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Recently there has been significant interest in measuring time-varyingfunctional connectivity (TVC) between different brain regions usingresting-state functional magnetic resonance imaging (rs-fMRI) data. One way toassess the relationship between signals from different brain regions is tomeasure their phase synchronization (PS) across time. However, this requiresthe \textit{a priori} choice of type and cut-off frequencies for the bandpassfilter needed to perform the analysis. Here we explore alternative approachesbased on the use of various mode decomposition (MD) techniques that circumventthis issue. These techniques allow for the data driven decomposition of signalsjointly into narrow-band components at different frequencies, thus fulfillingthe requirements needed to measure PS. We explore several variants of MD,including empirical mode decomposition (EMD), bivariate EMD (BEMD),noise-assisted multivariate EMD (na-MEMD), and introduce the use ofmultivariate variational mode decomposition (MVMD) in the context of estimatingtime-varying PS. We contrast the approaches using a series of simulations andapplication to rs-fMRI data. Our results show that MVMD outperforms otherevaluated MD approaches, and further suggests that this approach can be used asa tool to reliably investigate time-varying PS in rs-fMRI data.

Mode decomposition-based time-varying phase synchronization for fMRI Data