Evaluating phase synchronization methods in fMRI: a comparison study and new approaches
Autor: | Hamed Honari, Ann S. Choe, Martin A. Lindquist |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Synchronization Machine Learning (cs.LG) Functional connectivity 0302 clinical medicine Sliding window protocol Synchronization (computer science) Image Processing Computer-Assisted Cortical Synchronization Circular statistics Brain Mapping Time-varying phase synchronization 05 social sciences Brain Magnetic Resonance Imaging Neurology Metric (mathematics) Instantaneous phase synchronization Phase synchronization detection Cognitive Neuroscience Models Neurological Instantaneous phase Statistics - Applications Article 050105 experimental psychology Hilbert–Huang transform lcsh:RC321-571 Methodology (stat.ME) 03 medical and health sciences FOS: Electrical engineering electronic engineering information engineering Humans Computer Simulation Applications (stat.AP) 0501 psychology and cognitive sciences Resting-state fMRI Electrical Engineering and Systems Science - Signal Processing lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Statistics - Methodology Resting state fMRI business.industry Pattern recognition Filter (signal processing) Phase synchronization Artificial intelligence Relative phase business 030217 neurology & neurosurgery |
Zdroj: | NeuroImage, Vol 228, Iss, Pp 117704-(2021) NeuroImage |
Popis: | 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. |
Databáze: | OpenAIRE |
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