Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information

Maziar Yaesoubi, Elena A. Allen, Robyn L. Miller, Vince D. Calhoun

Research output: Contribution to journalArticle

Abstract

Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes.Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands.Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain.

Original languageEnglish (US)
Pages (from-to)133-142
Number of pages10
JournalNeuroImage
Volume120
DOIs
StatePublished - Oct 5 2015
Externally publishedYes

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Keywords

  • Functional connectivity
  • Functional network connectivity
  • Time-frequency analysis
  • Wavelet transform
  • Wavelet transform coherence

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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