Abstract
We studied the interactions between different temporal scales of the information flow in complex networks and found them to be stronger in scale-free (SF) than in Erdos-Renyi (ER) networks, especially for the case of phase-amplitude coupling (PAC)— the phenomenon where the phase of an oscillatory mode modulates the amplitude of another oscillation. We found that SF networks facilitate PAC between slow and fast frequency components of the information flow, whereas ER networks enable PAC between slow-frequency components. Nodes contributing the most to the generation of PAC in SF networks were non-hubs that connected with high probability to hubs. Additionally, brain networks from healthy controls (HC) and Alzheimer’s disease (AD) patients presented a weaker PAC between slow and fast frequencies than SF, but higher than ER. We found that PAC decreased in AD compared to HC and was more strongly correlated to the scores of two different cognitive tests than what the strength of functional connectivity was, suggesting a link between cognitive impairment and multi-scale information flow in the brain.
Highlights
The study of information flow and transport in complex biological and social networks by means of random walks has attracted increasing interest in recent years [1,2,3,4]
SF networks are constructed with the Barabasi and Albert’s (BA) model [16], or “rich-gets-richer” scheme, which assumes that new nodes in a network are not connected at random but with high probability to those which already possess a large number of connections
The correlation between Clinical Dementia Rating Sum of Boxes (CDRSB) and the right middle temporal area (r = 0.23, p = 0.237) was not significant, and neither were the correlations between the three areas and the Functional Activities Questionnaire (FAQ) test: left middle temporal (r = 0.31, p = 0.109), left inferior temporal (r = 0.34, p = 0.077), left pars orbitalis (r = −0.16, p = 0.411). These results suggest the existence of a relationship between cognitive impairment, functional decline and behavioral symptoms that characterize Alzheimer’s disease (AD) and the perturbations to the information flow in brain networks, as characterized by cross-frequency interactions and not by broadband interactions
Summary
The study of information flow and transport in complex biological and social networks by means of random walks has attracted increasing interest in recent years [1,2,3,4]. There has been considerable progress in characterizing first passage times, or the amount of time it takes a random walker to reach a target [6,7,8,9]. Previous works have neglected the study of the temporal dynamics of the information flow in the network, which depends on how the walkers move and not just on their arrival time. We lack knowledge about how the different temporal scales in the information flow arise from the topological structure of the network, whether they interact, and how they do it. This paper aims to address such knowledge gap
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