Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder marked by deficits in social communication and stereotyped behaviors. Functional magnetic resonance imaging (fMRI) can record the brain’s neural activity through detecting blood flow changes, which plays an important role in automatic ASD diagnosis. Graph convolutional network (GCN)-based methods using fMRI data and phenotypic data to construct population graphs aggregate information from different modalities and have achieved satisfactory performance. However, some existing GCNs cannot effectively integrate node features and learn topological structures from the population graph. In addition, they usually construct brain functional connectivity limited to one brain atlas, which did not consider the complementary spatial information between different atlases. To this end, we propose a scale adaptive graph convolutional network that employs adaptive multi-channel graph convolutional network (AM-GCN) for ASD diagnosis. We introduce mutual learning in two parallel AM-GCNs to integrate the complementary information from different atlases. To alleviate the over-smoothing problem, we add attention-based jumping connections into each network to reduce information loss of previous layers. We evaluate our method on the Autism Brain Imaging Data Exchange (ABIDE) and achieves 90.9% classification accuracy, which outperforms the state-of-the-art methods.
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References
Ktena, S.I., Parisot, S., Ferrante, E., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage 169, 431–442 (2018)
Yang, Y., Ye, C., Ma, T.: A deep connectome learning network using graph convolution for connectome-disease association study. Neural Netw. 164, 91–104 (2023)
Wen, G., Cao, P., Bao, H., et al.: MVS-GCN: a prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Comput. Biol. Med. 142, 105239 (2022)
Shao, L., Fu, C., Chen, X.: A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder. BMC Bioinform. 24(1), 363 (2023)
Parisot, S., et al.: Spectral graph convolutions for population-based disease prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 177–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_21
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, Toulon, France (2017)
Cosmo, L., Kazi, A., Ahmadi, S.-A., Navab, N., Bronstein, M.: Latent-graph learning for disease prediction. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 643–653. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_62
Zhang, S., Chen, X., Shen, X., et al.: A-GCL: adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders. Med. Image Anal. (90), 102932 (2023)
Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)
Wang, Y., Long, H., Zhou, Q., et al.: PLSNet: position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting. Comput. Biol. Med. 163, 107184 (2023)
Kullback, S.: Information Theory and Statistics.Courier Corporation (1997)
Zhang, H., Song, R., Wang, L., et al.: Classification of brain disorders in rs-fMRI via local-to-global graph neural networks. IEEE Trans. Med. Imaging 42(2), 444–455 (2022)
Heinsfeld, A.S., Franco, A.R., Craddock, R.C., et al.: Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clin. 17, 16–23 (2018)
Wang, X., Zhu, M., Bo, D., et al.: AM-GCN: adaptive multi-channel graph convolutional networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1243–1253 (2020)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S.: Identifying autism from resting-state fMRI using long short-term memory networks. In: Wang, Q., Shi, Y., Suk, H.-Il., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 362–370. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_42
Cao, M., Yang, M., Qin, C., et al.: Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data. Biomed. Sig. Process. Control 70, 103015 (2021)
Bi, X., Wang, Y., Shu, Q., et al.: Classification of autism spectrum disorder using random support vector machine cluster. Front. Genet. 9, 331287 (2018)
Kazeminejad, A., Sotero, R.C.: Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification. Front. Neurosci. 12, 414728 (2018)
Zhang, Y., Xiang, T., Hospedales, T.M., et al.: Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4320–4328 (2018)
Di Martino, A., Yan, C.G., Li, Q., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Huang, Y., Chung, A.C.S.: Disease prediction with edge-variational graph convolutional networks. Med. Image Anal. 77, 102375 (2022)
Müller, R.A., Shih, P., Keehn, B., et al.: Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cereb. Cortex 21(10), 2233–2243 (2011)
Zheng, S., et al.: Multi-modal graph learning for disease prediction. IEEE Trans. Med. Imaging 41(9), 2207–2216 (2022)
Khodatars, M., Shoeibi, A., Sadeghi, D., et al.: Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput. Biol. Med. 139, 104949 (2021)
Data & Statistics on Autism Spectrum Disorder (2022). https://www.cdc.gov/ncbddd/autism/data.html
Li, J., Chen, Z., Zhong, Y., et al.: Appearance-based gaze estimation for ASD diagnosis. IEEE Trans. Cybern. 52(7), 6504–6517 (2022)
Matthews, P.M., Jezzard, P.: Functional magnetic resonance imaging. J. Neurol. Neurosurg. Psychiatry 75(1), 6–12 (2004)
Li, L., Jiang, H., Wen, G., et al.: TE-HI-GCN: an ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis. Neuroinformatics 20, 353–375 (2022)
Acknowledgments
This work was supported by National Natural Science Foundation of China (62373280) and STI 2030—Major Projects (2021ZD0200500).
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Zhang, J., Jiang, C., Li, J., Ouyang, G. (2024). SA-GCN: Scale Adaptive Graph Convolutional Network for ASD Identification. In: Yap, M.H., Kendrick, C., Behera, A., Cootes, T., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2024. Lecture Notes in Computer Science, vol 14860. Springer, Cham. https://doi.org/10.1007/978-3-031-66958-3_9
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DOI: https://doi.org/10.1007/978-3-031-66958-3_9
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