Advertisement

Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning

  • Chen Zu
  • Yue Gao
  • Brent Munsell
  • Minjeong Kim
  • Ziwen Peng
  • Jessica R. Cohen
  • Daoqiang Zhang
  • Guorong Wu
Original Research

Abstract

The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.

Keywords

Hypergraph learning Brain network Biomarker Autism spectrum disorder Attention deficit hyperactivity disorder 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2018_9899_MOESM1_ESM.docx (36 kb)
ESM 1 (DOCX 36 kb)

References

  1. Agarwal, S., Lim, J., Zelnik-Manor, L., Perona, P., Kriegman, D., & Belongie, S. (2005). Beyond pairwise clustering. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (vol. 2, pp. 838–845). IEEE.Google Scholar
  2. Argyriou, A., Evgeniou, T., & Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73(3), 243–272.CrossRefGoogle Scholar
  3. Bu, J. et al. (2010) Music recommendation by unified hypergraph: combining social media information and music content. In Proceedings of the 18th ACM international conference on Multimedia (pp. 391–400). ACM.Google Scholar
  4. Chapelle, O., Scholkopf, B., & Zien, A. (2009). Semi-supervised learning (Chapelle, O. et al., Eds.; 2006)[Book reviews]. IEEE Transactions on Neural Networks, 20(3), 542–542.CrossRefGoogle Scholar
  5. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.Google Scholar
  6. Davison, E. N., Schlesinger, K. J., Bassett, D. S., Lynall, M. E., Miller, M. B., Grafton, S. T., & Carlson, J. M. (2015). Brain network adaptability across task states. PLoS Computational Biology, 11(1), e1004029.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Di Martino, A., 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.CrossRefPubMedGoogle Scholar
  8. Gao, Y., Wang, M., Tao, D., Ji, R., & Dai, Q. (2012). 3-D object retrieval and recognition with hypergraph analysis. IEEE Transactions on Image Processing, 21(9), 4290–4303.CrossRefPubMedGoogle Scholar
  9. Gao, Y., Adeli-M, E., Kim, M., Giannakopoulos, P., Haller, S., and Shen, D. (2015a) Medical image retrieval using multi-graph learning for MCI diagnostic assistance. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 86–93). Springer.Google Scholar
  10. Gao, Y. et al. (2015b) MCI identification by joint learning on multiple MRI data. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 78–85) Springer.Google Scholar
  11. Huang, Y., Liu, Q., Zhang, S., and Metaxas, D. N. (2010) Image retrieval via probabilistic hypergraph ranking. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 3376–3383: IEEE.Google Scholar
  12. Huang, Y., Liu, Q., Lv, F., Gong, Y., & Metaxas, D. N. (2011). Unsupervised image categorization by hypergraph partition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(6), 1266–1273.CrossRefPubMedGoogle Scholar
  13. Jie, B., Wee, C.-Y., Shen, D., & Zhang, D. (2016). Hyper-connectivity of functional networks for brain disease diagnosis. Medical Image Analysis, 32, 84–100.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Matthews, P., & Jezzard, P. (2004). Functional magnetic resonance imaging. Journal of Neurology, Neurosurgery & Psychiatry, 75(1), 6–12.Google Scholar
  15. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: simple building blocks of complex networks. Science, 298(5594), 824–827.CrossRefPubMedGoogle Scholar
  16. Minshew, N. J., & Williams, D. L. (2007). The new neurobiology of autism: cortex, connectivity, and neuronal organization. Archives of Neurology, 64(7), 945–950.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Nielsen, J. A. et al. (2013) Multisite functional connectivity MRI classification of autism: ABIDE results.Google Scholar
  18. Sporns, O., & Kötter, R. (2004). Motifs in brain networks. PLoS Biology, 2(11), e369.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Sun, L., Ji, S., & Ye, J. (2008). Hypergraph spectral learning for multi-label classification. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 668–676). ACM.Google Scholar
  20. Tao, D., Li, X., Hu, W., Maybank, S., and Wu, X. (2005) Supervised tensor learning. In Fifth IEEE International Conference on Data Mining (ICDM'05) (pp. 8). IEEE.Google Scholar
  21. Tian, Z., Hwang, T., & Kuang, R. (2009). A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge. Bioinformatics, 25(21), 2831–2838.CrossRefPubMedGoogle Scholar
  22. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (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.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Van Den Heuvel, M. P., & Pol, H. E. H. (2010). Exploring the brain network: a review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.CrossRefPubMedGoogle Scholar
  24. Yu, J., Tao, D., & Wang, M. (2012). Adaptive hypergraph learning and its application in image classification. IEEE Transactions on Image Processing, 21(7), 3262–3272.CrossRefPubMedGoogle Scholar
  25. Yu, J., Rui, Y., Tang, Y. Y., & Tao, D. (2014). High-order distance-based multiview stochastic learning in image classification. IEEE Transactions on Cybernetics, 44(12), 2431–2442.CrossRefPubMedGoogle Scholar
  26. Zass, R. and Shashua, A. (2008) Probabilistic graph and hypergraph matching. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8) IEEE.Google Scholar
  27. Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012). Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain, 135(5), 1498–1507.CrossRefPubMedGoogle Scholar
  28. Zhang, Z., Wang, J., & Zha, H. (2012). Adaptive manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2), 253–265.CrossRefPubMedGoogle Scholar
  29. Zhang, L., Gao, Y., Hong, C., Feng, Y., Zhu, J., & Cai, D. (2014). Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Transactions on Cybernetics, 44(8), 1408–1419.CrossRefPubMedGoogle Scholar
  30. Zhou, D., Huang, J., and Schölkopf, B. (2006). Learning with hypergraphs: Clustering, classification, and embedding. In Advances in neural information processing systems (pp. 1601–1608).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Chen Zu
    • 1
    • 2
  • Yue Gao
    • 3
  • Brent Munsell
    • 4
  • Minjeong Kim
    • 5
  • Ziwen Peng
    • 6
  • Jessica R. Cohen
    • 7
  • Daoqiang Zhang
    • 1
  • Guorong Wu
    • 2
  1. 1.Department of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.School of SoftwareTsinghua UniversityBeijingChina
  4. 4.Department of Computer ScienceCollege of CharlestonCharlestonUSA
  5. 5.Department of Computer ScienceUniversity of North CarolinaGreensboroUSA
  6. 6.Centre for Studies of Psychological Application, School of PsychologySouth China Normal UniversityGuangzhouChina
  7. 7.Department of Psychology and NeuroscienceUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations