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

Full text not available from this repository.

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
Date:	30 January 2008
Volume:	Volume 167
Number:	Number 2
Number of Pages:	9
Page Range:	pp. 367-375
Identification Number:	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.
URI:	http://wrap.warwick.ac.uk/id/eprint/30618

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item