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Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

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Abstract

Most previous studies of functional brain networks have been conducted on undirected networks despite the direction of information flow able to provide additional information on how one brain region influences another. The current study explores the application of normalized transfer entropy to EEG data to detect and identify the patterns of information flow in the functional brain networks during cognitive activity. Using a mix 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 results demonstrate not only the value of transfer entropy in evaluating the directed functional brain networks but more importantly in determining the information flow patterns and thus providing more insights into the dynamics of the neuronal clusters underpinning cognitive function.

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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. Nature Reviews Neuroscience 10, 186–198 (2009)

    Article  Google Scholar 

  3. Nandagopal, N.D., Vijayalakshmi, R., Cocks, B., Dahal, N., Dasari, N., Thilaga, M., Dharwez, S.: Computational Techniques for Characterizing Cognition Using EEG Data – New Approaches. Procedia Computer Science 22, 699–708 (2013)

    Article  Google Scholar 

  4. Vicente, R., Wibral, M., Lindner, M., Pipa, G.: Transfer entropy—a model-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience 30, 45–67 (2011)

    Article  MathSciNet  Google Scholar 

  5. Schreiber, T.: Measuring information transfer. Physical Review Letters 85, 461 (2000)

    Article  Google Scholar 

  6. Chávez, M., Martinerie, J., Le Van Quyen, M.: Statistical assessment of nonlinear causality: Application to epileptic EEG signals. Journal of Neuroscience Methods 124, 113–128 (2003)

    Article  Google Scholar 

  7. Gourévitch, B., Eggermont, J.J.: Evaluating information transfer between auditory cortical neurons. Journal of Neurophysiology 97, 2533–2543 (2007)

    Article  Google Scholar 

  8. 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: Data Mining in Biomedicine, pp. 483–503. Springer (2007)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Neymotin, S.A., Jacobs, K.M., Fenton, A.A., Lytton, W.W.: Synaptic information transfer in computer models of neocortical columns. Journal of Computational Neuroscience 30, 69–84 (2011)

    Article  MathSciNet  Google Scholar 

  11. Kim, S.P.: A review on the computational methods for emotional state estimation from the human EEG. Computational and Mathematical Methods in Medicine 2013 (2013)

    Google Scholar 

  12. Hanneman, R.A., Riddle, M.: Introduction to social network methods. University of California Riverside (2005), published in digital form at http://faculty.ucr.edu/~hanneman/

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

    Google Scholar 

  14. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Article  Google Scholar 

  15. Brain Connectivity Toolbox, https://sites.google.com/site/bctnet/measures/list

  16. Latora, V., Marchiori, M.: Economic small-world behavior in weighted networks. The European Physical Journal B-Condensed Matter and Complex Systems 32, 249–263 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Cognitive Neuroengineering Laboratory, Division of IT, Engineering and the Environments, University of South Australia, Adelaide, Australia

    Md. Hedayetul Islam Shovon, D (Nanda) Nandagopal, Jia Tina Du & Bernadine Cocks

  2. Department of Applied Mathematics and Computational Science, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Ramasamy Vijayalakshmi

Authors
  1. Md. Hedayetul Islam Shovon
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  2. D (Nanda) Nandagopal
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  3. Ramasamy Vijayalakshmi
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  4. Jia Tina Du
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  5. Bernadine Cocks
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Shovon, M.H.I., Nandagopal, D.(., Vijayalakshmi, R., Du, J.T., Cocks, B. (2014). 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) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-12637-1_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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