Estimation of time-varying coherence and its application in understanding brain functional connectivity

  • Authors:

  • Cheng Liu;William Gaetz;Hongmei Zhu

  • Affiliations:

  • Department of Mathematics and Statistics, York University, Toronto, ON, Canada;Biomagnetic Imaging Laboratory, Children's Hospital of Philadelphia, Philadelphia, PA;Department of Mathematics and Statistics, York University, Toronto, ON, Canada

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
  • Year:
  • 2010

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Abstract

Time-varying coherence is a powerful tool for revealing functional dynamics between different regions in the brain. In this paper, we address ways of estimating evolutionary spectrum and coherence using the general Cohen's class distributions. We show that the intimate connection between the Cohen's class-based spectra and the evolutionary spectra defined on the locally stationary time series can be linked by the kernel functions of the Cohen's class distributions. The time-varying spectra and coherence are further generalized with the Stockwell transform, amultiscale time-frequency representation. The Stockwell measures can be studied in the framework of the Cohen's class distributions with a generalized frequency-dependent kernel function. A magnetoencephalography study using the Stockwell coherence reveals an interesting temporal interaction between contralateral and ipsilateral motor cortices under the multisource interference task.