Reconstructing effective phase connectivity of oscillator networks from observations

Kralemann, Bjoern; Pikovsky, Arkady; Rosenblum, Michael G.

doi:10.1088/1367-2630/16/8/085013

Cited by 73 publications

(68 citation statements)

References 72 publications

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“…Establishing various forms of dynamical coupling and the mechanisms underlying interactions between pairs of organ systems and their respective structural and regulatory networks is an essential building block in network physiology to investigate how coordinated communications among multiple organ systems integrated as a network lead to distinct physiologic states and conditions. Utilizing phase-dynamics reconstruction analysis on triplets of network nodes, Kralemann et al [71] propose a novel approach to detect and quantify directional connectivity in dynamical networks of nonlinear oscillators from multivariate time series data. To probe the network of interaction between the brain and the heart, Faes et al [72] propose an information dynamics framework and entropy-based measures to investigate flows of information between these two systems compared to the information stored by each system separately, in order to explore changes in neural regulation across different sleep stages.…”

Section: New Data Science Methodology To Probe Physiologic Interactionsmentioning

confidence: 99%

Focus on the emerging new fields of network physiology and network medicine

2016

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Despite the vast progress and achievements in systems biology and integrative physiology in the last decades, there is still a significant gap in understanding the mechanisms through which (i) genomic, proteomic and metabolic factors and signaling pathways impact vertical processes across cells, tissues and organs leading to the expression of different disease phenotypes and influence the functional and clinical associations between diseases, and (ii) how diverse physiological systems and organs coordinate their functions over a broad range of space and time scales and horizontally integrate to generate distinct physiologic states at the organism level. Two emerging fields, network medicine and network physiology, aim to address these fundamental questions. Novel concepts and approaches derived from recent advances in network theory, coupled dynamical systems, statistical and computational physics show promise to provide new insights into the complexity of physiological structure and function in health and disease, bridging the genetic and sub-cellular level with inter-cellular interactions and communications among integrated organ systems and sub-systems. These advances form first building blocks in the methodological formalism and theoretical framework necessary to address fundamental problems and challenges in physiology and medicine. This ‘focus on’ issue contains 26 articles representing state-of-the-art contributions covering diverse systems from the sub-cellular to the organism level where physicists have key role in laying the foundations of these new fields.

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Section: New Data Science Methodology To Probe Physiologic Interactionsmentioning

confidence: 99%

Focus on the emerging new fields of network physiology and network medicine

2016

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“…Looking only at the network structure will in general give better results, but will also entirely neglect the dynamics. Certain methods give excellent results, but only when applied to cases of dynamics with specific properties, such as periodicity or synchronization [20,21]. Another distinction runs along the ability to interfere with the system.…”

Section: Discussionmentioning

confidence: 99%

Decoupling approximation robustly reconstructs directed dynamical networks

et al. 2018

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Methods for reconstructing the topology of complex networks from time-resolved observations of node dynamics are gaining relevance across scientific disciplines. Of biggest practical interest are methods that make no assumptions about the properties of the dynamics, and can cope with noisy, short and incomplete trajectories. Ideal reconstruction in such scenario requires an exhaustive approach of simulating the dynamics for all possible network configurations and matching the simulated against the actual trajectories, which of course is computationally too costly for any realistic application. Relying on insights from equation discovery and machine learning, we here introduce decoupling approximation of dynamical networks and propose a new reconstruction method based on it. Decoupling approximation consists of matching the simulated against the actual trajectories for each node individually rather than for the entire network at once. Despite drastic reduction of the computational cost that this approximation entails, we find our method's performance to be very close to that of the ideal method. In particular, we not only make no assumptions about the properties of the trajectories, but provide strong evidence that our methods' performance is largely independent of the dynamical regime at hand. Of crucial relevance for practical applications, we also find our method to be extremely robust to both length and resolution of the trajectories and relatively insensitive to noise.Actually, network reconstruction is becoming a field of its own within network science [15]. It brings together methodological disciplines such as computer science and statistics with domain sciences such as physics, sociology, biology and neuroscience. Within the context of physics, a myriad of methods have been proposed over the past decade. They are typically anchored in physical insights about network's collective/ emergent dynamics [16,17]. This primarily includes synchronization as the best researched paradigm of collective dynamics [9], in both its theoretical [18,19] and experimental aspect [20,21]. Methods applicable to sparse data have been developed [22], as well as methods that work in the presence of noise [23], or out of equilibrium [24]. Invasive methods assume that one is able to interfere with the system and extract the information from transients [25]. Other methods use compressive sensing [26,27] or elaborate statistics related to derivative-variable correlations [28,29]. Another set of methods attempts to grasp the situations relevant for inferring networks of neurons [30, 31], or even social networks based on infection statistics [32]. On the other hand, somewhat less effort has been invested in the development of methods that can reconstruct the interaction (coupling) functions and not necessarily the network topology [33].The problem of reconstructing the network from dynamical data is not to be confused with the problem of link prediction or network completion [34][35][36]. The latter refers to assessing the existence of ...

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“…Later, it was extended to characterize interactions in networks of coupled oscillators [20,21]. It was successfully applied not only in bi-variate but also in multivariate model systems and experimental data [4,15,[18][19][20][21][51][52][53][54].…”

Section: B Phase-based Approachmentioning

confidence: 99%

“…Among them are approaches based on state-space reconstruction [9][10][11][12][13][14], phases [15][16][17][18][19][20][21], information theory [22][23][24][25][26][27], linear correlation [28][29][30], dynamical Bayesian inferrence analysis [31][32][33][34][35] as well as on neural networks [36,37], among others. A comparison between many of these approaches was done in model systems and also in experimental data [21,35,[38][39][40][41][42]. In this study we apply a state-space approach [14] and a phase-based approach [15,18,19].…”

Section: Introductionmentioning

confidence: 99%

Coupling strength versus coupling impact in nonidentical bidirectionally coupled dynamics

Laiou

Andrzejak

2017

Phys. Rev. E

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The understanding of interacting dynamics is important for the characterization of real-world networks. In general real-world networks are heterogeneous in the sense that each node of the network is a dynamics with different properties. For coupled non-identical dynamics symmetric interactions are not straightforwardly defined from the coupling strength values. Thus, a challenging issue is whether we can define a symmetric interaction in this asymmetric setting. To address this problem we introduce the notion of the coupling impact. The coupling impact considers not only the coupling strength but also the energy of the individual dynamics which is conveyed via the coupling. To illustrate this concept, we follow a data-driven approach by analyzing signals from pairs of coupled model dynamics using two different connectivity measures. We find that the coupling impact, but not the coupling strength, correctly detects a symmetric interaction between pairs of coupled dynamics regardless of their degree of asymmetry. Therefore, this approach allows to reveal the real impact that one dynamics has on the other and hence to define symmetric interactions in pairs of non-identical dynamics.

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Reconstructing effective phase connectivity of oscillator networks from observations

Cited by 73 publications

References 72 publications

Focus on the emerging new fields of network physiology and network medicine

Focus on the emerging new fields of network physiology and network medicine

Decoupling approximation robustly reconstructs directed dynamical networks

Coupling strength versus coupling impact in nonidentical bidirectionally coupled dynamics

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