Abstract
Brain connectomes are heavily studied to characterize early symptoms of various neurodegenerative diseases such as Alzheimer’s Disease (AD). As the connectomes over different brain regions are naturally represented as a graph, variants of Graph Neural Networks (GNNs) have been developed to identify topological patterns for disease early diagnosis. However, existing GNNs heavily rely on the fixed local structure given by an initial graph as they aggregate information from a direct neighborhood of each node. Such an approach overlooks useful information from further nodes, and multiple layers for node aggregations have to be stacked across the entire graph which leads to an over-smoothing issue. In this regard, we propose a flexible model that learns adaptive scales of neighborhood for individual nodes of a graph to incorporate broader information from appropriate range. Leveraging an adaptive diffusion kernel, the proposed model identifies desirable scales for each node for feature aggregation, which leads to better prediction of diagnostic labels of brain networks. Empirical results show that our method outperforms well-structured baselines on Alzheimer’s Disease Neuroimaging Initiative (ADNI) study for classifying various stages towards AD based on the brain connectome and relevant node-wise features from neuroimages.
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References
Anderson, J., Nielsen, J., Froehlich, A., et al.: Functional connectivity magnetic resonance imaging classification of autism. Brain 134(12), 3742–3754 (2011)
Bruna, J., Zaremba, W., Szlam, A., Lecun, Y.: Spectral networks and deep locally connected networks on graphs. In: ICLR, Banff, Canada (2014)
Chung, F.: Spectral graph theory. American Mathematical Society (1997)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS, vol. 29, pp. 3844–3852 (2016)
Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1–15 (2010)
DeTure, M., Dickson, D.: The neuropathological diagnosis of Alzheimer’s disease. Mol. Neurodegeneration 14(32), 1–18 (2019)
Gao, H., Ji, S.: Graph U-Nets. In: ICML, pp. 2083–2092. Macao, China (2019)
Glorot, X., Bengio., Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, vol. 9, p. 249–256 (2010)
Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: IJCNN, pp. 729–734 (2005)
Guo, Z., Zhang, J., Liu, X., Hou, H., et al.: Neurometabolic characteristics in the anterior cingulate gyrus of Alzheimer’s disease patients with depression: a 1h magnetic resonance spectroscopy study. BMC Psychiatr. 15(1), 1–7 (2015)
Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR, San Diego, U.S. (2015)
Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR, Toulon, France (2017)
Klicpera, J., Weißenberger, S., Günnemann, S.: Diffusion improves graph learning. In: NIPS (2019)
McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
Oppenheim, A., Schafer, R., Buck, J.: Signal and Systems, 2nd edn. Prentice Hall, Upper Saddle River, N.J. (1999)
Scheff, S.W., Price, D.A., Schmitt, F.A., Scheff, M.A., Mufson, E.J.: Synaptic loss in the inferior temporal gyrus in mild cognitive impairment and Alzheimer’s disease. J. Alzheimers Dis. 24(3), 547–557 (2011)
Scott, M.R., Hampton, O.L., Buckley, R.F., et al.: Inferior temporal tau is associated with accelerated prospective cortical thinning in clinically normal older adults. Neuroimage 220, 116991 (2020)
Shuman, D., Ricaud, B., Vandergheynst, P.: Vertex-frequency analysis on graphs. ACHA 40(2), 260–291 (2016)
Stam, C.J., de Haan, W., Daffertshofer, A., et al.: Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132(1), 213–224 (2008)
Tekin, S., Mega, M.S., Masterman, D.M., Chow, T., et al.: Orbitofrontal and anterior cingulate cortex neurofibrillary tangle burden is associated with agitation in Alzheimer disease. Ann. Neurol. 49(3), 355–361 (2001)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: ICLR, Vancouver, Canada (2018)
Venneri, A., McGeown, W.J., Hietanen, H.M., Guerrini, C., Ellis, A.W., Shanks, M.F.: The anatomical bases of semantic retrieval deficits in early Alzheimer’s disease. Neuropsychologia 46(2), 497–510 (2008)
Wu, T., Wang, L., Chen, Y., Zhao, C., Li, K., Chan, P.: Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease. Neurosci. Lett. 460(1), 6–10 (2009)
Xu, B., Shen, H., Cao, Q., Cen, K., Cheng, X.: Graph convolutional networks using heat Kernel for semi-supervised learning. In: IJCAI, Macao, China (2019)
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: NIPS (2018)
Acknowledgements
This research was supported by NSF IIS CRII 1948510, NIH R03 AG070701 and partially supported by IITP-2019-0-01906 (AI Graduate Program at POSTECH), IITP-2022-2020-0-01461 (ITRC) and IITP-2022-0-00290 funded by Ministry of Science and ICT (MSIT).
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Choi, I., Wu, G., Kim, W.H. (2022). How Much to Aggregate: Learning Adaptive Node-Wise Scales on Graphs for Brain Networks. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_36
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