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• W4288045143 endingPage "119519" @default.
• W4288045143 startingPage "119519" @default.
• W4288045143 abstract "Recently, there has been significant interest in measuring time-varying functional connectivity (TVC) between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. However, this requires the a priori choice of type and cut-off frequencies for the bandpass filter needed to perform the analysis. Here we explore alternative approaches based on the use of various mode decomposition (MD) techniques that provide a more data driven solution to this issue. These techniques allow for the data driven decomposition of signals jointly into narrow-band components at different frequencies, thus fulfilling the 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 of multivariate variational mode decomposition (MVMD) in the context of estimating time-varying PS. We contrast the approaches using a series of simulations and application to rs-fMRI data. Our results show that MVMD outperforms other evaluated MD approaches, and further suggests that this approach can be used as a tool to reliably investigate time-varying PS in rs-fMRI data." @default.
• W4288045143 created "2022-07-27" @default.
• W4288045143 creator A5010490290 @default.
• W4288045143 creator A5045394227 @default.
• W4288045143 date "2022-11-01" @default.
• W4288045143 modified "2023-10-14" @default.
• W4288045143 title "Mode decomposition-based time-varying phase synchronization for fMRI" @default.
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• W4288045143 doi "https://doi.org/10.1016/j.neuroimage.2022.119519" @default.
• W4288045143 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35905810" @default.
• W4288045143 hasPublicationYear "2022" @default.
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