Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age

Nuzzi, Davide; Pellicoro, M.; Angelini, L.; Marinazzo, Daniele; Stramaglia, Sebastiano

doi:10.1162/netn_a_00146

Cited by 13 publications

(10 citation statements)

References 54 publications

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“…Our framework is inspired by the Ising Model representation of brain dynamics whereby selforganized patterns of connectivity are formed through the spontaneous fluctuations of random spins (Reichl & Luscombe, 1999). This model has been used to characterize complex microscale dynamics of the human brain (Deco et al, 2008;Kadirvelu et al, 2017;Ostojic & Brunel, 2011;Tkačik et al, 2015), as well as macro-scale interactions (Ezaki et al, 2017;Marinazzo et al, 2014;Nghiem et al, 2018;Niu et al, 2019;Nuzzi et al, 2020;Schneidman et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Our framework is inspired by the Ising model representation of brain dynamics whereby self-organized patterns of connectivity are formed through the spontaneous fluctuations of random spins ( Reichl & Luscombe, 1999 ). This model has been used to characterize complex microscale dynamics of the human brain ( Deco et al, 2008 ; Kadirvelu et al, 2017 ; Ostojic & Brunel, 2011 ; Tkačik et al, 2015 ), as well as macroscale interactions ( Ezaki et al, 2017 ; Marinazzo et al, 2014 ; Nghiem et al, 2018 ; Niu et al, 2019 ; Nuzzi et al, 2020 ; Schneidman et al, 2006 ). Unconstrained maximum entropy models (MEM) have been shown to accurately represent spatiotemporal coactivations in neuronal spike trains ( Roudi et al, 2009 ; Schneidman et al, 2006 ; Shlens et al, 2006 ) as well as patterns of BOLD activity ( Ashourvan et al, 2017 ; Cocco et al, 2017 ; Ezaki et al, 2020 ; Watanabe et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Fortel

Butler

Korthauer

et al. 2022

Network Neuroscience

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Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macro-scale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics; wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Fortel

Butler

Korthauer

et al. 2022

Network Neuroscience

View full text Add to dashboard Cite

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“…Such trends suggest alterations in the connectivity of the source EEG network occurring before the onset of focal epileptic seizures. The use of information-theoretic measures as an indicator of phase transitions has been demonstrated using classic spin model in physics [ 50 ], as well as in financial systems [ 51 ] and in the brain: recent studies have shown that the maximal amount of information transfer among units is representative of the critical state on a brain network [ 52 ], and that synergy measures derived from information transfer in a multivariate fashion peak before the transition from disordered to ordered phases [ 53 ]. In this context, the use of measures of information transfer like those presented in this study, possibly integrated with multivariate measures obtained in the context of information dynamics [ 54 ], may have important implications in seizure prediction.…”

Section: Discussionmentioning

confidence: 99%

A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

et al. 2020

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This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.

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“…Speaking qualitatively, in a complex system, the synergy measures its capacity to make integration of information whilst redundancy provides robustness to the system; decomposing interactions between variables into synergistic and redundant components illuminates how the system addresses the trade-off between robustness and integration. For example, recent works focusing on the brain at the macroscale have identified high synergy brain regions which support higher cognitive function [13] and are affected by the aging process [14].…”

Section: Introductionmentioning

confidence: 99%

Pairwise and high-order dependencies in the cryptocurrency trading network

Scagliarini¹,

Pappalardo²,

Biondo³

et al. 2022

Preprint

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In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high-order statistical dependencies, quantified by Granger causality and O-information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid-19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high-order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high-order descriptions of complex financial systems provide complementary information for cryptocurrency analysis.

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Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age

Cited by 13 publications

References 54 publications

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

Pairwise and high-order dependencies in the cryptocurrency trading network

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