A new methodology for phase-locking value: a measure of true dynamic functional connectivity

Tianhu Lei; K. Ty Bae; Timothy P. L. Roberts

doi:10.1117/12.912725

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14 April 2012 A new methodology for phase-locking value: a measure of true dynamic functional connectivity

Tianhu Lei, K. Ty Bae, Timothy P. L. Roberts

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Proceedings Volume 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging; 83170T (2012) https://doi.org/10.1117/12.912725
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Phase-Locking value (PLV) is used to measure phase synchrony of narrowband signals, therefore, it is able to provide a measure of dynamic functional connectivity (DFC) of brain interactions. Currently used PLV methods compute the convolution of the signal at the target frequency with a complex Gabor wavelet centered at that frequency. The phase of this convolution is extracted for all time-bins over trials for a pair of neural signals. These time-bins set a limit on the temporal resolution for PLV, hence, for DFC. Therefore, these methods cannot provide a true DFC in a strict sense. PLV is defined as the absolute value of the characteristic function of the difference of instantaneous phases (IP) of two analytic signals evaluated at s = 1. It is a function of the time. For the narrowband signal in the stationary Gaussian white noise, we investigated statistics of (i) its phase, (ii) the maximum likelihood estimate of its phase, and (iii) the phase-lock loop (PLL) measurement of its phase, derived the analytic form of the probability density function (pdf) of the difference of IP, and expressed this pdf in terms of signal-to-noise ratio (SNR) of signals. PLV is finally given by analytic formulas in terms of SNRs of a pair of neural signals under investigation. In this new approach, SNR, hence PLV, is evaluated at any time instant over repeated trials. Thus, the new approach can provide a true DFC via PLV. This paper presents detailed derivations of this approach and results obtained by using simulations for magnetoencephalography (MEG) data.

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Tianhu Lei, K. Ty Bae, and Timothy P. L. Roberts "A new methodology for phase-locking value: a measure of true dynamic functional connectivity", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83170T (14 April 2012); https://doi.org/10.1117/12.912725

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