Project: BOLD Connectivity Dynamics
FIM Authors:
Authors:
- Joshua Faskowitz
- Peter Bandettini
- Javier Gonzalez-Castillo
Resting-state fMRI time series are punctuated by spontaneous moments of high-amplitude activity lasting mere seconds. Previous research has demonstrated that such moments may contain a disproportionate amount of information and can be used to recapitulate maps of distributed brain activity or to recreate spatial functional connectivity patterns. Ultimately, this body of work has established that modeling neurovascular activity as a succession of spontaneous, punctuated moments is an effective approach for understanding cortex-wide brain activity. Here, we expand on this line of work by focusing our attention on the spatiotemporal properties of such punctuated moments, particularly on their duration. For this, we turn to an edge time series approach to resolve the dynamics of functional connectivity, identify moments of prominent synchrony, and record their duration. This procedure allows us to differentiate such punctuated moments by the time scales at which they unfold. By mapping moment duration to the cortex, we find that connectivity emanating from brain’s primary sensory areas transpires with the longest durations. We further construct spatial patterns of connectivity unfolding over distinct durations, demonstrating how time scales differentially relate to traditionally constructed functional connectivity. Finally, we show how the longest connectivity moments could convey information about fluctuations in subjects’ vigilance. Overall, the information that we have gleaned about prominent connectivity moments and their duration would otherwise be largely obscured when using other prevalent methods. Here we highlight an additional feature of functional connectivity to further our characterization of the brain’s spatiotemporal organization.
Code
Journal: Imaging Neuroscience
Volume: 4
URL: https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.1126/134944/Mapping-high-amplitude-fMRI-edge-time-series
DOI: https://doi.org/10.1162/IMAG.a.1126