Characterizing dynamic local functional connectivity in the human brain

Abstract

Functional connectivity (FC), obtained from functional magnetic resonance imaging (fMRI), brings insights into the functional organization of the brain. Recently, rich and complex behaviour of brain has been revealed by the dynamic fluctuation of FC, which had previously been regarded as confounding ‘noise’. While the dynamics of long-distance, inter-regional FC has been extensively studied, the dynamics of local FC within a few millimetres in space remains largely unexplored. In this study, the local FC was depicted by regional homogeneity (ReHo), and the dynamics of local FC was obtained using sliding windows method. We observed a robust positive correlation between ReHo and its temporal variability, which was shown to be an intrinsic feature of the brain rather than a pure stochastic effect. Furthermore, fluctuation of ReHo was associated with global functional organization: (i) brain regions with higher centrality of inter-regional FC tended to possess higher ReHo variability; (ii) coherence of ReHo fluctuation was higher within brain’s functional modules. Finally, we observed alteration of ReHo variability during a motor task compared with resting-state. Our findings associated the temporal fluctuation of ReHo with brain function, opening up the possibility of dynamic local FC study in the future.

Replicability of time-varying connectivity patterns in large resting state fMRI samples

“…Zalesky and Breakspear (2015) reviewed these findings providing statistical support for their proposed rule of thumb, but additionally making a point that the proposed lower limit is overly conservative especially in moderate SNR conditions. The latter findings are corroborated in several studies where varying the window length parameter over a range beyond a certain safety limit did not change the overall observed dynamics (Allen, Damaraju et al 2012, Li, Zhu et al 2014, Deng, Sun et al 2016, Liégeois, Ziegler et al 2016, Preti, Bolton et al 2016. Hindriks, Adhikari et al (2016) evaluated the ability of the sliding-window correlations in revealing dFNC and also highlighted the importance of appropriate statistical tests to detect dFNC.…”

Physical activity is positively related to local functional connectivity in adolescents’ brains

“…Interestingly, an earlier study found that executing a hand-closing-opening task motor task acutely decreased ReHo in the right supramarginal gyrus relative to a resting state (Deng et al, 2016). Changes in ReHo have also been reported in relation to a finger tapping task (Lv et al, 2013).…”

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

“…26 . Under such a framework, a range of FC metrics used in analyses of static connectivity (e.g., correlations between regions of interest or functional networks 15,16,29–32 , regional homogeneity 33,34 , and independent component analysis (ICA) 35 ), as well as newly proposed metrics for dynamic analysis 36,37 can be examined. This practice complements analyses of static connectivity in distinguishing between populations of individuals with neuropsychiatric disorders versus healthy controls 16,38,39 .…”