The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Validation of in vitro probabilistic tractography
Abstract
Diffusion weighted imaging (DWI) and tractography allow the non-invasive study of anatomical brain connectivity. However, a gold standard for validating tractography of complex connections is lacking. Using the porcine brain as a highly gyrated brain model, we quantitatively and qualitatively assessed the anatomical validity and reproducibility of in vitro multi-fiber probabilistic tractography against two invasive tracers: the histochemically detectable biotinylated dextran amine and manganese enhanced magnetic resonance imaging. Post mortem DWI was used to ensure that most of the sources known to degrade the anatomical accuracy of in vivo DWI did not influence the tracking results. We demonstrate that probabilistic tractography reliably detected specific pathways. Moreover, the applied model allowed identification of the limitations that are likely to appear in many of the current tractography methods. Nevertheless, we conclude that DWI tractography can be a precise tool in studying anatomical brain connectivity.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Ex vivo imaging and tracing17–23 or physically24–30 and numerically simulated phantoms31–34 allow validation to some extent, and in specific circumstances such as basic fiber configurations.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [17]
In the citing paper
“However, these evaluations cannot validate the accuracy of reconstructions due to the lack of ground truth information17.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [17]
In the citing paper
“However, there is one domain where histological postmortem studies have shown a consistent failure of dMRI, which is in regions of transition between white and gray matter, where orientation errors of as much as 90° have been observed (49, 50).”
From Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates (2022) · cites this paper as [49]
Citations
Cites
Parallel organization of functionally segregated circuits linking basal ganglia and cortex
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.09.030186.002041 · Cited by 8,527
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970
Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999) · Annals of Neurology · doi:10.1002/1531-8249(199902)45:2 · Cited by 3,513
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471
Characterization and propagation of uncertainty in diffusion-weighted MR imaging
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen‐Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10609 · Cited by 3,038
Alexander, G. E., Crutcher, M. D., & DeLong, M. R. (1990) · PubMed · Cited by 2,422
(1991) · Progress in Brain Research · doi:10.1016/s0079-6123(08)62678-3 · Cited by 1,961
Tuch, D. S. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20279 · Cited by 1,880
Tournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.037 · Cited by 1,635
High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10268 · Cited by 1,548 · cited 1×
“…(DTI) (Basser et al., 1994) and related methods such as q-ball imaging (Tuch, 2004), tensor-mixture models (Alexander, 2005; Assaf et al., 2004; Tuch et al., 2002), PASMRI (Jansons and Alexander, 2003) and spherical deconvolution (Frank, 2002; Tournier et al., 2004) quantify water-diffusion…”
MethodLongitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey
Selemon, L. D., & Goldman-Rakic, P. S. (1985) · Journal of Neuroscience · doi:10.1523/jneurosci.05-03-00776.1985 · Cited by 1,074
DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking
Jiang, H., van Zijl, P. C. M., Kim, J., Pearlson, G. D., & Mori, S. (2006) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2005.08.004 · Cited by 999
Artifacts and pitfalls in diffusion MRI
Bihan, D. L., Poupon, C., Amadon, A., & Lethimonnier, F. (2006) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20683 · Cited by 805
Detection and modeling of non‐Gaussian apparent diffusion coefficient profiles in human brain data
Alexander, D. C., Barker, G. J., & Arridge, S. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10209 · Cited by 538
Self-citationParker, G. J. M., Haroon, H. A., & Wheeler‐Kingshott, C. A. M. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10350 · Cited by 527
Self-citationCharacterization of anisotropy in high angular resolution diffusion‐weighted MRI
Frank, L. R. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10156 · Cited by 520
Assaf, Y., Freidlin, R. Z., Rohde, G. K., & Basser, P. J. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20274 · Cited by 519
The use of pigs in neuroscience: Modeling brain disorders
Lind, N. M., Moustgaard, A., Jelsing, J., Vajta, G., Cumming, P. D., & Hansen, A. K. (2007) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2007.02.003 · Cited by 515
Self-citationIn vivo neuronal tract tracing using manganese‐enhanced magnetic resonance imaging
Pautler, R. G., Silva, A. C., & Koretsky, A. P. (1998) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910400515 · Cited by 446
Current concepts in neuroanatomical tracing
Köbbert, C., Apps, R., Bechmann, I., Lanciego, J. L., Mey, J., & Thanos, S. (2000) · Progress in Neurobiology · doi:10.1016/s0301-0082(00)00019-8 · Cited by 419
Parker, G. J. M., & Alexander, D. C. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1639 · Cited by 339
Self-citationProbabilistic segmentation of white matter lesions in MR imaging
Anbeek, P., Vincken, K. L., van Osch, M. J. P., Bisschops, R. H. C., & van der Grond, J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.10.012 · Cited by 323
Persistent angular structure: new insights from diffusion magnetic resonance imaging data
Jansons, K. M., & Alexander, D. C. (2003) · Inverse Problems · doi:10.1088/0266-5611/19/5/303 · Cited by 305
Self-citationParker, G. J. M., Wheeler‐Kingshott, C. A. M. G., & Barker, G. J. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.1009386 · Cited by 303
Self-citationThe anatomical relationships of the prefrontal cortex with limbic structures and the basal ganglia
Groenewegen, H. J., Wright, C. I., & Uylings, H. B. M. (1997) · Journal of Psychopharmacology · doi:10.1177/026988119701100202 · Cited by 268
Heiervang, E., Behrens, T. E. J., Mackay, C. E., Robson, M. D., & Johansen-Berg, H. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.037 · Cited by 259
Multiple‐Fiber Reconstruction Algorithms for Diffusion MRI
Alexander, D. C. (2005) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1340.018 · Cited by 239
Self-citationParker, G. J. M., & Alexander, D. C. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45087-0_57 · Cited by 228
Self-citationFrom diffusion tractography to quantitative white matter tract measures: a reproducibility study
Ciccarelli, O., Parker, G. J. M., Toosy, A. T., Wheeler-Kingshott, C. A. M., Barker, G. J., Boulby, P. A., Miller, D. H., & Thompson, A. J. (2003) · NeuroImage · doi:10.1016/s1053-8119(02)00042-3 · Cited by 228
Self-citationMagnetic Resonance Imaging of Neuronal Connections in the Macaque Monkey
Saleem, K. S., Pauls, J., Augath, M. A., Trinath, T., Prause, B. A., Hashikawa, T., & Logothetis, N. K. (2002) · Neuron · doi:10.1016/s0896-6273(02)00718-3 · Cited by 218
Lin, C., Tseng, W. I., Cheng, H.-C., & Chen, J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0882 · Cited by 184
An error analysis of white matter tractography methods: synthetic diffusion tensor field simulations
Lazar, M., & Alexander, A. L. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00277-5 · Cited by 169
Lori, N. F., Akbudak, E., Shimony, J. S., Cull, T. S., Snyder, A. Z., Guillory, R., & Conturo, T. E. (2002) · NMR in Biomedicine · doi:10.1002/nbm.779 · Cited by 164
On the effects of gating in diffusion imaging of the brain using single shot EPI
Skare, S. T., & Andersson, J. (2001) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(01)00415-5 · Cited by 157
Pautler, R. G. (2004) · NMR in Biomedicine · doi:10.1002/nbm.942 · Cited by 141
PASTA: Pointwise assessment of streamline tractography attributes
Jones, D. K., Travis, A. R., Eden, G., Pierpaoli, C. M., & Basser, P. J. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20484 · Cited by 135
Leergaard, T. B., Bjaalie, J. G., Devor, A., Wald, L. L., & Dale, A. M. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.009 · Cited by 102
Tracing neural circuits in vivo with Mn-enhanced MRI
Murayama, Y., Weber, B., Saleem, K. S., Augath, M. A., & Logothetis, N. K. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2005.12.031 · Cited by 79
Mathematical framework for simulating diffusion tensor MR neural fiber bundles
Leemans, A., Sijbers, J., Verhoye, M., der Linden, A. V., & Dyck, D. V. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20418 · Cited by 79
Jelsing, J., Hay‐Schmidt, A., Dyrby, T. B., Hemmingsen, R., Uylings, H. B. M., & Pakkenberg, B. (2006) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2006.06.009 · Cited by 62
Self-citationModelling uncertainty in brain fibre orientation from diffusion-weighted magnetic resonance imaging.
Cook, P. A. (2006) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
Basser (2000) · doi:10.1002/1522-2594(200010)44:4
Flagged first, then by citations
Research works printed but not linked: 1
- Oztas, 2003, Neuronal tracing, Neuroanatomy, 2, 2
Cited by
Sorted by citations
Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,376
Self-citationThomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725
Tractography: Where Do We Go from Here?
Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614
Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings
Mukherjee, P., Berman, J., Chung, S. W., Hess, C. P., & Henry, R. G. (2008) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1051 · Cited by 547
Diffusion MRI at 25: exploring brain tissue structure and function
Le Bihan, D., & Johansen-Berg, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.006 · Cited by 498
Metadata
- Authors
- Dyrby, T. B., Søgaard, L. V., Parker, G. J., Alexander, D. C., Lind, N. M., Baaré, W. F. C., Hay-Schmidt, A., Eriksen, N., Pakkenberg, B., Paulson, O. B., & Jelsing, J.
- Affiliations
- Hvidovre Hospital (DK)
- University College London (GB)
- University of Copenhagen (DK)
- Bispebjerg Hospital (DK)
- Corresponding author
- Dyrby, Tim B.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2007-10
- Volume
- 37
- Issue
- 4
- Pages
- 1267-1277
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1979286792
- Cited by
- 236
- References
- 42
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 13 · 2020: 16 · 2019: 13 · 2018: 18 · 2017: 16 · 2016: 11 · 2015: 17 · 2014: 15 · 2013: 18 · 2012: 19
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Funders
- Velux Stiftung
- Lundbeckfonden
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council
- 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
- probabilistic tractography, diffusion-weighted imaging, porcine brain, biotinylated dextran amine, manganese-enhanced MRI
- MeSH terms
- Algorithms, Animals, Biotin, Brain, Cerebral Cortex, Dextrans, Fluorescent Dyes, Male, Manganese, Nerve Fibers, Neural Pathways, Swine, Swine, Miniature, Reproducibility of Results, Models, Statistical, Diffusion Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review