Evaluating phase synchronization methods in fMRI: A comparison study and new approaches

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2021-03, Vol.228, p.117704-117704, Article 117704
• Main Authors: Honari, Hamed, Choe, Ann S., Lindquist, Martin A.
• Format: Article
• Language: English
• Subjects: Brain - physiology; Brain mapping; Brain Mapping - methods; Circular statistics; Computer Simulation; Cortical Synchronization - physiology; Decomposition; Functional connectivity; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Instantaneous phase synchronization; Magnetic Resonance Imaging - methods; Models, Neurological; Neural networks; Neuroimaging; Phase synchronization detection; Resting-state fMRI; Synchronization; Time series; Time-varying phase synchronization
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117704

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.