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Noise-assisted instantaneous coherence analysis of brain connectivity.

Hu M, Liang H - Comput Intell Neurosci (2012)

Bottom Line: Characterizing brain connectivity between neural signals is key to understanding brain function.In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data.Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, Science & Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA.

ABSTRACT
Characterizing brain connectivity between neural signals is key to understanding brain function. Current measures such as coherence heavily rely on Fourier or wavelet transform, which inevitably assume the signal stationarity and place severe limits on its time-frequency resolution. Here we addressed these issues by introducing a noise-assisted instantaneous coherence (NAIC) measure based on multivariate mode empirical decomposition (MEMD) coupled with Hilbert transform to achieve high-resolution time frequency representation of neural coherence. In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data. Such power spectra are typically sparse and of high resolution, that is, there usually exist many zero values, which result in numerical problems for directly computing coherence. Hence, we propose to add random noise onto the spectra, making coherence calculation feasible. Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise. Computer simulations are first performed to verify the effectiveness of NAIC. Local field potentials collected from visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data.

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Related in: MedlinePlus

Construction of the synthetic trivariate nonstationary signal [X Y Z] and their theoretic coherence between channels. In this figure, the first three rows (C1, C2, and C3) show the components used to generate the synthetic data (the fourth row). The last row shows theoretical coherence between different channels of the synthetic data.
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fig2: Construction of the synthetic trivariate nonstationary signal [X Y Z] and their theoretic coherence between channels. In this figure, the first three rows (C1, C2, and C3) show the components used to generate the synthetic data (the fourth row). The last row shows theoretical coherence between different channels of the synthetic data.

Mentions: In this simulation, we generated a nonstationary three-channel signal [X, Y, Z] by concatenating and superposing three sinusoid waves, each with different frequency. Figure 2 showed how nonstationary three-channel signal was constructed and its theoretical coherence between channels. We used this synthetic signal to verify the effectiveness of our NAIC in offering a high-resolution time-frequency coherence spectrum of nonstationary time series.


Noise-assisted instantaneous coherence analysis of brain connectivity.

Hu M, Liang H - Comput Intell Neurosci (2012)

Construction of the synthetic trivariate nonstationary signal [X Y Z] and their theoretic coherence between channels. In this figure, the first three rows (C1, C2, and C3) show the components used to generate the synthetic data (the fourth row). The last row shows theoretical coherence between different channels of the synthetic data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368308&req=5

fig2: Construction of the synthetic trivariate nonstationary signal [X Y Z] and their theoretic coherence between channels. In this figure, the first three rows (C1, C2, and C3) show the components used to generate the synthetic data (the fourth row). The last row shows theoretical coherence between different channels of the synthetic data.
Mentions: In this simulation, we generated a nonstationary three-channel signal [X, Y, Z] by concatenating and superposing three sinusoid waves, each with different frequency. Figure 2 showed how nonstationary three-channel signal was constructed and its theoretical coherence between channels. We used this synthetic signal to verify the effectiveness of our NAIC in offering a high-resolution time-frequency coherence spectrum of nonstationary time series.

Bottom Line: Characterizing brain connectivity between neural signals is key to understanding brain function.In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data.Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, Science & Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA.

ABSTRACT
Characterizing brain connectivity between neural signals is key to understanding brain function. Current measures such as coherence heavily rely on Fourier or wavelet transform, which inevitably assume the signal stationarity and place severe limits on its time-frequency resolution. Here we addressed these issues by introducing a noise-assisted instantaneous coherence (NAIC) measure based on multivariate mode empirical decomposition (MEMD) coupled with Hilbert transform to achieve high-resolution time frequency representation of neural coherence. In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data. Such power spectra are typically sparse and of high resolution, that is, there usually exist many zero values, which result in numerical problems for directly computing coherence. Hence, we propose to add random noise onto the spectra, making coherence calculation feasible. Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise. Computer simulations are first performed to verify the effectiveness of NAIC. Local field potentials collected from visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data.

Show MeSH
Related in: MedlinePlus