Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-06-007 ISMRM Abstract

Longitudinal Brain Connectivity Prediction with Edge Graph Recurrent Network

Primary: Brain Function and fMRI
Secondary: Neuro
560-06-007 · AI Image Processing · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row A
Keywords: Brain Connectivity Neuroimaging Longitudinal Studies Graph Neural Networks
Accepted
Muhammad Adeel Ijaz 1, Meklit Mesfin Atlaw1, Xinrui Chen1, Shizhou Zhang1, Geng Chen1
1School of Computer Science, Northwestern Polytechnical University, Xi'An, China
Presenting Author: Muhammad Adeel Ijaz

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References

1. Farahani, F.V., Karwowski, W. and Lighthall, N.R., 2019. Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review. Frontiers in Neuroscience, 13, p.585.
2. Tekin, A., Nebli, A. and Rekik, I., 2021, September. Recurrent brain graph mapper for predicting time-dependent brain graph evaluation trajectory. In MICCAI Workshop on Domain Adaptation and Representation Transfer (pp. 180-190). Cham: Springer International Publishing.
3. Xiao, S. and Rekik, I., 2024, October. DynGNN: Dynamic Memory-Enhanced Generative GNNs for Predicting Temporal Brain Connectivity. In International Workshop on PRedictive Intelligence In MEdicine (pp. 111-123). Cham: Springer Nature Switzerland.
4. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M. and Solomon, J.M., 2019. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics, 38(5), pp.1-12.
5. Chung, J., Gulcehre, C., Cho, K. and Bengio, Y., 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
6. Weiner, M.W., Veitch, D.P., Aisen, P.S., Beckett, L.A., Cairns, N.J., Green, R.C., Harvey, D., Jack, C.R., Jagust, W., Morris, J.C., Petersen, R.C., Saykin, A.J., Shaw, L.M., Siuciak, J.A., Soares, H., Toga, A.W. and Trojanowski, J.Q., 2017. The Alzheimer’s Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement. Alzheimer’s & Dementia, 13(5), pp.561–571.
7. Wang, J., Lytle, M.N., Weiss, Y., Yamasaki, B.L. and Booth, J.R., 2022. A longitudinal neuroimaging dataset on language processing in children ages 5, 7, and 9 years old. Scientific Data, 9(1), p.4.

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http://echo.ismrm.org/p/ISMRM2026/560-06-007