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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
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
Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999) · Annals of Neurology · doi:10.1002/1531-8249(199902)45:2 · Cited by 3,513
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 501–600 of 2,327
Neuroimaging Biomarkers of a History of Concussion Observed in Asymptomatic Young Athletes
Orr, C., Albaugh, M. D., Watts, R., Garavan, H., Andrews, T. J., Nickerson, J. P., Gonyea, J. V., Hipko, S. G., Zweber, C., Logan, K., et al. (2015) · Journal of Neurotrauma · doi:10.1089/neu.2014.3721 · Cited by 51
“26 Fractional anisotropy (FA) measures the directionality of water motion.”
Structural network underlying visuospatial imagery in humans
Whittingstall, K., Bernier, M., Houde, J., Fortin, D., & Descoteaux, M. (2013) · Cortex · doi:10.1016/j.cortex.2013.02.004 · Cited by 55
“Finding diffusion metrics that reflect connectivity strength remains a challenge and an open question for the dMRI community (Jones et al., 2012).”
BackgroundInfluentialKraft, I., Schreiber, J., Cafiero, R., Metere, R., Schaadt, G., Bräuer, J., Neef, N. E., Müller, B., Kirsten, H., Wilcke, A., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.004 · Cited by 55
“The fact that 63–90% of voxels in the brain contain crossing fibers impairs a specific connection of diffusion indices such as FA to physiological consequences (Jones et al., 2013).”
BackgroundLeipold, S., Klein, C., & Jäncke, L. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1985-20.2020 · Cited by 58
Calamante, F., Smith, R. E., Tournier, J., Raffelt, D. A., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.070 · Cited by 54
“Despite the well-known limitations with interpretation of track counts (Jones et al., 2013), there has been great interest in the use of streamlines density as a surrogate measure of local axonal density.”
BackgroundMethodWinston, G. P., Vos, S. B., Caldairou, B., Hong, S.-J., Czech, M., Wood, T. C., Wastling, S. J., Barker, G. J., Bernhardt, B. C., Bernasconi, N., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102231 · Cited by 57
Ba, R., Wang, X., Zhang, Z., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09623-y · Cited by 53
“The SNR difference may be one of the reasons why the short-td group had a better fitting performance, although it was considered SNR greater than 10 is sufficient for dMRI calculation [50].”
BackgroundPires, P., Vives, A. S., Vives‐Gilabert, Y., Webb, S. M., Sainz, A., Resmini, E., Crespo, I., de Juan-Delago, M., & Gómez‐Ansón, B. (2015) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4322 · Cited by 55
“The observed increases in RD should, nevertheless, be interpreted with caution, especially in regions containing white matter tract crossings, as has been previously reported.(31) Our results expand the findings not only to patients in long-term remission but also in those with active hypercortisolism and those with r…”
BackgroundResultZhang, Y., Walter, R., Ng, P., Luong, P. N., Dutt, S., Heuer, H. W., Rojas-Rodriguez, J. C., Tsai, R. M., Litvan, I., Dickerson, B. C., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157218 · Cited by 54
Kosa, P. F., Ghazali, D., Tanigawa, M., Barbour, C. R., Cortese, I. C. M., Kelley, W. N., Snyder, B., Ohayon, J. M., Fenton, K. M., Lehky, T., et al. (2016) · Frontiers in Neurology · doi:10.3389/fneur.2016.00131 · Cited by 53
“While these confounding factors are easily controlled in animal experiments from which pathological-MRI correlations have been derived (30, 31), they are impossible to eliminate in the real-world experience of human clinical trials performed on multiple MRI scanners and spanning several years.”
BackgroundGenc, S., Malpas, C. B., Ball, G., Silk, T. J., & Seal, M. L. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1658-5 · Cited by 56
“inference of axon and myelin ‘organisation’ (Jones et al. 2013; Beaulieu 2002).”
BackgroundThe Superficial White Matter in Autism and Its Role in Connectivity Anomalies and Symptom Severity
Hong, S., Hyung, B., Paquola, C., & Bernhardt, B. C. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy321 · Cited by 53
“In ASD, recent studies leveraged diffusion-weighted imaging (DWI), a technique sensitive to regional tissue microstructure and fiber architecture through an analysis of water displacement properties (Beaulieu 2002; Jones et al. 2013; Jbabdi et al. 2015).”
MethodJavadi, S. A. H., Nabavi, A., Giordano, M., et al. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001347 · Cited by 53
Andica, C., Kamagata, K., Kirino, E., Uchida, W., Irie, R., Murata, S., & Aoki, S. (2021) · Molecular Autism · doi:10.1186/s13229-021-00456-4 · Cited by 53
“As mentioned in the Introduction section, DTI is reportedly sensitive but not specific to microstructural changes [14, 81].”
BackgroundDeep White Matter in Huntington's Disease
Phillips, O. R., Squitieri, F., Sánchez‐Castañeda, C., Elifani, F., Caltagirone, C., Sabatini, U., & Paola, M. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109676 · Cited by 52
Diffusion weighted imaging of prefrontal cortex in prodromal huntington's disease
Matsui, J., Vaidya, J. G., Johnson, H. J., Magnotta, V. A., Long, J. D., Mills, J. A., Lowe, M. J., Sakaie, K., Rao, S. M., Smith, M. M., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22273 · Cited by 53
Rosso, C., Valabrègue, R., Arbizu, C., Sophie Ferrieux, A., Vargas, P., Humbert, F., Attal, Y., Messé, A., Zavanone, C., Meunier, S., et al. (2013) · Brain stimulation · doi:10.1016/j.brs.2013.08.007 · Cited by 53
“The measurement of tract volume and its biological meaning is still a matter of debate [62] since for example tract volume depends on the size of the regions of interest chosen as seeds for probabilistic tractography.”
Grinberg, F., Maximov, I. I., Farrher, E., Neuner, I., Amort, L., Thönneßen, H., Oberwelland, E., Konrad, K., & Shah, N. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.033 · Cited by 52
“Due to growing evidence that a significant fraction, or even a majority of voxels contain complex fibres (Jones et al., 2013; Vos et al., 2012), this limitation of DTI gains more and more awareness.”
BackgroundJu, Y., Ou, W., Su, J., Averill, C. L., Liu, J., Wang, M., Wang, Z., Zhang, Y., Liu, B., Li, L., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.01.047 · Cited by 52
López, M. J. R., León, I., Góngora, D., Hernandez-Cabrera, J. A., Byrne, S., & Bobes, M. A. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw080 · Cited by 50
“narrowing of the tract, perturbation by crossing fibers), (Jones et al., 2013).”
BackgroundCottam, N. C., Ofori, K., Stoll, K. T., Bryant, M., Rogge, J. R., Hekmatyar, K., Sun, J., & Charvet, C. J. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1429-24.2025 · Cited by 59
Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy
Kamiya, K., Amemiya, S., Suzuki, Y., Kunii, N., Kawai, K., Mori, H., Kunimatsu, A., Saito, N., Aoki, S., & Ohtomo, K. (2015) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.2015-0027 · Cited by 51
Sun, Y., Yu, C., Lee, R., Bezerianos, A., Collinson, S. L., & Sim, K. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.01.025 · Cited by 52
Nashiro, K., Guevara‐Aguirre, J., Braskie, M. N., Hafzalla, G. W., Velasco, R., Balasubramanian, P., Wei, M., Thompson, P. M., Mather, M., Nelson, M. D., et al. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1929-16.2016 · Cited by 51
“Both FA and MD may also be influenced by factors such as fiber coherence and fiber crossings, which do not necessarily reflect white matter integrity (Jones et al., 2013).”
BackgroundGaudio, S., Quattrocchi, C. C., Piervincenzi, C., Zobel, B. B., Montecchi, F. R., Dakanalis, A., Riva, G., & Carducci, F. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.06.011 · Cited by 52
“…between our results and the others are mainly related to the direction and laterality of WM microstructural changes; although interpretations of WM measures are not completely explained (Jones et al., 2013), these differences could be mainly due to the different composition of the samples.”
Gong, S., Zhang, F., Norton, I., Essayed, W. I., Unadkat, P., Rigolo, L., Pasternak, O., Rathi, Y., Hou, L., Golby, A. J., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0197056 · Cited by 51
Zekelman, L., Zhang, F., Makris, N., He, J., Chen, Y., Xue, T., Liera, D., Drane, D. L., Yogesh Rathi, A., Golby, A. J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118739 · Cited by 51
“Additionally, we note that these measures are all sensitive to a variety of tissue properties including the myelination, axonal density, cell membranes, and microtubules (Beaulieu, 2009; Jones et al., 2013).”
InfluentialRozanski, V. E., Vollmar, C., Cunha, J. P. S., Tafula, S. M. N., Ahmadi, S., Patzig, M., Mehrkens, J., & Bötzel, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.009 · Cited by 50
“At present, there is no method providing unbiased statistical information on connectivity strength (Jones et al., 2013) and we are aware of the limitations of the statistical method applied in this study.”
BackgroundMethodPrefrontal cortex white matter tracts in prodromal H untington disease
Matsui, J., Vaidya, J. G., Wassermann, D., Kim, R. E. Y., Magnotta, V. A., Johnson, H. J., Paulsen, J. S., & PREDICT‐HD Investigators and Coordinators of the Huntington Study Group (2015) · Human Brain Mapping · doi:10.1002/hbm.22835 · Cited by 51
“Diffusion tensor scalar measures are, therefore, numerical representations of diffusion anisotropy degree or magnitude and are computed at the voxel level or averaged over a region of interest [Basser, 1995; Basser and Pierpaoli, 1996; Jones et al., 2013].”
BackgroundMethodVogel, K., Timmers, I., Kumar, V., Nickl‐Jockschat, T., Bastiani, M., Roebroeck, A. F., Herpertz‐Dahlmann, B., Konrad, K., Goebel, R., & Seitz, J. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.04.002 · Cited by 51
Wen, M., Heng, H. S. E., Ng, S. Y., Tan, L. C. S., Chan, L. L., & Tan, E. K. (2016) · Scientific Reports · doi:10.1038/srep35601 · Cited by 52
Measuring Compensation in Neurodegeneration using MRI
Gregory, S., Long, J., Tabrizi, S., & Rees, G. (2017) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000469 · Cited by 41
“anatomical connectivity or underlying white matter microstructure, as measured by diffusionweighted imaging may (with caution in terms of biological interpretation), also give some clue as to the biological changes, such as demyelination and axonal degeneration that occur during neurodegeneration (32-34).”
Amygdala habituation and uncinate fasciculus connectivity in adolescence: A multi-modal approach
Hein, T. C., Mattson, W. I., Dotterer, H. L., Mitchell, C. M., Lopez‐Duran, N. L., Thomason, M. E., Peltier, S., Welsh, R. C., Hyde, L. W., & Monk, C. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.058 · Cited by 54
Tschentscher, N., Ruisinger, A., Blank, H., Díaz, B., & von Kriegstein, K. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1435-18.2018 · Cited by 50
“Because gray matter and cerebrospinal fluid have low anisotropy, tracking only from and to gray matter voxels (instead of white matter voxels adjacent to the gray matter ROI) yields less robust probabilistic tractography results (Jones et al., 2013).”
BackgroundWhite matter alterations in anorexia nervosa: Evidence from a voxel-based meta-analysis
Barona, M., Brown, M., Clark, C. A., Frangou, S., White, T. J. H., & Micali, N. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.03.002 · Cited by 52
“Usually, FA is described as a marker of WM integrity, however it can be altered due to a variety of reasons (72, 73), such as larger axon diameter and lower packing density of fibers (both contributing to fewer barriers to diffusion leading to lower”
BackgroundCaballero, M. Á. A., Song, Z., Rubinski, A., Duering, M., Dichgans, M., Park, D. C., & Ewers, M. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12062 · Cited by 52
Madden, D. J., Jain, S., Monge, Z. A., Cook, A. D., Lee, A., Huang, H., Howard, C. M., & Cohen, J. R. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.09.010 · Cited by 58
“Diffusion weighted imaging measures the mobility of water molecules and is not a direct measure of white matter integrity or connectivity strength (Jones et al., 2013).”
Maffei, C., Girard, G., Schilling, K. G., Aydogan, D. B., Adluru, N., Zhylka, A., Wu, Y., Mancini, M., Hamamcı, A., Sarica, A., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119327 · Cited by 49
“We computed the SNR as the mean (signal)/standard deviation (noise) (Jones et al., 2013) for the b = 0 image (training: 63.”
MethodDissociable roles of the inferior longitudinal fasciculus and fornix in face and place perception
Hodgetts, C. J., Postans, M., Shine, J. P., Jones, D. K., Lawrence, A. D., & Graham, K. S. (2015) · eLife · doi:10.7554/elife.07902 · Cited by 51
“, 2013]) can influence the hindrance and restriction of water, as has been described elsewhere (Jones et al., 2013), the interpretation of DTI and its specific metrics (including MD and FA) is not straightforward.”
BackgroundSelf-citationChechlacz, M., Humphreys, G. W., Sotiropoulos, S. N., Kennard, C., & Cazzoli, D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2610-15.2015 · Cited by 51
“…assumes that greater inhibition and lateralization depends on stronger connectivity, there is no evidence to support a linear relationship between FA/HMOA and connectivity (i.e., that higher FA/HMOA is equivalent to higher connectivity; Beaulieu, 2002; Dell’acqua et al., 2013; Jones et al., 2013).”
BackgroundInfluentialChen, Z., Tie, Y., Olubiyi, O. I., Zhang, F., Mehrtash, A., Rigolo, L., Kahali, P., Norton, I. H., Pasternak, O., Yogesh Rathi, A., et al. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1344-5 · Cited by 49
Mikoláš, P., Hlinka, J., Škoch, A., Pitra, Z., Frodl, T., Španiel, F., & Hájek, T. (2018) · BMC Psychiatry · doi:10.1186/s12888-018-1678-y · Cited by 49
“more crossing fibers or that the membranes are more permeable [9].”
BackgroundMethodDevelopment of a transcallosal tractography template and its application to dementia
Archer, D. B., Coombes, S. A., McFarland, N. R., DeKosky, S. T., & Vaillancourt, D. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.065 · Cited by 46
Tunç, B., Yankowitz, L., Parker, D., Alappatt, J. A., Pandey, J., Schultz, R., & Verma, R. (2019) · Molecular Autism · doi:10.1186/s13229-019-0301-5 · Cited by 47
“logical processes is not well described [115].”
Geeraert, B., Chamberland, M., Lebel, R. M., & Lebel, C. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233244 · Cited by 54
“Fractional anisotropy (FA) and mean diffusivity (MD) describe total water diffusion and are simultaneously sensitive to many microstructural factors [20-23].”
BackgroundKamiya, K., Hori, M., Irie, R., Miyajima, M., Nakajima, M., Kamagata, K., Tsuruta, K., Saito, A., Nakazawa, M., Suzuki, Y., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.03.003 · Cited by 49
Wu, D., & Zhang, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.051 · Cited by 48
“Furthermore, the labeling fluorescent proteins used in AMBCA are distributed relatively uniformly throughout the axons and dendrites of infected neurons via active transport, whereas tractography infers potential macroscopic connections indirectly by following locally estimated water molecule diffusion profiles based…”
Tsao, H., Pannek, K., Boyd, R. N., & Rose, S. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0656-x · Cited by 52
“Given that streamline counts cannot be compared between participants (Jones et al. 2013), an asymmetry index (AI) was generated for each thalamocortical connection to allow comparison between TDC and CP groups (Rose et al. 2011).”
MethodInnocenti, G. M., Dyrby, T. B., Andersen, K. W., Rouiller, E. M., & Caminiti, R. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw161 · Cited by 48
“Given the current enhanced interest in the striatal and corticostriatal functions (Kress et al. 2013; Smith and Graybiel 2013; Stephenson-Jones et al. 2013; Znamenskiy and Zador 2013; Reig and Silberberg 2014) also related to their role in psychosis (Koch et al. 2014; Burguière et al. 2014; Nakamae…”
Assessing White Matter Microstructure in Brain Regions with Different Myelin Architecture Using MRI
Groeschel, S., Hagberg, G. E., Schultz, T., Balla, D. Z., Klose, U., Hauser, T., Nägele, T., Bieri, O., Prasloski, T., MacKay, A. L., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167274 · Cited by 47
White matter connectivity disruptions in early and chronic schizophrenia
Biase, M. A. D., Cropley, V., Baune, B. T., Olver, J. S., Amminger, G. P., Phassouliotis, C., Bousman, C., McGorry, P. D., Everall, I., Pantelis, C., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291717001313 · Cited by 49
“Although tractography provides a more direct estimate of underlying connectivity, this method is still susceptible to noise (Jones et al. 2013) and suffers from various spatial biases that preclude detection of all white matter connections (Thomas et al. 2014; Zalesky et al. 2016).”
BackgroundHo, N. F., Li, Z., Ji, F., Wang, M., Kuswanto, C. N., Sum, M. Y., Tng, H. Y., Sitoh, Y. Y., Sim, K., & Zhou, J. H. (2017) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.160090 · Cited by 48
The cingulum and cingulate U‐fibers in children and adolescents with autism spectrum disorders
Hau, J., Aljawad, S., Baggett, N., Fishman, I., Carper, R. A., & Müller, R. (2019) · Human Brain Mapping · doi:10.1002/hbm.24586 · Cited by 48
Diffusion MRI anisotropy in the cerebral cortex is determined by unmyelinated tissue features
Reveley, C., Ye, F. Q., Mars, R. B., Matrov, D., Chudasama, Y., & Leopold, D. A. (2022) · Nature Communications · doi:10.1038/s41467-022-34328-z · Cited by 51
Normalisation of brain connectivity through compensatory behaviour, despite congenital hand absence
Hahamy, A., Sotiropoulos, S. N., Slater, D. H., Malach, R., Johansen‐Berg, H., & Makin, T. R. (2015) · eLife · doi:10.7554/elife.04605 · Cited by 44
“This measurement is thought to reflect myelination and fibre density, but is also influenced by other micro-features (e.g., membrane permeability, tissue geometry, see ‘Discussion’) (Zatorre et al., 2012; Jones et al., 2013; Sampaio-Baptista et al., 2013).”
The association between the 5-HTTLPR and neural correlates of fear conditioning and connectivity
Klucken, T., Schweckendiek, J., Blecker, C. R., Walter, B., Kuepper, Y., Hennig, J., & Stark, R. M. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsu108 · Cited by 47
Epstein, K. A., & Kumra, S. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.10.010 · Cited by 46
“Finally, it is important to note that that the observed alterations in FA trajectory should be interpreted cautiously (Jones et al., 2013).”
BackgroundAnatomical Brain Networks on the Prediction of Abnormal Brain States
Iturria‐Medina, Y. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0122 · Cited by 51
“Additionally, the impossibility to discriminate between afferent and efferent connections and the not less-important issue of defining interpretable and accurate fiber pathway integrity measures are major problems ( Jones et al., 2012).”
Prediction of Selective Serotonin Reuptake Inhibitor Response Using Diffusion-Weighted MRI
DeLorenzo, C., Delaparte, L., Thapa-Chhetry, B., Miller, J. M., Mann, J. J., & Parsey, R. V. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00005 · Cited by 47
“Recent publications have highlighted several issues related to DW-MRI interpretation (Jones, 2010; Jones et al., 2012).”
BackgroundGlobal loss of myelin water over 5 years in multiple sclerosis normal-appearing white matter
Vavasour, I. M., Huijskens, S. C., Li, D. K. B., Traboulsee, A. L., Mädler, B., Kolind, S., Rauscher, A., Moore, G. W., MacKay, A. L., & Laule, C. (2017) · Multiple Sclerosis Journal · doi:10.1177/1352458517723717 · Cited by 49
Weinstein, M., Green, D., Rudisch, J., et al. (2017) · European Journal of Paediatric Neurology · doi:10.1016/j.ejpn.2017.09.012 · Cited by 47
Altered white matter development in children born very preterm
Young, J. M., Vandewouw, M. M., Morgan, B. R., Smith, M. L., Sled, J. G., & Taylor, M. J. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1614-4 · Cited by 53
“With increasing GA and ongoing development, FA is found to increase while RD and mean diffusivity (MD) decrease, due to contributing factors such as increasing myelination, improved coherence of fibres, increasing axonal density and reduced axonal caliber (Beaulieu 2002; Jones et al. 2013; Pannek et al. 2014).”
InfluentialDewey, D., Thompson, D. K., Kelly, C. E., Spittle, A. J., Cheong, J. L. Y., Doyle, L. W., & Anderson, P. J. (2019) · Acta Paediatrica · doi:10.1111/apa.14786 · Cited by 49
“These diffusion tensor imaging measures suggested that the children at risk for DCD had altered overall microstructural organisation and coherency of brain white matter, which could reflect underlying cellular differences such as reduced myelination, reduced axon size or density, or less coherent organisation of axons…”
BackgroundShen, X., Adams, M. J., Ritakari, T. E., Cox, S. R., McIntosh, A. M., & Whalley, H. C. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.06.011 · Cited by 48
“Changes in FA, which could result, for example, from increased transverse diffusion that is due to myelin and axonal disruption (51), may be masked by cooccurring processes such as fiber reorganization and glial reactivity.”
BackgroundJütten, K., Mainz, V., Gauggel, S., Patel, H., Binkofski, F., Wiesmann, M., Clusmann, H., & Na, C.-H. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00536 · Cited by 49
Imaging Alzheimer's genetic risk using diffusion MRI: A systematic review
Harrison, J. R., Bhatia, S., Tan, Z. X., Mirza-Davies, A., Benkert, H., Tax, C. M. W., & Jones, D. K. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102359 · Cited by 50
“On the other hand, the fODF is a function that characterises the probability of finding a fibre oriented along a particular axis (Jones et al., 2013).”
Self-citationKumar, R., Waisberg, E., Ong, J., Paladugu, P., Amiri, D., Saintyl, J., Yelamanchi, J., Nahouraii, R., Jagadeesan, R., & Tavakkoli, A. (2024) · Brain Sciences · doi:10.3390/brainsci14121266 · Cited by 52
Structural Connectivity is Differently Altered in Dementia with Lewy Body and Alzheimer’s Disease
Pizzi, S. D., Franciotti, R., Taylor, J., Esposito, R., Tartaro, A., Thomas, A., ONOFRJ, M. C., & Bonanni, L. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00208 · Cited by 45
“However, the use of DA and RD remain controversial because a change in RD can cause a fictitious change in DA and vice-versa in voxels characterized by crossing fibers (Wheeler-Kingshott and Cercignani, 2009; Jones et al., 2013).”
BackgroundDBSproc: An open source process for DBS electrode localization and tractographic analysis
Lauro, P. M., Vanegas‐Arroyave, N., Huang, L., Taylor, P. A., Zaghloul, K. A., Lungu, C., Saad, Z. S., & Horovitz, S. G. (2015) · Human Brain Mapping · doi:10.1002/hbm.23039 · Cited by 46
Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography
Dhollander, T., Emsell, L., Hecke, W. V., Maes, F., Sunaert, S. G., & Suetens, P. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.047 · Cited by 49
“As the amount of available techniques is rapidly growing, leading scientists in the general domain of DWI are still emphasizing the issue of overinterpretation of track counts (Jones et al., 2013).”
Network efficiency in autism spectrum disorder and its relation to brain overgrowth
Lewis, J. D., Theilmann, R. J., Townsend, J., & Evans, A. C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00845 · Cited by 50
Limbic white matter microstructure plasticity reflects recovery from depression
Bracht, T., Jones, D. K., Müller, T., Wiest, R., & Walther, S. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.08.031 · Cited by 51
Self-citationCurran, K. M., Emsell, L., & Leemans, A. (2015) · Springer eBooks · doi:10.1007/978-1-4939-3118-7_5 · Cited by 45
Dorsal and ventral language pathways in persistent developmental stuttering
Kronfeld-Duenias, V., Amir, O., Ezrati-Vinacour, R., Civier, O., & Ben‐Shachar, M. (2016) · Cortex · doi:10.1016/j.cortex.2016.04.001 · Cited by 48
Dupuy, M., & Chanraud, S. (2016) · International review of neurobiology · doi:10.1016/bs.irn.2016.04.003 · Cited by 46
White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging
Hansen, B., Khan, A. R., Shemesh, N., Lund, T. E., Sangill, R., Eskildsen, S. F., Østergaard, L., & Jespersen, S. N. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3741 · Cited by 46
“using a Siemens Trio 3T equipped with a 32 channel head coil. A twice refocused spin echo DW EPI sequence was used and inversion recovery was used to suppress cerebrospinal fluid (CSF) signal as per (Jones et al., 2013). Head motion during the acquisitions was avoided by inserting padding between the coil and the subj…”
MethodWu, Y., Zhang, F., Makris, N., Ning, Y., Norton, I. H., She, S., Peng, H., Yogesh Rathi, A., Feng, Y., Wu, H., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.019 · Cited by 46
Reijmer, Y. D., van den Heerik, M. S., Heinen, R., Leemans, A., Hendrikse, J., Vis, J. B. D., van der Kleij, L. A., Lucci, C., Hendriks, M. E., van Zandvoort, M. J. E., et al. (2018) · Stroke · doi:10.1161/strokeaha.118.021622 · Cited by 46
von Schwanenflug, N., Müller, D. K., King, J. A., Ritschel, F., Bernardoni, F., Mohammadi, S., Geisler, D., Roessner, V., Biemann, R., Marxen, M., et al. (2018) · Psychological Medicine · doi:10.1017/s003329171800212x · Cited by 47
“Further measures include axial (AD) and radial diffusivity (RD), which represent the diffusivity along the principal direction of fibers (AD) and perpendicular (RD) to it (Jones et al., 2013).”
BackgroundThe contribution of brain imaging to the understanding of psychopathy
Pujol, J., Harrison, B. J., Contreras‐Rodríguez, O., & Cardoner, N. (2018) · Psychological Medicine · doi:10.1017/s0033291718002507 · Cited by 52
Carpenter, K. L. H., Major, S. T., Tallman, C. W., Chen, L. W., Franz, L., Sun, J. M., Kurtzberg, J., Song, A. W., & Dawson, G. (2019) · Stem Cells Translational Medicine · doi:10.1002/sctm.18-0251 · Cited by 47
Goetschius, L. G., Hein, T. C., Mattson, W. I., Lopez‐Duran, N. L., Dotterer, H. L., Welsh, R. C., Mitchell, C. M., Hyde, L. W., & Monk, C. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.009 · Cited by 49
White Matter Indices of Medication Response in Major Depression: A Diffusion Tensor Imaging Study
Davis, A. D., Hassel, S., Arnott, S. R., Harris, J. K., Lam, R. W. M., Milev, R. V., Rotzinger, S., Zamyadi, M., Frey, B. N., Minuzzi, L., et al. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.05.016 · Cited by 46
Mapping the principal gradient onto the corpus callosum
Friedrich, P., Forkel, S. J., & de Schotten, M. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117317 · Cited by 47
The Vulnerability to Suicidal Behavior is Associated with Reduced Connectivity Strength
Bijttebier, S., Caeyenberghs, K., van den Ameele, H., Achten, E., Rujescu, D., Titeca, K., & Heeringen, C. V. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00632 · Cited by 46
“Previous studies showed that this method is not able to resolve crossing fiber bundles (Mori and van Zijl, 2002; Tournier et al., 2011; Jones et al., 2013).”
BackgroundMethodResultInfluentialWhite matter abnormalities in adults with bipolar disorder type-II and unipolar depression
Manelis, A., Soehner, A. M., Halchenko, Y. O., Satz, S., Ragozzino, R., Lucero, M. M., Swartz, H. A., Phillips, M. L., & Versace, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-87069-2 · Cited by 46
Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness
Edlow, B. L., Olchanyi, M., Freeman, H. J., Li, J., Maffei, C., Snider, S. B., Zöllei, L., Iglesias, J. E., Augustinack, J. C., Bodien, Y. G., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adj4303 · Cited by 55
Umarova, R. M., Reisert, M., Beier, T., Kiselev, V. G., Klöppel, S., Kaller, C. P., Glauche, V., Mader, I., Beume, L.-A., Hennig, J., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22503 · Cited by 44
“FA combines the contributions from the different subcompartments of the white matter into a single metric [Jones et al., 2013].”
BackgroundMotor and cortico-striatal-thalamic connectivity alterations in intrauterine growth restriction
Eixarch, E., Muñoz‐Moreno, E., Bargalló, N., Batallé, D., & Gratacós, E. (2015) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2015.12.028 · Cited by 47
Creative females have larger white matter structures: Evidence from a large sample study
Takeuchi, H., Taki, Y., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Sekiguchi, A., Iizuka, K., Yamamoto, Y., Hanawa, S., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23369 · Cited by 48
“In addition, there are differences in the neural correlates of individual differences of creativity between subjects with lower intelligence and those with higher intelligence [Jauk et al., 2015; Jung et al., 2009].”
BackgroundSchilling, K. G., Gao, Y., Stepniewska, I., Janve, V. A., Landman, B. A., & Anderson, A. W. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.09.004 · Cited by 48
Resting state networks in empirical and simulated dynamic functional connectivity
Glomb, K., Ponce‐Alvarez, A., Gilson, M., Ritter, P., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.065 · Cited by 42
“Lastly, fiber tracking results exhibit a high variability across subjects and sessions (Jones et al., 2013; Jeurissen et al., 2013).”
BackgroundMailleux, L., Franki, I., Emsell, L., et al. (2019) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2019.103569 · Cited by 45
Kennedy, K. M., & Raz, N. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00068-3 · Cited by 48
Unique white matter microstructural patterns in ADHD presentations—a diffusion tensor imaging study
Svatkova, A., Nestrasil, I., Rudser, K., Goldenring Fine, J., Bledsoe, J., & Semrud‐Clikeman, M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23243 · Cited by 44
Xu, J., McClintock, C. H., Balodis, I. M., Miller, L. F., & Potenza, M. N. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00330 · Cited by 44
“However, increases in both λ1 and FA have been linked to axonal loss, whereas increases in both MD and λT have been associated with demyelination (Song et al., 2002; Jones et al., 2013).”
BackgroundAnomalous White Matter Morphology in Adults Who Stutter
Cieslak, M., Ingham, R. J., Ingham, J. C., & Grafton, S. T. (2015) · Journal of Speech Language and Hearing Research · doi:10.1044/2015_jslhr-s-14-0193 · Cited by 48
“…is preferable to comparing stuttering behavior measures with a magnitude variable such as streamline count because (a) streamline count does not readily correspond to an interpretable physical quantity (Jones et al., 2013) and (b) the control group has no variance in the speech behavior measures.”
BackgroundDirect Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging
Koppers, S., & Merhof, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47157-0_7 · Cited by 43
“The b-value is set to b = 2700 smm2 and a Rician noise with SNR0 = 1 σ = 30 is added, which is chosen to be similar to the acquired real data.”
BackgroundInfluentialCapuani, S., Guerreri, M., Antonelli, A., Bernardo, S., Porpora, M. G., Giancotti, A., Catalano, C., & Manganaro, L. (2017) · Placenta · doi:10.1016/j.placenta.2017.08.003 · Cited by 44
Guan, X., Huang, P., Zeng, Q., Liu, C., Wei, H., Xuan, M., Gu, Q., Xu, X., Wang, N., Yu, X., et al. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9842-z · Cited by 44
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
