Frequency-phase analysis of resting-state functional MRI

Abstract

We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.

Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data

“…For the application of the method to functional MRI (fMRI) data, there is a need to assume that the hemodynamic lag faithfully reproduces temporal precedence at the neuronal level. Based on our 7 and others results, we will make this simplifying assumption to demonstrate the potential usefulness of our method for fMRI. Previous fMRI studies 8 – 11 showed that time-lag propagates within conventionally known resting-state networks and therefore can be used to infer MRI signal progression and its directionality 10 , 11 .…”

Spatiotemporal functional interactivity among large-scale brain networks

“…Several computational approaches have been suggested to explore directed intrinsic connectivity (e.g., Roebroeck, Formisano et al 2005, Kim, Zhu et al 2007, Blinowska, Trzaskowski et al 2009, Chen, Glen et al 2011, Xu, Spreng et al 2017). In parallel, several studies have revealed that correlated, yet temporally asynchronous, patterns of BOLD signal may reflect the timing of information transfer in the brain (e.g., Mitra, Snyder et al 2015, Yuste and Fairhall 2015, Goelman, Dan et al 2017, Xu, Spreng et al 2017. Together, these advances open the possibility of identifying not only the spatial properties of these intrinsic brain networks but also the direction and rate of information flow through this network architecture.…”

Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra

“…Conversely, improved estimation of phase lags may also be informative when inferring directionality in network connections using fMRI data or in comparing the spectral content of fMRI timecourses across different brain regions. Recently researchers have proposed several ideas that compute the extremum of the cross-covariance [8] or perform a frequency-phase analysis [3] to discover this lag structure in rfMRI connectivity.…”