Noise-Assisted Multivariate Variational Mode Decomposition On fMRI Data For Phase Synchronization Analysis

Recently, there has been a growing interest in analyzing resting-state fMRI (rs-fMRI) signals using Time-Varying Phase Synchronization (TVPS) measures. TVPS serves as a functional connectivity metric, allowing for the quantification of phase synchronization between different brain regions. However, extracting the phase from fMRI signals poses challenges due to inherent noise and insufficient bandlimitation. Traditional filtering methods struggle to effectively eliminate noise and necessitate prior knowledge of cutoff frequencies. In this context, data-driven multivariate decomposition techniques present promising solutions for extracting narrow-band components suitable for TVPS analyses. Previous studies have identified Multivariate Variational Mode Decomposition (MVMD) as particularly suitable for fMRI decomposition. However, MVMD's requirement for predefining the number of extracted modes K limits its analytical capabilities. To address this limitation, we employ an enhanced MVMD scheme called Noise-Assisted MVMD (NA-MVMD), designed to reduce sensitivity to parameters and enhance decomposition quality. We apply NA-MVMD to synthetic signals, showcasing improved decomposition quality, noise robustness, and reduced sensitivity in setting the K parameter.