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Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal

Brain Topography volume 29, pages 13–26 (2016)Cite this article

Abstract

Neurophysiological field-potential signals consist of both arrhythmic and rhythmic patterns indicative of the fractal and oscillatory dynamics arising from likely distinct mechanisms. Here, we present a new method, namely the irregular-resampling auto-spectral analysis (IRASA), to separate fractal and oscillatory components in the power spectrum of neurophysiological signal according to their distinct temporal and spectral characteristics. In this method, we irregularly resampled the neural signal by a set of non-integer factors, and statistically summarized the auto-power spectra of the resampled signals to separate the fractal component from the oscillatory component in the frequency domain. We tested this method on simulated data and demonstrated that IRASA could robustly separate the fractal component from the oscillatory component. In addition, applications of IRASA to macaque electrocorticography and human magnetoencephalography data revealed a greater power-law exponent of fractal dynamics during sleep compared to wakefulness. The temporal fluctuation in the broadband power of the fractal component revealed characteristic dynamics within and across the eyes-closed, eyes-open and sleep states. These results demonstrate the efficacy and potential applications of this method in analyzing electrophysiological signatures of large-scale neural circuit activity. We expect that the proposed method or its future variations would potentially allow for more specific characterization of the differential contributions of oscillatory and fractal dynamics to distributed neural processes underlying various brain functions.

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Acknowledgments

The research was supported in part by NIH R01MH104402. The authors are thankful to Dr. Shao-Chin Hung for proof reading and constructive comments, to Drs. Masaki Fukunaga and Jeff Duyn for assistance in collecting the MEG data, and to Dr. Naotaka Fujii for making the macaque ECoG data publicly available.

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Authors and Affiliations

  1. Weldon School of Biomedical Engineering, College of Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Zhongming Liu

  2. School of Electrical and Computer Engineering, College of Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Haiguang Wen & Zhongming Liu

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  1. Haiguang Wen
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  2. Zhongming Liu
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Correspondence to Zhongming Liu.

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Wen, H., Liu, Z. Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal. Brain Topogr 29, 13–26 (2016). https://doi.org/10.1007/s10548-015-0448-0

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  • DOI: https://doi.org/10.1007/s10548-015-0448-0

Keywords

  • Fractal
  • Oscillation
  • Power-law
  • Self-affinity
  • Scale-free