The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Metric learning with spectral graph convolutions on brain connectivity networks
Open accessAbstract
ABSTRACT Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non‐trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s‐GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non‐matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. HighlightsMetric learning approach for similarity estimation between brain connectivity graphs.The method employs spectral graph convolutions to learn localised feature maps.Quantitative and qualitative evaluation on ABIDE and UK Biobank databases.Global loss function leads to improved results on heterogeneous datasets.
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.
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020
Network modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., Ugurbil, K., Barch, D. M., Van Essen, D. C., & Miller, K. L. (2015) · Nature Neuroscience · doi:10.1038/nn.4125 · Cited by 859
BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J. G., & Hamarneh, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.046 · Cited by 813
Geodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., & Varoquaux, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.045 · Cited by 701
Cited by
Graph Neural Networks in Network Neuroscience
Bessadok, A., Mahjoub, M. A., & Rekik, I. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3209686 · Cited by 341
A novel graph convolutional feature based convolutional neural network for stock trend prediction
Chen, W., Jiang, M., Zhang, W.-G., & Chen, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2020.12.068 · Cited by 281
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271
Perraudin, N., Defferrard, M., Kacprzak, T., & Sgier, R. (2019) · Astronomy and Computing · doi:10.1016/j.ascom.2019.03.004 · Cited by 184
“…to their versatility, graph neural networks have been used in a variety of tasks, such as identifying diseases from brain connectivity networks [61] or population graphs [62], designing drugs using molecular graphs [63], segmenting 3D point clouds [64], optimizing shapes to be aerodynamic…”
MethodChen, Z., Liu, X., Yang, Q., Wang, Y.-J., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.1671 · Cited by 43
Metadata
- Authors
- Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., & Rueckert, D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 169
- Pages
- 431-442
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2779020697
- Cited by
- 376
- References
- 13
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Metric learning with spectral graph convolutions on brain connectivity networks. https://app.cochat.ai/papers/10.1016/j.neuroimage.2017.12.052
- The paper
- Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., & Rueckert, D. (2018). Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.12.052
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
