Persistence of information flow: A multiscale characterization of human brain

Benigni, Barbara; Ghavasieh, Arsham; Corso, Alessandra; d’Andrea, Valeria; Domenico, Manlio De

doi:10.1162/netn_a_00203

Cited by 13 publications

(10 citation statements)

References 86 publications

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“…Resolving the discussed issues related to the previous definitions, the network Gibbs state has been introduced [26,27,37] to quantify and compare the information content of complex networks. This framework has been successfully applied to a wide variety of problems in network science, from multilayer reducibility and its effect on the transport phenomena [28], to the human microbiome [27], the human brain [38,39], network robustness [40] and pan-viral interactomes [29].…”

Section: Statistical Physics Of Complex Information Dynamicsmentioning

confidence: 99%

“…Comparing the Von Neumann entropy of empirical networks with their configuration models-i.e., a model that generates a random network with the same degree distribution as the real network under study [42]-or types of randomized versions, has been used to investigate the benefits of topological complexity often observed in real systems for their functional diversity [26,39].…”

Section: Functional Diversitymentioning

confidence: 99%

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Multiscale Information Propagation in Emergent Functional Networks

Ghavasieh

Domenico

2021

Entropy

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Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for the information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

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Section: Statistical Physics Of Complex Information Dynamicsmentioning

confidence: 99%

Section: Functional Diversitymentioning

confidence: 99%

Multiscale Information Propagation in Emergent Functional Networks

Ghavasieh

Domenico

2021

Entropy

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“…Resolving the discussed issues related to the previous definitions, the network Gibbs state has been introduced [26,36,37] to quantify and compare the information content of complex networks. This framework has been successfully applied to a wide variety of problems in network science, from multilayer reducibility and its effect on the transport phenomena [38], to the human microbiome [36], the human brain [39,40], network robustness [41] and pan-viral interactomes [42].…”

Section: Statistical Physics Of Complex Information Dynamicsmentioning

confidence: 99%

“…Comparing the Von Neumann entropy of empirical networks with their configuration models-i.e., a model that generates a random network with the same degree distribution as the real network under study [44]-, or types of randomized versions, has been used to investigate the benefits of topological complexity often observed in real systems for their functional diversity [26,40].…”

Section: Functional Diversitymentioning

confidence: 99%

Multiscale Information Propagation in Emergent Functional Networks

Ghavasieh

Domenico

2021

Preprint

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Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

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“…I), a property crucial for system's functionality. For instance, it has been shown that by analyzing the perturbation of information flow, one can distinguish the healthy brain from the pathological one, even in absence of significant structural differences among the two types of connectomes [14]. Accordingly, a number of methods have been developed to capture unit-unit communications, considering the coupling between the structure and dynamical processes governing the flow of information [15][16][17] and to account for the heterogeneity and intervening of temporal and spatial information propagation scales [18,19].…”

Section: Introductionmentioning

confidence: 99%

Dismantling the information flow in complex interconnected systems

Ghavasieh¹,

Bertagnolli²,

Domenico³

2022

Preprint

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Microscopic structural damage, such as lesions in neural systems or disruptions in urban transportation networks, can impair the dynamics crucial for systems' functionality, such as electrochemical signals or human flows, or any other type of information exchange, respectively, at larger topological scales. Damage is usually modeled by progressive removal of components or connections and, consequently, systems' robustness is assessed in terms of how fast their structure fragments into disconnected sub-systems. Yet, this approach fails to capture how damage hinders the propagation of information across scales, since system function can be degraded even in absence of fragmentation -e.g., pathological yet structurally integrated human brain. Here, we probe the response to damage of dynamical processes on the top of complex networks, to study how such an information flow is affected. We find that removal of nodes central for network connectivity might have insignificant effects, challenging the traditional assumption that structural metrics alone are sufficient to gain insights about how complex systems operate. Using a damaging protocol explicitly accounting for flow dynamics, we analyze synthetic and empirical systems, from biological to infrastructural ones, and show that it is possible to drive the system towards functional fragmentation before full structural disintegration.

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Persistence of information flow: A multiscale characterization of human brain

Cited by 13 publications

References 86 publications

Multiscale Information Propagation in Emergent Functional Networks

Multiscale Information Propagation in Emergent Functional Networks

Multiscale Information Propagation in Emergent Functional Networks

Dismantling the information flow in complex interconnected systems

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