Cognitive Load Driven Directed Information Flow in Functional Brain Networks

  • Md. Hedayetul Islam ShovonEmail author
  • D. (Nanda) Nandagopal
  • Ramasamy Vijayalakshmi
  • Jia Tina Du
  • Bernadine Cocks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9492)


The human brain connectome analysis describes the patterns of structural and functional brain networks and has become one of the most studied topics in computational neuroscience in recent years. Detailed investigation of functional brain networks based on the direction of information flow has subsequently gained significance. This study identifies changes in information flow direction between different brain regions during cognitive activity compared to baseline state using Normalized Transfer Entropy (NTE) estimated from electroencephalogram (EEG) signals. An algorithm is proposed for finding the cognitive state specific information flow direction patterns (IFDP) among various regions (lobes) of the brain. Results clearly demonstrate that IFDP based analysis is able to detect the changing information flow directional patterns during cognitive activity among four different brain regions: Frontal, Central, Parietal and Occipital. During cognitive activity, noticeable long range interconnections are established in the directed functional brain network from frontal to central, parietal and occipital lobes, and as well as from the central to occipital lobe. This suggests that the IFDP approach may have potential applications in the detection of cognitive impairments as well as in the clinical research e.g., for finding seizure foci in epilepsy.


Transfer entropy Information flow Directed functional brain network EEG Cognitive activity 



The authors wish to acknowledge partial support provided by the Defence Science and Technology Organisation (DSTO), Australia. The assistance and technical support provided by fellow researchers Mr Nabaraj Dahal and Mr Naga Dasari are greatly appreciated.


  1. 1.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  2. 2.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)CrossRefGoogle Scholar
  3. 3.
    Shovon, M.I., Nandagopal, D., Vijayalakshmi, R., Du, J.T., Cocks, B.: Transfer entropy and information flow patterns in functional brain networks during cognitive activity. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 1–10. Springer, Heidelberg (2014)Google Scholar
  4. 4.
    Liao, W., Ding, J., Marinazzo, D., Xu, Q., Wang, Z., Yuan, C., Zhang, Z., Lu, G., Chen, H.: Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI. Neuroimage 54, 2683–2694 (2011)CrossRefGoogle Scholar
  5. 5.
    Yan, C., He, Y.: Driving and driven architectures of directed small-world human brain functional networks. PLoS ONE 6, e23460 (2011)CrossRefGoogle Scholar
  6. 6.
    Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85, 461 (2000)CrossRefGoogle Scholar
  7. 7.
    Vicente, R., Wibral, M., Lindner, M., Pipa, G.: Transfer entropy—a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30, 45–67 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lindner, M., Vicente, R., Priesemann, V., Wibral, M.: TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy. BMC Neurosci. 12, 119 (2011)CrossRefGoogle Scholar
  9. 9.
    Wibral, M., Vicente, R., Lindner, M.: Transfer entropy in neuroscience. In: Wibral, M., Vicente, R., Lizier, J.T. (eds.). UCS, vol. 93, pp. 3–36Springer, Heidelberg (2014)CrossRefGoogle Scholar
  10. 10.
    Sabesan, S., Narayanan, K., Prasad, A., Iasemidis, L., Spanias, A., Tsakalis, K.: Information flow in coupled nonlinear systems: Application to the epileptic human brain. In: Pardalos, P.M., Boginski, V.L., Vazacopoulos, A. (eds.) Data Mining in Biomedicine, pp. 483–503. Springer, New York (2007)CrossRefGoogle Scholar
  11. 11.
    Gourévitch, B., Eggermont, J.J.: Evaluating information transfer between auditory cortical neurons. J. Neurophysiol. 97, 2533–2543 (2007)CrossRefGoogle Scholar
  12. 12.
    Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100, 118703 (2008)CrossRefGoogle Scholar
  13. 13.
    Fagiolo, G.: Clustering in complex directed networks. Phys. Rev. E 76, 026107 (2007)CrossRefGoogle Scholar
  14. 14.
    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    CURRY 7 EEG Acquisition and Analysis Software. Compumedics Neuroscan USA LtdGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Md. Hedayetul Islam Shovon
    • 1
    Email author
  • D. (Nanda) Nandagopal
    • 1
  • Ramasamy Vijayalakshmi
    • 2
  • Jia Tina Du
    • 1
  • Bernadine Cocks
    • 1
  1. 1.Cognitive Neuroengineering and Computational Neuroscience Laboratory, School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia
  2. 2.Department of Applied Mathematics and Computational SciencePSG College of TechnologyCoimbatoreIndia

Personalised recommendations