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The universality of this organizing principle gave birth to the field of network physiology ( Bashan et al., 2012 Ivanov and Bartsch, 2014 Bartsch et al., 2015 Ivanov et al., 2016), aiming at unfolding the mechanisms through which diverse physiological systems interact.
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These may emerge at various scales from molecular pathways ( Covert, 2006 Prentki et al., 2020) to the brain connectome ( Sporns, 2011) and even at the level of the entire organism ( Bashan et al., 2012 Bartsch et al., 2015).
#DIFFERENCE BETWEEN MATLAB 2018B AND 2019A SERIES#
Physiological systems are integrated through a series of intricate connections giving rise to networks of dynamically interacting elements. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections.
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Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence.