Skip to main content
Springer Nature Link
Log in

Applications of Granger Causality Model to Connectivity Network Based on fMRI Time Series

  • Conference paper
Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

Included in the following conference series:

  • 1414 Accesses

Abstract

The connectivity network with direction of brain is a significant work to reveal interaction and coordination between different brain areas. Because Granger causality model can explore causal relationship between time series, the direction of the network can be specified when the model is applied to connectivity network of brain. Although the model has been used in EEG time sires more and more, it was seldom used in fMRI time series because of lower time resolution of fMRI time series. In this paper, we introduced a pre-processing method to fMRI time series in order to alleviate the magnetic disturbance, and then expand the time series to fit the requirement of time-variant algorism. We applied recursive least square (RLS) algorithm to estimate time-variant parameters of Granger model, and introduced a time-variant index to describe the directional connectivity network in a typical finger tapping fMRI experiment. The results showed there were strong directional connectivity between the activated motor areas and gave a possibility to explain them.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)

    Article  Google Scholar 

  2. Kaminski, M.J., Ding, M., Truccolo, W.A., Bressler, S.L.: Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 85, 145–157 (2001)

    Article  MATH  Google Scholar 

  3. Goebel, R., Roebroeck, A., Kim, D.S., Formisano, E.: Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging 21, 1251–1261 (2003)

    Article  Google Scholar 

  4. Moller, E., Schack, B., Arnold, M., Witte, H.: Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models. J. Neurosci. Methods 105, 143–158 (2001)

    Article  Google Scholar 

  5. Ding, M., Bressler, S.L., Yang, W., Liang, H.: Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modelling: data preprocessing, model validation, and variability assessment. Biol. Cybern. 83, 35–45 (2000)

    Article  MATH  Google Scholar 

  6. van de Ven, V.G., Formisano, E., Prvulovic, D., et al.: Functional Connectivity as Revealed by Spatial Independent Component Analysis of fMRI Measurements During Rest. Hum. Brain Mapp. 22, 165–178 (2004)

    Article  Google Scholar 

  7. Astolfi, L., Cincotti, F., Mattia, D., et al.: Estimation of the effective and functional human cortical connectivity with structural quation modeling and directed transfer function applied to high-resolution EEG. Magn. Reson. Imaging 22, 1457–1470 (2004)

    Article  Google Scholar 

  8. Geweke, J.: Measurement of linear dependence and feedback between multiple time series. J. Amer. Statist. Assoc. 77, 304–324 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  9. Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, pp. 417–420. Springer, New York (1987)

    MATH  Google Scholar 

  10. Haykin, S.: Adaptive Filter Theory, pp. 381–407. Prentice-Hall, Englewood Cliffs (1986)

    Google Scholar 

  11. Theiler, J., Eubank, Longtin, S.J., Galdrikian, B., Farmer, J.D.: Testing for nonlinearity in time series: The method of surrogate data. Phys. D 58, 77–94 (1992)

    Article  MATH  Google Scholar 

  12. Wiley, J.: Programs for Digital Signal Processing. IEEE Press, New York (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Beijing Normal University, Beijing, 100088, China

    Xiao-Jie Zhao & Li Yao

  2. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100088, China

    Xiao-Tong Wen, Xiao-Jie Zhao, Li Yao & Xia Wu

Authors
  1. Xiao-Tong Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Jie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xia Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., 710071, Xi’an, P.R. China

    Xin-bo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wen, XT., Zhao, XJ., Yao, L., Wu, X. (2006). Applications of Granger Causality Model to Connectivity Network Based on fMRI Time Series. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_31

Download citation

  • DOI: https://doi.org/10.1007/11881070_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics