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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InfluentialImaging of Motor Cortex Physiology in Parkinson's Disease
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Mapping population-based structural connectomes
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“However, that count is not considered to be reliable when measuring the coupling strength (Fornito et al., 2013; Smith et al., 2013; Jones et al., 2013).”
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Besseling, R. M. H., Jansen, J. F. A., Overvliet, G. M., van der Kruijs, S. J. M., Ebus, S. C. M., de Louw, A. J. A., Hofman, P. A. M., Vles, J. S. H., Aldenkamp, A. P., & Backes, W. H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083568 · Cited by 91
“FA is at best an indirect measure for white matter integrity, and its accuracy is compromised at regions of fiber crossing, kissing, bending and fanning [61,62].”
A fiber coherence index for quality control of B-table orientation in diffusion MRI scans
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Parcellation influence on the connectivity‐based structure–function relationship in the human brain
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High-Resolution MR Imaging of the Human Brainstem In vivo at 7 Tesla
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“DTI, however, is commonly limited by the relatively coarse spatial resolution available, which makes detailed analysis of smaller fiber tracts difficult, and often fails in regions of crossing fibers (Jones et al., 2013).”
BackgroundSelf-citationDimensionality reduction of diffusion MRI measures for improved tractometry of the human brain
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““streamlines” (Jones et al., 2013; Mukherjee et al., 2008).”
Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls
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MethodInfluentialBrain white matter structure and information processing speed in healthy older age
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“Although ascribing these biomarkers directly to white matter ‘integrity’ is problematic (Jones et al. 2013), a number of authors have used them to investigate links between brain structure and processing speed in older age.”
White matter microstructure alterations of the medial forebrain bundle in melancholic depression
Bracht, T., Horn, H., Strik, W. K., Federspiel, A., Schnell, S., Höfle, O., Stegmayer, K., Wiest, R., Dierks, T., Müller, T., et al. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.10.048 · Cited by 90
“Therefore, novel white matter imaging techniques should be used in future studies (Jones et al., 2013).”
MethodLongitudinal reliability of tract‐based spatial statistics in diffusion tensor imaging
Madhyastha, T. M., Mérillat, S., Hirsiger, S., Bezzola, L., Liem, F., Grabowski, T. J., & Jäncke, L. (2014) · Human Brain Mapping · doi:10.1002/hbm.22493 · Cited by 94
Diffusional kurtosis imaging of the developing brain
Paydar, A., Fieremans, E., Nwankwo, J. I., Lazar, M., Sheth, H. D., Adisetiyo, V., Helpern, J. A., Jensen, J. H., & Milla, S. S. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3764 · Cited by 91
“It has been proposed that axial diffusivity characterizes axonal architecture, while radial diffusivity reflects myelin integrity,(9,12,14) though such simplified interpretations should be regarded with caution.(15) From diffusion tensor eigenvalues, fractional anisotropy (FA), which is a primary index of diffusional…”
BackgroundTractostorm: The what, why, and how of tractography dissection reproducibility
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. (2020) · Human Brain Mapping · doi:10.1002/hbm.24917 · Cited by 95
“…in connectivity matrix or density maps) is still very much present as a way to compare groups (Girard, Whittingstall, Deriche, & Descoteaux, 2014; Jones, Knösche, & Turner, 2013; Sotiropoulos & Zalesky, 2017), despite not being directly related to anatomy or connection strength (Jones, 2010;…”
Levit, A., Hachinski, V., & Whitehead, S. N. (2020) · GeroScience · doi:10.1007/s11357-020-00164-6 · Cited by 96
“Despite this, and with some controversy (Jones et al. 2013), DTI measures such as FA are widely inferred as a measure of white matter integrity.”
BackgroundInfluentialQuantitative streamlines tractography: methods and inter-subject normalisation
Smith, R. E., Raffelt, D. A., Tournier, J., & Connelly, A. (2022) · Aperture Neuro · doi:10.52294/apertureneuro.2022.2.neod9565 · Cited by 91
Doxey, C. R., & Kirwan, C. B. (2014) · Hippocampus · doi:10.1002/hipo.22389 · Cited by 97
“This could be because although measures of FA are typically associated with indications of WM integrity, this may not be the most accurate description (Jones et al., 2013).”
BackgroundRémy, F., Vayssière, N., Saint‐Aubert, L., Barbeau, E. J., & Pariente, J. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.01.014 · Cited by 87
“Although the strength of hippocampal connectionswith other limbic structures cannot be simply derived from our anatomical data (Jones et al., 2013), it is important to note that our results are coherent with functional connectivity studies in AD (Allen et al., 2007; Bai et al., 2009; Greicius et…”
ResultAsymmetry and Structure of the Fronto-Parietal Networks Underlie Visuomotor Processing in Humans
Budisavljević, S., Dell’Acqua, F., Zanatto, D., Begliomini, C., Miotto, D., Motta, R., & Castiello, U. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv348 · Cited by 90
“First, the volumetric measures can depend on the underlying tractography algorithm and the experimental conditions (Jones and Cercignani 2010; Jones et al. 2012).”
InfluentialMyelin changes at the early stage of 5XFAD mice
Gu, L., Wu, D., Tang, X., Qi, X., Li, X., Bai, F., Chen, X., Ren, Q., & Zhang, Z. (2018) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2017.12.013 · Cited by 87
The superficial white matter in Alzheimer's disease
Phillips, O. R., Joshi, S. H., Piras, F., Orfei, M. D., Iorio, M., Narr, K. L., Shattuck, D. W., Caltagirone, C., Spalletta, G., & Paola, M. D. (2016) · Human Brain Mapping · doi:10.1002/hbm.23105 · Cited by 87
“While we don’t know the precise biological processes underlying variations in DTI parameters (for review see: [Jones et al., 2012]) and caution should be used in interpreting the results, these large increases in diffusivity strongly suggest changes in superficial white matter tissue structure.”
BackgroundEffects of bilingualism on white matter integrity in older adults
Anderson, J. A. E., Grundy, J. G., de Frutos‐Lucas, J., Barker, R. M., Grady, C. L., & Bialystok, E. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.038 · Cited by 87
Kelly, C. E., Cheong, J. L. Y., Fam, L. G., Leemans, A., Seal, M. L., Doyle, L. W., Anderson, P. J., Spittle, A. J., & Thompson, D. K. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9361-0 · Cited by 98
Functional connectomics from a “big data” perspective
Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.031 · Cited by 85
“Given that the accurate biological interpretation for these indices remains ambiguous and controversial (Jones et al., 2013), the relevant results should be carefully explained to avoid overestimation.”
BackgroundThe subcortical and neurochemical organization of the ventral and dorsal attention networks
Alves, P. N., Forkel, S. J., Corbetta, M., & de Schotten, M. T. (2022) · Communications Biology · doi:10.1038/s42003-022-04281-0 · Cited by 89
Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI
Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Cardoso, M. J., Modat, M., Bainbridge, A., Kendall, G. S., Robertson, N. J., Marlow, N., & Ourselin, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.010 · Cited by 84
“From the transformations, we rotated the bvectors and modulated the image intensities by the Jacobian determinant (Jones et al., 2013), assuming that the largest part of the rotation in the affine transformation was due to bulk motion (Leemans and Jones, 2009).”
Girard, G., Caminiti, R., Battaglia-Mayer, A., St-Onge, E., Ambrosen, K. S., Eskildsen, S. F., Krug, K., Dyrby, T. B., Descoteaux, M., Thiran, J.-P., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117201 · Cited by 83
Toward valid and reliable brain imaging results in eating disorders
Frank, G., Favaro, A., Marsh, R., Ehrlich, S., & Lawson, E. A. (2018) · International Journal of Eating Disorders · doi:10.1002/eat.22829 · Cited by 85
“DWI measures the diffusion of water molecules in brain pathways (Jones et al., 2013).”
Lundell, H., Nilsson, M., Dyrby, T. B., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-45235-7 · Cited by 85
Dimitrova, R., Pietsch, M., Ciarrusta, J., Fitzgibbon, S. P., Williams, L. Z. J., Christiaens, D., Cordero‐Grande, L., Batallé, D., Makropoulos, A., Schuh, A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118488 · Cited by 97
Dall’Acqua, P., Johannes, S., Mica, L., Simmen, H., Glaab, R., Fandino, J., Schwendinger, M., Meier, C., Ulbrich, E. J., Müller, A. F., et al. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00280 · Cited by 91
“Lastly, confounding sources not completely controllable affecting intrasession DTI variability include subject head motion (Jones and Cercignani, 2010; Wang et al., 2012; Jones et al., 2013).”
BackgroundLyall, D. M., Cox, S. R., Lyall, L. M., Celis-Morales, C., Cullen, B., Mackay, D. F., Ward, J., Strawbridge, R. J., McIntosh, A. M., Sattar, N., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00069-9 · Cited by 97
Orientation dependence of magnetization transfer parameters in human white matter
Pampel, A., Müller, D. K., Anwander, A., Marschner, H., & Möller, H. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.068 · Cited by 84
“The assumption of a distinct fiber population does not hold for the majority of WM voxels (’crossing fiber problem’) [28], and even in regions with relatively coherent fiber bundles, such as the corpus callosum, axons are not perfectly parallel but demonstrate some degree of spreading or bending [29–31].”
BackgroundTryptophan Metabolism and White Matter Integrity in Schizophrenia
Chiappelli, J., Postolache, T. T., Kochunov, P., Rowland, L. M., Wijtenburg, S. A., Shukla, D. K., Tagamets, M. A., Du, X. M., Savransky, A., Lowry, C. A., et al. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.66 · Cited by 84
“Although the proper interpretation of FA regarding microanatomy and physiology remains disputed (Jones et al, 2013), this parameter is robust for clinical significance and reliability.”
BackgroundProix, T., Spiegler, A., Schirner, M., Rothmeier, S., Ritter, P., & Jirsa, V. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.016 · Cited by 86
The visual white matter: The application of diffusion MRI and fiber tractography to vision science
Rokem, A., Takemura, H., Bock, A. S., Scherf, K. S., Behrmann, M., Wandell, B. A., Fine, I., Bridge, H., & Pestilli, F. (2017) · Journal of Vision · doi:10.1167/17.2.4 · Cited by 92
Connectomic approaches before the connectome
Catani, M., de Schotten, M. T., Slater, D. A., & Dell’Acqua, F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.109 · Cited by 87
Structural Brain Network Characteristics Can Differentiate CIS from Early RRMS
Muthuraman, M., Fleischer, V., Kolber, P. L., Luessi, F., Zipp, F., & Groppa, S. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00014 · Cited by 85
“The diffusion-weighted MR signal provides useful qualitative results on the connection patterns of brain regions, however they are only the best possible guesses as the probability is rather an indication for increased connectivity (Jones et al., 2013).”
BackgroundBudinger, T. F., Bird, M. D., Frydman, L., Long, J. R., Mareci, T. H., Rooney, W. D., Rosen, B. R., Schenck, J. F., Schepkin, V. D., Sherry, A. D., et al. (2016) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-016-0561-4 · Cited by 83
“Diffusion-weighted imaging (DWI) provides a unique view of white matter in the brain through measurement of restricted translational diffusion of water by the axons [57, 58].”
BackgroundAnatomy and Connectivity of the Subthalamic Nucleus in Humans and Non-human Primates
Emmi, A., Antonini, A., Macchi, V., Porzionato, A., & De, R. (2020) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2020.00013 · Cited by 84
“As far as DWI is concerned, the anatomical accuracy of brain connections derived from diffusion MRI tractography appears to be inherently limited (Jones et al., 2013; Thomas et al., 2014).”
BackgroundDissecting the Fornix in Basic Memory Processes and Neuropsychiatric Disease: A Review
Benear, S. L., Ngo, C. T., & Olson, I. R. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0749 · Cited by 95
“DWI also cannot tell you the biological reason underlying differences in microstructure ( Filley and Fields, 2016; see Assaf et al., 2017; Jones et al., 2013; O’Donnell and Pasternak, 2015).”
Fiber clustering versus the parcellation-based connectome
O'Donnell, L. J., Golby, A. J., & Westin, C.-F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.066 · Cited by 83
“This example of one crucial tract illustrates the importance of choosing a tractography algorithm and/or modeling method that accounts for crossing fibers (Bastiani et al., 2012; Jones et al., 2013), as well as the inherent uncertainty in mapping of the brain's connections via neuroimaging, especially the difficulty o…”
MethodMagnetic Resonance Imaging of Human Olfactory Dysfunction
Han, P., Zang, Y., Joshi, A., & Hummel, T. R. (2019) · Brain Topography · doi:10.1007/s10548-019-00729-5 · Cited by 87
Yeh, C., Smith, R. E., Liang, X., Calamante, F., & Connelly, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.047 · Cited by 81
“Despite promising applications of this analysis approach, a number of problems associated with quantification of the structural connectome based on streamlines tractograms are well-recognised (see for example (Jones et al., 2013)).”
BackgroundMethodInfluentialA macaque connectome for large-scale network simulations in TheVirtualBrain
Shen, K., Bezgin, G., Schirner, M., Ritter, P., Everling, S., & McIntosh, A. R. (2019) · Scientific Data · doi:10.1038/s41597-019-0129-z · Cited by 78
Wright, A. D., Jarrett, M., Vavasour, I. M., Shahinfard, E., Kolind, S., van Donkelaar, P., Taunton, J. A., Li, D., & Rauscher, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150215 · Cited by 82
“In fact, a fundamental limitation of interpreting changes in DTI metrics is the inability of this technique to distinguish between multiple factors that influence them, including fiber diameter and density, fiber orientation, membrane permeability, edema, and demyelination [80].”
Avena‐Koenigsberger, A., Mišić, B., Hawkins, R. X. D., Griffa, A., Hagmann, P., Goñi, J., & Sporns, O. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1238-5 · Cited by 79
“…to perform better at the integration measures (S(RH) [ S(LH)) are colored with cyan (top and bottom right panels) Brain Struct Funct count, or fiber density) and the physiological or anatomical traits determining information flow across white matter pathways remains unclear (Jones et al. 2013).”
BackgroundAvesani, P., McPherson, B., Hayashi, S., Caiafa, C. F., Henschel, R., Garyfallidis, E., Kitchell, L. M., Bullock, D. N., Patterson, A. J., Olivetti, E., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0073-y · Cited by 81
Merluzzi, A. P., Dean, D. C., Adluru, N., Suryawanshi, G. S., Okonkwo, O. C., Oh, J. M., Hermann, B. P., Sager, M. A., Asthana, S., Zhang, H., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.03.026 · Cited by 78
“…et al., 2006; Madden et al., 2004; Nazeri et al., 2015a; Salat et al., 2005; Sullivan and Pfefferbaum, 2006), it is inherently a nonspecific technique; a change in FA could reflect changes in myelination, axon diameter, axon packing density, or increased membrane permeability (Jones et al., 2013).”
Neuroimaging in vascular cognitive impairment: a state-of-the-art review
Heiss, W., Rosenberg, G. A., Thiel, A., Berlot, R., & Reuck, J. D. (2016) · BMC Medicine · doi:10.1186/s12916-016-0725-0 · Cited by 86
“matter integrity, and it is advantageous that several metrics are used to provide a fuller characterization of microstructural alterations [44, 45].”
BackgroundRodríguez‐Cruces, R., Bernhardt, B. C., & Concha, L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116706 · Cited by 78
A Critical Review of White Matter Changes in Huntington’s Disease
Casella, C., Lipp, I., Rosser, A., Jones, D. K., & Metzler‐Baddeley, C. (2020) · Movement Disorders · doi:10.1002/mds.28109 · Cited by 82
“However, an increase in RD might not necessarily correspond to myelin loss; rather, it can have multiple meanings, including axonal loss.(67) Furthermore, in regions of crossing fibers, increased RD within a specific tract could be attributed to a less coherent alignment of fibers, more crossing fibers from other bund…”
BackgroundInfluentialSelf-citation
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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