Neuronal dynamics enable the functional differentiation of resting state networks in the human brain

Abstract

Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals.Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders. K E Y W O R D S electroencephalography, fractal dimension, neuronal oscillations, resting state networks

Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis

“…Moreover, it has been demonstrated that higher values of FD correspond to the presence of higher frequencies in the signal's Fourier spectrum and vice versa. [50][51][52] This relationship might not be linear: in our previous EEG study, 9 we showed that it was rather quadratic. There are many methods used to calculate FD, but the widely accepted ones are Katz's and Higuchi's methods, respectively, 24,53 with the latter considered the most accurate to estimate FD.…”

“…in the absence of external stimulation and response demands) have consistently been documented to be organized into large-scale networks, [1][2][3][4] each of them characterised by specific structural and functional architectures. [5][6][7][8] These are known as resting-state networks (RSNs), and have been widely reported in numerous neuroimaging 5,6 and electrophysiological [9][10][11][12] studies. The spatial pattern of RSNs is stable within individuals, consistent across healthy subjects and yet also contains considerable characteristic information about individuals' brain function [13][14][15] making them a powerful tool for neuroscientific enquiry.…”

“…In this study, we hypothesise that different RSNs studied using the Blood Oxygen Level Dependent (BOLD) technique might display different brain dynamics, as already shown using electroencephalography (EEG) data by our group. 9 Specifically, we aimed to investigate how the dynamics of BOLD activity at rest can be used to differentiate RSNs and link them to behavioural and perceptual states.…”

Haemodynamic Activity Characterization of Resting State Networks by Fractal Analysis and Thalamocorticalmorpho functional Integrity in Chronic Migraine

“…FD2, FD3, FD4, …., FD24). These three measures were averaged to give one mean value of FD for each k, for each subject [30][31][32]. The process was then repeated for every subject and every RSN.…”

Haemodynamic activity characterization of Resting State Networks by Fractal Analysis and thalamocortical morphofunctional integrity in chronic migraine

“…FD2, FD3, FD4, …., FD24). These three measures were averaged to give one mean value of FD for each k, for each subject [14,38,39]. The process was then repeated for every subject and every RSN (in Porcaro and colleagues [40] it is shown in detail the procedure and additional analyses demonstrating that the FD measurements were not dependent on the choice of window length or overlapping windows).…”