Journal
FRONTIERS IN PHYSIOLOGY
Volume 11, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fphys.2020.615961
Keywords
scale-free; bivariate; multifractal; functional connectivity; network physiology; electroencephalography
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Funding
- 'Development of scientific workshops for medical, health sciences and pharmaceutical training' Project [EFOP-3.6.3-VEKOP-162017-00009]
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The study introduces bivariate focus-based multifractal analysis to capture scale-free relations in neural processes. Most connections in the cortical regions exhibit true bivariate multifractality, indicating genuine scale-free coupling of neural dynamics. Differences in long-term autocorrelation and multifractality strength are found in within- and between-resting state networks connections.
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. 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. 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. 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. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. 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.
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