TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis | Neuroinformatics
Skip to main content

Advertisement

Springer Nature Link
Log in

TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer’s disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.

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

Access this article

Log in via an institution

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

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • A Khan, S., A Khan, S., R Narendra, A., Mushtaq, G., A Zahran, S., Khan, S., & A Kamal, M. (2016). Alzheimer’s disease and autistic spectrum disorder: Is there any association? CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 15(4), 390–402.

  • Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., & Varoquaux, G. (2017). Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. NeuroImage, 147, 736–745.

    Article  Google Scholar 

  • Aghdam, M. A., Sharifi, A., & Pedram, M. M. (2018). Combination of rs-fmri and smri data to discriminate autism spectrum disorders in young children using deep belief network. Journal of Digital Imaging, 31(6), 895–903.

    Article  Google Scholar 

  • Anirudh, R., & Thiagarajan, J. J. (2019). Bootstrapping graph convolutional neural networks for autism spectrum disorder classification. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 3197–3201.

  • Arslan, S., Ktena, S. I., Glocker, B., & Rueckert, D. (2018). Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity. In Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities. Springer, pp. 3–13.

  • Bajestani, G. S., Behrooz, M., Khani, A. G., Nouri-Baygi, M., & Mollaei, A. (2019). Diagnosis of autism spectrum disorder based on complex network features. Computer Methods and Programs in Biomedicine, 177, 277–283.

    Article  Google Scholar 

  • Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (compcor) for bold and perfusion based fmri. Neuroimage, 37(1), 90–101.

    Article  Google Scholar 

  • Betzel, R. F., & Bassett, D. S. (2017). Multi-scale brain networks. Neuroimage, 160, 73–83.

    Article  Google Scholar 

  • Chen, X., Zhang, H., Lee, S. -W., & Shen, D. (2017). Hierarchical high-order functional connectivity networks and selective feature fusion for mci classification. Neuroinformatics, 15(3), 271–284.

    Article  Google Scholar 

  • Craddock, C., Benhajali, Y., Chu, C., Chouinard, F., Evans, A., Jakab, A., Khundrakpam, B. S., Lewis, J. D., Li, Q., Milham, M., et al. (2013). The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics, 7.

  • Dadi, K., Rahim, M., Abraham, A., Chyzhyk, D., Milham, M., Thirion, B., et al. (2019). Benchmarking functional connectome-based predictive models for resting-state fmri. NeuroImage, 192, 115–134.

    Article  Google Scholar 

  • Di Martino, A., Yan, C. -G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., et al. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667.

    Article  Google Scholar 

  • Duc, N. T., Ryu, S., Qureshi, M. N. I., Choi, M., Lee, K. H., & Lee, B. (2020). 3d-deep learning based automatic diagnosis of alzheimer’s disease with joint mmse prediction using resting-state fmri. Neuroinformatics, 18(1), 71–86.

    Article  Google Scholar 

  • Dvornek, N. C., Ventola, P., & Duncan, J. S. (2018). Combining phenotypic and resting-state fmri data for autism classification with recurrent neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp. 725–728.

  • Dvornek, N. C., Ventola, P., Pelphrey, K. A., & Duncan, J. S. (2017). Identifying autism from resting-state fmri using long short-term memory networks. In International Workshop on Machine Learning in Medical Imaging, Springer, pp. 362–370.

  • Ebrahimighahnavieh, M. A., Luo, S., & Chiong, R. (2020). Deep learning to detect alzheimer’s disease from neuroimaging: A systematic literature review. Computer Methods and Programs in Biomedicine, 187, 105242.

    Article  Google Scholar 

  • Eid, O. M., & Eid, M. M. (2019). The implications of genetic factors in autism spectrum disorder and alzheimer’s disease. Neurological Disorders and Imaging Physics.

  • Eslami, T., Mirjalili, V., Fong, A., Laird, A. R., & Saeed, F. (2019). Asd-diagnet: a hybrid learning approach for detection of autism spectrum disorder using fmri data. Frontiers in Neuroinformatics, 13, 70.

    Article  Google Scholar 

  • Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, 102(27), 9673–9678.

    Article  CAS  Google Scholar 

  • Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. -P., Frith, C. D., & Frackowiak, R. S. (1994). Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2(4), 189–210.

    Article  Google Scholar 

  • Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178.

    Article  CAS  Google Scholar 

  • Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., et al. (2013). The minimal preprocessing pipelines for the human connectome project. Neuroimage, 80, 105–124.

    Article  Google Scholar 

  • Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 855–864.

  • Guo, H., Liu, L., Chen, J., Xu, Y., & Jie, X. (2017). Alzheimer classification using a minimum spanning tree of high-order functional network on fmri dataset. Frontiers in Neuroscience, 11, 639.

    Article  Google Scholar 

  • Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage: Clinical 17, 16–23.

  • Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., et al. (2017). Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146, 1038–1049.

    Article  Google Scholar 

  • Kazeminejad, A., & Sotero, R. C. (2020). The importance of anti-correlations in graph theory based classification of autism spectrum disorder. Frontiers in Neuroscience, 14, 676.

    Article  Google Scholar 

  • Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2016). Application of advanced machine learning methods on resting-state fmri network for identification of mild cognitive impairment and alzheimer’s disease. Brain Imaging and Behavior 10(3), 799–817.

  • Khosla, M., Jamison, K., Kuceyeski, A., & Sabuncu, M. R. (2019). Ensemble learning with 3d convolutional neural networks for functional connectome-based prediction. NeuroImage, 199, 651–662.

    Article  Google Scholar 

  • Khosla, M., Jamison, K., Ngo, G. H., Kuceyeski, A., & Sabuncu, M. R. (2019). Machine learning in resting-state fmri analysis. Magnetic Resonance Imaging, 64, 101–121.

    Article  Google Scholar 

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

  • Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., & Rueckert, D. (2018). Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage, 169, 431–442.

    Article  Google Scholar 

  • Lee, W. H., & Frangou, S. (2017). Linking functional connectivity and dynamic properties of resting-state networks. Scientific Reports, 7(1), 1–10.

    Article  Google Scholar 

  • Li, G., Muller, M., Thabet, A., & Ghanem, B. (2019). Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9267–9276.

  • Li, Q., Han, Z., & Wu, X. -M. (2018). Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 32.

  • Li, X., & Duncan, J. (2020). Braingnn: Interpretable brain graph neural network for fmri analysis. bioRxiv.

  • Li, X., Dvornek, N. C., Zhou, Y., Zhuang, J., Ventola, P., & Duncan, J. S. (2019). Graph neural network for interpreting task-fmri biomarkers. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 485–493.

  • Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.

    Article  Google Scholar 

  • Lund, T. E., Nørgaard, M. D., Rostrup, E., Rowe, J. B., & Paulson, O. B. (2005). Motion or activity: their role in intra-and inter-subject variation in fmri. Neuroimage, 26(3), 960–964.

    Article  Google Scholar 

  • Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on mri. Zeitschrift für Medizinische Physik, 29(2), 102–127.

    Article  Google Scholar 

  • Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits. Biological Psychiatry, 74(3), 212–219.

    Article  Google Scholar 

  • Ma, Y., Wang, S., Aggarwal, C. C., & Tang, J. (2019). Graph convolutional networks with eigenpooling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 723–731.

  • Mantini, D., Corbetta, M., Perrucci, M. G., Romani, G. L., & Del Gratta, C. (2009). Large-scale brain networks account for sustained and transient activity during target detection. Neuroimage, 44(1), 265–274.

    Article  Google Scholar 

  • Mier, W., & Mier, D. (2015). Advantages in functional imaging of the brain. Frontiers in Human Neuroscience, 9, 249.

    Article  Google Scholar 

  • Nasrat, A. M., Nasrat, R. M., & Nasrat, M. M. (2017). Autism and alzheimer; the etiopathologic twins. American Journal of Medicine and Medical Sciences.

  • Nebel, M. B., Eloyan, A., Nettles, C. A., Sweeney, K. L., Ament, K., Ward, R. E., et al. (2016). Intrinsic visual-motor synchrony correlates with social deficits in autism. Biological Psychiatry, 79(8), 633–641.

    Article  Google Scholar 

  • Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., et al. (2013). Multisite functional connectivity mri classification of autism: Abide results. Frontiers in Human Neuroscience, 7, 599.

    Article  Google Scholar 

  • Parisot, S., Ktena, S. I., Ferrante, E., Lee, M., Guerrero, R., Glocker, B., & Rueckert, D. (2018). Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer’s disease. Medical Image Analysis 48, 117–130.

  • Parisot, S., Ktena, S. I., Ferrante, E., Lee, M., Moreno, R. G., Glocker, B., & Rueckert, D. (2017). Spectral graph convolutions for population-based disease prediction. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 177–185.

  • Qi, S., Meesters, S., Nicolay, K., ter Haar Romeny, B. M., & Ossenblok, P. (2015). The influence of construction methodology on structural brain network measures: A review. Journal of Neuroscience Methods, 253, 170–182.

    Article  Google Scholar 

  • Raghu, M., Zhang, C., Kleinberg, J., & Bengio, S. (2019). Transfusion: Understanding transfer learning for medical imaging. arXiv preprint arXiv:1902.07208.

  • Sherkatghanad, Z., Akhondzadeh, M., Salari, S., Zomorodi-Moghadam, M., Abdar, M., Acharya, U. R., et al. (2020). Automated detection of autism spectrum disorder using a convolutional neural network. Frontiers in Neuroscience, 13, 1325.

    Article  Google Scholar 

  • Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015). Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077.

  • Tang, W., Lu, Z., & Dhillon, I. S. (2009). Clustering with multiple graphs. In 2009 Ninth IEEE International Conference on Data Mining, IEEE, pp. 1016–1021.

  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage, 15(1), 273–289.

    Article  CAS  Google Scholar 

  • Vaishali, S., Rao, K. K., & Rao, G. S. (2015). A review on noise reduction methods for brain mri images. In 2015 International Conference on Signal Processing and Communication Engineering Systems, IEEE, pp. 363–365.

  • Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., Consortium, W.-M. H., et al. (2013). The wu-minn human connectome project: an overview. Neuroimage 80, 62–79.

  • Wang, J., Zuo, X., & He, Y. (2010). Graph-based network analysis of resting-state functional mri. Frontiers in Systems Neuroscience, 4, 16.

    PubMed  PubMed Central  Google Scholar 

  • Wang, M., Hao, X., Huang, J., Wang, K., Shen, L., Xu, X., et al. (2020). Hierarchical structured sparse learning for schizophrenia identification. Neuroinformatics, 18(1), 43–57.

    Article  Google Scholar 

  • Wang, X., Zhen, X., Li, Q., Shen, D., & Huang, H. (2018). Cognitive assessment prediction in alzheimer’s disease by multi-layer multi-target regression. Neuroinformatics, 16(3), 285–294.

    Article  Google Scholar 

  • Wong, E., Anderson, J. S., Zielinski, B. A., & Fletcher, P. T. (2018). Riemannian regression and classification models of brain networks applied to autism. In International Workshop on Connectomics in Neuroimaging, Springer, pp. 78–87.

  • Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems.

  • Xing, X., Ji, J., & Yao, Y. (2018). Convolutional neural network with element-wise filters to extract hierarchical topological features for brain networks. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp. 780–783.

  • Yao, D., Liu, M., Wang, M., Lian, C., Wei, J., Sun, L., Sui, J., & Shen, D. (2019). Triplet graph convolutional network for multi-scale analysis of functional connectivity using functional mri. In International Workshop on Graph Learning in Medical Imaging, Springer, pp. 70–78.

  • Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., et al. (2020). Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics, 36(4), 1241–1251.

    Article  CAS  PubMed  Google Scholar 

  • Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., et al. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.

    Article  Google Scholar 

  • Zhou, Z., Sodha, V., Siddiquee, M. M. R., Feng, R., Tajbakhsh, N., Gotway, M. B., & Liang, J. (2019). Models genesis: Generic autodidactic models for 3d medical image analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 384–393

  • Zhu, Y., Qi, S., Zhang, B., He, D., Teng, Y., Hu, J., & Wei, X. (2019). Connectome-based biomarkers predict subclinical depression and identify abnormal brain connections with the lateral habenula and thalamus. Frontiers in Psychiatry, 10, 371.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No.62076059) and the Fundamental Research Funds for the Central Universities (No. N2016001).

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Northeastern University, Shenyang, China

    Lanting Li, Hao Jiang, Guangqi Wen, Peng Cao, Mingyi Xu & Jinzhu Yang

  2. Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China

    Lanting Li, Guangqi Wen, Peng Cao & Jinzhu Yang

  3. Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Xiaoli Liu

  4. Amii, University of Alberta, Edmonton, Alberta, Canada

    Osmar Zaiane

Authors
  1. Lanting Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangqi Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mingyi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoli Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jinzhu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Osmar Zaiane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Cao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Jiang, H., Wen, G. et al. TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis. Neuroinform 20, 353–375 (2022). https://doi.org/10.1007/s12021-021-09548-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-021-09548-1

Keywords

Search

Navigation