Multi-modal Brain Tensor Factorization: Preliminary Results with AD Patients

  • Göktekin Durusoy
  • Abdullah Karaaslanlı
  • Demet Yüksel Dal
  • Zerrin Yıldırım
  • Burak AcarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)


Global brain network parameters suffer from low classification performance and fail to provide an insight into the neurodegenerative diseases. Besides, the variability in connectivity definitions poses a challenge. We propose to represent multi-modal brain networks over a population with a single 4D brain tensor (B) and factorize B to get a lower dimensional representation per case and per modality. We used 7 known functional networks as the canonical network space to get a 7D representation. In a preliminary study over a group of 20 cases, we assessed this representation for classification. We used 6 different connectivity definitions (modalities). Linear discriminant analysis results in 90–95% accuracy in binary classification. The assessment of the canonical coordinates reveals Salience subnetwork to be the most powerful in classification consistently over all connectivity definitions. The method can be extended to include functional networks and further be used to search for discriminating subnetworks.


Functional networks Tensor factorization Structural networks Brain connectome Alzheimer’s Disease 



This work was in part supported by the Turkish Ministry of Development under the TAM Project number DPT2007K120610, and in part by TUBITAK-ARDEB project number 114E053.


  1. 1.
    Fornito, A., Zalesky, A., Bullmore, E.T.: Fundamentals of Brain Network Analysis. Academic Press, San Diego (2016)Google Scholar
  2. 2.
    Zalesky, A., Fornito, A., Bullmore, E.T.: Network based statistic: identifying differences in brain networks. Neuroimage 53(4), 1197–1207 (2010)CrossRefGoogle Scholar
  3. 3.
    Kaiser, M.: A tutorial in connectome analysis: topological and spatial features of brain networks. Neuroimage 57, 892–907 (2011)CrossRefGoogle Scholar
  4. 4.
    Karahan, E., Rojas-Lopez, P.A., Bringas-Vega, M.L., Valdes-Hernandez, P.A., Valdes-Sosa, P.A.: Tensor analysis and fusion of multimodal brain images. Proc. IEEE 103, 1531–1559 (2015)CrossRefGoogle Scholar
  5. 5.
    Williams, A.H., et al.: Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron (2018)Google Scholar
  6. 6.
    Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1–15 (2010)CrossRefGoogle Scholar
  7. 7.
    Tench, C.R., Morgan, P.S., Wilson, M., Blumhardt, L.D.: White matter mapping using diffusion tensor MRI. Magn. Reson. Med. 47(5), 967–972 (2002)CrossRefGoogle Scholar
  8. 8.
    Moyer, D., Gutman, B.A., Faskowitz, J., Jahanshad, N., Thompson, P.M.: Continuous representations of brain connectivity using spatial point processes. Med. Image Anal. 41, 32–39 (2017)CrossRefGoogle Scholar
  9. 9.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kiers, H.A.L.: Towards a standardized notation and terminology in multiway analysis. J. Chemometrics 14, 105–122 (2000)CrossRefGoogle Scholar
  11. 11.
    Yeo, B.T., et al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–65 (2011)CrossRefGoogle Scholar
  12. 12.
    Kolda, Tamara G.: Multilinear operators for higher-order decompositions, Technical report 2081. SANDIA, Albuquerque, NM (2006)Google Scholar
  13. 13.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  14. 14.
    Onnela, J.P., Saramäki, J., Kertész, J., Kaski, K.: Intensity and Coherence of motifs weighted complex networks. Phys. Rev. E. 71(6), 065103 (2005)CrossRefGoogle Scholar
  15. 15.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U.: Complex networks: structure and dynamics. Phys. Rep. 424(4–5), 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Humphries, M.D., Gurney, K.: Network small-world-ness: a quantitative method for determining canonical network equivalence. PLOS ONE 3(4), 110,04 (2008)CrossRefGoogle Scholar
  17. 17.
    Seeley, W.W., Crawford, R.K., Zhou, J., Miller, B.L., Greicius, M.D.: Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52 (2009)CrossRefGoogle Scholar
  18. 18.
    Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Göktekin Durusoy
    • 1
  • Abdullah Karaaslanlı
    • 1
  • Demet Yüksel Dal
    • 1
  • Zerrin Yıldırım
    • 2
  • Burak Acar
    • 1
    Email author
  1. 1.Department of Electrical and Electronics Engineering, VAVlabBoğaziçi UniversityIstanbulTurkey
  2. 2.Aziz Sancar Experimental Medical Research Institute, Department of NeuroscienceIstanbul UniversityIstanbulTurkey

Personalised recommendations