LAUSR

INTRODUCTION The selection of an appropriate window size, window function and functional connectivity (FC) metric in the sliding window method, is not straightforward due to the absence of ground truth. METHODS A previously proposed wavelet-based method was accordingly adjusted for estimating time-varying functional connectivity (TVFC) and was applied on a large high-quality, low-motion dataset of 400 resting-state fMRI data. Specifically, the wavelet coherence magnitude and relative phase were averaged across wavelet (frequency) scales to yield TVFC and synchronization patterns. To assess whether the observed fluctuations in TVFC were statistically significant (dynamic FC [dFC]; the distinction between TVFC and dFC is intentional), surrogate data were generated using the multivariate Phase (MVPR) and multivariate Auto-regressive Randomization (MVAR) methods to define the null hypothesis of dFC absence. RESULTS By averaging across all frequencies, core regions of the Default Mode Network (DMN; medial prefrontal and posterior cingulate cortices, inferior parietal lobes, hippocampal formation) were found to exhibit dFC (test-retest reproducibility of 90%) and were also synchronized in activity (-15°≤phase≤15°). When averaging across distinct frequency bands, the same dynamic connections were identified, with the majority of them identified in the frequency range (0.01, 0.198] Hz, though with lower test-retest reproducibility (<66%). Additional analysis suggested that MVPR method better preserved properties (p<10-10), including time-averaged coherence, of the original data compared to MVAR approach. CONCLUSIONS The wavelet-based approach identified dynamic associations between the core DMN regions with fewer choices in parameters, compared to sliding window method.