Abstract
Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e. brain networks constructed by fMRI. One way to interpret important features is through looking at how the classification probability changes if the features are occluded or replaced. The major limitation of this approach is that replacing values may change the distribution of the data and lead to serious errors. Therefore, we develop a 2-stage pipeline to eliminate the need to replace features for reliable biomarker interpretation. Specifically, we propose an inductive GNN to embed the graphs containing different properties of task-fMRI for identifying ASD and then discover the brain regions/sub-graphs used as evidence for the GNN classifier. We first show GNN can achieve high accuracy in identifying ASD. Next, we calculate the feature importance scores using GNN and compare the interpretation ability with Random Forest. Finally, we run with different atlases and parameters, proving the robustness of the proposed method. The detected biomarkers reveal their association with social behaviors and are consistent with those reported in the literature. We also show the potential of discovering new informative biomarkers. Our pipeline can be generalized to other graph feature importance interpretation problems.
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Adebayo, J., et al.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems, pp. 9505–9515 (2018)
Cangea, C., et al.: Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287 (2018)
Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika 35(3), 283–319 (1970)
Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3), 968–980 (2006)
Destrieux, C., et al.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53(1), 1–15 (2010)
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. CoRR abs/1903.02428 (2019)
Gilmer, J., et al.: Neural message passing for quantum chemistry. In: ICML 2017, pp. 1263–1272. JMLR.org (2017)
Goldani, A.A., et al.: Biomarkers in autism. Front. Psychiatry 5, 100 (2014)
Kaiser, M.D., et al.: Neural signatures of autism. Proc. Nat. Acad. Sci. 107(49), 21223–21228 (2010)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Ktena, S.I., et al.: Distance metric learning using graph convolutional networks: application to functional brain networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 469–477. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_54
Loe, C.W., Jensen, H.J.: Comparison of communities detection algorithms for multiplex. Physica A: Stat. Mech. Appl. 431, 29–45 (2015)
Nishii, R.: Box-Cox Transformation. Encyclopedia of Mathematics. Springer, New York (2001)
Yang, D., et al.: Brain responses to biological motion predict treatment outcome in young children with autism. Transl. Psychiatry 6(11), e948 (2016)
Yarkoni, T., et al.: Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8(8), 665 (2011)
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This work was supported by NIH Grant R01 NS035193.
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Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P., Duncan, J.S. (2019). Graph Neural Network for Interpreting Task-fMRI Biomarkers. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_54
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DOI: https://doi.org/10.1007/978-3-030-32254-0_54
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