ArticleNeuroImage2021
Evaluating phase synchronization methods in fMRI: A comparison study and new approaches.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 3 citations in OpenAlex.
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- st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.Human brain mapping · 2025Article
- Studying Time-Resolved Functional Connectivity via Communication Theory: On the Complementary Nature of Phase Synchronization and Sliding Window Pearson Correlation.Brain connectivity · 2025Article
- COMPLEX-VALUED PHASE SYNCHRONY REVEALS DIRECTIONAL COUPLING IN FMRI AND TRACKS MEDICATION EFFECTS.ArXiv · 2025Article
- Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.bioRxiv : the preprint server for biology · 2025Article
- Measuring functional connectivity in frequency-domain helps to better characterize brain function.Human brain mapping · 2024Article
- EiDA: A lossless approach for dynamic functional connectivity; application to fMRI data of a model of ageing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.Network neuroscience (Cambridge, Mass.) · 2024Article
- The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Impact of Amplitude and Phase of fMRI time series for Functional Connectivity Analysis.Magnetic resonance imaging · 2023Article
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- Response to photic stimulation as a measure of cortical excitability in epilepsy patients.Frontiers in neuroscience · 2023Article
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- Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity.NeuroImage · 2022Article
- Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality.Entropy (Basel, Switzerland) · 2021Article
- The Comet Toolbox: Improving robustness in network neuroscience through multiverse analysis.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
In recent years there has been growing interest in measuring time-varying functional connectivity 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. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.