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
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–110 of 110
Zalesky, A., Sotiropoulos, S. N., Jbabdi, S., & Fornito, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00007-0
Self-citationYang, L., & Wang, Y. (2025) · Journal of X-Ray Science and Technology · doi:10.1177/08953996241313321
Howell, A. M., Warrington, S., Fonteneau, C., Cho, Y., Sotiropoulos, S. N., Murray, J. D., & Anticevic, A. (2025) · eLife · doi:10.7554/elife.95018.2
Self-citationBell, N. M., Johnson, C., Yu, F. F., Urban, J. E., Proskovec, A. L., Madhuranthakam, A. J., Maldjian, J. A., & Davenport, E. M. (2025) · Neurotrauma Reports · doi:10.1177/2689288x251371125 · Cited by 1
An image quality transfer approach for localising deep brain stimulation targets
Zheng, Y., Akram, H., Li, Z., Smith, S. M., & Jbabdi, S. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1005
Self-citationHuang, Z., Chen, M., Charalambous, C. C., Zhu, L., Spiro, E., Shekhar, S., Feld, J., Jiang, X., Yao, J., & Feng, W. (2025) · Journal of Visualized Experiments · doi:10.3791/69236
10.5 Tesla High-Resolution Macaque Brain MRI for In vivo and Ex vivo Connectivity StudiesPreprint
Warrington, S., Selim, M. K., Tendler, B. C., Moeller, S., Farooq, H., Wu, W., Pisharady, P. K., Adriany, G., Auerbach, E. J., Folloni, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.22.695917
Self-citationImaging in Epilepsy: Current Clinical Practice and Future Directions
Hadar, P., & Cole, A. J. (2025) · Seminars in Neurology · doi:10.1055/a-2761-2049
Curtis, M., Yendiki, A., Cheng, T., Omary, A., Flournoy, J. C., Kandala, S., Sanders, A. F. P., Harms, M. P., Somerville, L. H., & Barch, D. M. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1270
Bueno, J. A. R., Paredes‐Pacheco, J., Muñoz-García, J. B., Alfaro-Rubio, F., Ramos-Rodríguez, J. R., Ariza-Corbo, M. J., Rioja, J., García-Arnés, J. A., & García‐Casares, N. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01199-7
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
