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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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]
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“Previous studies in this field have used the diffusion tensor model to estimate local axonal orientations and obtain connectivity metrics, however this model has inherent limitations (Jones et al., 2013).”
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“FA in a given region may further be influenced by factors such as axon diameter, packing density (Takahashi et al., 2002), or membrane permeability (i.e., reduced boundary effectiveness; Jones et al., 2013).”
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“The NODDI model r 3082 r separates the diffusion signal into three compartments: intracellular, extracellular, and CSF [Zhang et al., 2012].”
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“…of this work, FA is perhaps the most widely used diffusion MRI metric in studies of dementia, but recent studies suggest that metrics AX and RD might have more specificity for certain pathologies and may be more appropriate descriptors of neurodegeneration (Mahoney et al. 2014; Jones et al. 2013).”
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“Because tracts with highly aligned axons will exhibit a high degree of directionality of diffusion, FA is a useful though indirect measure of the microstructural organization of white-matter (Jones et al., 2013).”
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“…in their orientation (Axer and Keyserlingk, 2000), CBd and FMin contain multiple subregions and crossing fibers (Catheline et al., 2010; Jones et al., 2013a; Malykhin et al., 2011), and inferior association fibers (IFOF) may include multiple functionally and anatomically specific…”
InfluentialNew Insights in Trigeminal Anatomy: A Double Orofacial Tract for Nociceptive Input
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“A post-mortem diffusion tensor imaging (DTI) study based on a diffusion weighted MRI (DW-MRI) scan could contribute to our insights in the trigeminal fibers, because this is currently the only capable method of mapping the detailed architecture of white matter fibers in human brain specimens (Jones et al., 2013).”
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“…& Turner, 2013; Wheeler-Kingshott & Cercignani, 2009) including increased myelination, changes in axonal membrane structure, increases in axon diameter, fibre packing density or increased fibre number (Beaulieu, 2002; Beaulieu, 2014; Jones et al., 2013; WheelerKingshott & Cercignani, 2009).”
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“FA is one of many diffusion indices, and has a number of determinants in white matter, including tissue architecture, myelination, fiber diameter and density, such that it is not normally possible to anatomically interpret an FA difference without at least an additional myelin measurement such as T2-relaxometry [31].”
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“…works have commonly focussed on the use of diffusion MRI derived metrics such as fractional anisotropy (FA) and mean diffusivity (MD) to describe axon geometry/organization and axon density (Alexander, Lee, Lazar, & Field, 2007; Jones, Knösche, & Turner, 2013; Tardif et al., 2016).”
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“…remains a plausible hypothesis, one needs to remain very cautious when attempting to derive physiological explanations from purely structural data (Jbabdi and Johansen-Berg 2011; Jones et al. 2012) and further experiments are absolutely required to better understand the basis of this association.”
BackgroundNeural connections foster social connections: a diffusion-weighted imaging study of social networks
Hampton, W. H., Unger, A., Heide, R. J. V. D., & Olson, I. R. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv153 · Cited by 56
Caeyenberghs, K., Clemente, A., Imms, P. E., Egan, G. F., Hocking, D. R., Leemans, A., Metzler‐Baddeley, C., Jones, D. K., & Wilson, P. H. (2018) · Neurorehabilitation and neural repair · doi:10.1177/1545968317753076 · Cited by 58
“61 Taken together, FA is quite a nonspecific tool and it remains unclear what biological factors underpin training-induced FA changes.”
Self-citationYoung, J. M., Vandewouw, M. M., Mossad, S. I., Morgan, B. R., Lee, W., Smith, M. L., Sled, J. G., & Taylor, M. J. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101855 · Cited by 57
Mallas, E., Carletti, F., Chaddock, C. A., Shergill, S. S., Woolley, J., Picchioni, M., McDonald, C., Toulopoulou, T., Kravariti, E., Kalidindi, S., et al. (2016) · Genes Brain & Behavior · doi:10.1111/gbb.12355 · Cited by 57
“Fractional anisotropy (FA) measured using diffusion tensor imaging (DTI) is a putative proxy of WM microstructural integrity (Jones et al. 2013) and decreases in FA can thus be regarded as being indicative of reduced WM integrity.”
Backgroundde Lange, A. G., Bråthen, A. C. S., Rohani, D. A., Grydeland, H., Fjell, A. M., & Walhovd, K. B. (2017) · Human Brain Mapping · doi:10.1002/hbm.23756 · Cited by 62
“Although the signal may be modulated by cellular properties and myelination, it is also influenced by how axons are laid out within the voxel, as the gradient is applied along a given axis [Jones et al., 2013].”
BackgroundLunkova, E., Guberman, G. I., Ptito, A., & Saluja, R. S. (2021) · Human Brain Mapping · doi:10.1002/hbm.25630 · Cited by 64
“For example, Keightley et al. (2014) showed lower task-related activity in adolescents with mTBI due to an inability to involve compensatory strategies.”
BackgroundRegional White Matter Variation Associated with Domain-specific Metacognitive Accuracy
Baird, B., Cieslak, M., Smallwood, J., Grafton, S. T., & Schooler, J. W. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00741 · Cited by 57
Seydell‐Greenwald, A., Raven, E. P., Leaver, A. M., Turesky, T. K., & Rauschecker, J. P. (2014) · Neural Plasticity · doi:10.1155/2014/145943 · Cited by 57
“As pointed out by Jones and colleagues [36], due to the relatively large voxel sizes necessary for maintaining reasonable signalto-noise ratio (SNR), it can be assumed that at least 90% of voxels contain more than one fiber population, thus violating the core assumption behind the tensor model.”
BackgroundInfluentialYang, X., Wang, Y., Wang, D.-F., Tian, K., Cheung, E. F. C., Xie, G., & Chan, R. C. K. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.04.005 · Cited by 60
“Other parameters, including mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) can be used to reflect different aspects of WM characteristics (Jones et al., 2013).”
Stolp, H. B., Ball, G., So, P.-W., Tournier, J.-D., Jones, M., Thornton, C., & Edwards, A. D. (2018) · Scientific Reports · doi:10.1038/s41598-018-22295-9 · Cited by 59
McDougall, S., Riad, W. V., Silva-Gotay, A., Tavares, E. R., Harpalani, D., Li, G., & Richardson, H. N. (2018) · eNeuro · doi:10.1523/eneuro.0203-18.2018 · Cited by 60
“It should be noted, however, that FA is a measurement sensitive to many tissue properties aside from myelination, including axonal orientation and density (Jones et al., 2013).”
BackgroundZemmoura, I., Serres, B., Andersson, F., Barantin, L., Tauber, C., Filipiak, I., Cottier, J.-P., Venturini, G., & Destrieux, C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.016 · Cited by 56
“…axons by the cellular membrane (the two compartment theory, which postulates that the cellular membrane is impermeable), while the exact mechanisms leading to anisotropy in neural tissues have not been fully elucidated (Jones et al., 2013; Le Bihan and Johansen-Berg, 2012; Tournier et al., 2011).”
BackgroundGogliettino, A. R., Potenza, M. N., & Yip, S. W. (2016) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2016.02.015 · Cited by 56
“Using the resultant data, it is possible to quantify diffusion within a given voxel in the brain, and this has been used to infer whitematter microstructural characteristics (Basser, 1995; Basser and Pierpaoli, 1996); for reviews see (Sullivan et al., 2010; Jones et al., 2013; Soares et al., 2013).”
MethodLanguage Without Speech: Segregating Distinct Circuits in the Human Brain
Finkl, T., Hahne, A., Friederici, A. D., Gerber, J. C., Mürbe, D., & Anwander, A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz128 · Cited by 54
“We defined the pMTG ROI parallel to the one in pSTG with the superior temporal sulcus representing its superior border D ow nloaded from https://academ ic.oup.com /cercor/advance-article-abstract/doi/10.1093/cercor/bhz128/5543064 by guest on 19 August 2019 and the middle temporal sulcus the inferior one.”
BackgroundInfluentialMerits and Limits of Tractography Techniques for the Uninitiated
Nimsky, C., Bopp, M. H. A., & Carl, B. (2015) · Advances and technical standards in neurosurgery · doi:10.1007/978-3-319-21359-0_2 · Cited by 56
Detection of the arcuate fasciculus in congenital amusia depends on the tractography algorithm
Chen, J. L., Kumar, S., Williamson, V. J., Scholz, J., Griffiths, T. D., & Stewart, L. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00009 · Cited by 53
“Early to bed, early to rise”: Diffusion tensor imaging identifies chronotype-specificity
Rosenberg, J., Maximov, I. I., Reske, M., Grinberg, F., & Shah, N. J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.086 · Cited by 56
“However, it should be mentioned that according to Jones et al. (2013), the term 'streamline count' is sometimes used which offers a more unambiguous way of reporting results.”
Seitz, J., Zuo, J. X., Lyall, A. E., Makris, N., Kikinis, Z., Sylvain Bouix, A., Pasternak, O., Fredman, E., Duskin, J. A., Goldstein, J. M., et al. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbv171 · Cited by 57
Mander, B. A., Zhu, A. H., Lindquist, J. R., Villeneuve, S., Rao, V. R., Lu, B. S., Saletin, J., Ancoli‐Israel, S., Jagust, W. J., & Walker, M. P. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3033-16.2017 · Cited by 61
“However, without confirmation, differences in any DTI measure may merely reflect individual differences in the distribution of orientation of axons within white matter fiber tracts (Jones et al., 2013).”
The human corticocortical vestibular network
Raiser, T. M., Flanagin, V. L., Duering, M., Ombergen, A. V., Ruehl, R. M., & zu Eulenburg, P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117362 · Cited by 65
Kawadler, J. M., Kirkham, F. J., Clayden, J. D., Hollocks, M. J., Seymour, E. L., Edey, R., Telfer, P. T., Robins, A., Wilkey, O., Barker, S., et al. (2015) · Stroke · doi:10.1161/strokeaha.115.008721 · Cited by 55
Fan, Q., Nummenmaa, A. R., Witzel, T., Zanzonico, R., Keil, B. R., Cauley, S., Polimeni, J. R., Tisdall, M. D., Dijk, K. R. A. V., Buckner, R. L., et al. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0305 · Cited by 53
“…from higher b -values provides improved contrast in the angular domain (Alexander and Barker, 2005; Cho et al., 2008; Clark and Le Bihan, 2000; Descoteaux et al., 2009; Jensen et al., 2005; Jones et al., 2013; Tournier et al., 2004, 2008; Tuch, 2004; Yoshiura et al., 2001; Zhan and Yang, 2006).”
White Matter Differences Among Adolescents Reporting Psychotic Experiences
O’Hanlon, E., Leemans, A., Kelleher, I., Clarke, M. C., Roddy, S., Coughlan, H., Harley, M., Amico, F., Hoscheit, M. J., Tiedt, L., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0137 · Cited by 57
Silk, T. J., Vilgis, V., Adamson, C. L., Chen, J., Smit, L., Vance, A. L. A., & Bellgrove, M. A. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9470-9 · Cited by 57
“Although many attribute a difference in FA to represent a difference in myelination, it can also be modulated by many other factors including but not limited to axon diameter, fiber bundle density, crossing fibers, myelin thickness or the cytoskeleton (Jones et al. 2013; Winston 2012).”
The effects of hippocampal lesions on MRI measures of structural and functional connectivity
Henson, R. N., Greve, A., Cooper, E., Gregori, M., Simons, J. S., Geerligs, L., Erzinçlioğlu, S., Kapur, N., & Browne, G. (2016) · Hippocampus · doi:10.1002/hipo.22621 · Cited by 52
Structural Network Disorganization in Subjects at Clinical High Risk for Psychosis
Schmidt, A., Crossley, N. A., Harrisberger, F., Smieskova, R., Lenz, C., Riecher‐Rössler, A., Lang, U. E., McGuire, P. K., Fusar‐Poli, P., & Borgwardt, S. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw110 · Cited by 55
Connectomic correlates of response to treatment in first-episode psychosis
Crossley, N. A., Marques, T. R., Taylor, H., Chaddock, C., Dell’Acqua, F., Reinders, A. A. T. S., Mondelli, V., Forti, M. D., Simmons, A., David, A. S., et al. (2016) · Brain · doi:10.1093/brain/aww297 · Cited by 53
“Connectomic approaches have themselves several limitations, ranging from the choice of template used (Zalesky et al., 2010b), to the meaning of structural connectivity as number of streamlines (Jones et al., 2013).”
BackgroundTrack-weighted imaging methods: extracting information from a streamlines tractogram
Calamante, F. (2017) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-017-0608-1 · Cited by 56
Khalsa, S., Hale, J. R., Goldstone, A., Wilson, R. S., Mayhew, S. D., Bagary, M. S., & Bagshaw, A. P. (2017) · Neurobiology of Sleep and Circadian Rhythms · doi:10.1016/j.nbscr.2017.03.001 · Cited by 56
“, 2007), and may be altered by a variety of changes to the underlying white matter, such as increases or decreases in myelination, the extent of coherent alignment of fibres, the presence and orientation of crossing fibres from other bundles etc (Jones et al., 2013), as well as short term plasticity (Engvig et al.”
BackgroundVisualizing the Human Subcortex Using Ultra-high Field Magnetic Resonance Imaging
Keuken, M. C., Isaacs, B. R., Trampel, R., van der Zwaag, W., & Forstmann, B. U. (2018) · Brain Topography · doi:10.1007/s10548-018-0638-7 · Cited by 57
Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging
Griffa, A., Baumann, P., Klauser, P., Mullier, E., Cleusix, M., Jenni, R., van den Heuvel, M. P., Kim, Q., Conus, P., & Hagmann, P. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0392-y · Cited by 54
Delettre, C., Messé, A., Dell, L.-A., Foubet, O., Heuer, K., Larrat, B., Meriaux, S., Mangin, J.-F., Reillo, I., de Juan Romero, C., et al. (2019) · Network Neuroscience · doi:10.1162/netn_a_00098 · Cited by 55
Tarumi, T., Tomoto, T., Repshas, J., Wang, C., Hynan, L. S., Cullum, C. M., Zhu, D. C., & Zhang, R. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117512 · Cited by 56
Brain morphometry shows effects of long-term musical practice in middle-aged keyboard players
Gärtner, H., Minnerop, M., Pieperhoff, P., Schleicher, A., Zilles, K., Altenmüller, E., & Amunts, K. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00636 · Cited by 53
“Additionally, the meaning of the different diffusion parameters is merely adequately understood, so that the authors have to be careful with the interpretation of their data (Jones et al., 2012) and tractography methods have to be further validated with postmortem data (Dell’Acqua and Catani, 2012).”
BackgroundLoui, P., Raine, L. B., Chaddock‐Heyman, L., Kramer, A. F., & Hillman, C. H. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01198 · Cited by 65
Adisetiyo, V., Tabesh, A., Martino, A. D., Falangola, M. F., Castellanos, F. X., Jensen, J. H., & Helpern, J. A. (2013) · Human Brain Mapping · doi:10.1002/hbm.22317 · Cited by 54
Rich Club Organization and Cognitive Performance in Healthy Older Participants
Baggio, H. C., Segura, B., Junqué, C., de Reus, M. A., Sala‐Llonch, R., & van den Heuvel, M. P. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00821 · Cited by 54
Hyperconnectivity in juvenile myoclonic epilepsy: A network analysis
Caeyenberghs, K., Powell, R., Thomas, R. H., Brindley, L. M., Church, C., Evans, J., Muthukumaraswamy, S. D., Jones, D. K., & Hamandi, K. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.11.018 · Cited by 52
Self-citationApplication of Advanced Neuroimaging Modalities in Pediatric Traumatic Brain Injury
Ashwal, S., Tong, K. A., Ghosh, N., Bartnik‐Olson, B., & Holshouser, B. A. (2014) · Journal of Child Neurology · doi:10.1177/0883073814538504 · Cited by 54
“If used correctly, appropriate utilization of computational tools have great potential to facilitate neuroimaging-based traumatic brain injury research.(132)”
MethodGálvez, J. F., Keser, Z., Mwangi, B., Ghouse, A. A., Fenoy, A. J., Schulz, P. E., Sanches, M., Quevedo, J., Selvaraj, S., Gajwani, P., et al. (2014) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2014.12.003 · Cited by 53
Structural MRI connectome in development: challenges of the changing brain
Tymofiyeva, O., Hess, C. P., Xu, D., & Barkovich, A. J. (2014) · British Journal of Radiology · doi:10.1259/bjr.20140086 · Cited by 55
The power of neuroimaging biomarkers for screening frontotemporal dementia
McMillan, C. T., Avants, B., Cook, P. A., Ungar, L., Trojanowski, J. Q., & Grossman, M. (2014) · Human Brain Mapping · doi:10.1002/hbm.22515 · Cited by 58
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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