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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“Each dMRI scalar, however, can be affected by different tissue properties.(2) In our study, FA values explained the highest amount of variability across groups, in agreement with previous studies.”
BackgroundResultInfluentialMicrostructural maturation of white matter tracts in encephalopathic neonates
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“These results are inconsistent with those of a DTI study in 1065 young adults [13], since no significant correlation was found with PSQI-J sleep duration component scores.”
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“…fanning and crossing (Mollink et al., 2017; Zhang et al., 2012), and FA can be influenced by how coherently fibres within a voxel are organized (Jones, Knosche, & Turner, 2013; Pierpaoli et al., 1996), PC2 was interpreted as relating to a ‘ complexity ’ property of the fibres (the dispersion…”
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Arezza, N. J. J., Santini, T., Omer, M., & Baron, C. A. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1074730 · Cited by 8
“Kaniso describes the variance in the dMRI powder average signal due to the eccentric shape of neuron fibers and other compartments (Jones et al., 2013); for example, a dMRI acquisition in the direction parallel to neuronal axons will yield a lower signal than one perpendicular to the axons.”
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Charvet, C. (2023) · Brain, Behavior and Evolution · doi:10.1159/000530317 · Cited by 8
Gkotsoulias, D., Müller, R., Jäger, C., Schlumm, T., Mildner, T., Eichner, C., Pampel, A., Jaffe, J., Gräßle, T., Alsleben, N., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120202 · Cited by 8
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“, 2018), although RD may also be affected by axonal loss (Jones et al., 2013) or differences in axonal packing density or diameter (Alexander et al.”
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Jaatela, J., Nurmi, T., Vallinoja, J., Mäenpää, H., Sairanen, V., & Piitulainen, H. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02692-1 · Cited by 9
“Furthermore, the interpretation of FA and MD metrics at a macrostructural level remains challenging (Tournier et al. 2011; Jones et al. 2013).”
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“14 In addition, DTI-based models cannot accurately describe reductions in fibre counts and as such cannot distinguish inflammatory and degenerative changes accurately.”
Tato-Fernández, C., Ekblad, L. L., Pietilä, E. M., Saunavaara, V., Helin, S., Parkkola, R. K., Zetterberg, H., Blennow, K., Rinne, J. O., & Snellman, A. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106439 · Cited by 9
“DTI is sensitive to the underlying tissue microstructure; however, changes in FA, MD, RD and AxD should be interpreted with caution in healthy subjects (Jones et al., 2013).”
BackgroundA prominent vertical occipital white matter fasciculus unique to primate brains
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Tracking the Neurodevelopmental Correlates of Mental State Inference in Early Childhood
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“…mechanisms underlying diffusion anisotropy measures remain unclear; thus we are unable to make conclusions about underlying anatomy, although there is some evidence that FA values reflect tissue properties such as axonal ordering, axonal density, and degree of myelination (Jones et al., 2013).”
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“Probabilistic tractography has several advantages over deterministic tractography (45), in particular in the detection of tracts among crossing fibers (46) as well as the reconstruction of fibers also in areas of low anisotropy (47).”
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“Due to its many short fibers, the cingulum is composed of distinct sub-regions that are associated with different neural functions (Heilbronner and Haber, 2014; Jones et al., 2013a; Schmahmann and Pandya, 2006).”
BackgroundMethodResultInfluentialWhite matter fiber analysis using kernel dictionary learning and sparsity priors
Kumar, K., Kaleem Siddiqi, A., & Desrosiers, C. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.06.002 · Cited by 8
White matter pathways as both a target and mediator of health behaviors
Porter, A., Leckie, R. L., & Verstynen, T. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13708 · Cited by 9
Zhang, X., Yu, X., Bao, Q., Yang, L., Sun, Y., & Qi, P. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01953-8 · Cited by 8
Hunt, D., Dighe, M., Gatenby, C., & Studholme, C. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2932681 · Cited by 8
“the direct measurement of strongly coherent bundles, or more accurate mappings between cortical regions and the shapes of these regions themselves) instead of relying entirely or in part on fiber count or average bundle quantities like FA [62].”
Yablonski, M., & Ben‐Shachar, M. (2020) · Cortex · doi:10.1016/j.cortex.2020.03.015 · Cited by 8
Dias, J. W., McClaskey, C. M., Eckert, M. A., Jensen, J. H., & Harris, K. C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116792 · Cited by 8
“The failure of the anterior SLF to predict ITD digit segregation may be due to the susceptibility of fiber tracts to noise resulting from crossing fibers and pruning errors, which can affect measurements of FA (Beaulieu, 2002; Jones et al., 2013; Mädler et al., 2008).”
Lacerda, L. M., Clayden, J. D., Handley, S. E., Winston, G. P., Kaden, E., Tisdall, M. M., Cross, J. H., Liasis, A. C., & Clark, C. A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00269 · Cited by 8
Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture
Mournet, S., Okubo, G., Koubiyr, I., Zhang, B., Kusahara, H., Prévost, V., Ichinose, N., Triaire, B., Hiba, B., Dousset, V., et al. (2020) · Neuroradiology · doi:10.1007/s00234-020-02462-4 · Cited by 8
A quality control pipeline for probabilistic reconstruction of white-matter pathways
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“The diffusion gradient vectors were also rotated accordingly, based on the estimated movement parameters (Jones et al., 2013).”
BackgroundDiffusion imaging changes in the treated tract following focused ultrasound thalamotomy for tremor
Mazerolle, E. L., Warwaruk-Rogers, R., Romo, P., Sankar, T., Scott, S., Rockel, C., Pichardo, S., Martino, D., Kiss, Z., & Pike, G. (2021) · Neuroimage: Reports · doi:10.1016/j.ynirp.2021.100010 · Cited by 8
Vieira, E. V., Arantes, P. R., Hamani, C., Iglésio, R., Duarte, K. P., Teixeira, M. J., Miguel, E. C., Lopes, A. C., & Godinho, F. L. F. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680484 · Cited by 8
Investigating white matter structure in social anxiety disorder using fixel-based analysis
Gorman, B. D. A., Calamante, F., Civier, O., DeMayo, M. M., Demetriou, E. A., Hickie, I. B., & Guastella, A. J. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.08.028 · Cited by 8
Li, Y., Yan, S., Zhang, G., Shen, N., Wu, D., Lu, J., Zhou, Y., Liu, Y., Zhu, H., Li, L., et al. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27911 · Cited by 8
Laczkovics, C., Nenning, K., Wittek, T., Schmidbauer, V. U., Schwarzenberg, J., Maurer, E. S., Wagner, G., Seidel, S., Philipp, J., Prayer, D., et al. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111427 · Cited by 8
Calabrese, J. R., Goetschius, L. G., Murray, L., Kaplan, M. R., Lopez‐Duran, N. L., Mitchell, C. M., Hyde, L. W., & Monk, C. S. (2022) · Brain Research · doi:10.1016/j.brainres.2022.147803 · Cited by 8
Kuang, L., Gao, W., Long, Z., Cao, W., Cui, D., Guo, Y., Jiao, Q., Qiu, J., Su, L., & Lu, G. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.750798 · Cited by 8
Reduced structural connectivity in the corpus callosum in patients with anorexia nervosa
Maier, S. J., Joos, A. A. B., van Elst, L. T., Ebert, D., Endres, D., Domschke, K., Lahmann, C., Zeeck, A., Runge, K., Denzel, D., et al. (2022) · European Eating Disorders Review · doi:10.1002/erv.2894 · Cited by 8
Early path dominance as a principle for neurodevelopment
Razban, R. M., Pachter, J. A., Dill, K. A., & Mujica-Parodi, L. R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218007120 · Cited by 7
“These include both fasciculation (increasing fiber count) (42, 46, 47) as well increasing myelination (25).”
BackgroundControllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients
Meyer‐Baese, A., Jütten, K., Meyer-Baese, U. H., Amani, A. M., Malberg, H., Stadlbauer, A., Kinfe, T. M., & Na, C.-H. (2023) · Cancers · doi:10.3390/cancers15102714 · Cited by 8
“Further methodological limitations are grounded in the imaging modalities applied, with diffusion MRI only providing gross estimates of white matter connectivity on a macroscopic scale, with known shortcomings especially related to fibre curvature and fibre crossings [41].”
BackgroundKoerte, I. K., & Muehlmann, M. (2014) · Springer eBooks · doi:10.1007/978-3-642-54542-9_5 · Cited by 8
Diffusion MRI in Neurological Disorders
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Hsu, J.-L., Chen, W., Bai, C., Leu, J.-G., Hsu, C.-Y., Viergever, M. A., & Leemans, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116330 · Cited by 8
Wang, W., Song, C.-L., Liang, H., et al. (2015) · Medicine · doi:10.1097/md.0000000000001909 · Cited by 8
“Demyelination, a lower axonal packing density, decreased axonal number, and intravoxel orientation difference caused by axonal compression and deformation will contribute to the relatively decreased FA values.(25) The PMP22 gene deletion has been considered to be the cause of HNPP, which is also observed in CNS.”
BackgroundTudela, R., Muñoz‐Moreno, E., López-Gil, X., & Sòria, G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170703 · Cited by 8
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Ahn, S. J., Park, H., Lee, B. R., & Lee, H. J. (2019) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.18-25849 · Cited by 8
“Changes in DTI parameters may reflect alterations in white matter fiber diameter/density, myelination, or membrane permeability.(31) Therefore, our results may indicate differences in white matter maturation between preterm and full-term infants and between infants with and without severe ROP.”
ResultRoine, T., Roine, U., Tokola, A. M., Balk, M. H., Mannerkoski, M., Åberg, L. E., Lönnqvist, T. R. I., & Autti, T. H. (2019) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6306 · Cited by 8
“However, a large part of the white matter remains outside of these analyses.(3,4) Recently, graph theoretical analysis has been applied to investigate structural brain connectivity networks.”
BackgroundRoelofs, E. F., Bas‐Hoogendam, J. M., van Ewijk, H., Ganjgahi, H., van der Werff, S. J. A., Barendse, M. E. A., Westenberg, P. M., Vermeiren, R. R. J. M., & van der Wee, N. J. A. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102493 · Cited by 9
Kliamovich, D., Jones, S. A., Chiapuzio, A. M., Baker, F. C., Clark, D. B., & Nagel, B. J. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111324 · Cited by 8
White Matter Tractography and Diffusion‐Weighted Imaging
Vettel, J. M., Cooper, N., Garcia, J. O., Yeh, F.-C. F., & Verstynen, T. (2017) · Wiley eBooks · doi:10.1002/9780470015902.a0027162 · Cited by 9
It’s About Time: Towards a Longitudinal Cognitive Neuroscience of IntelligencePreprint
Kievit, R., & Simpson-Kent, I. L. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/n2yg7 · Cited by 7
CHIASM, the human brain albinism and achiasma MRI dataset
Puźniak, R. J., McPherson, B., Ahmadi, K., Herbik, A., Kaufmann, J., Liebe, T., Gouws, A. D., Morland, A. B., Gottlob, I., Hoffmann, M. B., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-01080-w · Cited by 8
Yalçınkaya, B. H., Ziaeemehr, A., Fousek, J., Hashemi, M., Lavanga, M., Solodkin, A., McIntosh, A. R., Jirsa, V., & Petkoski, S. (2023) · medRxiv · doi:10.1101/2023.01.11.23284438 · Cited by 8
Dziemian, S., Baranczuk‐Turska, Z., & Langer, N. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02117-3 · Cited by 12
“However, FA measures are further influenced by the axon diameter, number of axons, and axon branching [101].”
BackgroundKato, S., Kurokawa, R., Suzuki, F., Amemiya, S., Shinozaki, T., Takanezawa, D., Kohashi, R., & Abe, O. (2023) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2022-0099 · Cited by 7
“13 However, DTI has its limitations.”
Liu, S., Ma, X., Yu, S., Weng, X., Li, M., Zhu, J., Liu, C.-F., & Hu, H. (2024) · Clinical Interventions in Aging · doi:10.2147/cia.s438782 · Cited by 9
“9 Furthermore, its capability in handling complex crossed WM fiber tracts is restricted, and variations in the presence of WM voxels pose challenges in accurately attributing them to specific fiber pathways, 10 but WM changes manifest in multiple forms in neurodegenerative diseases, such as fiber…”
BackgroundBrain signaling becomes less integrated and more segregated with age
Razban, R. M., Antal, B. B., Dill, K. A., & Mujica-Parodi, L. R. (2024) · Network Neuroscience · doi:10.1162/netn_a_00389 · Cited by 8
“However, –8– previous results employed the more simple diffusion tension imaging (DTI) method which is known to be less accurate at performing tractography (Garyfallidis et al., 2014; Jones, Kn¨osche, & Turner, 2013; Rokem et al., 2015).”
Lissaman, R., Rajagopal, S., Kearley, J., Pasvanis, S., & Rajah, M. N. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.05.017 · Cited by 7
Cox, S. R. (2024) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-010923-102441 · Cited by 12
Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis
Hernández-Gutiérrez, E., Coronado‐Leija, R., Edde, M., Dumont, M., Houde, J., Baraković, M., Magon, S., Ramírez-Manzanares, A., & Descoteaux, M. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1467786 · Cited by 6
Translational network neuroscience: Nine roadblocks and possible solutions
Fekonja, L. S., Forkel, S. J., Aydogan, D. B., Lioumis, P., Cacciola, A., Lucas, C. W., Tournier, J., Vergani, F., Ritter, P., Schenk, R., et al. (2025) · Network Neuroscience · doi:10.1162/netn_a_00435 · Cited by 8
“…different regions of the brain. dMRI therefore offers insights into how brain regions are physically connected to process cognitive functions and behavior (Basser, Mattiello, & LeBihan, 1994; Hagmann et al., 2008; Jeurissen, Descoteaux, Mori, & Leemans, 2019; Jones, Knösche, & Turner, 2013).”
Mendis, S. B., Welstead, M., & Tan, M. P. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106066 · Cited by 7
The cognitive cerebellum: linking microstructure to cognitive functions in a healthy population
Urbini, N., McNabb, C. B., Jones, D. K., Hedge, C., Messaritaki, E., Laguna, P. A. L., & Cercignani, M. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121356 · Cited by 7
“Thus, a variation in FA most likely reflects some difference in one of the aspects of connectivity, although it is difficult to define which precise aspect and the direction of the change (Jones et al., 2013).”
Self-citationCan structure predict function at individual level in the human connectome?
Smolders, L., Baene, W. D., Rutten, G.-J. M., van der Hofstad, R., & Florack, L. M. J. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02796-2 · Cited by 7
“Integrity measures such as Fractional Anisotropy or Mean Diffusivity may be more meaningful to quantify connection strength, although these are accompanied by some problems e.g. in and around crossing fibers (Jones 2010; Jones et al. 2013 ; Vos et al. 2012).”
Background
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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