Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Directed Connectivity Analysis of Functional Brain Networks during Cognitive Activity Using Transfer Entropy

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Most previous studies of functional brain networks have been conducted on undirected networks despite the fact that direction of information flow is able to provide additional information on how one brain region influences another. The current study explores the application of normalized transfer entropy (NTE) to detect and identify the patterns of information flow in the functional brain networks derived from EEG data during cognitive activity. Using a combination of signal processing, information and graph-theoretic techniques, this study has identified and characterized the changing connectivity patterns of the directed functional brain networks during different cognitive tasks. The functional brain networks constructed from EEG data using non-linear measure NTE also exhibit small-world property. An exponential truncated power-law fits the in-degree and out-degree distribution of directed functional brain networks. The empirical results demonstrate not only the application of transfer entropy in evaluating the directed functional brain networks, but also in determining the information flow patterns and thus provide more insights into the dynamics of the neuronal clusters underpinning cognitive function.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  Google Scholar 

  2. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198

    Article  Google Scholar 

  3. Nandagopal DN, Vijayalakshmi R, Cocks B, Dahal N, Dasari N, Thilaga M, Dharwez SS (2013) Computational techniques for characterizing cognition using EEG data-new approaches. Proc Comput Sci 22:699–708

    Article  Google Scholar 

  4. Shovon MHI, Nandagopal DN, Vijayalakshmi R, Du JT, Cocks B (2014) Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity. In: Neural information processing. Lecture notes in computer science (LNCS 8834), Part I., Springer, New York, pp 1–10

  5. Leicht EA, Newman ME (2008) Community structure in directed networks. Phys Rev Lett 100(11):118703

    Article  Google Scholar 

  6. Fagiolo G (2007) Clustering in complex directed networks. Phys Rev E 76(2):026107

    Article  MathSciNet  Google Scholar 

  7. Vicente R, Wibral M, Lindner M, Pipa G (2011) Transfer entropy—a model-free measure of effective connectivity for the neurosciences. J Comput Neurosci 30(1):45–67

    Article  MathSciNet  Google Scholar 

  8. Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85(2):461

    Article  Google Scholar 

  9. Wibral M, Vicente R, Lindner M (2014) Transfer entropy in neuroscience. In: Wibral M, Vicente R, Lizier JT (eds) Directed information measures in neuroscience. Springer, Berlin, pp 3–36

    Chapter  Google Scholar 

  10. Chávez M, Martinerie J, Le Van QM (2003) Statistical assessment of nonlinear causality: application to epileptic EEG signals. J Neurosci Methods 124(2):113–128

    Article  Google Scholar 

  11. Gourévitch B, Eggermont JJ (2007) Evaluating information transfer between auditory cortical neurons. J Neurophysiol 97(3):2533–2543

    Article  Google Scholar 

  12. Sabesan S, Narayanan K, Prasad A, Iasemidis L, Spanias A, Tsakalis K (2007) Information flow in coupled nonlinear systems: Application to the epileptic human brain. In: Pardalos PM, Boginski VL, Alkis V (eds) Data mining in biomedicine. Springer, Berlin, pp 483–503

    Chapter  Google Scholar 

  13. Kaiser A, Schreiber T (2002) Information transfer in continuous processes. Physica D 166(1):43–62

    Article  MathSciNet  MATH  Google Scholar 

  14. Lindner M, Vicente R, Priesemann V, Wibral M (2011) TRENTOOL: a Matlab open source toolbox to analyse information flow in time series data with transfer entropy. BMC Neurosci 12(1):119

    Article  Google Scholar 

  15. Neymotin SA, Jacobs KM, Fenton AA, Lytton WW (2011) Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci 30(1):69–84

    Article  MathSciNet  Google Scholar 

  16. Kim MK, Kim M, Oh E, Kim SP (2013) A review on the computational methods for emotional state estimation from the human EEG. Comput Math Methods Med 2013:573734

  17. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308

    Article  MathSciNet  Google Scholar 

  18. Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  MATH  Google Scholar 

  19. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827

    Article  Google Scholar 

  20. McDonnell MD, Yaveroğlu ÖN, Schmerl BA, Iannella N, Ward LM (2014) Motif-role-fingerprints: the building-blocks of motifs, clustering-coefficients and transitivities in directed networks. PloS One 9(12):e114503

    Article  Google Scholar 

  21. Sporns O, Kötter R (2004) Motifs in brain networks. PLoS Biol 2(11):e369

    Article  Google Scholar 

  22. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26(1):63–72

    Article  Google Scholar 

  23. Liao W, Ding J, Marinazzo D, Xu Q, Wang Z, Yuan C, Zhang Z, Lu G, Chen H (2011) Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI. Neuroimage 54(4):2683–2694

    Article  Google Scholar 

  24. Yan C, He Y (2011) Driving and driven architectures of directed small-world human brain functional networks. PLoS One 6(8):e23460

    Article  Google Scholar 

  25. Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–442

    Article  Google Scholar 

  26. Latora V, Marchiori M (2003) Economic small-world behavior in weighted networks. Eur Phys J B 32(2):249–263

    Article  Google Scholar 

  27. Humphries MD, Gurney K (2008) Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PloS One 3(4):e0002051

    Article  Google Scholar 

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

  29. Nuamps EEG Amplifier (Model 7181). Compumedics Neuroscan USA Ltd

  30. STIM 2 Stimulus Delivery and Experiment Control Solution. Compumedics Neuroscan USA Ltd

  31. Hosseini SH, Hoeft F, Kesler SR (2012) GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PloS One 7(7):e40709

    Article  Google Scholar 

  32. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  Google Scholar 

  33. Stam CJ (2004) Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’network? Neurosci Lett 355(1):25–28

    Article  Google Scholar 

  34. Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12(6):512–523

    Article  Google Scholar 

  35. Micheloyannis S, Pachou E, Stam CJ, Vourkas M, Erimaki S, Tsirka V (2006) Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neurosci Lett 402(3):273–277

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge the partial support provided by the Defence Science and Technology (DST) Group, Australia.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Hedayetul Islam Shovon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shovon, M.H.I., Nandagopal, N., Vijayalakshmi, R. et al. Directed Connectivity Analysis of Functional Brain Networks during Cognitive Activity Using Transfer Entropy. Neural Process Lett 45, 807–824 (2017). https://doi.org/10.1007/s11063-016-9506-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-016-9506-1

Keywords