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Cognitive Load Driven Directed Information Flow in Functional Brain Networks

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9492))

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Abstract

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.

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References

  1. Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010)

    Article  Google Scholar 

  2. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  5. Yan, C., He, Y.: Driving and driven architectures of directed small-world human brain functional networks. PLoS ONE 6, e23460 (2011)

    Article  Google Scholar 

  6. Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85, 461 (2000)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  11. Gourévitch, B., Eggermont, J.J.: Evaluating information transfer between auditory cortical neurons. J. Neurophysiol. 97, 2533–2543 (2007)

    Article  Google Scholar 

  12. Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100, 118703 (2008)

    Article  Google Scholar 

  13. Fagiolo, G.: Clustering in complex directed networks. Phys. Rev. E 76, 026107 (2007)

    Article  Google Scholar 

  14. Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  15. CURRY 7 EEG Acquisition and Analysis Software. Compumedics Neuroscan USA Ltd

    Google Scholar 

Download references

Acknowledgement

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.

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Correspondence to Md. Hedayetul Islam Shovon .

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Shovon, M.H.I., Nandagopal, D.(., Vijayalakshmi, R., Du, J.T., Cocks, B. (2015). Cognitive Load Driven Directed Information Flow in Functional Brain Networks. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_40

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  • DOI: https://doi.org/10.1007/978-3-319-26561-2_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26560-5

  • Online ISBN: 978-3-319-26561-2

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