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
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 2,001–2,100 of 2,327
Can we see the living brain? A review on in vivo visualization of the brain
Walter, G. F. (2018) · International Neuroscience Journal · doi:10.1515/inj-2017-0002 · Cited by 1
Three Testing Perspectives on Connectome Data
Cabassi, A., Casa, A., Fontana, M., Russo, M., & Farcomeni, A. (2018) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-030-00039-4_3 · Cited by 1
Roebroeck, A. F. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_6 · Cited by 1
Functional MRI and Diffusion Tensor Imaging
Shah, G. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-62120-9_6 · Cited by 1
Imaging early brain structural and functional development
Ouyang, M., Smyser, C. D., Neil, J. J., & Huang, H. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00018-1 · Cited by 1
Análisis bromatológico y aislamiento de microorganismos con potencial probiótico del pulque
Dante Israel León de la, O., Colín, D. S. M., Padilla, D. P. R., Hurle, L. P., García, F. I. S., & Brito, R. S. (2012) · Dialnet (Universidad de la Rioja) · Cited by 1
Attention-deficit/hyperactivity disorder
Klein, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85654-6.00029-0 · Cited by 1
van Vliet, E. A., Immonen, R. J., Prager, O., et al. (2022) · Epilepsia Open · doi:10.1002/epi4.12643 · Cited by 1
“69 This need for large numbers of imaging shells necessitates rapid image acquisitions, typically EPI-based, which benefit from high-powered gradients with short rise times.”
Wegdan, A., Saad, A., Ahmed, S., et al. (2023) · THE NEW ARMENIAN MEDICAL JOURNAL · doi:10.56936/18290825-2023.17.70-76 · Cited by 1
Doughty, M., Danielli, E., Boshra, R., Ruiter, K. I., Minuzzi, L., Connolly, J. F., & Noseworthy, M. D. (2023) · Journal of Concussion · doi:10.1177/20597002231200372 · Cited by 1
Tvrdík, T., Melicherčík, Ľ., Šebeková, K., Szabó, J., Maková, M., Gogola, D., & Kašparová, S. (2023) · Measurement Science Review · doi:10.2478/msr-2023-0030 · Cited by 1
“These data quantify diffusion and its anisotropy in the whole voxel, where several populations of crossing white matter tracts are usually present [18]; therefore, we should interpret the results with caution [19].”
BackgroundConventional and advanced magnetic resonance imaging for degenerative cervical myelopathy
Seif, M., Dávid, G., Martin, A. R., & Freund, P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-95334-4.00011-5 · Cited by 1
Knowledge atlas of white matter microstructure: a bibliometric analysis
Song, W., Chu, Y., Yao, Z., Dong, M., Yang, S., Zhou, L., Qin, C., Zhu, H., Pan, D., Xiao, J., et al. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1397 · Cited by 1
“The second and third ranked most cited articles pertained to the use of diffusion-weighted MRI and general linear model in imaging neuroscience, respectively (17,18).”
Thurston, L. T., Skorska, M. N., Lobaugh, N. J., Zucker, K. J., Chakravarty, M. M., Lai, M., Chavez, S., & VanderLaan, D. P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300139 · Cited by 1
Windon, C., & Elahi, F. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-49963-0_30 · Cited by 1
Neukam, P. T., Müller, D. K., Deza-Lougovski, Y. I., Pooseh, S., Witt, S. H., Rietschel, M., & Smolka, M. N. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126666 · Cited by 1
Fan, F., Beare, R., Genc, S., Shapiro, J., Takagi, M., Hearps, S. J. C., Parkin, G. M., Rausa, V. C., Anderson, N., Fabiano, F., et al. (2024) · Journal of Neurosurgery: Pediatrics · doi:10.3171/2024.6.peds2499
Janko, M., Santaniello, S. D., Brockmann, C., Wolf, M. N., Grauhan, N. F., Schöffling, V., Dimova, V., Ponto, K. A., Hoffmann, E. M., Kleinekofort, W., et al. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16490 · Cited by 1
“In most settings, pure streamline count is not sufficient as a measurement criteria, especially so in terms of connection strength (Jones et al., 2013; Le Bihan et al., 2006).”
Microstructural abnormalities of the right hemisphere in preschool autism spectrum disorders
Yi, T., Liu, Y., Wei, W., He, S., & Jin, K. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.10.020 · Cited by 1
Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging
Martin, P., Altbach, M. I., & Bilgin, A. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.110309 · Cited by 1
Chamberland, M., Poirier, C., Hendriks, T., Shastin, D., Vilanova, A., & Leemans, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00002-1 · Cited by 1
Ghanem, J., Totzek, J. F., Henri-Bellemare, C., Raucher‐Chéné, D., Kiar, G., Patel, R., Chakravarty, M. M., Shah, J. L., Joober, R., MALLA, A. K., et al. (2025) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2025.111294 · Cited by 1
Reliable Multimodal Brain Signatures Predict Mental Health Outcomes in Children
Manning, K. Y., Llera, A., & Lebel, C. A. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.03.003 · Cited by 3
Adamo, M., Gayer, M., Jacobs, A., Raynaud, Q., Sebbah, R., Domenicantonio, G. D., Latypova, A., Vionnet, N., Kherif, F., Lutti, A., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf117 · Cited by 1
“77 Using the neurite orientation density and dispersion imaging model, we calculated maps of ICVF from multi-shell diffusion data across all acquired b -values as implemented in the AMICO toolbox.”
Sutkus, L. T., Li, Z., Sommer, K. M., & Dilger, R. N. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1576274 · Cited by 1
No Support for White Matter Alterations in Adults With Dyslexia: A Fixel-Based Diffusion MRI Study
Verhelst, H., Gerrits, R., & Karlsson, E. M. (2025) · Neurobiology of Language · doi:10.1162/nol.a.17 · Cited by 2
“Newer and more advanced models and frameworks have been developed to overcome the limitations of the DTI model (Jeurissen et al., 2013; Jones et al., 2013; Tournier et al., 2011).”
Horne, M. A., Lindgren, M., Nilsson, M., Roll, M., Юрьевич, Ш. Ю., Ståhlberg, F., & Topgaard, D. (2015) · doi:10.3389/978-2-88919-480-3 · Cited by 1
Roy, A., Turan, N., & Pradilla, G. (2019) · Springer eBooks · doi:10.1007/978-3-319-64379-3_14 · Cited by 1
Hernández, N. R., Hernández‐Montelongo, R., Bernal, J. M. C., & González, J. R. O. (2019) · Health and Technology · doi:10.1007/s12553-019-00376-7 · Cited by 1
Reid, A. A. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.90400 · Cited by 2
“Furthermore, these measures are not specific to any one of them and this causes difficulties in interpretation of the results [45].”
BackgroundYang, J., Carl, B., Nimsky, C., & Bopp, M. H. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233474 · Cited by 1
Q-Space Quantitative Diffusion MRI Measures Using a Stretched-Exponential Representation
Pieciak, T., Afzali, M., Bogusz, F., et al. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-73018-5_10 · Cited by 1
“The most common and simplest assumption in dMRI is the Gaussian assumption [18] on the spin displacement, which results in a mono-exponential decay of a diffusion signal versus the b-value parameter [10].”
BackgroundSelf-citationCortico-cortical Communication Dynamics
(2014) · Frontiers research topics · doi:10.3389/978-2-88919-288-5 · Cited by 1
Lesion-Behavior Awake Mapping with Direct Cortical and Subcortical Stimulation
Riès, S., Jordan, K., Knight, R. T., & Berger, M. S. (2022) · Neuromethods · doi:10.1007/978-1-0716-2225-4_14 · Cited by 1
A symmetry-based method to infer structural brain networks from tractography dataPreprint
Shadi, K., Bakhshi, S., Gutman, D. A., Mayberg, H. S., & Dovrolis, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.03788 · Cited by 1
“, the Number of Streamlines, or NoS, in deterministic tractography) should not be viewed as equivalent to “fiber count”, and similarly, the connectivity likelihoods inferred from probabilistic tractography should not be viewed as “connection strengths” (Jones et al., 2013).”
BackgroundReliability and Uncertainty in Diffusion MRI Modelling
Charles, C. N. (2016) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 1
Shafer, A. T. (2015) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3cc0v533 · Cited by 1
Brain functional connectivity in regions that exhibit age-related cortical thinning
Vieira, B. H. (2018) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.59.2018.tde-17042018-130342 · Cited by 1
Robert, M. T., Gutterman, J., Ferre, C. L., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/609313 · Cited by 1
“The estimated number of streamlines may be an oversimplified approach that may neglect potential micro-structural changes [48].”
BackgroundTractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tractPreprint
Rheault, F., BENEDICTIS, A. D., Daducci, A., Maffei, C., Tax, C. M. W., Romascano, D., Caverzasi, E., Morency, F. C., Corrivetti, F., Pestilli, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/623892 · Cited by 1
Gong, T., Tong, Q., He, H., Li, Z., & Zhong, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.13075 · Cited by 1
“…in the intensity values of the acquired diffusion weighted images (DWI), which reflects the amount of hindrance experienced by water molecules along the applied direction of diffusion gradient owing to barriers and obstacles imposed by micro-structures [Assaf and Cohen, 2013; Jones et al., 2013].”
BackgroundFritz, F. J., Poser, B. A., & Roebroeck, A. F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.15.098269 · Cited by 1
“…as Fractional Anisotropy (FA) is sensitive, it is also unspecific, since differences in FA can reflect different axonal properties such as axon density, diameter distribution and myelination (Assaf et al., 2004; Assaf and Pasternak, 2008; Beaulieu, 2002; De Santis et al., 2014; Jones et al., 2013).”
Webb, C. E., Perez, P. M. V., Hoagey, D. A., Gonen, C., Rodrigue, K. M., & Kennedy, K. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.04.368423 · Cited by 1
Hardy, P. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27417 · Cited by 1
In vivo super-resolution track-density imaging for thalamic nuclei identificationPreprint
Basile, G. A., Bertino, S., Bramanti, A., Anastasi, G., Milardi, D., & Cacciola, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.03.425122 · Cited by 1
“…well-known limitations concerning the interpretation of streamline density (for tractography-based methods) and between-voxel correlation/covariance of blood oxygen level dependent (BOLD) signal (for resting-state fMRI) as measures of neural connectivity (Buckner et al. 2013; Jones et al. 2013).”
BackgroundMolecular simulation and drug design.
Lybrand, T. P. (1991) · PubMed · Cited by 1
Neuroimaging correlates of hearing loss, tinnitus, and hyperacusis
Koops, E. A. (2021) · doi:10.33612/diss.177289799 · Cited by 1
Grotheer, M., Rosenke, M., Wu, H., Kular, H., Querdasi, F. R., Natu, V. S., Yeatman, J. D., & Kalanit Grill-spector, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.29.437583 · Cited by 1
Brignoni‐Pérez, E., Dubner, S. E., Ben‐Shachar, M., et al. (2021) · doi:10.31234/osf.io/phcw6 · Cited by 1
McCormick, E. M., & Kievit, R. (2022) · doi:10.31234/osf.io/8xf53 · Cited by 1
Microvascular and blood-brain barrier dysfunction in Alzheimer’s disease
van de Haar, H. J. (2016) · Research Publications (Maastricht University) · doi:10.26481/dis.20161110hh · Cited by 1
Fast sensorimotor learning in middle-aged adultsPreprint
Kyriazis, D. A., Uji, M., Bouyagoub, S., Cercignani, M., Ford, P. R., & Sigala, N. (2022) · Research Square · doi:10.21203/rs.3.rs-1922630/v1 · Cited by 1
Neurosurgical treatment planning
Bastos, D. C. A., Bi, W. L., Olubiyi, O. I., Tharin, S. A., & Golby, A. J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822835-7.00041-x · Cited by 1
Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI
Estéban, O. (2013) · Hispana · Cited by 1
Enhancing Precision in Human NeurosciencePreprint
Nebe, S., Reutter, M., Baker, D. H., Bölte, J., Domes, G., Gamer, M., Gärtner, A., Gießing, C., Mann, C., Hilger, K., et al. (2023) · doi:10.31234/osf.io/m8c4k · Cited by 1
Microscopic fractional anisotropy asymmetry in unilateral temporal lobe epilepsyPreprint
Arezza, N. J. J., Abbas, H. H., Chadwick, C., et al. (2023) · medRxiv · doi:10.1101/2023.05.10.23289785 · Cited by 1
Dimitriadis, S. I. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.31.543029 · Cited by 1
Abnormal white matter development during early childhood in autism and developmental disabilityPreprint
Liu, Y., Zhang, J., Rolls, E. T., Dai, Y., Geng, S., Deng, L., Chen, Z., Zhang, Y., Tao, M., Zhang, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.15.567183 · Cited by 1
MRI analysis of brain connectivity in a mouse model of Alzheimer’s disease
Degiorgis, L. (2019) · theses.fr (ABES) · doi:10.70675/5334d86bz9cc5z451cz865fzcf91895a5400 · Cited by 1
HARP: Hierarchical Anatomical Refinement of Pathways for Whole-Brain TractographyPreprint
Leserri, S., Rockland, K. S., & Aydogan, D. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728127 · Cited by 1
“…divergence of layer-specific projections (Coudé et al., 2018; Kita & Kita, 2012; Parent & Parent, 2005), do not readily transfer to tractography, due to the fundamental mismatch between data from single-neurons/axon measurements and the fiber bundles represented by streamlines (Jones et al., 2013).”
Leung, S. (2014) · YorkSpace (York University) · Cited by 1
Han, X., Hu, R., Ping, X., Chen, J., Liu, J., Liu, Z., Jin, X., Gao, X., Lin, M., Zhao, Y., et al. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71908
“16 Brain tissue contains multiple barriers and compartments, including cell membranes, neurites, myelin, extracellular space, and pathological deposits, which cause diffusion to deviate from Gaussian behavior.”
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“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).”
Experience Dependent Plasticity over short and long timescales
Lyness, C. R. (2015) · UCL Discovery (University College London)
Extraction of Structural Metrics from Crossing Fiber Models
Riffert, T. (2014) · Qucosa (Saxon State and University Library Dresden)
Programmable Modified Fractional Comb Decimation Filter
Babić, D., & Renfors, M. K. (2002) · INFM-OAR (INFN Catania) · doi:10.5281/zenodo.37888
Diffusion Tensor Imaging as a Diagnostic and Research Tool: A Study on Preterm Infants
Saunavaara, V. (2014) · UTUPub (University of Turku)
Braakman, H. M. H. (2013) · Research Publications (Maastricht University) · doi:10.26481/dis.20130614hb
Morphological alterations in frontotemporal dementia:
Santillo, A. F. (2014) · Lund University Publications (Lund University)
White matter integrity and age related differences in reaction time components
Yang, Y. (2013) · Human Biology
Tax, C. M. W., & Leemans, A. (2014) · Neuropraxis · doi:10.1007/s12474-014-0050-3
LUCCONI, G., Romeo, C., BONETTI, R., et al. (2015) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519415400205
Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI
Estéban, O. (2015) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.38431 · Cited by 1
Garraux, G., Ziegler, E., Rouillard, M., André, E., Coolen, T., Stender, J., Balteau, E., & Phillips, C. L. M. (2014) · Neurology · doi:10.1212/wnl.82.10_supplement.p6.066
Action control in uncertain environments
Smittenaar, P. (2015) · UCL Discovery (University College London)
Resilience and brain connectivity
Galinowski, A., Miranda, R., Lemaître, H., et al. (2013) · European Psychiatry · doi:10.1016/j.eurpsy.2013.09.156
OPINION Neuroimaging insights into network-based neurodegeneration
Greicius, M. D., & Kimmel, D. L. (2012)
Structural connectivity based on diffusion Kurtosis imaging
Loução, R. (2015) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Neurocognitive networks in higher-level visual perception
Postans, M. (2015) · ORCA Online Research @Cardiff (Cardiff University)
A connectivity-based approach to the pathophysiology of hemiballism
Rozanski, V. E., Wick, F., da Silva, N. M., et al. (2016) · Basal Ganglia · doi:10.1016/j.baga.2016.02.002
Characterizing white matter microstructure of the reward system in depression
Bracht, T. (2015) · ORCA Online Research @Cardiff (Cardiff University)
Structural and functional connectivity in multiple sclerosis
Sampaio, A. (2015) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.34626/9atj-na03
The role of thalamocortical networks in the symptomatology of autism spectrum disorders
Nair, A. (2015) · eScholarship (California Digital Library)
Jun, J. M., & Castellanos, F. X. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139035491.017
Motor, sensory and executive function brain networks in children with unilateral cerebral palsy
Scheck, S. M. (2016) · The University of Queensland · doi:10.14264/uql.2016.526
Charvet, C. J., & Krienen, F. M. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00075-0
matter microstructure alterations at term-equivalent age relative to term-born controls
Kelly, C. E., Cheong, J. L. Y., Matthews, L. G., Leemans, A., Seal, M. L., Doyle, L. W., Anderson, P. J., Spittle, A. J., & Thompson, D. K. (2016)
Kim, S. (2017) · MPG.PuRe (Max Planck Society)
Neural Substrates of Parkinson's Disease
Koshimori, Y. (2016) · TSpace (University of Toronto)
Auning, E. (2015) · NORA - Norwegian Open Research Archives
Imaging in Neurology Research II: Exploring Plasticity and Cognitive Networks by In Vivo MRI
Hamaide, J., Ruijssevelt, L. V., Kara, F., Groof, G. D., & der Linden, A. V. (2017) · Springer eBooks · doi:10.1007/978-3-319-42202-2_29
Kucyi, A. (2014) · TSpace (University of Toronto)
Domen (2017) · doi:10.26481/dis.20170412pd
Wang, G. (2013) · TSpace (University of Toronto)
Functional and structural organisation of the visual system in human albinism
Alvarez, I. (2017) · UCL Discovery (University College London)
Serpa, M. H. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2017.tde-19062017-075614
fMRT und Traktografie in der Gliomchirurgie
Nimsky, C., & Carl, B. (2017) · Springer eBooks · doi:10.1007/978-3-662-48694-8_10
Ailion, A. S. (2022) · Digital Archive @ GSU · doi:10.57709/10755972
Ashwal, S., Ghosh, N., Barnes, S. R., Oyoyo, U. E., Tong, K., & Holshouser, B. A. (2017) · European Journal of Paediatric Neurology · doi:10.1016/j.ejpn.2017.04.1085
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
