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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BackgroundSpurious group differences due to head motion in a diffusion MRI study
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“Tractography is an indirect method for inferring connectivity and various methodological confounds introduce noise and/or bias (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013; Van Essen et al., 2014).”
MethodImaging human connectomes at the macroscale
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“…such as cell death, edema, inflammation, demyelination, increase in extracellular or intracellular water and partial volume effects, may cause similar changes in FA (Assaf and Pasternak, 2008; Bihan and Johansen-Berg, 2012; Jones et al., 2013; Vos et al., 2012; O'Donnell and Pasternak, 2015).”
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“For this reason, they are error prone and the results can be more difficult to quantify compared with those of corresponding invasive methods.(25) They also offer substantially lower spatial resolution than chemical tracers and microscopic techniques and they cannot estimate the directionality of connections.”
BackgroundIndividual brain structure and modelling predict seizure propagation
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“The number of streamlines may not directly reflect the strength of signal transmission between two areas but simply the probability that a streamline between two regions is found by the tractography algorithm (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013).”
BackgroundDiffusion tensor imaging in Parkinson's disease: Review and meta-analysis
Atkinson-Clément, C., Pinto, S., Eusébio, A., & Coulon, O. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.07.011 · Cited by 277
“Jones et al. (2013) recommended to use at least 30 diffusion directions, in order “to obtain robust estimates of tensor-derived properties”; Ni et al. (2006) only observed differences of FA between 6, 21 and 31 directions for regions with low anisotropy (Ni et al., 2006).”
Network connectivity determines cortical thinning in early Parkinson’s disease progression
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The effects of SIFT on the reproducibility and biological accuracy of the structural connectome
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“This is however contrary to a fundamental limitation of streamlines tractography approaches that is well-established in thefield: streamline count is not a validmarker of axonalfibre count (Jones et al., 2013).”
Evaluation and statistical inference for human connectomes
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259
“It is time consuming, the neural tissue is distorted and shrunk during the histological preparation, and the method can be applied to only a few cubic millimeters of white-matter volume of a single pathway 48 .”
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Liu, H., Yang, Y., Xia, Y., Zhu, W., Leak, R. K., Wei, Z., Wang, J., & Hu, X. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.11.006 · Cited by 276
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COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
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“In fact, the structural connectivity between different brain regions is usually quantified by counting the number of recovered pathways or averaging some scalar maps along them [14].”
BackgroundAdolescent impatience decreases with increased frontostriatal connectivity
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“Indeed, the complex interactions between these factors and the resulting inability to ascribe apparent diffusion changes to any particular cause, is why (Jones et al., 2013) and others have advocated for researchers to exercise caution when interpreting DTI data and avoid drawing conclusions about microstructural or t…”
BackgroundAssociation of Amyloid Pathology With Myelin Alteration in Preclinical Alzheimer Disease
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The development of brain network hubs
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Connectometry: A statistical approach harnessing the analytical potential of the local connectome
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“Mapping end-to-end connections has been a challenge due to methodological limitations (Jones et al., 2013; Reveley et al., 2015; Thomas et al., 2014), and a reliable and reproducible approach is still under active research.”
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Essayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.011 · Cited by 249
“Several indepth review articles are available (Assaf and Pasternak, 2008; Jones et al., 2013; O'Donnell and Westin, 2011), including recent neurosurgery-specific reviews and commentaries (Bi and Chiocca, 2014; Nimsky et al., 2016a), so here we present a brief overview of the most relevant…”
BackgroundLasič, S., Szczepankiewicz, F., Eriksson, S., Nilsson, M., & Topgaard, D. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00011 · Cited by 248
“Because fiber orientation dispersion and several other tissue properties are inherently entangled in the echo attenuation of the PGSE experiment, changes in FA are not specific to any particular tissue characteristics [16].”
BackgroundMethodConnectomics: A new paradigm for understanding brain disease
Fornito, A., & Bullmore, E. T. (2014) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2014.02.011 · Cited by 247
Traumatic and nontraumatic spinal cord injury: pathological insights from neuroimaging
Dávid, G., Mohammadi, S., Martin, A. R., Cohen‐Adad, J., Weiskopf, N., Thompson, A. J., & Freund, P. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0270-5 · Cited by 243
Imaging structural and functional brain networks in temporal lobe epilepsy
Bernhardt, B. C., Hong, S., Bernasconi, A., & Bernasconi, N. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00624 · Cited by 244
“While somewhat challenged in regions where different fiber populations intersect (Behrens et al., 2003; Jones et al., 2012), such as the cortical gray matter, tractography can generate consistent results, particularly in deep white matter.”
BackgroundTractometer: Towards validation of tractography pipelines
Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228
“Hence, as mentioned in (Jones et al., 2013), fiber count should be avoided.”
BackgroundStructural connectivity differences in left and right temporal lobe epilepsy
Besson, P., Dinkelacker, V., Valabrègue, R., Thivard, L., Leclerc, X., Baulac, M., Sammler, D., Olivier Colliot, A., Lehéricy, S., Samson, S., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.071 · Cited by 238
“However, we cannot amend the global limitations of tractography in yielding good signal to noise ratio close to the cortical ribbon (Jones et al., 2013).”
BackgroundMethodInfluentialPalesi, F., Rinaldis, A. D., Castellazzi, G., Calamante, F., Muhlert, N., Chard, D., Tournier, J., Magenes, G., D'Angelo, E. U., & Wheeler‐Kingshott, C. A. M. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-13079-8 · Cited by 248
Roalf, D. R., Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K., Gennatas, E. D., Calkins, M. E., Moore, T. M., Hopson, R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.068 · Cited by 199
“…spikes) and susceptibility artifacts (Anderson, 2001; Bastin et al., 1998; Skare et al., 2000), which have come under scrutiny (Heim et al., 2004; Jones et al., 1999, 2013; Lauzon et al., 2013; Owens et al., 2012; Tournier et al., 2011; Yendiki et al., 2014), and are the focus of several new…”
Imaging biomarkers in neurodegeneration: current and future practices
Young, P. N. E., Estarellas, M., Coomans, E. M., Srikrishna, M., Beaumont, H., Maaß, A., Venkataraman, A. V., Lissaman, R., Jiménez, D., Betts, M. J., et al. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00612-7 · Cited by 244
Specificity and robustness of long-distance connections in weighted, interareal connectomes
Betzel, R. F., & Bassett, D. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720186115 · Cited by 223
Learning to Read Improves the Structure of the Arcuate Fasciculus
de Schotten, M. T., Cohen, L., Amemiya, E., Braga, L. W., & Dehaene, S. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs383 · Cited by 241
Fast track to the neocortex: A memory engram in the posterior parietal cortex
Brodt, S., Gais, S., Beck, J., Erb, M., Scheffler, K., & Schönauer, M. (2018) · Science · doi:10.1126/science.aau2528 · Cited by 224
Multidimensional diffusion MRI
Topgaard, D. (2016) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2016.12.007 · Cited by 223
Neuroimaging in functional neurological disorder: State of the field and research agenda
Perez, D. L., Nicholson, T. R., Asadi-Pooya, A. A., Bègue, I., Butler, M., Carson, A. J., David, A. S., Deeley, Q., Diez, I., Edwards, M. J., et al. (2021) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102623 · Cited by 216
“This procedure requires that images be transformed to a common anatomical space to assure correspondence across subjects, generally using the Statistical Parametric Mapping (SPM) (Ashburner and Friston, 2000) or FMRIB Software Library (FSL) VBM analysis pipelines (Jenkinson et al., 2012).”
MethodAssessment of system dysfunction in the brain through MRI-based connectomics
Filippi, M., van den Heuvel, M. P., Fornito, A., Yong He, A., Pol, H. E. H., Agosta, F., Comı, G., & Rocca, M. A. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70144-3 · Cited by 219
Gadie, A., Shafto, M. A., Leng, Y., Cam-CAN, & Kievit, R. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-014920 · Cited by 224
Rasmussen, J. M., Graham, A. M., Entringer, S., Gilmore, J. H., Styner, M. A., Fair, D. A., Wadhwa, P. D., & Buß, C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.020 · Cited by 215
“…et al., 2017; Dubois et al., 2014) However, given the speculative nature of interpreting microscopic properties from macroscopic DTI observations (Jones et al., 2013), future efforts in animal models aimed at elucidating the microstructural correlates of macroscopic diffusion measures are…”
From Phineas Gage and Monsieur Leborgne to H.M.: Revisiting Disconnection Syndromes
de Schotten, M. T., Dell’Acqua, F., Ratiu, P., Leslie, A., Howells, H., Cabanis, E. A., Iba-Zizen, M. T., Plaisant, O., Simmons, A., Dronkers, N. F., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv173 · Cited by 223
Modelling white matter with spherical deconvolution: How and why?
Dell’Acqua, F., & Tournier, J. (2018) · NMR in Biomedicine · doi:10.1002/nbm.3945 · Cited by 223
Palesi, F., Tournier, J., Calamante, F., Muhlert, N., Castellazzi, G., Chard, D., D'Angelo, E. U., & Wheeler‐Kingshott, C. A. M. G. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0861-2 · Cited by 212
“While recognizing that tractography provides only indirect evidence of anatomical connectivity between regions and cannot distinguish between direct connections and pathways involving synapses (like the cerebello-thalamo-cortical pathway) (Catani et al. 2012; Jones et al. 2013), we aimed to assess the usefulness of tr…”
BackgroundInfluentialLampinen, B., Szczepankiewicz, F., Mårtensson, J., van Westen, D., Sundgren, P. C., & Nilsson, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.053 · Cited by 200
“A similar situation faced users of DTI, who early on tried to interpret the FA parameter as an index of 'white matter integrity' (Jones et al. 2013).”
Influentialvan den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4105-13.2014 · Cited by 178
“For example, numerous structural properties likely contribute to tract likelihood, including axon caliber, axonal density, membrane permeability, and myelination (Jones et al., 2013).”
BackgroundDevelopmental changes in brain network hub connectivity in late adolescence
Baker, S. T. E., Lubman, D. I., Yucel, M., Allen, N. B., Whittle, S., Fulcher, B. D., Zalesky, A., & Fornito, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5043-14.2015 · Cited by 168
“…of changes in connectivity measures derived from diffusion MRI, and it is possible that changes in fiber trajectory, organization, density, or other parameters related to axonal integrity may also affect the measures examined in our analysis (Paus, 2010; Fornito et al., 2013; Jones et al., 2013).”
BackgroundMapping Structural Connectivity Using Diffusion MRI : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187
Self-citationChang, E. H., Argyelan, M., Aggarwal, M., Chandon, T.-S. S., Karlsgodt, K. H., Mori, S., & Malhotra, A. K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.068 · Cited by 186
“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, 20…”
InfluentialJones, D. K., Alexander, D. C., Bowtell, R. W., Cercignani, M., Dell’Acqua, F., McHugh, D. J., Miller, K. L., Palombo, M., Parker, G. J. M., Rudrapatna, U. S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.047 · Cited by 187
Self-citationAsymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain
Fernandez-Miranda, J. C., Wang, Y., Pathak, S., Stefaneau, L., Verstynen, T., & Yeh, F.-C. F. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0751-7 · Cited by 185
“Recent reviews in diffusion fiber tracking (Jones and Cercignani 2010; Jbabdi and Johansen-Berg 2011; Jones et al. 2013) are against using the number of streamlines to derive a quantitative index due to poor reproducibility, and therefore we preferred the volume of streamlines as a more reliable…”
Betzel, R. F., Griffa, A., Hagmann, P., & Mišić, B. (2018) · Network Neuroscience · doi:10.1162/netn_a_00075 · Cited by 172
Finding the Elusive Psychiatric “Lesion” With 21st-Century Neuroanatomy: A Note of Caution
Weinberger, D. R., & Radulescu, E. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15060753 · Cited by 173
White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease
Araque Caballero, M. Á., Suárez-Calvet, M., Duering, M., Franzmeier, N., Benzinger, T., Fagan, A. M., Bateman, R. J., Jack, C. R., Levin, J., Dichgans, M., et al. (2018) · Brain · doi:10.1093/brain/awy229 · Cited by 184
“, 2007), although this is still a matter of intense research (Jones et al., 2013).”
BackgroundInfluentialKhalsa, S., Mayhew, S. D., Chechlacz, M., Bagary, M. S., & Bagshaw, A. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.022 · Cited by 193
“, since there are contributions from several methodological and physiological factors which are not related to the underlying connectivity (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013).”
Gomez, J., Pestilli, F., Witthoft, N., Golarai, G., Liberman, A., Poltoratski, S., Yoon, J. M. D., & Kalanit Grill-spector, A. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.027 · Cited by 191
“The physiological factors underlying the diffusivity properties of our reported fiber tracts are likely diverse (Beaulieu, 2002) Neuron 85, 216–22 and difficult to directly link to microscopic tissue properties (Jones et al., 2013).”
BackgroundKodiweera, C., Alexander, A. L., Harezlak, J., McAllister, T. W., & Wu, Y.-C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.033 · Cited by 179
“…crossing fibers in the brain white matter is ~90%, which may explain the lower than expected pathologic specificity of DTI metrics in complex white matter areas of the brain (Sullivan and Pfefferbaum 2006, Jones and Cercignani 2010, Tournier et al. 2011, Jeurissen et al. 2013, Jones et al. 2013).”
Interactions between brain structure and behavior: The corpus callosum and bimanual coordination
Gooijers, J., & Swinnen, S. P. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.03.008 · Cited by 184
Hecht, E. E., Gutman, D. A., Bradley, B. A., Preuss, T. M., & Stout, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.039 · Cited by 175
From Cajal to Connectome and Beyond
Swanson, L. W., & Lichtman, J. W. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-033954 · Cited by 178
Characterizing longitudinal white matter development during early childhood
Dean, D. C., O’Muircheartaigh, J., Dirks, H., Waskiewicz, N., Walker, L., Doernberg, E., Piryatinsky, I., & Deoni, S. C. L. (2015) · Brain Structure and Function · doi:10.1007/s00429-014-0763-3 · Cited by 174
“2001) and are influenced by a broad range of other microstructural changes (Jones et al. 2013).”
The physical and biological basis of quantitative parameters derived from diffusion MRI.
Winston, G. P. (2012) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2223-4292.2012.12.05 · Cited by 174
A review of white matter microstructure alterations of pathways of the reward circuit in depression
Bracht, T., Linden, D. E. J., & Keedwell, P. A. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.06.041 · Cited by 178
“…different sub-compartments of the CB (e.g. the subgenual CB or the parahippocampal cingulum) since white matter microstructure alterations may only be present and detectable in sub-compartments of this large fi bre bundle (Bracht et al., 2015b; Carballedo et al., 2012; Jones et al., 2013a).”
BackgroundMethodPujol, S., Wells, W., Pierpaoli, C., Brun, C., Gee, J., Cheng, G., Vemuri, B., Commowick, O., Prima, S., Stamm, A., et al. (2015) · Journal of Neuroimaging · doi:10.1111/jon.12283 · Cited by 171
Tractography-based Parcellation of the Human Middle Temporal Gyrus
Xu, J., Wang, J., Fan, L., Li, H., Zhang, W., Hu, Q., & Jiang, T. (2015) · Scientific Reports · doi:10.1038/srep18883 · Cited by 190
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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