Evaluating phase synchronization methods in fMRI: a comparison study and new approaches
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Abstract (arXiv)
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 here 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 necessity of band-pass filtering the data to obtain valid results. 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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Sep 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2025Voxel-Level Brain States Prediction Using Swin Transformercited by 5
- 2026Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selectioncited by 1
- 2022Mode decomposition-based time-varying phase synchronization for fMRI Datacited by 0
- 2026Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorderscited by 0
- 2026Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders14 Mar 2026
- 2026Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection5 Feb 2026
- 2025Voxel-Level Brain States Prediction Using Swin Transformer13 Jun 2025
- 2022Mode decomposition-based time-varying phase synchronization for fMRI Data26 Mar 2022