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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“Finally, diffusion tensor metrics such as FA can only provide a crude estimate of the structural coherence of a tract, and the tracts that cross the AIns-NAcc tract further decrease the validity of the measure (Jones et al., 2013).”
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“Although FA is clearly related to white matter microstructure, it may be misleading to use it as a marker of microstructural integrity (Jones et al. 2013).”
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“Further, FA has also demonstrated low sensitivity to a single neurobiological process 81 making it difficult to characterize the exact mechanism behind neuroplasticity changes, specifically the decrease in measures noted in males.”
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“Moreover, tractography in general does not differentiatethedirectionoftracts,meaningthechangesinconnectivitymay bothbeafferentandefferentinnature 42 .”
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“…quantitatively assess the diffusion of water molecules, independent of direction (Moseley et al., 1991; Pierpaoli and Basser, 1996; Andre and Bammer, 2010; Cheran et al., 2011; Buchy et al., 2012; Zatorre et al., 2012; Han et al., 2013; Jones et al., 2013; Tseng et al., 2013; Abhinav et al., 2014).”
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“Other aspects such as crossing fibers, axonal rearrangement or partial volume effects are known to have some impact on the same metrics (Jones et al. 2013).”
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“In the context of deterministic tractography, similar arguments were discussed by Jones and colleagues (Jones et al. 2013) for the relation between 'streamlines count' and 'fibers counts'.”
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“Fiber-specific metrics are quantified by the generally unreliable fiber-count (Jones et al., 2012) or ROI-based approaches.”
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“Traditionally, these measures have typically been reported as indirect indices of “white matter integrity,” but the relationships between thesemeasures and themicrostructural properties of white matter are now acknowledged to be more nuanced (Jones et al., 2013).”
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“A single-shot echo-planar diffusion tensor imaging sequence was applied with 32 measurement directions and the following scan parameters (Jones and Leemans 2011): TR = 10,004 ms, TE = 56 ms, FOV = 220 × 220 mm2 with an acquisition matrix of 112 × 110, 2.00 mm slice thickness, transversal slice…”
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Inherent spatial structure in myelin water fraction maps
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“…the GWM boundary in ASD had not yet been characterized based on measures of diffusion as assessed using DTI, which have been linked to several aspects of underlying white matter neuroarchitecture including myelination and fiber architecture (Beaulieu, 2002; Jbabdi et al., 2015; Jones et al., 2013).”
Reprint of “Hypomyelinating disorders: An MRI approach
Barkovich, A. J., & Deon, S. (2016) · Neurobiology of Disease · doi:10.1016/j.nbd.2015.10.022 · Cited by 15
“The accuracy of information can be increased by the use of advanced diffusion imaging techniques: high angular resolution diffusion imaging (HARDI) with multiple b values are optimal, but these require strong magnetic fi elds and rather long examination times (Jones et al., 2013).”
BackgroundMagnetic resonance imaging of the dopamine system in schizophrenia – A scoping review
Schulz, J., Zimmermann, J., Sorg, C., Menegaux, A., & Brandl, F. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.925476 · Cited by 14
Mürner-Lavanchy, I. M., Koenig, J., Ando, A., Henze, R., Schell, S., Resch, F., Brunner, R. M., & Kaess, M. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100812 · Cited by 15
Peterson, B. S., Liu, J., Dantec, L., Newman, C., Sawardekar, S., Goh, S., & Bansal, R. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13531 · Cited by 15
Pearce, A. M., Marr, C., Dewar, M., & Gow, A. J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.815439 · Cited by 16
“It is therefore suggested that these measures are not automatically interpreted as indicating WM integrity (Jones et al., 2013).”
BackgroundCharvet, C. J., Ofori, K., Baucum, C., Sun, J., Modrell, M. S., Hekmatyar, K., Edlow, B. L., & van der Kouwe, A. J. W. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1506-21.2022 · Cited by 18
Integration of white matter architecture to stereo-EEG better describes epileptic spike propagation
Azeem, A., von Ellenrieder, N., Royer, J., et al. (2022) · Clinical Neurophysiology · doi:10.1016/j.clinph.2022.10.012 · Cited by 14
Jossinger, S., Yablonski, M., Amir, O., & Ben‐Shachar, M. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00098 · Cited by 14
“As the tensor model is notoriously known for its inability to account for multiple orientations within the same voxel (Jones et al., 2013), our individual tractograms were generated based on the CSD model.”
BackgroundMethodLongitudinal Trajectories of White Matter Development in Attention-Deficit/Hyperactivity Disorder
Fuelscher, I. T., Hyde, C. E., Thomson, P., Vijayakumar, N., Sciberras, E., Efron, D., Anderson, V. A., Hazell, P., & Silk, T. J. (2023) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2023.03.008 · Cited by 15
Amil, A. S., Nowacki, A., Debove, I., Petermann, K., Tinkhauser, G., Krack, P., Pollo, C., & Nguyen, T. A. K. (2023) · Human Brain Mapping · doi:10.1002/hbm.26390 · Cited by 13
“But these counts have limitations and may not be a good indicator of the biological density of axons (Jones et al., 2013; Smith et al., 2015).”
BackgroundCarrozzi, A., Gramegna, L. L., Sighinolfi, G., Zoli, M., Mazzatenta, D., Testa, C. M., Lodi, R., Tonon, C., & Manners, D. N. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103494 · Cited by 14
Lateralization of major white matter tracts during infancy is time-varying and tract-specific
Ford, A., Ammar, Z., Li, L., & Shultz, S. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad277 · Cited by 15
“Additional methodological limitations include (i) DTI metrics (FA and other diffusivity measures), can be less robust and reliable in curved structures with crossing fibers, like the AF and Fx (Jones and Cercignani 2010; Jones et al. 2013), (ii) our study also does not include additional measures of white matter micro…”
BackgroundMeisler, S. L., Gabrieli, J. D. E., & Christodoulou, J. A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00108 · Cited by 13
“…unspecific due to the variety of factors that can influence diffusion of water in voxel-sized regions and the confounding impact of crossing fibers ( De Santis et al., 2014; Jones et al., 2013), which can impact as many as 90% of white matter voxels ( Behrens et al., 2007; Jeurissen et al., 2013).”
BackgroundNetwork Analysis in Multiple Sclerosis and Related Disorders
Pontillo, G., Barrantes-Cepas, M., Broeders, T. A. A., Koubiyr, I., & Schoonheim, M. M. (2024) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2024.03.008 · Cited by 13
Gong, Z., Faulkner, M. E., Akhonda, M. A. B. S., Guo, A., Bae, J., Laporte, J. P., Church, S., D’Agostino, J., Bergeron, J., Bergeron, C. M., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01392-w · Cited by 12
Chen, Y., Zhang, F., Wang, M., Zekelman, L., Karayumak, S. C., Xue, T., Zhang, C., Song, Y., Rushmore, J., Makris, N., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103476 · Cited by 14
“…feature, though widely used and considered the top performer for many prediction tasks (Yeung et al., 2023a), is known to vary across tractography algorithms and is influenced by parameters like the number of seed points for tractography initialization (Girard et al., 2014; Jones et al., 2013).”
Subject–Motion Correction in HARDI Acquisitions: Choices and Consequences
Elhabian, S., Gur, Y., Vachet, C., Piven, J., Styner, M. A., Leppert, I. R., Pike, G. B., & Gerig, G. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00240 · Cited by 14
“INTRODUCTION Diffusion-weighted (DW)-MRI enables probing the fiber architecture of biological tissues – in vivo – by encoding the microscopic direction and speed of the diffusion of water molecules (4), while reflecting the amount of hindrance experienced by such molecules along the axis of the applied diffusion gradi…”
BackgroundKristensen, T. D., Mandl, R. C. W., Jepsen, J. R. M., Rostrup, E., Glenthøj, L. B., Nordentoft, M., Glenthoj, B. Y., & Ebdrup, B. H. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.013 · Cited by 14
“4, E) may be expected, which could affect the diffusion profile of WM-tracts (Jones et al., 2013a; Jones et al., 2013b).”
Validation of diffusion tensor imaging measures of nigrostriatal neurons in macaques
Shimony, J. S., Rutlin, J. R., Karimi, M., Tian, L., Snyder, A. Z., Loftin, S. K., Norris, S. A., & Perlmutter, J. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202201 · Cited by 14
Baro, V., Landi, A., Brigadoi, S., et al. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.01.099 · Cited by 14
White matter disconnection is related to age-related phonological deficits
Troutman, S. B. W., & Diaz, M. T. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00086-8 · Cited by 14
“…et al. 2013), a growing number of sources suggests that DTI signal differences can be attributed to differences in white matter, broadly speaking (Jones et al. 2013; Song et al. 2002; Takahashi et al. 2002; Pierpaoli Electronic supplementary material The online version of this article…”
BackgroundMethodInfluentialZanin, J., Dhollander, T., Rance, G., Yu, L., Lan, L., Wang, H.-Y., Lou, X., Connelly, A., Nayagam, B., & Wang, Q.-J. (2020) · Ear and Hearing · doi:10.1097/aud.0000000000000890 · Cited by 12
“(2019) for review), new dMRI acquisition methods in conjunction with higher-order diffusion models of microstructure, such as constrained spherical deconvolution (CSD) modeling techniques, provide more accurate representations of underlying white matter architecture than the widely adopted diffusion tensor model (Tour…”
BackgroundNicolas, R., Hiba, B., Dilharreguy, B., Barse, E., Baillet, M., Edde, M., Pelletier, A., Periot, O., Helmer, C., Michèle Allard, A., et al. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00218 · Cited by 13
“…use of the diffusion metric pattern to interpret the underlying microstructural state of the tissue must be taken with caution since this association is a very complex phenomenon which depends on brain regions, pathological state of the tissue, and acquisition parameters used (Jones et al., 2013).”
BackgroundLiu, S., Liu, J., Chen, W., Zhang, L., Wu, S., Wang, F., Pan, J., Luo, M., Liu, X., & Zhang, S. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27322 · Cited by 14
“The interpretation of DTI results is complicated.(22) DTI allows for the quantitative evaluation of water diffusion properties in the anisotropy direction.”
BackgroundKanel, D., Counsell, S., & Nosarti, C. (2020) · Journal of Perinatology · doi:10.1038/s41372-020-00865-y · Cited by 14
Zhang, F., Cho, K. I. K., Tang, Y., Zhang, T., Kelly, S., Biase, M. A. D., Xu, L., Li, H., Keshevan, M., Whitfield, S., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117564 · Cited by 14
“However, DTI cannot identify the biological compartment responsible for the changes in FA, since FA is sensitive to multiple biological and geometrical effects that may not provide a good approximation of white matter integrity (Jones et al., 2013).”
Meijer, A., Pouwels, P. J. W., Smith, J., Visscher, C., Bosker, R., Hartman, E., Oosterlaan, J., & Königs, M. (2021) · Journal of Neuroscience Research · doi:10.1002/jnr.24851 · Cited by 15
“Although DTI parameters are measures of water diffusion and thus indirect measures of structural connectivity (Jones et al., 2013), a profile of higher FA and AD and lower RD is thought to be compatible with higher white matter integrity.”
BackgroundFritz, F. J., Poser, B. A., & Roebroeck, A. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118285 · Cited by 15
Kennedy, E., Poppe, T., Tottman, A. C., & Harding, J. E. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102730 · Cited by 15
Structural Connectivity Alterations in Operculo-Insular Epilepsy
Obaïd, S., Rheault, F., Edde, M., Guberman, G. I., St‐Onge, E., Sidhu, J., Bouthillier, A., Daducci, A., Ghaziri, J., Bojanowski, M. W., et al. (2021) · Brain Sciences · doi:10.3390/brainsci11081041 · Cited by 17
“Through its ability to take into account the tracking bias related to variations in bundle width, the COMMIT weight constitutes a more biological proxy than the frequently used, but debated, streamline count [41,42].”
BackgroundMethodInfluentialThe Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI
Moody, J. F., Adluru, N., Alexander, A. L., & Field, A. S. (2021) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2021.07.007 · Cited by 14
Duan, J., Wei, Y., Womer, F. Y., Zhang, X., Chang, M., Zhu, Y., Liu, Z., Li, C., Yin, Z., Zhang, R., et al. (2021) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.200166 · Cited by 15
Brain tissue microstructural and free-water composition 13 years after very preterm birth
Kelly, C. E., Dhollander, T., Harding, I. H., Khan, W., Beare, R. J., Cheong, J. L. Y., Doyle, L. W., Seal, M. L., Thompson, D. K., Inder, T. E., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119168 · Cited by 14
Thaler, C. P., Tian, Q., Wintermark, M., Ghanouni, P., Halpern, C. H., Henderson, J. M., Airan, R. D., Zeineh, M., Goubran, M., Leuze, C. W., et al. (2022) · Brain Connectivity · doi:10.1089/brain.2021.0157 · Cited by 13
“In addition, for optimizing intra-voxel crossing fiber detection we followed recent recommendations, including sufficient spatial resolution (1.875 · 1.875 · 2.0 mm),highb-values(2500 s/mm 2 ),andahighnum-ber of diffusion encoding directions (60 gradients) (Jones et al., 2013).”
InfluentialThiel, K., Meinert, S. L., Winter, A., Lemke, H., Waltemate, L., Breuer, F., Gruber, M., Leenings, R., Wüste, L., Rüb, K., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722001490 · Cited by 14
Machado, C., Rodríguez‐Rojas, R., Estévez, M., Leisman, G., Melillo, R. J., Chinchilla, M., & Portela, L. (2015) · Brain Connectivity · doi:10.1089/brain.2014.0335 · Cited by 13
“…et al., 2013; Dacosta-Aguayo et al., 2015; Garcia-Penton et al., 2014; Hernandez et al., 2013; Iturria-Medina et al., 2011, 2013, 2014; Jones et al., 2013; Keehn et al., 2012; Li et al., 2014; McKinney et al., 2004; Perez et al., 2014; Pontabry et al., 2013; Shen et al., 2012;…”
Tanner, J. J., Levy, S.-A. T., Schwab, N. A., Hizel, L. P., Nguyen, P., Okun, M. S., & Price, C. C. (2016) · The Clinical Neuropsychologist · doi:10.1080/13854046.2016.1251973 · Cited by 14
“Due to concerns about variability in diffusion fiber tracking results between the caudate and frontal lobe (Kuhn et al., 2016), we chose to use fractional anisotropy, which is a well-established but simplistic metric (Jones et al., 2013).”
Hinton, K. E., Lahey, B. B., Villalta‐Gil, V., Boyd, B. D., Yvernault, B., Werts, K. B., Plassard, A. J., Applegate, B. E., Woodward, N. D., Landman, B. A., et al. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00127 · Cited by 18
“, Q-ball imaging) that better take into account these limitations to determine if the results still hold (Tuch et al., 2003; Jeurissen et al., 2013; Jones et al., 2013).”
BackgroundWhite Matter Microstructure in Illiterate and Low-Literate Elderly Brazilians: Preliminary Findings
Resende, E. D. P. F., Tovar-Moll, F. F., Ferreira, F. M., Bramati, I. E., de Souza, L. C., Carmona, K. C., Guimarães, H. C., Carvalho, V. A., Barbosa, M. T., & Caramelli, P. (2018) · Cognitive and Behavioral Neurology · doi:10.1097/wnn.0000000000000173 · Cited by 15
Zanin, J., Dhollander, T., Farquharson, S., Rance, G., Connelly, A., & Nayagam, B. (2019) · Hearing Research · doi:10.1016/j.heares.2019.02.014 · Cited by 10
Gurholt, T. P., Haukvik, U. K., Lonning, V., Jönsson, E. G., Pasternak, O., & Agartz, I. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00056 · Cited by 13
“However, the FA measure may not provide a good representation of white matter integrity due to several methodological issues (10), including partial volume effects e.”
BackgroundPain Processing in Older Adults and Its Association with Prefrontal Characteristics
Bunk, S., Emch, M., Koch, K., Lautenbacher, S., Zuidema, S. U., & Miriam Kunz, A. (2020) · Brain Sciences · doi:10.3390/brainsci10080477 · Cited by 14
“When interpreting the direction of FA, there is the difficulty that FA is influenced by several factors, including myelination, axon diameter, and the space between axons [56].”
BackgroundRapid microscopic fractional anisotropy imaging via an optimized linear regression formulation
Arezza, N. J. J., Tse, D. H. Y., & Baron, C. A. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.04.015 · Cited by 14
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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