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Detecting causality between different frequencies

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Wu, Jianhua, Liu, Xuguang and Feng, Jianfeng. (2008) Detecting causality between different frequencies. Journal of Neuroscience Methods, Volume 167 (Number 2). pp. 367-375. ISSN 0165-0270

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Official URL: http://dx.doi.org/10.1016/j.jneumeth.2007.08.022

Abstract

Biological systems are usually non-linear and, as a result, the driving signal frequency (say, MHz) is in general not identical with the output frequency (say, NHz). Coherence and causality analysis have been well-developed to measure the (directional) correlation between input and output signals with identical frequencies (N = M), but they are not applicable to the cases with different frequencies (N A M). In this paper, we propose a novel method called frequency-modified causality (coherence) analysis to resolve the issue. The input or output signal is first modulated by up-sampling or down-sampling, coherence and causality analysis are then applied to the frequency modulated and filtered signals. An optimal coherence and causality is found, revealing the true input-output relationship between signals. The method is successfully tested on data generated from a toy model, the van der Pol oscillator and then employed to analyze data recorded from Parkinson's disease (PD) patients. (c) 2007 Elsevier B.V. All rights reserved.

Item Type:

Journal Article

Subjects:

Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry

Divisions:

Faculty of Science > Centre for Scientific Computing
Faculty of Science > Computer Science

Library of Congress Subject Headings (LCSH):

Time-series analysis, Parkinson's disease -- Research, Neural networks (Computer science), Causation

Journal or Publication Title:

Journal of Neuroscience Methods

Publisher:

Elsevier BV

ISSN:

0165-0270

Official Date:

30 January 2008

Dates:

Date	Event
30 January 2008	Published

Volume:

Volume 167

Number:

Number 2

Number of Pages:

Page Range:

pp. 367-375

DOI:

10.1016/j.jneumeth.2007.08.022

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Restricted or Subscription Access

Adapted As:

References:

Albo Z, Di Prisco GV, Chen YH, Rangarajan G, Truccolo W, Feng JF, et al.
Is partial coherence a viable technique for identifying generators of neural
oscillations. Biol Cybern 2004;90:318–26.
AkaikeH.Anewlook at the statistical model identification. IEEE Trans Automat
Contr 1974;19:716–23.
Bezruchko B, Ponomarenko V, Rosenblum MG, Pikovsky AS. Characterizing
direction of coupling from experimental observations. Chaos
2003;13:179–84.
Brovelli A, Ding MZ, Ledberg A, Chen YH, Nakamura R, Bressler SL. Beta
oscillations in a large-scale sensorimotor cortical network: directional influences
revealed by Granger causality. PNAS 2004;101:9849–54.
Chavez M, Martinerie J, Le Van Quyen M. Statistical assessment of nonlinear
causality: application to epileptic EEG signals. J Neurosci Meth
2003;124:113–28.
Chen YH, Rangarajan G, Feng JF, Ding MZ. Analyzing multiple non-linear time
series with extended Granger causality. Phys Lett A 2004;324:26–35.
Collis W, White PR, Hammond JK. Higher-order spectra: the bispectrum and
trispectrum. Mech Syst Signal Process 1998;14:375–94.
Cristi R. Modern digital signal processing. New York: Brooks, Cole; 2004.
Ding MZ, Bressler SL, Yang WM, Liang HL. Short-window spectral analysis
of cortical event-related potentials by adaptive multivariate autoregressive
modeling: data preprocessing, model validation, and variability assessment.
Biol Cybern 2004;83:35–45.
Feng JF, Jost J, Qian MP. Network: from biology to theory. London: Springer-
Verlag; 2006.
Guo SX, Feng JF. Partial Granger causality, eliminating exogenous inputs and
latent variables, in preparation.
Geweke J. Measurement of linear dependence and feedback between multiple
time series. J Am Stat Assoc 1982;77:304–24.
Glass L. From clocks to chaos: the rhythms of life. Princeton University Press;
1987.
Granger C. Investigating causal relations by econometric models and crossspectral
methods. Econometrica 1969;37:428–38.
Haykin S, Kesler S. Nonlinear methods of spectral analysis. Berlin Heidelberg
New York: Springer; 1983.
Jamˇsek J, Stefanovska A, McClintock P. Nonlinear cardio-respiratory interactions
revealed by time-phase bispectral analysis. Phys Med Biol
2004;49:4407–25.
Le Van Quyen M, Foucher J, Lachaux J, Rodriguez E, Lutz A, Martinerie J, et
al. Comparison of Hilbert transform and wavelet methods for the analysis
of neuronal synchrony. J Neurosci Meth 2001;111:83–98.
Lii L, Helland K. Cross-Bispectrum Computation and Variance Estimation.
ACM Trans Math Software 1981;7:284–94.
Llinas RR, Ribary U, Jeanmonod D, Kronberg E, Mitra P. Thalamocortical
dysrhythmia: a neurological and neuropsychiatric syndrome characterized
by magnetoencephalography. PNAS 1999;96:15222–7.
Liu X, Nandi D, Winter JL. Deep brain stimulation of the pedunculopontine
region in the normal non-human primate. Clin Neurophysiol
2002;113:1667–72.
Morf M, Vieira A, Lee DTL, Kailath T. Recursive multichannel maximum
entropy spectral estimation. IEEE Trans Geosci Electron 1978;16:
85–94.
Pollok B, Gross J, Dirks M, Timmermann L, Schnitzler A. The cerebral oscillatory
network of voluntary tremor. J Neurophysiol 2004;554:871–8.
Rosenblum MG, Pikovsky AS. Phase synchronization of chaotic oscillators.
Phys Rev E 2001;64:45202.
Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP. Causal proteinsignaling
networks derived from multiparameter single-cell data. Science
2005;308:523–9.
Tass P, Rosenblum MG, Weule J, Kurths J, Pikovsky A, Volkmann J, et al.
Detection of n:m phase-locking from noisy data: application to magnetoencephalography.
Phys Rev Lett 1998;81:3291–4.
Timmer J. Handbook on time series analysis. Wiley; 2006.
Timmermann L, Gross J, Dirks M, Volkmann J, Freund HJ, Schnitzler A.
The cerebral oscillatory network of Parkinsonian resting tremor. Brain
2003;126:199–212.
Varela F, Lachaux JP, Rodriguez E, Martinerie J. The brainweb: phase synchronization
and large-scale integration. Nat Rev Neurosci 2001;2:229–39.
Wang SY, Chen YH, Ding MZ, Feng JF, Stein JF, Aziz TZ, et al. Revealing
the dynamic correlation between neural and muscular signals using timedependent
Granger causality analysis. Proc IEE Med Appl Signal Process
2005;2:99–105.
Wang SY, Aziz TZ, Stein JF, Bain PG, Liu XG. The physiological and harmonic
components in neural and muscular coherence in Parkinsonian tremor. Clin
Neurophysiol 2006;117:1487–98.