015) · Human Brain Mapping · doi:10.1002/hbm.23096 · Cited by 28
Impact of brain shift on subcallosal cingulate deep brain stimulation
Choi, K. S., Noecker, A. M., Riva‐Posse, P., Rajendra, J. K., Gross, R. E., Mayberg, H. S., & McIntyre, C. C. (2017) · Brain stimulation · doi:10.1016/j.brs.2017.12.001 · Cited by 27
Griffa, A., Ville, D. V. D., Herrmann, F. R., & Allali, G. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.02.023 · Cited by 29
“For example, an increased Dradial can be interpreted as myelination loss, edema, or increased axonal diameter, whereas Daxial has been associated with axonal injury, axonal density and fiber orientation spread (Jones et al., 2013).”
BackgroundEarly parenting is associated with the developing brains of children born very preterm
Treyvaud, K., Thompson, D. K., Kelly, C. E., Loh, W. Y., Inder, T. E., Cheong, J. L. Y., Doyle, L. W., & Anderson, P. J. (2020) · The Clinical Neuropsychologist · doi:10.1080/13854046.2020.1811895 · Cited by 30
In vivo biomarkers of structural and functional brain development and aging in humans
Franke, K., Bublak, P., Hoyer, D., Billiet, T., Gaser, C., Witte, O. W., & Schwab, M. G. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.002 · Cited by 29
Tondo, L. P., Viola, T. W., Fries, G. R., Kluwe‐Schiavon, B., Rothmann, L. M., Cupertino, R. B., Ferreira, P., Franco, A. R., Lane, S. D., Stertz, L., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01367-x · Cited by 27
Sex differences in myelin content of white matter tracts in adolescents with depression
Ho, T. C., Sisk, L. M., Kulla, A., Teresi, G. I., Hansen, M. M., Wu, H., & Gotlib, I. H. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01078-3 · Cited by 29
“While these metrics are a proxy for the degree of myelination, they are also sensitive to other related tissue properties, including axonal packing and fiber density [7].”
BackgroundIntegrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods
Venkadesh, S., & Horn, J. D. V. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.752332 · Cited by 25
“However, the fractional anisotropy, which is used in tractography, is sensitive to features such as the degree of myelination and curvature in axonal pathways (Jones et al., 2013).”
BackgroundBrain and cognitive ageing: The present, and some predictions (…about the future)
Cox, S. R., & Deary, I. J. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100032 · Cited by 29
Impact of asthma on the brain: evidence from diffusion MRI, CSF biomarkers and cognitive decline
Nair, A. K., Hulle, C. A. V., Bendlin, B. B., Zetterberg, H., Blennow, K., Wild, N., Kollmorgen, G., Suridjan, I., Busse, W. W., Dean, D., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad180 · Cited by 29
“Traditional dMRI methods such as diffusion tensor imaging (DTI), provide a range of metrics that offer valuable information (fractional anisotropy, mean, radial, and axial diffusivity), but have limitations in their biological inference.(30) Using multi-shell diffusion imaging, the three-compartment neurite orienta-”
Durkee, C. A., Sarlls, J. E., Hommer, D. W., & Momenan, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080952 · Cited by 27
“FA reflects a complex mixture of tissue properties including coherence of fiber orientation, myelination, and axonal density [15].”
BackgroundPediatric frontal lobe epilepsy: white matter abnormalities and cognitive impairment
Braakman, H. M. H., Vaessen, M. J., Jansen, J. F. A., Hall, M. H. J. A. D.-V., de Louw, A. J. A., Hofman, P. A. M., Vles, J. S. H., Aldenkamp, A. P., & Backes, W. H. (2013) · Acta Neurologica Scandinavica · doi:10.1111/ane.12183 · Cited by 28
Epilepsy-related cytoarchitectonic abnormalities along white matter pathways
Glenn, G. R., Jensen, J. H., Helpern, J. A., Spampinato, M. V., Kuzniecky, R., Keller, S. S., & Bonilha, L. (2016) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-312980 · Cited by 28
Cortical magnetization transfer abnormalities and connectome dysconnectivity in schizophrenia
Wei, Y., Collin, G., Mandl, R. C. W., Cahn, W., Keunen, K., Schmidt, R., Kahn, R. S., & van den Heuvel, M. P. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.05.029 · Cited by 28
Neuroimaging in the Diagnosis of Chronic Traumatic Encephalopathy: A Systematic Review
Sparks, P., Lawrence, T. P., & Hinze, S. (2017) · Clinical Journal of Sport Medicine · doi:10.1097/jsm.0000000000000541 · Cited by 32
Diffusion tensor imaging in metachromatic leukodystrophy
van Rappard, D. F., Königs, M., Steenweg, M. E., Boelens, J. J., Oosterlaan, J., van der Knaap, M. S., Wolf, N. I., & Pouwels, P. J. W. (2018) · Journal of Neurology · doi:10.1007/s00415-018-8765-3 · Cited by 27
“It results in axial diffusivity (AD), radial diffusivity (RD) and the derived fractional anisotropy (FA) and mean diffusivity (MD) [11].”
Horbrügger, M., Loewe, K., Kaufmann, J., Wagner, M., Schippling, S., Pawlitzki, M., & Schoenfeld, M. A. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101740 · Cited by 31
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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