An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

Abstract

Background: Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization. Methods: In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals. Results: The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment. Conclusions: In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data.

Exploring MEG brain fingerprints: evaluation, pitfalls, and interpretations

“…Additionally, we considered four phase-based measures: i) the Phase Locking Value (PLV) which evaluates the time varying phase difference, as a measure of phase-locking, between two brain signals (Lachaux, Rodriguez, Martinerie, & Varela, 1999); ii) the Phase-Lag Index (PLI) which estimates the asymmetry around zero of the distribution of the phase differences between two signals (Cornelis J. Stam, Nolte, & Daffertshofer, 2007); iii) the weighted Phase Lag Index (wPLI) which weights the PLI by the magnitude of the imaginary component of the cross-spectrum (Vinck, Oostenveld, van Wingerden, Battaglia, & Pennartz, 2011); and iv) the Phase Linearity Measurement (PLM) which measures the synchronization between brain regions by monitoring their phase differences in time while accounting for narrow differences in the main frequency components of the two signals (Baselice, Sorriso, Rucco, & Sorrentino, 2019; Sorrentino, Ambrosanio, Rucco, & Baselice, 2019). While the PLI and the wPLI are intrinsically insensitive to spatial leakage since they discard zero phase-lag interactions between brain regions, the PLV is susceptible to spatial leakage artefacts.…”

Functional brain network topology across the menstrual cycle is sex hormone dependent and correlates with the individual well-being

“…After the signal had been filtered in each canonical frequency band (i.e. delta, theta, alpha, beta and gamma – see later), the Phase Linearity Measurement (PLM) 82 was computed, to provide an estimate of synchronization between any two region that is purely based upon the phases of the signals, and unaffected by volume conduction. The PLM is defined as 31 : where the Δ∅( t ) represent the phase difference between two signals, the 2B is the frequency band range, set to 1 Hz, f is the frequency and T is the observation time interval.…”

“…After the signal had been filtered in each canonical frequency band (i.e. delta, theta, alpha, beta and gammasee later), the Phase Linearity Measurement (PLM) 82 was computed, to provide an estimate of synchronization between any two region that is purely based upon the phases of the signals, and unaffected by volume conduction. The PLM is defined as 31 :…”

Fading of brain network fingerprint in Parkinson’s disease predicts motor clinical impairment

“…Then, we obtained the time series of 116 regions of interest (ROIs), based on the AAL atlas 45 , using the volume conduction model proposed by Nolte 46 , and applying the Linearly Constrained Minimum Variance 47 beamformer algorithm included in the Fieldtrip toolbox 42 . The resulting time series were band-pass filtered in each canonical frequency band (i.e., delta (0.5 -4 Hz), theta (4 -8 Hz), alpha (8 -13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)). Only 90 ROIs were selected for further analysis, since we excluded ROIs related to the cerebellum because of low reliability.…”