IECE Transactions on Intelligent Systematics
ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)
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[1] Park, H. J., & Friston, K. (2013). Structural and functional brain networks: from connections to cognition. Science, 342(6158), 1238411.
[2] Yuan, H., Ding, L., Zhu, M., Zotev, V., Phillips, R., & Bodurka, J. (2016). Reconstructing large-scale brain resting-state networks from high-resolution EEG: spatial and temporal comparisons with fMRI. Brain connectivity, 6(2), 122-135.
[3] Stam, C. V., & Van Straaten, E. C. W. (2012). The organization of physiological brain networks. Clinical neurophysiology, 123(6), 1067-1087.
[4] Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology, 7(1), 113-140.
[5] Zhang, W., Feng, J., Liu, W., Zhang, S., Yu, X., Liu, J., ... & Ma, L. (2024). Investigating Sea-Level Brain Predictors for Acute Mountain Sickness: A Multimodal MRI Study before and after High-Altitude Exposure. American Journal of Neuroradiology, 45(6), 809-818.
[6] Wei, C., Gong, S., Zou, Q., Zhang, W., Kang, X., Lu, X., ... & Shan, B. (2021). A comparative study of structural and metabolic brain networks in patients with mild cognitive impairment. Frontiers in Aging Neuroscience, 13, 774607.
[7] Sporns, O. (2018). Graph theory methods: applications in brain networks. Dialogues in clinical neuroscience, 20(2), 111-121.
[8] Hong, S., Lv, C., Zhao, T., Wang, B., Wang, J., & Zhu, J. (2016). Cascading failure analysis and restoration strategy in an interdependent network. Journal of Physics A: Mathematical and Theoretical, 49(19), 195101.
[9] Hong, S., Zhu, J., Braunstein, L. A., Zhao, T., & You, Q. (2017). Cascading failure and recovery of spatially interdependent networks. Journal of Statistical Mechanics: Theory and Experiment, 2017(10), 103208.
[10] Bessadok, A., Mahjoub, M. A., & Rekik, I. (2022). Graph neural networks in network neuroscience. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5833-5848.
[11] Wein, S., Malloni, W. M., Tomé, A. M., Frank, S. M., Henze, G. I., Wüst, S., ... & Lang, E. W. (2021). A graph neural network framework for causal inference in brain networks. Scientific reports, 11(1), 8061.
[12] Gauthier, C. J., & Fan, A. P. (2019). BOLD signal physiology: models and applications. Neuroimage, 187, 116-127.
[13] Kim, S. G., Richter, W., & Ugurbil, K. (1997). Limitations of temporal resolution in functional MRI. Magnetic resonance in medicine, 37(4), 631-636.
[14] da Silva, F. L. (2013). EEG and MEG: relevance to neuroscience. Neuron, 80(5), 1112-1128.
[15] Zhang, W., Jiang, M., Teo, K. A. C., Bhuvanakantham, R., Fong, L., Sim, W. K. J., ... & Guan, C. (2024). Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study. NeuroImage, 293, 120629.
[16] Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature reviews neuroscience, 10(3), 186-198.
[17] Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12), 7821-7826.
[18] Barthelemy, M. (2004). Betweenness centrality in large complex networks. The European physical journal B, 38(2), 163-168.
[19] Ravasz, E., & Barabási, A. L. (2003). Hierarchical organization in complex networks. Physical review E, 67(2), 026112.
[20] Bassett, D. S., & Bullmore, E. T. (2017). Small-world brain networks revisited. The Neuroscientist, 23(5), 499-516.
[21] van den Heuvel, M. P., Stam, C. J., Boersma, M., & Pol, H. H. (2008). Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage, 43(3), 528-539.
[22] Hosseini, S. H., Hoeft, F., & Kesler, S. R. (2012). GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PloS one, 7(7), e40709.
[23] Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Frontiers in human neuroscience, 9, 386.
[24] Kruschwitz, J. D., List, D., Waller, L., Rubinov, M., & Walter, H. (2015). GraphVar: a user-friendly toolbox for comprehensive graph analyses of functional brain connectivity. Journal of neuroscience methods, 245, 107-115.
[25] Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A. A. C., Lukemire, J., ... & Yang, C. (2022). Braingb: a benchmark for brain network analysis with graph neural networks. IEEE transactions on medical imaging, 42(2), 493-506.
[26] Abboud, R., Ceylan, I. I., Grohe, M., & Lukasiewicz, T. (2020). The surprising power of graph neural networks with random node initialization. arXiv preprint arXiv:2010.01179.
[27] Li, J., Song, Y., Song, X., & Wipf, D. (2023, July). On the initialization of graph neural networks. In International Conference on Machine Learning (pp. 19911-19931). PMLR.
[28] Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in neural information processing systems, 30.
[29] Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. stat, 1050(20), 10-48550.
[30] Mesquita, D., Souza, A., & Kaski, S. (2020). Rethinking pooling in graph neural networks. Advances in Neural Information Processing Systems, 33, 2220-2231.
[31] Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., & Leskovec, J. (2018). Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems, 31.
[32] Lee, J., Lee, I., & Kang, J. (2019, May). Self-attention graph pooling. In International conference on machine learning (pp. 3734-3743). pmlr.
[33] Zhang, Z., Bu, J., Ester, M., Zhang, J., Li, Z., Yao, C., ... & Wang, C. (2021). Hierarchical multi-view graph pooling with structure learning. IEEE Transactions on Knowledge and Data Engineering, 35(1), 545-559.
[34] Ranjan, E., Sanyal, S., & Talukdar, P. (2020, April). Asap: Adaptive structure aware pooling for learning hierarchical graph representations. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 04, pp. 5470-5477).
[35] Murphy, R., Srinivasan, B., Rao, V., & Ribeiro, B. (2019, May). Relational pooling for graph representations. In International Conference on Machine Learning (pp. 4663-4673). PMLR.
[36] Chen, Z., Chen, F., Zhang, L., Ji, T., Fu, K., Zhao, L., ... & Lu, C. T. (2020). Bridging the gap between spatial and spectral domains: A survey on graph neural networks. arXiv preprint arXiv:2002.11867.
[37] Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019). Graph convolutional networks: a comprehensive review. Computational Social Networks, 6(1), 1-23.
[38] Zhang, H., Song, R., Wang, L., Zhang, L., Wang, D., Wang, C., & Zhang, W. (2022). Classification of brain disorders in rs-fMRI via local-to-global graph neural networks. IEEE transactions on medical imaging, 42(2), 444-455.
[39] Klepl, D., He, F., Wu, M., Blackburn, D. J., & Sarrigiannis, P. (2022). EEG-based graph neural network classification of Alzheimer’s disease: An empirical evaluation of functional connectivity methods. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2651-2660.
[40] Cao, J., Yang, L., Sarrigiannis, P. G., Blackburn, D., & Zhao, Y. (2024). Dementia classification using a graph neural network on imaging of effective brain connectivity. Computers in Biology and Medicine, 168, 107701.
[41] Li, X., Dvornek, N. C., Zhou, Y., Zhuang, J., Ventola, P., & Duncan, J. S. (2019). Graph neural network for interpreting task-fmri biomarkers. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V 22 (pp. 485-493). Springer International Publishing.
[42] Chan, Y. H., Girish, D., Gupta, S., Xia, J., Kasi, C., He, Y., ... & Rajapakse, J. C. (2024). Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review. arXiv preprint arXiv:2405.00577.
[43] Li, X., Zhou, Y., Dvornek, N. C., Zhang, M., Zhuang, J., Ventola, P., & Duncan, J. S. (2020). Pooling regularized graph neural network for fmri biomarker analysis. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII 23 (pp. 625-635). Springer International Publishing.
[44] Yang, C., Wang, P., Tan, J., Liu, Q., & Li, X. (2021). Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Computers in biology and medicine, 139, 104963.
[45] Li, Z., Hwang, K., Li, K., Wu, J., & Ji, T. (2022). Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity. Scientific reports, 12(1), 18998.
[46] Zhu, Y., Cui, H., He, L., Sun, L., & Yang, C. (2022, July). Joint embedding of structural and functional brain networks with graph neural networks for mental illness diagnosis. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 272-276). IEEE.
[47] Zheng, K., Yu, S., & Chen, B. (2024). Ci-gnn: A granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis. Neural Networks, 172, 106147.
[48] Kim, B. H., Ye, J. C., & Kim, J. J. (2021). Learning dynamic graph representation of brain connectome with spatio-temporal attention. Advances in Neural Information Processing Systems, 34, 4314-4327.
[49] Kim, M., Kim, J., Qu, J., Huang, H., Long, Q., Sohn, K. A., ... & Shen, L. (2021, December). Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1381-1384). IEEE.
[50] Dong, Z., Wu, Y., Xiao, Y., Chong, J. S. X., Jin, Y., & Zhou, J. H. (2023, October). Beyond the snapshot: Brain tokenized graph transformer for longitudinal brain functional connectome embedding. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 348-357). Cham: Springer Nature Switzerland.
[51] Yang, Y., Ye, C., Guo, X., Wu, T., Xiang, Y., & Ma, T. (2023). Mapping multi-modal brain connectome for brain disorder diagnosis via cross-modal mutual learning. IEEE Transactions on Medical Imaging.
[52] Cui, H., Dai, W., Zhu, Y., Li, X., He, L., & Yang, C. (2022, September). Interpretable graph neural networks for connectome-based brain disorder analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 375-385). Cham: Springer Nature Switzerland.
[53] Sebenius, I., Campbell, A., Morgan, S. E., Bullmore, E. T., & Liò, P. (2021, October). Multimodal graph coarsening for interpretable, MRI-based brain graph neural network. In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
[54] Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826.
[55] Liu, N., Feng, Q., & Hu, X. (2022). Interpretability in graph neural networks. Graph neural networks: foundations, frontiers, and applications, 121-147.
[56] Wang, Z., Xu, Y., Peng, D., Gao, J., & Lu, F. (2023). Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression. Cerebral Cortex, 33(10), 6407-6419.
[57] Zhang, W., Zhang, T., Pan, T., Zhao, S., Nie, B., Liu, H., ... & Alzheimer’s Disease Neuroimaging Initiative. (2021). Deep learning with 18F-fluorodeoxyglucose-PET gives valid diagnoses for the uncertain cases in memory impairment of Alzheimer’s disease. Frontiers in Aging Neuroscience, 13, 764272.
[58] Yu, L., Shen, J., Li, J., & Lerer, A. (2020). Scalable graph neural networks for heterogeneous graphs. arXiv preprint arXiv:2011.09679.
[59] Zhao, S., Khoo, S., Ng, S. C., & Chi, A. (2022). Brain functional network and amino acid metabolism association in females with subclinical depression. International Journal of Environmental Research and Public Health, 19(6), 3321.
[60] Stickel, S., Wagels, L., Wudarczyk, O., Jaffee, S., Habel, U., Schneider, F., & Chechko, N. (2019). Neural correlates of depression in women across the reproductive lifespan–An fMRI review. Journal of affective disorders, 246, 556-570.
IECE Transactions on Intelligent Systematics
ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)
Email: [email protected]
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