Regularized Partial Phase Synchrony Index Applied to Dynamical Functional Connectivity Estimation

Gaetan Frusque (1) , Julien Jung (2) , Pierre Borgnat (3) , Paulo Gonçalves (1)

1 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
2 CRNL - Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center
3 Phys-ENS - Laboratoire de Physique de l'ENS Lyon

We study the inference of conditional independence graph from the partial Phase Locking Value (PLV) index of mul-tivariate time series. A typical application is the inference of temporal functional connectivity from brain data. We extend the recently proposed time-varying graphical lasso to the measurement of partial locking values, yielding a sparse and temporally coherent dynamical graph that characterizes the evolution of the phase synchrony between each pair of signals. Cast as an optimization problem, we solve it using the alternating direction method of multipliers. The approach is validated on simulated Gaussian multivariate signals and Roessler oscillators. The potential of this regularized partial PLV is then illustrated on actual iEEG data during an epileptic seizure.

Mots clés: Phase Locking value; multivariate; dynamical networks; iEEG; time-varying graphical lasso; graph signal processing

Domaines: Traitement du signal et de l'image [eess.SP]; Neurosciences [q-bio.NC]

Gaetan Frusque, Julien Jung, Pierre Borgnat, Paulo Gonçalves. Regularized Partial Phase Synchrony Index Applied to Dynamical Functional Connectivity Estimation. ICASSP 2020 - IEEE International Conference on Acoustic Speech and Signal Processing, May 2020, Barcelona, Spain. pp.1-5. ⟨hal-02459821⟩