Persistence of hierarchical network organization and emergent topologies in models of functional connectivity
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Brain networkFunctional connectivityHierarchical modular networksSegregation integration
Publisher version: A. Safari, P. Moretti, I. Diez et al., Persistence of hierarchical network organization and emergent topologies in models of func-tional connectivity, Neurocomputing [https://doi.org/10.1016/j.neucom.2021.02.096]
SponsorshipConsejería de Conocimiento; Investigación Universidad, Junta de Andalucía A-FQM-175-UGR18; Spanish Ministry; National Institutes of Health NIH; NIH Blueprint for Neuroscience Research; McDonnell Center for Systems Neuroscience; Deutsche Forschungsgemeinschaft MO 3049/1-1,MO 3049/3-1 DFG; European Regional Development Fund ERDF; Agencia Estatal de Investigación FIS2017-84256-P AEI
Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of neural activity on the underlying anatomical or structural network of synaptic connections. This latter is well known to be organized 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 (i.e. segregation) and global integration by inheriting the hierarchical organization of the underlying structural architecture.