Abstract
Currently, accurately identifying autism spectrum disorders (ASD) still have challenges. However, graph neural network used for ASD diagnosis only focus on the part of abnormal brain functional connectivity, ignoring the effects between brain regions and the help of phenotypic information, and the kernel is also pre-defined. To solve the above problems, a graph neural network based on self-attention graph pooling and self-adjust filter is proposed for ASD diagnosis. Specifically, first, self-attention graph pooling is used for feature extraction of the fMRI to account for the influence of nodes with abnormal brain functional connectivity and activity between brain regions in fMRI data. Then, image features were taken as graph nodes and phenotypic information as edges to form a population graph. Finally, to focus on both high and low frequency information in the graph, a graph neural network based on self-adjust filter is used to learn node embedding. Experimental results on ABIDE I showed the effectiveness of the proposed method.
This work was supported by the Natural Science Foundation of Shandong Province, China (ZR2022MF237 and ZR2020MF041).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amaral, D.G., Schumann, C.M., Nordahl, C.W.: Neuroanatomy of autism. Trends Neurosci. 31(3), 137–145 (2008)
American Psychiatric Association, D., Association, A.P., et al.: Diagnostic and statistical manual of mental disorders: DSM-5, vol. 5. American psychiatric association Washington, DC (2013)
Baio, J., et al.: Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, united states, 2014. MMWR Surveill. Summ. 67(6), 1 (2018)
Cao, M.: Using deepgcn to identify the autism spectrum disorder from multi-site resting-state data. Biomed. Signal Process. Control 70, 103015 (2021)
Craddock, C., et al.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinform. 7, 27 (2013)
Di Martino, A., 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)
Jiang, H., Cao, P., Xu, M., Yang, J., Zaiane, O.: HI-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput. Biol. Med. 127, 104096 (2020)
Kazi, A., Shekarforoush, S., Kortuem, K., Albarqouni, S., Navab, N., et al.: Self-attention equipped graph convolutions for disease prediction. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1896–1899. IEEE (2019)
Khazaee, A., Ebrahimzadeh, A., Babajani-Feremi, A.: Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin. Neurophysiol. 126(11), 2132–2141 (2015)
Kong, Y., Gao, J., Xu, Y., Pan, Y., Wang, J., Liu, J.: Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing 324, 63–68 (2019)
Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage 169, 431–442 (2018)
Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: International Conference on Machine Learning, pp. 3734–3743. PMLR (2019)
Li, J., Zhong, Y., Han, J., Ouyang, G., Li, X., Liu, H.: Classifying ASD children with LSTM based on raw videos. Neurocomputing 390, 226–238 (2020)
Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P., Duncan, J.S.: Graph neural network for interpreting task-fMRI biomarkers. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 485–493. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_54
Li, X.: Braingnn: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)
Li, X., et al.: Pooling regularized graph neural network for fMRI biomarker analysis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 625–635. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_61
Liu, J., Li, M., Lan, W., Wu, F.X., Pan, Y., Wang, J.: Classification of alzheimer’s disease using whole brain hierarchical network. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(2), 624–632 (2016)
Liu, J., Wang, X., Zhang, X., Pan, Y., Wang, X., Wang, J.: Mmm: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning. Multimedia Tools Appl. 77, 29651–29667 (2018)
Mhiri, I., Rekik, I.: Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism. Med. Image Anal. 60, 101596 (2020)
Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)
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
Silverstein, B.H., Bressler, S.L., Diwadkar, V.A.: Inferring the dysconnection syndrome in schizophrenia: interpretational considerations on methods for the network analyses of fmri data. Front. Psych. 7, 132 (2016)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wen, G., Cao, P., Bao, H., Yang, W., Zheng, T., Zaiane, O.: MVS-GCN: a prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Comput. Biol. Med. 142, 105239 (2022)
Yan, Y., Zhu, J., Duda, M., Solarz, E., Sripada, C., Koutra, D.: Groupinn: Grouping-based interpretable neural network for classification of limited, noisy brain data. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 772–782 (2019)
Zhang, Z., et al.: Hierarchical graph pooling with structure learning. arXiv preprint arXiv:1911.05954 (2019)
Zhu, M., Wang, X., Shi, C., Ji, H., Cui, P.: Interpreting and unifying graph neural networks with an optimization framework. In: Proceedings of the Web Conference 2021, pp. 1215–1226 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dong, A., Zhang, X., Lv, G., Zhao, G., Zhai, Y. (2024). Autism Spectrum Disorder Diagnosis Using Graph Neural Network Based on Graph Pooling and Self-adjust Filter. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_35
Download citation
DOI: https://doi.org/10.1007/978-981-99-8558-6_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8557-9
Online ISBN: 978-981-99-8558-6
eBook Packages: Computer ScienceComputer Science (R0)