The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project
Open accessAbstract
Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Construction of the connectivity matrix has been described in several reviews (Sotiropoulos et al., 2016; Yeh et al., 2020) and includes two major steps. First, parcellation of the whole-brain tractogram is needed to compute all possible fiber pathways in the brain, i.e., white matter parcels connecting all pairs of gray matter regions (as described in Section 4.2). Then, a metric of “connectivity”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Sotiropoulos et al., 2016
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient
Stejskal, E. O., & Tanner, J. E. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1695690 · Cited by 8,239
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
Self-citationThe minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
Self-citationDeep Brain Stimulation for Treatment-Resistant Depression
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., Schwalb, J. M., & Kennedy, S. H. (2005) · Neuron · doi:10.1016/j.neuron.2005.02.014 · Cited by 4,059
Accurate and robust brain image alignment using boundary-based registration
Greve, D. N., & Fischl, B. R. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.060 · Cited by 3,553
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
Self-citationAndersson, J. L. R., Skare, S., & Ashburner, J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00336-7 · Cited by 3,469
Self-citationAndersson, J., & Sotiropoulos, S. N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.019 · Cited by 3,439
Self-citationNODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288
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
Self-citationJensen, J. H., Helpern, J. A., Ramani, A., Lu, H., & Kaczynski, K. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20508 · Cited by 2,488
Toward a quantitative assessment of diffusion anisotropy
Pierpaoli, C. M., & Basser, P. J. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910360612 · Cited by 2,392
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191
Reproducibility of quantitative tractography methods applied to cerebral white matter
Wakana, S., Caprihan, A., Panzenboeck, M. M., Fallon, J. H., Perry, M., Gollub, R. L., Hua, K., Zhang, J., Jiang, H., Dubey, P., et al. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.049 · Cited by 1,686
Tournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.037 · Cited by 1,635
Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A., & Sijbers, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.061 · Cited by 1,484
Jeurissen, B., Leemans, A., Tournier, J., Jones, D. K., & Sijbers, J. (2013) · Human Brain Mapping · doi:10.1002/hbm.22099 · Cited by 1,088
Architectonic subdivision of the human orbital and medial prefrontal cortex
Öngür, D., Ferry, A. T., & Price, J. L. (2003) · Journal of Comparative Neurology · doi:10.1002/cne.10609 · Cited by 944
Advances in diffusion MRI acquisition and processing in the Human Connectome Project
Sotiropoulos, S. N., Jbabdi, S., Xu, J., Andersson, J., Moeller, S., Auerbach, E. J., Glasser, M. F., Hernandez, M., Sapiro, G., Jenkinson, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.057 · Cited by 889
Self-citationAxcaliber: A method for measuring axon diameter distribution from diffusion MRI
Assaf, Y., Blumenfeld‐Katzir, T., Yovel, Y., & Basser, P. J. (2008) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21577 · Cited by 849
Neural Information Processing Systems (NIPS)
Shang, X., Zhu, Z., Leimkuhler, B., & Storkey, A. (2015) · Cited by 831
Uğurbil, K., Xu, J., Auerbach, E. J., Moeller, S., Vu, A. T., Duarte-Carvajalino, J. M., Lenglet, C., Wu, X., Schmitter, S., Van de Moortele, P. F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.012 · Cited by 812
Self-citationDeterministic and probabilistic tractography based on complex fibre orientation distributions
Descoteaux, M., Deriche, R., Knosche, T. R., & Anwander, A. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004424 · Cited by 698
An analytical model of restricted diffusion in bovine optic nerve
Stanisz, G. J., Wright, G. A., Henkelman, R. M., & Szafer, A. (1997) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910370115 · Cited by 501
Bennett, K. M., Schmainda, K. M., Bennett, R., Rowe, D. B., Lu, H., & Hyde, J. S. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10581 · Cited by 469
Measurement of fiber orientation distributions using high angular resolution diffusion imaging
Anderson, A. W. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20667 · Cited by 461
Reveley, C., Seth, A. K., Pierpaoli, C., Silva, A. C., Yu, D., Saunders, R. C., Leopold, D. A., & Ye, F. Q. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418198112 · Cited by 417
Jbabdi, S., Sotiropoulos, S. N., Savio, A. M., Graña, M., & Behrens, T. E. J. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24204 · Cited by 399
Self-citationDetermining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI
Jones, D. K. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10331 · Cited by 371
The Human Connectome Project and beyond: Initial applications of 300mT/m gradients
McNab, J. A., Edlow, B. L., Witzel, T., Huang, S. Y., Bhat, H., Heberlein, K., Feiweier, T., Liu, K., Keil, B. R., Cohen‐Adad, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.074 · Cited by 352
A new view of automatic relevance determination
Wipf, D., & Nagarajan, S. S. (2008) · Cited by 312
Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner
Miller, K. L., Stagg, C. J., Douaud, G., Jbabdi, S., Smith, S. M., Behrens, T. E. J., Jenkinson, M., Chance, S. A., Esiri, M. M., Voets, N., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.070 · Cited by 273
Self-citationAndersson, J., & Sotiropoulos, S. N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.067 · Cited by 249
Self-citationLasič, S., Szczepankiewicz, F., Eriksson, S., Nilsson, M., & Topgaard, D. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00011 · Cited by 248
High resolution whole brain diffusion imaging at 7 T for the Human Connectome Project
Vu, A. T., Auerbach, E. J., Lenglet, C., Moeller, S., Sotiropoulos, S. N., Jbabdi, S., Andersson, J., Yacoub, E. S., & Uǧurbil, K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.004 · Cited by 222
Self-citationJbabdi, S., Lehman, J. F., Haber, S. N., & Behrens, T. E. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2457-12.2013 · Cited by 213
Self-citationA Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging
Dell’Acqua, F., Rizzo, G., Scifo, P., Clarke, R. A., Scotti, G., & Fazio, F. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.888830 · Cited by 192
Sotiropoulos, S. N., Moeller, S., Jbabdi, S., Xu, J., Andersson, J., Auerbach, E. J., Yacoub, E. S., Feinberg, D. A., Setsompop, K., Wald, L. L., et al. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24623 · Cited by 192
Self-citationHernández, M., Guerrero, G. D., Cecilia, J. M., García, J. M., Inuggi, A., Jbabdi, S., Behrens, T. E. J., & Sotiropoulos, S. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061892 · Cited by 191
Self-citationParametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging
Kaden, E., Knösche, T. R., & Anwander, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.012 · Cited by 190
Ball and rackets: inferring fiber fanning from diffusion-weighted MRI
Sotiropoulos, S. N., Behrens, T. E. J., & Jbabdi, S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.056 · Cited by 156
Self-citationMaximum Entropy Spherical Deconvolution for Diffusion MRI
Alexander, D. C. (2005) · Lecture notes in computer science · doi:10.1007/11505730_7 · Cited by 145
́ndez, M. H., Guerrero, G. D., Cecilia, J. M., García, J. M., Inuggi, A., & Sotiropoulos, S. N. (2012) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp.2012.46 · Cited by 139
Self-citationInterpolation of diffusion weighted imaging datasets
Dyrby, T. B., Lundell, H., Burke, M., Reislev, N. L., Paulson, O. B., Ptito, M., & Siebner, H. R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.005 · Cited by 135
Layer-Specific Intracortical Connectivity Revealed with Diffusion MRI
Leuze, C. W., Anwander, A., Bazin, P., Dhital, B., Stüber, C., Reimann, K., Geyer, S., & Turner, R. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs311 · Cited by 134
Heidemann, R. M., Anwander, A., Feiweier, T., Knösche, T. R., & Turner, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.081 · Cited by 125
Graham, M. S., Drobnjak, I., & Zhang, H. G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.006 · Cited by 118
Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI
Alexander, D. C., Zikic, D., Zhang, J., Zhang, H. G., & Criminisi, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_29 · Cited by 113
Mapping Connections in Humans and Non-Human Primates
Essen, D. C. V., Jbabdi, S., Sotiropoulos, S. N., Chen, C., Dikranian, K. T., Coalson, T., Harwell, J., Behrens, T. E. J., & Glasser, M. F. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-396460-1.00016-0 · Cited by 109
Self-citationIn vivo mapping of the fast and slow diffusion tensors in human brain
Clark, C. A., Hedehus, M., & Moseley, M. E. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10118 · Cited by 101
High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T
Roebroeck, A. F., Galuske, R. A. W., Formisano, E., Chiry, O., Bratzke, H., Ronen, I., Kim, D., & Goebel, R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.015 · Cited by 100
Nilsson, M., Szczepankiewicz, F., van Westen, D., & Hansson, O. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141825 · Cited by 89
Goh, A., Lenglet, C., Thompson, P. M., & Vidal, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_108 · Cited by 58
Self-citationRubiX: Combining Spatial Resolutions for Bayesian Inference of Crossing Fibers in Diffusion MRI
Sotiropoulos, S. N., Jbabdi, S., Andersson, J., Woolrich, M. W., Uǧurbil, K., & Behrens, T. E. J. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2231873 · Cited by 40
Self-citationAbout the Geometry of Asymmetric Fiber Orientation Distributions
Reisert, M., Kellner, E., & Kiselev, V. G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2187916 · Cited by 38
Pisharady, P. K., Duarte‐Carvajalino, J. M., Sotiropoulos, S. N., Sapiro, G., & Lenglet, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_15 · Cited by 4
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090
Self-citationImaging brain microstructure with diffusion MRI: practicality and applications
Alexander, D. C., Dyrby, T. B., Nilsson, M., & Zhang, H. G. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3841 · Cited by 456
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics
Demirtaş, M., Burt, J. B., Helmer, M., Ji, J. L., Adkinson, B. D., Glasser, M. F., Essen, D. C. V., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.017 · Cited by 343
Self-citationThe Human Connectome Project: A retrospective
Elam, J. S., Glasser, M. F., Harms, M. P., Sotiropoulos, S. N., Andersson, J., Burgess, G. C., Curtiss, S. W., Oostenveld, R., Larson‐Prior, L. J., Schoffelen, J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118543 · Cited by 340
Self-citationZhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302
“Construction of the connectivity matrix has been described in several reviews [513, 423] and in-”
Metadata
- Authors
- Sotiropoulos, S. N., Hernández-Fernández, M., Vu, A. T., Andersson, J. L., Moeller, S., Yacoub, E., Lenglet, C., Ugurbil, K., Behrens, T. E. J., & Jbabdi, S.
- Affiliations
- University of Oxford (GB)
- University of Minnesota (US)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Sotiropoulos, Stamatios N.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 134
- Pages
- 396-409
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6318224
- OpenAlex
- W2328941635
- Cited by
- 110
- References
- 57
- Citations by year
- 2026: 2 · 2025: 14 · 2024: 15 · 2023: 6 · 2022: 13 · 2021: 13 · 2020: 13 · 2019: 8 · 2018: 11 · 2017: 10 · 2016: 4
- FWCI
- 9.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Funders
- Human Connectome Project (1U54MH091657-01)
- NIH (P41 EB015894)
- UK EPSRC (EP/L023067/1)
- UK MRC (MR/L009013/1)
- 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 MRI, data fusion, Human Connectome Project, brain connectivity, generative models, high-field MRI
- MeSH terms
- Adult, Algorithms, Brain, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Diffusion Tensor Imaging, Connectome, White Matter
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
