Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity

TLDR

Functional brain activity and connectivity are traditionally examined using correlation measures, but sliding‑window approaches trade temporal resolution for statistical reliability. This study demonstrates that instantaneous phase synchronization can be used to achieve higher temporal resolution in dynamic functional connectivity analysis. The authors applied phase synchronization to fMRI data from 12 volunteers watching a film, using a 0.04–0.07 Hz band and defining seed‑based, intersubject, and intersubject seed‑based PS metrics that are applicable to both naturalistic and controlled paradigms. The PS‑based metrics agree with conventional correlation methods over the full time series while offering single‑TR resolution, and the FUNPSY MATLAB toolbox is freely available for implementation.

Abstract

Functional brain activity and connectivity have been studied by calculating intersubject and seed-based correlations of hemodynamic data acquired with functional magnetic resonance imaging (fMRI). To inspect temporal dynamics, these correlation measures have been calculated over sliding time windows with necessary restrictions on the length of the temporal window that compromises the temporal resolution. Here, we show that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity. We applied PS on an fMRI dataset obtained while 12 healthy volunteers watched a feature film. Narrow frequency band (0.04–0.07 Hz) was used in the PS analysis to avoid artifactual results. We defined three metrics for computing time-varying functional connectivity and time-varying intersubject reliability based on estimation of instantaneous PS across the subjects: (1) seed-based PS, (2) intersubject PS, and (3) intersubject seed-based PS. Our findings show that these PS-based metrics yield results consistent with both seed-based correlation and intersubject correlation methods when inspected over the whole time series, but provide an important advantage of maximal single-TR temporal resolution. These metrics can be applied both in studies with complex naturalistic stimuli (e.g., watching a movie or listening to music in the MRI scanner) and more controlled (e.g., event-related or blocked design) paradigms. A MATLAB toolbox FUNPSY (http://becs.aalto.fi/bml/software.html) is openly available for using these metrics in fMRI data analysis.