Skip to main content

Advertisement

Log in

On the Evaluation of Information Flow in Multivariate Systems by the Directed Transfer Function

  • Original Paper
  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

The directed transfer function (DTF) has been proposed as a measure of information flow between the components of multivariate time series. In this paper, we discuss the interpretation of the DTF and compare it with other measures for directed relationships. In particular, we show that the DTF does not indicate multivariate or bivariate Granger causality, but that it is closely related to the concept of impulse response function and can be viewed as a spectral measure for the total causal influence from one component to another. Furthermore, we investigate the statistical properties of the DTF and establish a simple significance level for testing for the null hypothesis of no information flow.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Achermann P, Hartmann R, Gunzinger A, Guggenbühl W, Borbély AA (1994) All night sleep and artificial stochastic control have similar correlation dimension. Electroencephalogr Clin Neurophysiol 90:384–387

    Article  PubMed  CAS  Google Scholar 

  • Baccalá LA, Sameshima K (2001) Partial directed coherence: a new concept in neural structure determination. Biol Cybern 84:463–474

    Article  PubMed  Google Scholar 

  • Blinowska KJ, Kuś R, Kamiński M (2004). Granger causality and information flow in multivariate processes. Phys Rev E 70:050902(R)

    Article  Google Scholar 

  • Blinowska KJ, Malinowski M (1991) Non-linear and linear forecasting of the EEG time series. Biol Cybern 66:159–165

    Article  PubMed  CAS  Google Scholar 

  • Brockwell PJ, Davis RA (1991) Time series: theory and methods 2nd edn. Springer Berlin Heidelberg, New York

    Google Scholar 

  • Brovelli A, Ding MAL, Chen Y, Nakamura R, Bressler SL (2004) Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by granger causality. Proc Nat Acad Sci USA 101:9849–9854

    Article  PubMed  CAS  Google Scholar 

  • Chatfield C (2003) The analysis of time series: an introduction, 6th edn. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  • Dahlhaus R, Eichler M (2002) Causality and graphical models for multivariate time series and point processes. In: Hutten H, Kroesl P (eds) IFMBE Proceedings EMBEC 2002, vol 3(2), pp 1430–1431

  • Dufour JM, Renault E (1998) Short run and long run causality in time series: theory. Econometrica 66:1099–1125

    Article  Google Scholar 

  • Eichler M (2002) Granger-causality and path diagrams for multivariate time series. Journal of Econometrics (to appear)

  • Eichler M (2005) A graphical approach for evaluating effective connectivity in neural systems. Philos Trans R Soc B 360:953–967

    Article  Google Scholar 

  • Eichler M, Dahlhaus R, Sandkühler J (2003) Partial correlation analysis for the identification of synaptic connections. Biol Cybern 89:289–302

    Article  PubMed  Google Scholar 

  • Franaszczuk PJ, Bergey GK (1998) Application of the directed transfer function method to mesial and lateral onset temporal lobe seizures. Brain Topogr 11:13–21

    Article  PubMed  CAS  Google Scholar 

  • Franaszczuk PJ, Blinowska KJ, Kowalczyk M (1985) The application of parametric multichannel spectral estimates in the study of electrical brain activity. Biol Cybern 51:239–247

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438

    Article  Google Scholar 

  • Granger CWJ (1980) Testing for causality, a personal viewpoint. J Econ Dynam Control 2:329–352

    Article  Google Scholar 

  • Harrison L, Penny WD, Friston KJ (2003) Multivariate autoregressive modeling of fMRI time series. Neuroimage 4:1477–91

    Article  Google Scholar 

  • Harville DA (1997) Matrix algebra from a statistician’s perspective. Springer Berlin Heidelberg, New York

    Google Scholar 

  • Hayo B (1999) Money-output Granger causality revisited: an empirical analysis of EU countries. Appl Econ 31:1489–1501

    Article  Google Scholar 

  • Hesse W, Möller E, Arnold M, Schack B (2003) The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. J Neurosci Methods 124:27–44

    Article  PubMed  Google Scholar 

  • Hsiao C (1982) Autoregressive modeling and causal ordering of econometric variables. J Econ Dynam Control 4:243–259

    Article  Google Scholar 

  • Kamiński M (2005) Determination of transmission patterns in multichannel data. Philos Trans R Soc B 360:947–952

    Article  Google Scholar 

  • Kamiński M, Blinowska KJ, Szelenberger W (1997) Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness. Electroencephalogr Clin Neurophysiol 102: 216–277

    Article  PubMed  Google Scholar 

  • Kamiński M, Ding M, Truccolo WA, Bressler SL (2001) Evaluating causal relations in neural systems: Granger causality, directed transfer function and statisdtical assessment of significance. Biol Cybern 85:145–157

    Article  PubMed  Google Scholar 

  • Kamiński MJ, Blinowska KJ (1991) A new method of the description of the information flow in the brain structures. Biol Cybern 65:203–210

    Article  PubMed  Google Scholar 

  • Korzeniewska A, Kasicki S, Kamiński M, Blinowska KJ (1997) Information flow between hippocampus and related structures during various types of rat’s behaviour. J Neurosci Methods 73:49–60

    Article  PubMed  CAS  Google Scholar 

  • Kuś R, Kamiński M, Blinowska KJ (2004) Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Trans Biomed Eng 51:1501–1510

    Article  PubMed  Google Scholar 

  • Liang H, Ding M, Nakamura R, Bressler SL (2000) Causal influences in primate cerebral cortex during visual pattern discrimination. NeuroReport 11:2875–2880

    Article  PubMed  CAS  Google Scholar 

  • Lütkepohl H (1993) Introduction to multiple time series analysis. Springer Berlin Heidelberg, New York

    Google Scholar 

  • Medvedev A, Willoughby JO (1999) Autoregressive modeling of the EEG in systemic kainic acid-induced epileptogenesis. Int J Neurosci 97:149–167

    Article  PubMed  CAS  Google Scholar 

  • Pijn JPM, Van Neerven DN, Noest A, Lopes de Silva FH (1991) Chaos or noise in EEG signals: dependence on state and brain site. Electroencephalogr Clin Neurophysiol 79:371–381

    Article  PubMed  CAS  Google Scholar 

  • Pijn JPM, Velis DN, van der Heyden MJ, DeGoede J, van Velen CWM, Lopes de Silva FH (1997) Nonlinear dynamics of epileptic seizure on basis of intracranial EEG recordings. Brain Topogr 9:249–270

    Article  PubMed  CAS  Google Scholar 

  • Reinsel GC (2003) Elements of multivariate time series analysis 2nd edn. Springer Berlin Heidelberg, New York

    Google Scholar 

  • Sameshima K, Baccalá LA (1999) Using partial directed coherence to describe neuronal ensemble interactions. J Neurosci Methods 94:93–103

    Article  PubMed  CAS  Google Scholar 

  • Sandkühler J, Eblen-Zajjur AA (1994) Identification and characterization of rhythmic nociceptive and non-reciceptive spinal dorsal horn neurons in the rat. Neuroscience 61:991–1006

    Article  PubMed  Google Scholar 

  • Schack B, Rappelsberger P, Weiss S, Möller E (1999) Adaptive phase estimation and its application in EEG analysis of word processing. J Neurosci Methods 93:49–59

    Article  PubMed  CAS  Google Scholar 

  • Schelter B, Winterhalder M, Eichler M, Peifer M, Hellwig B, Guschlbauer B, Lücking CH, Dahlhaus R, Timmer J (2005) Testing for directed influences in neuroscience using partial directed coherence. J Neurosci Methods (in press)

  • Sims CA (1980) Macroeconomics and reality. Econometrica 48:1–4

    Article  Google Scholar 

  • Stam C, Pijn JPM, Suffczynski P, Lopes da Silva FH (1999) Dynamics of the human alpha rhythm: evidence for non-linearity. Clin Neurophysiol 110:1801–1813

    Article  PubMed  CAS  Google Scholar 

  • Toda HY, Philipps PCB (1993) Vector autoregressions and causality. Econometrica 61:1367–1393

    Article  Google Scholar 

  • Valdés-Sosa PA (2004) Spatio-temporal autoregressive models defined over brain manifolds. Neuroinformatics 2:239–250

    Article  PubMed  Google Scholar 

  • Veeramani B, Narayanan K, Prasad A, Spanias A, Iasemidis LD (2003) On the use of directed transfer function for nonlinear systems. In: Hamza MH (eds) Simulation and modelling. IASTED/ACTA Press, Calgary, pp 270–274

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Eichler.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eichler, M. On the Evaluation of Information Flow in Multivariate Systems by the Directed Transfer Function. Biol Cybern 94, 469–482 (2006). https://doi.org/10.1007/s00422-006-0062-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00422-006-0062-z

Keywords

Navigation