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This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels.
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Edelsbrunner, H., Letscher, D., Zomorodian, A.: Topological persistence and simplification. Discrete Comput Geom 28, 511–533 (2002)
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Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE transactions on pattern analysis and machine intelligence 29(11), 1944–1957 (2007)
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Jung, R.E., Haier, R.J.: The parieto-frontal integration theory (p-fit) of intelligence: converging neuroimaging evidence. Behav. brain Sci. 30, 135–154 (2007)
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Akshoomoff, N., Beaumont, J.L., Bauer, P.J., Dikmen, S.S., Gershon, R.C., Mungas, D., Slotkin, J., Tulsky, D., Weintraub, S., Zelazo, P.D., et al.: VIII. NIH Toolbox Cognition Battery (CB): composite scores of crystallized, fluid, and overall cognition. Monogr. Soc. Res. Child Dev. 78(4), 119–132 (2013)
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Lee, H., Chung, M.K., Kang, H., Lee, D.S.: Hole detection in metabolic connectivity of alzheimer’s disease using k-laplacian. In: 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014. pp. 297–304. Springer Verlag (2014)
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Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems. pp. 3844–3852 (2016)
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Li, X., Duncan, J.: BrainGNN: Interpretable brain graph neural network for fMRI analysis. bioRxiv (2020)
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Hu, J., Cao, L., Li, T., Dong, S., Li, P.: Gat-li: a graph attention network based learning and interpreting method for functional brain network classification. BMC bioinformatics 22(1), 1–20 (2021)
2021
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Huang, S.G., Chung, M.K., Qiu, A.: Revisiting convolutional neural network on graphs with polynomial approximations of laplace–beltrami spectral filtering. Neural Computing and Applications 33(20), 13693–13704 (2021)
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Jo, J., Baek, J., Lee, S., Kim, D., Kang, M., Hwang, S.J.: Edge representation learning with hypergraphs. Advances in Neural Information Processing Systems 34, 7534–7546 (2021)
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Kawahara, J., Brown, C.J., Miller, S.P., Booth, B.G., Chau, V., Grunau, R.E., Zwicker, J.G., Hamarneh, G.: BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049 (2017)
2017
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Shen, X., Finn, E.S., Scheinost, D., Rosenberg, M.D., Chun, M.M., Papademetris, X., Constable, R.T.: Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 12(3), 506–518 (2017)
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Jiang, X., Ji, P., Li, S.: Censnet: Convolution with edge-node switching in graph neural networks. In: IJCAI. pp. 2656–2662 (2019)
2019
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Wee, C.Y., Liu, C., Lee, A., Poh, J.S., Ji, H., Qiu, A., Initiative, A.D.N.: Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. NeuroImage: Clinical 23, 101929 (2019)
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Cui, H., Dai, W., Zhu, Y., Li, X., He, L., Yang, C.: Interpretable graph neural networks for connectome-based brain disorder analysis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 375–385. Springer (2022)
2022
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Huang, S.G., Xia, J., Xu, L., Qiu, A.: Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity. Medical Image Analysis 77, 102370 (2022)
2022
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Zhao, K., Duka, B., Xie, H., Oathes, D.J., Calhoun, V., Zhang, Y.: A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in adhd. Neuroimage 246, 118774 (2022)
2022
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