Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state

Abstract

Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages ("rBeta") can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas.

Dynamic Repertoire of Intrinsic Brain States Is Reduced in Propofol-Induced Unconsciousness

“…Here, we applied the ReHo method to a dynamic analysis of correlated brain activity for the first time. The CTC method (as we call it) was designed to quantify spontaneous spatiotemporal correlation patterns at high temporal resolution representing the BOLD signal as a point process (Tagliazucchi et al, 2011). The method has been shown to yield networks that are consistent with those obtained by independent component analysis (ICA) on a longer time scale and has been shown to track changes in state of vigilance (Tagliazucchi et al, 2012b).…”

“…Liu and associates (Liu and Duyn, 2013;Liu et al, 2013b) applied a variation of the point process method (Tagliazucchi et al, 2011) followed by k-means clustering in terms of the spatial similarity of BOLD signal activations or deactivations to derive spontaneous coactivation patterns (CAPs). In Liu and colleagues (2013b), they admitted 10% of the highest and 5% of the lowest signal values (after demeaning and normalizing to temporal SD) and averaged the signal patterns obtained at various time points within each cluster.…”

On the distinguishability of HRF models in fMRI

“…Although no explicit external inputs exist in resting-state fMRI acquisitions, it has been observed that discrete neuronal events do occur (Deco and Jirsa, 2012). Most interestingly, it has been recently suggested that such events can be identified as peaks of relatively large BOLD signal amplitude (Tagliazucchi et al, 2011), and restingstate fMRI data can then be seen as "spontaneous event-related" data (Wu et al, 2013).…”

Dynamic BOLD functional connectivity in humans and its electrophysiological correlates

“…Dynamic functional connectivity has only recently been demonstrated for Blood Oxygen Level-Dependent (BOLD) signals, measured with functional Magnetic Resonance Imaging (fMRI) (Chang and Glover, 2010; Tagliazucchi et al, 2010a,b, 2012; Handwerker et al, 2012; Hutchison et al, 2012; Petridou et al, 2012; Smith et al, 2012). Given the precise spatial resolution and whole brain coverage allowed by fMRI, understanding the relationship between brain rhythms and BOLD connectivity could contribute to the identification of the functional roles of each oscillation at rest and during a task.…”