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Multi-modal Brain Connectivity Study Using Deep Collaborative Learning

  • Wenxing Hu
  • Biao Cai
  • Vince Calhoun
  • Yu-Ping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11044)

Abstract

Functional connectivities in the brain explain how different brain regions interact with each other when conducting a specific activity. Canonical correlation analysis (CCA) based models, have been used to detect correlations and to analyze brain connectivities which further help explore how the brain works. However, the data representation of CCA lacks label related information and may be limited when applied to functional connectivity study. Collaborative regression was proposed to address the limitation of CCA by combining correlation analysis and regression. However, both prediction and correlation are sacrificed as linear collaborative regression use the same set of projections on both correlation and regression. We propose a novel method, deep collaborative learning (DCL), to address the limitations of CCA and collaborative regression. DCL improves collaborative regression by combining correlation analysis and label information using deep networks, which may lead to better performance both for classification/prediction and for correlation detection. Results demonstrated the out-performance of DCL over other conventional models in terms of classification accuracy. Experiments showed the difference of brain connectivities between different age groups may be more significant than that between different cognition groups.

Keywords

Canonical correlation Deep network fMRI Functional connectivity 

Notes

Acknowledgment

The authors would like to thank the NIH (R01 GM109068, R01 MH104680, R01 MH107354, P20 GM103472, R01 REB020407, R01 EB006841) and NSF (#1539067) for the partial support.

References

  1. 1.
    Calhoun, V.D., Adali, T.: Time-varying brain connectivity in fMRI data: whole-brain data-driven approaches for capturing and characterizing dynamic states. IEEE Sig. Process. Mag. 33(3), 52–66 (2016)CrossRefGoogle Scholar
  2. 2.
    Calhoun, V.D., Miller, R., Pearlson, G., Adalı, T.: The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84(2), 262–274 (2014)CrossRefGoogle Scholar
  3. 3.
    Braun, U., Muldoon, S.F., Bassett, D.S.: On human brain networks in health and disease. eLS, 1–9 (2001)Google Scholar
  4. 4.
    Cai, B., Zille, P., Stephen, J.M., Wilson, T.W., Calhoun, V.D., Wang, Y.P.: Estimation of dynamic sparse connectivity patterns from resting state fMRI. IEEE Trans. Med. Imaging 37(5), 1224–1234 (2018)CrossRefGoogle Scholar
  5. 5.
    Deng, S.-P., Hu, W., Calhoun, V.D., Wang, Y.-P.: Integrating imaging genomic data in the quest for biomarkers for schizophrenia disease. IEEE/ACM Trans. Comput. Biol. Bioinform. (2017)Google Scholar
  6. 6.
    Calhoun, V.D., Liu, J., Adalı, T.: A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(1), S163–S172 (2009)CrossRefGoogle Scholar
  7. 7.
    Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)CrossRefGoogle Scholar
  8. 8.
    Cao, S., Qin, H., Gossmann, A., Deng, H.-W., Wang, Y.-P.: Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations. Bioinformatics 32(3), 330–337 (2015)CrossRefGoogle Scholar
  9. 9.
    Hu, W., et al.: Adaptive sparse multiple canonical correlation analysis with application to imaging (epi) genomics study of schizophrenia. IEEE Trans. Biomed. Eng. 65(2), 390–399 (2018)Google Scholar
  10. 10.
    Li, Y.-O., Adali, T., Wang, W., Calhoun, V.D.: Joint blind source separation by multiset canonical correlation analysis. IEEE Trans. Sig. Process. 57(10), 3918–3929 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lin, D., Calhoun, V.D., Wang, Y.-P.: Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Med. Image Anal. 18(6), 891–902 (2014)CrossRefGoogle Scholar
  12. 12.
    Du, L., et al.: Structured sparse canonical correlation analysis for brain imaging genetics: an improved graphnet method. Bioinformatics 32(10), 1544–1551 (2016)CrossRefGoogle Scholar
  13. 13.
    Gross, S.M., Tibshirani, R.: Collaborative regression. Biostatistics 16(2), 326–338 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Andrew, G., Arora, R., Bilmes, J., Livescu, K.: Deep canonical correlation analysis. In: International Conference on Machine Learning, pp. 1247–1255 (2013)Google Scholar
  15. 15.
    Satterthwaite, T.D., et al.: Neuroimaging of the philadelphia neurodevelopmental cohort. Neuroimage 86, 544–553 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Wilkinson, G.S., Robertson, G.J.: Wide Range Achievement Test. Psychological Assessment Resources (2006)Google Scholar
  17. 17.
    Hu, W., Lin, D., Calhoun, V.D., Wang, Y.-P.: Integration of SNPs-FMRI-methylation data with sparse multi-CCA for schizophrenia study. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 3310–3313. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wenxing Hu
    • 1
  • Biao Cai
    • 1
  • Vince Calhoun
    • 2
  • Yu-Ping Wang
    • 1
  1. 1.Biomedical Engineering DepartmentTulane UniversityNew OrleansUSA
  2. 2.The Mind Research Network and Department of ECEUniversity of New MexicoAlbuquerqueUSA

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