Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of  activity on the anatomical, or structural network of synaptic connections , which possess a URLanization. While it is assumed that structural networks shape their functional counterparts, it is also hypothesized that alterations of brain activity may come with transformations of functional connectivity , and possibly its deviation from the hierarchical topology . In this computational study, we introduce a novel methodology to monitor the persistence and breakdown of hierarchical order in functional networks, generated from simulations of activity spreading on both synthetic and real structural connectomes. We show that hierarchical connectivity is persistent in the quasi-critical regime associated with optimal processing capabilities and normal brain function (Griffiths phase) and breaks down in states deviating from this regime , often associated with pathological conditions. Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of tuning patterns of activity and information flow , and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective balance between local specialized processing and global integration  .
Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of neural activity on the anatomical, or structural network of synaptic connections . This last is well known to URLanized in hierarchical and modular way. While it is assumed that structural networks shape their functional counterparts, it is also hypothesized that alterations of brain dynamics come with transformations of functional connectivity . In this computational study, we introduce a novel methodology to monitor the persistence and breakdown of hierarchical order in functional networks, generated from computational models of activity spreading on both synthetic and real structural connectomes. We show that hierarchical connectivity appears in functional networks in a persistent way if the dynamics is set to be in the quasi-critical regime associated with optimal processing capabilities and normal brain function , while it breaks down in other (supercritical) dynamical regimes , often associated with pathological conditions. Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of controlling patterns of activity and information flow . We conclude that functional connectivity patterns achieve optimal balance between local specialized processing and global integration , by inheriting the URLanization of the underlying structural architecture .
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neural
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, which possess a URLanization.
. This last is well known to URLanized in hierarchical and modular way.
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activity may
dynamics
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, and possibly its deviation from the hierarchical topology
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simulations
computational models
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is persistent
appears in functional networks in a persistent way if the dynamics is set to be
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(Griffiths phase) and
, while it
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states deviating from this regime
other (supercritical) dynamical regimes
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tuning
controlling
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, and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective
. We conclude that functional connectivity patterns achieve optimal
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, by inheriting the URLanization of the underlying structural architecture