Abstract
Assessing the functional connectivity (FC) of the brain has proven valuable in enhancing our understanding of brain function. Recent developments in the field demonstrated that FC fluctuates even in the resting state, which has not been taken into account by the widely applied static approaches introduced earlier. In a recent study using functional near-infrared spectroscopy (fNIRS) global dynamic functional connectivity (DFC) has also been found to fluctuate according to scale-free i.e., fractal dynamics evidencing the true multifractal (MF) nature of DFC in the human prefrontal cortex. Expanding on these findings, we performed electroencephalography (EEG) measurements in 14 regions over the whole cortex of 24 healthy, young adult subjects in eyes open (EO) and eyes closed (EC) states. We applied dynamic graph theoretical analysis to capture DFC by computing the pairwise time-dependent synchronization between brain regions and subsequently calculating the following dynamic graph topological measures: Density, Clustering Coefficient, and Efficiency. We characterized the dynamic nature of these global network metrics as well as local individual connections in the networks using focus-based multifractal time series analysis in all traditional EEG frequency bands. Global network topological measures were found fluctuating–albeit at different extent–according to true multifractal nature in all frequency bands. Moreover, the monofractal Hurst exponent was found higher during EC than EO in the alpha and beta bands. Individual connections showed a characteristic topology in their fractal properties, with higher autocorrelation owing to short-distance connections–especially those in the frontal and pre-frontal cortex–while long-distance connections linking the occipital to the frontal and pre-frontal areas expressed lower values. The same topology was found with connection-wise multifractality in all but delta band connections, where the very opposite pattern appeared. This resulted in a positive correlation between global autocorrelation and connection-wise multifractality in the higher frequency bands, while a strong anticorrelation in the delta band. The proposed analytical tools allow for capturing the fine details of functional connectivity dynamics that are evidently present in DFC, with the presented results implying that multifractality is indeed an inherent property of both global and local DFC.
Highlights
Functions of the brain emerge from dynamic interactions between the elements of its complex neuronal networks (Chialvo, 2010; Werner, 2010)
We showed that dynamic global functional connectivity of the brain—when investigated by EEG mapping and captured in dynamic graph theoretical measures—fluctuates according to a multifractal temporal pattern
Our results revealed that several network topological aspects exhibit different characteristics
Summary
Functions of the brain emerge from dynamic interactions between the elements of its complex neuronal networks (Chialvo, 2010; Werner, 2010). This phenomenon is present across a broad range of spatial scales from the microanatomical level of individual neurons through neuronal cell assemblies to macroanatomical brain regions (Sporns, 2011). FC studies did reveal the existence of several resting-state brain networks such as the default mode network (Raichle et al, 2001; Greicius et al, 2003) or the task-positive network (Fox et al, 2005), and showed that FC properties responded to changes in physiological conditions e.g., sleep (Horovitz et al, 2009; Liu et al, 2015) or cognitive stimulation (Esposito et al, 2006; Racz et al, 2017). Altered FC was found in pathological conditions like those in degenerative dementias (Pievani et al, 2011), schizophrenia (Liu et al, 2008), or multiple sclerosis (Cader et al, 2006)
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