Complexity Synchronization of Organ Networks

Abstract

The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks. Information flows from dynamic networks at a higher level of complexity to those at lower levels of complexity, as summarized in the ‘complexity matching effect’, and the flow is maximally efficient when the complexities are equal. Herein, we use the scaling of empirical datasets from the brain, cardiovascular and respiratory networks to support the hypothesis that complexity synchronization occurs between scaling indices or equivalently with the matching of the time dependencies of the networks’ multifractal dimensions.

Complexity synchronization in living matter: a mini review

“…The complexity (scaling) of EEG time series data (64-channel electroencephalogram) were shown to be multifractal as were the respiratory and cardiovascular time series in addition to which the multifractal dimension (MFD) scaling of the three kinds of simultaneously measured datasets were shown to be in synchronization with one another ( Mahmoodi et al, 2023a ; Mahmoodi et al, 2023b ; West et al, 2023 ). The MFDs of these time series have also been identified using pairwise correlations between time series to identify an appropriate mechanism ( Bartsch and Ivanov, 2014 ).…”

“…The major distinction between the present work and such theoretical works as those of Plamen Ivanov and his group ( Ivanov et al, 2016 ) is that they are working towards understanding, “complexity ... compounded by various couplings and feedback interactions among different systems, the nature of which is not understood” from a network physiology and/or network medicine theory perspective. We on the other hand, are working towards the same end from the other side which is to say through understanding the generic empirical properties that have only recently been uncovered and precisely how such phenomena as CS can and do constrain theory development is only now being glimpsed ( Mahmoodi et al, 2023c ; West et al, 2023 ).…”

“…Note the similarity of the individual multi-fractal dimensions of the three kinds of time series, particularly their quasi-periodic behavior. Adapted from ( West et al, 2023 ) with permission.…”

“…As pointed out by West et al ( West et al, 2023 ) the science motif of CS is based on scaling determined by the 1/f-variability of complex dynamic ONs and the need for a NoONs to transport information internally during intra-ON dynamics and externally exchange information which is the interaction among ONs during inter-network dynamics among the members of the NoONs. The term 1/f-variability is used throughout rather than the more familiar term 1/f-noise because the author finds the latter term to be directed toward random fluctuations that carry no useful information whereas the former term addresses time series with inverse power law (IPL) statistics.…”

“…In a recent series of papers Mahmoodi et al ( Mahmoodi et al, 2023a ; Mahmoodi et al, 2023b ) and West et al ( West et al, 2023 ) have used the scaling behavior of dynamically generated time series to hypothesize the existence of a new form of synchronization, called complexity synchronization (CS), having to do with the optimally efficient exchange of information between and among the organ-networks (ONs) within the human body viewed as a network-of-ONs (NoONs). This complexity synchronization (CS) involves the matching of the multifractal dimensions (MFDs) for time series and was identified by processing a 64-channel electroencephalogram (EEG) of the human brain with each channel treated as a distinct network, while the brain continuously interacts with the respiratory and cardiovascular networks.…”