Advertisement

Multi-modality Low-Rank Learning Fused First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia

  • Huijie Li
  • Qi ZhuEmail author
  • Rui Zhang
  • Daoqiang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11936)

Abstract

The brain functional connectivity network (BFCN) based methods for diagnosing brain diseases have shown great advantages. At present, most BFCN construction strategies only calculate the first-order correlation between brain areas, such as the Pearson correlation coefficient method. Although the work of the low-order and high-order BFCN construction methods exists, there is very little work to integrate them, that is, to design a multi-modal BFCN feature selection and classification method to combine low-order and high-order information. This may affect the performance of brain disease diagnosis. To this end, we propose a multi-modality low-rank learning framework jointly learning first-order and second-order BFCN information and apply it to the diagnosis of schizophrenia. The proposed method not only embeds the correlation information of multi-modality data in the learning model, but also encourages the cooperation between the first-order and the second-order BFCN by combining the ideal representation term. The experimental results of the three schizophrenia datasets (totally including 168 patients and 163 normal controls) show that our proposed method achieves promising classification results in the diagnosis of schizophrenia.

Keywords

Multi-modality learning Low rank representation Brain functional connection network 

Notes

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Nos. 61501230, 61732006, 61876082 and 81771444), National Science and Technology Major Project (No. 2018ZX10201002), and the Fundamental Research Funds for the Central Universities (No. NJ2019010).

References

  1. 1.
    Bluhm, R.L., et al.: Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr. Bull. 33, 1004–1012 (2007)CrossRefGoogle Scholar
  2. 2.
    Richiardi, J., Achard, S., Bunke, H., Van De Ville, D.: Machine learning with brain graphs: predictive modeling approaches for functional imaging in systems neuroscience. IEEE Signal Process. Mag. 30, 58–70 (2013).  https://doi.org/10.1109/MSP.2012.2233865CrossRefGoogle Scholar
  3. 3.
    Guo, S., Kendrick, K.M., Yu, R., Wang, H.L.S., Feng, J.: Key functional circuitry altered in schizophrenia involves parietal regions associated with sense of self. Hum. Brain Mapp. 35, 123–139 (2014).  https://doi.org/10.1002/hbm.22162CrossRefGoogle Scholar
  4. 4.
    Zhu, Q., Li, H., Huang, J., Xu, X., Guan, D., Zhang, D.: Hybrid functional brain network with first-order and second-order information for computer-aided diagnosis of schizophrenia. Front. Neurosci. 13, 603 (2019).  https://doi.org/10.3389/fnins.2019.00603CrossRefGoogle Scholar
  5. 5.
    Huang, S., et al.: Identifying Alzheimer’s disease-related brain areas from multi-modality neuroimaging data using sparse composite linear discrimination analysis. In: Advances in Neural Information Processing Systems, vol. 1431–1439 (2011)Google Scholar
  6. 6.
    Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59, 895–907 (2012).  https://doi.org/10.1016/j.neuroimage.2011.09.069CrossRefGoogle Scholar
  7. 7.
    Candès, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix completion. IEEE Trans. Inf. Theory 56, 2053–2080 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Zhu, Q., Li, H., Huang, J., Xu, X., Guan, D., Zhang, D.: Hybrid functional brain network with first-order and second-order information for computer-aided diagnosis of schizophrenia. Front. Neurosci. 13, 603 (2019).  https://doi.org/10.3389/fnins.2019.00603CrossRefGoogle Scholar
  9. 9.
    Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35, 171–184 (2013).  https://doi.org/10.1109/TPAMI.2012.88CrossRefGoogle Scholar
  10. 10.
    Zhang, N., Yang, J.: Low-rank representation based discriminative projection for robust feature extraction. Neurocomputing 111, 13–20 (2013).  https://doi.org/10.1016/j.neucom.2012.12.012CrossRefGoogle Scholar
  11. 11.
    Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9, 717–772 (2009).  https://doi.org/10.1007/s10208-009-9045-5MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Fazel, M.: Matrix Rank Minimization with Applications. Dissertation (2002)Google Scholar
  13. 13.
    Jie, B., Zhang, D., Cheng, B., Shen, D.: Manifold regularized multitask feature learning for multimodality disease classification. Hum. Brain Mapp. 36, 489–507 (2015)CrossRefGoogle Scholar
  14. 14.
    Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35, 171–184 (2013).  https://doi.org/10.1109/TPAMI.2012.88CrossRefGoogle Scholar
  15. 15.
    Wright, J., Ganesh, A., Rao, S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices, vol. 1, pp. 289–298 (2009). 58Google Scholar
  16. 16.
    Zhu, C., Wei, L., Zhou, R., Wang, X., Wu, A.: Robust subspace segmentation by self-representation constrained low-rank representation. In: Neural Processing Letters, pp. 1671–1691 (2018)  https://doi.org/10.1007/s11063-018-9783-yCrossRefGoogle Scholar
  17. 17.
    Lin, Z., Chen, M., Wu, L., Ma, Y.: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. Eprint Arxiv. 9 (2010)Google Scholar
  18. 18.
    Zhang, Z., Liu, L., Shen, F., Shen, H.T., Shao, L.: Binary multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1 (2018)CrossRefGoogle Scholar
  19. 19.
    Yang, J., Yin, W., Zhang, Y., Wang, Y.: A fast algorithm for edge-preserving variational multichannel image restoration. SIAM J. Imaging Sci. 2, 569–592 (2009)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Zhang, X., Jia, Y.: A linear discriminant analysis framework based on random subspace for face recognition. Pattern Recogn. 40, 2585–2591 (2007).  https://doi.org/10.1016/j.patcog.2006.12.002CrossRefzbMATHGoogle Scholar
  21. 21.
    Chang, C., Lin, C.: LIBSVM. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).  https://doi.org/10.1145/1961189.1961199CrossRefGoogle Scholar
  22. 22.
    Cai, D., He, X., Han, J.: Speed up kernel discriminant analysis. VLDB J. 20, 21–33 (2011)CrossRefGoogle Scholar
  23. 23.
    Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011).  https://doi.org/10.1016/j.neuroimage.2011.01.008CrossRefGoogle Scholar
  24. 24.
    Foroughi, H., Shakeri, M., Ray, N., Zhang, H.: Face recognition using multi-modal low-rank dictionary learning. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 1082–1086. IEEE (2017).  https://doi.org/10.1109/ICIP.2017.8296448

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Huijie Li
    • 1
    • 2
  • Qi Zhu
    • 1
    • 2
    Email author
  • Rui Zhang
    • 1
  • Daoqiang Zhang
    • 1
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

Personalised recommendations