Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer’s Diseases

  • Conference paper
  • First Online:

Abstract

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer’s Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11210
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14013
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Van Den Driessche, G.B., Lespiau, J.B., Damoc, B., Clark, A., et al.: Improving language models by retrieving from trillions of tokens. In: International conference on machine learning. pp. 2206–2240. PMLR (2022)

    Google Scholar 

  2. Cui, H., Dai, W., Zhu, Y., Li, X., He, L., Yang, C.: Interpretable graph neural networks for connectome-based brain disorder analysis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 375–385. Springer (2022)

    Google Scholar 

  3. Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis: I. segmentation and surface reconstruction. Neuroimage9(2), 179–194 (1999)

    Google Scholar 

  4. Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al.: An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest. Neuroimage31(3), 968–980 (2006)

    Article  Google Scholar 

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprintarXiv:1810.04805 (2018)

  6. Gilmer, J., Schoenholz, S.S., et al.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning. pp. 1263–1272. PMLR (2017)

    Google Scholar 

  7. Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., Leskovec, J.: Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems33, 22118–22133 (2020)

    Google Scholar 

  8. Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E.: Few-shot learning with retrieval augmented language models. arXiv preprintarXiv:2208.03299 (2022)

  9. Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprintarXiv:1611.01144 (2016)

  10. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprintarXiv:1609.02907 (2016)

  11. LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A.G., et al.: Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease. MedRxiv pp. 2019–12 (2019)

    Google Scholar 

  12. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.t., Rocktäschel, T., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems33, 9459–9474 (2020)

    Google Scholar 

  13. Liang, S., Shao, J., Zhang, J., Cui, B.: Graph-based non-sampling for knowledge graph enhanced recommendation. IEEE Transactions on Knowledge and Data Engineering35(9), 9462–9475 (2023)

    Article  Google Scholar 

  14. Lin, X., Quan, Z., Wang, Z.J., Ma, T., Zeng, X.: Kgnn: Knowledge graph neural network for drug-drug interaction prediction. In: IJCAI. vol. 380, pp. 2739–2745 (2020)

    Google Scholar 

  15. Luo, Z., Xu, C., Zhao, P., Geng, X., Tao, C., Ma, J., Lin, Q., Jiang, D.: Augmented large language models with parametric knowledge guiding. arXiv preprintarXiv:2305.04757 (2023)

  16. Lyu, Z., Wu, Y., Lai, J., Yang, M., Li, C., Zhou, W.: Knowledge enhanced graph neural networks for explainable recommendation. IEEE Transactions on Knowledge and Data Engineering35(5), 4954–4968 (2022)

    Google Scholar 

  17. Mackin, R.S., Insel, P.S., Landau, S., Bickford, D., Morin, R., Rhodes, E., Tosun, D., Rosen, H.J., Butters, M., Aisen, P., et al.: Late-life depression is associated with reduced cortical amyloid burden: Findings from the alzheimer’s disease neuroimaging initiative depression project. Biological psychiatry89(8), 757–765 (2021)

    Article  Google Scholar 

  18. Subramaniapillai, S., Rajagopal, S., Snytte, J., Otto, A.R., Einstein, G., Rajah, M.N., Group, P.A.R., et al.: Sex differences in brain aging among adults with family history of alzheimer’s disease and apoe4 genetic risk. NeuroImage: Clinical30, 102620 (2021)

    Google Scholar 

  19. Tang, H., Ma, G., et al.: Commpool: An interpretable graph pooling framework for hierarchical graph representation learning. Neural Networks143, 669–677 (2021)

    Article  Google Scholar 

  20. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage15(1), 273–289 (2002)

    Article  Google Scholar 

  21. Veličković, P., Cucurull, G., et al.: Graph attention networks. arXiv preprintarXiv:1710.10903 (2017)

  22. Wang, Y., Wang, Y.G., Hu, C., Li, M., Fan, Y., Otter, N., Sam, I., Gou, H., Hu, Y., Kwok, T., et al.: Cell graph neural networks enable the precise prediction of patient survival in gastric cancer. NPJ precision oncology6(1),  45 (2022)

    Article  Google Scholar 

  23. Williamson, J., Yabluchanskiy, A., Mukli, P., Wu, D.H., Sonntag, W., Ciro, C., Yang, Y.: Sex differences in brain functional connectivity of hippocampus in mild cognitive impairment. Frontiers in Aging Neuroscience14, 959394 (2022)

    Article  Google Scholar 

  24. WOLTERINK, J., SUK, J.: Geometric deep learning for precision medicine. KEY ENABLING TECHNOLOGY FOR SCIENTIFIC MACHINE LEARNING60

    Google Scholar 

  25. Xia, T., Ku, W.S.: Geometric graph representation learning on protein structure prediction. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 1873–1883 (2021)

    Google Scholar 

  26. Xiong, J., Xiong, Z., Chen, K., Jiang, H., Zheng, M.: Graph neural networks for automated de novo drug design. Drug Discovery Today26(6), 1382–1393 (2021)

    Article  Google Scholar 

  27. Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprintarXiv:1810.00826 (2018)

Download references

Acknowledgments

This project was partially supported by the NSF (IIS 2045848 and IIS 2319450), the NIH (R01AG071243, R01MH125928, and U01AG068057), and the CCI Hub (HC-4Q24-059). It was supported by resources provided by the Office of Research Computing at George Mason University (URL:https://orc.gmu.edu) and funded in part by grants from the National Science Foundation (Award Number 2018631).

Author information

Authors and Affiliations

  1. George Mason University, Fairfax, VA, 22032, USA

    Zhepeng Wang, Lei Yang & Weiwen Jiang

  2. GE Healthcare, Bellevue, WA, 98004, USA

    Runxue Bao

  3. University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Yawen Wu & Liang Zhan

  4. University of Maryland, College Park, MD, 20742, USA

    Guodong Liu

  5. Southern University of Science and Technology, Shenzhen, 518055, GD, China

    Feng Zheng

  6. William and Mary, Williamsburg, VA, 23185, USA

    Yanfu Zhang

Authors
  1. Zhepeng Wang

    You can also search for this author inPubMed Google Scholar

  2. Runxue Bao

    You can also search for this author inPubMed Google Scholar

  3. Yawen Wu

    You can also search for this author inPubMed Google Scholar

  4. Guodong Liu

    You can also search for this author inPubMed Google Scholar

  5. Lei Yang

    You can also search for this author inPubMed Google Scholar

  6. Liang Zhan

    You can also search for this author inPubMed Google Scholar

  7. Feng Zheng

    You can also search for this author inPubMed Google Scholar

  8. Weiwen Jiang

    You can also search for this author inPubMed Google Scholar

  9. Yanfu Zhang

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toYanfu Zhang.

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

Ethics declarations

Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

1Electronic supplementary material

Below is the link to the electronic supplementary material.

Rights and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Z.et al. (2024). Self-guided Knowledge-Injected Graph Neural Network for Alzheimer’s Diseases. In: Linguraru, M.G.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15002. Springer, Cham. https://doi.org/10.1007/978-3-031-72069-7_36

Download citation

Publish with us

Societies and partnerships

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11210
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14013
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp