Measures for brain connectivity analysis: nodes centrality and their invariant patterns

Silva, Laysa Mayra Uchôa da; Baltazar, Carlos Arruda; Silva, Camila Aquemi; Ribeiro, Mauricio Watanabe; Aratanha, Maria Adelia; Deolindo, Camila Sardeto; Rodrigues, Abner Cardoso; Machado, Birajara Soares

doi:10.1140/epjst/e2016-60400-2

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…In [45] the authors investigated the high dynamical complexity of the brain, as a small world topology. They compared four nonlinear connectivity measures and show that each method characterizes distinct features of brain interactions.…”

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confidence: 99%

Complexity, Chaos and Fluctuations

Banerjee

Colangeli

2017

Eur. Phys. J. Spec. Top.

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This EPJ special topics issue is a collection of contributions about some recent developments in statistical mechanics and dynamical complexity. The various articles report results on statistical fluctuations, non equilibrium models, chaos and nonlinear interactions, complexity and its various associated measures.The long-standing endeavor of Statistical Mechanics is to establish a mathematical framework which allows to encompass different levels of description of a many-particle system: (i) the microscopic setting, stemming from the Boltzmann-Gibbs statistical ensemble theory [1, 2]; (ii) the mesoscopic scale, typically described in the framework of kinetic theory [3-5], interacting particle systems [6][7][8][9][10][11][12] or even more general stochastic processes [13,14]; (iii) the macroscopic level, which is the standard set-up in contexts such as the derivation of hydrodynamic equations, turbulence [15][16][17], Irreversible Thermodynamics [18], etc. Equilibrium phenomena have been investigated and understood much more thoroughly than non-equilibrium ones. At present, the equilibrium theory may be considered complete, for what concerns the microscopic foundations of equilibrium thermodynamics, including the theory of phase transitions and critical phenomena. On the other hand, in spite of the long-standing research activity, Nonequilibrium Statistical Mechanics is still an open field of research. One of the reasons is that nonequilibrium phenomena are so numerous, diverse and complex that attaining a unified description may still appear as an elusive ambition. In particular, away from equilibrium one cannot bypass the analysis of the microscopic dynamics even in the study of stationary states, that are the simplest beyond equilibrium.Nevertheless, relevant progresses were made in this field in the last decades, cf.[19]. We refer, for instance, to the discovery of the Fluctuation Relations for deterministic and stochastic dynamics [20][21][22]. Much effort was needed, in particular, to identify the minimal mathematical ingredients as well as the physical mechanisms lying beneath the validity of such relations [23][24][25]. Another result, whose mathematical theory was originally dug out in the sixties of the last century [26,27] and was then gradually a

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confidence: 99%

Complexity, Chaos and Fluctuations

Banerjee

Colangeli

2017

Eur. Phys. J. Spec. Top.

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“…To study the centrality properties of networks and define their hubs, we used a measure based on the shortest path length concept, called betweenness centrality (

). This measure was used alongside the method for hub definition proposed by da Silva et al [ 32 ]. For two given nodes k and j from the set of nodes N , the shortest path length is defined by

, where

belongs to a connectivity matrix and

is the shortest path length between the nodes i and j [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…Following this measure, we applied the method of da Silva et al [ 32 ] to identify hubs from nodes. We used a left-sided Mann–Whitney test with significance level of

to compare the

value of each node with all remaining nodes.…”

Section: Methodsmentioning

confidence: 99%

Connective Core Structures in Cognitive Networks: The Role of Hubs

Baltazar

Guinle

Caron

et al. 2019

Entropy

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Complex network analysis applied to the resting brain has shown that sets of highly interconnected networks with coherent activity may support a default mode of brain function within a global workspace. Perceptual processing of environmental stimuli induces architectural changes in network topology with higher specialized modules. Evidence shows that during cognitive tasks, network topology is reconfigured and information is broadcast from modular processors to a connective core, promoting efficient information integration. In this paper, we explored how the brain adapts its effective connectivity within the connective core and across behavioral states. We used complex network metrics to identify hubs and proposed a method of classification based on the effective connectivity patterns of information flow. Finally, we interpreted the role of the connective core and each type of hub on the network effectiveness. We also calculated the complexity of electroencephalography microstate sequences across different tasks. We observed that divergent hubs contribute significantly to the network effectiveness and that part of this contribution persists across behavioral states, forming an invariant structure. Moreover, we found that a large quantity of multiple types of hubs may be associated with transitions of functional networks.

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Brain connectivity in patients with dystonia during motor tasks

et al. 2020

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Objective. This study aims to investigate alterations of brain connectivity using multivariate electroencephalographic data to provide new insights of the brain connectivity dynamics of dystonia. Approach. We recorded electroencephalography (EEG) of patients with right upper limb idiopathic focal dystonia and paired controls during resting state, writing-from-memory, and finger-tapping tasks. We applied power spectrum analyses considering the mu, beta and gamma rhythms of the motor cortex and analyzed brain connectivity networks and microstates (MS). Main results. The power spectra results showed that patients had a loss of desynchronization of the beta rhythm during the writing task. We observed differences in the structure of the connective core in beta rhythm, as well as, in the intensity of the patient’s hubs observed with basis in path length measures in mu and beta rhythms. Abnormalities were also identified in MS of default mode networks of patients associated with its performances during motor tasks. Significance. The EEG connectivity analyses provided interesting insights about the cortical electrophysiological patterns in dystonia, such as loss of event-related desynchronization, changes in the effective connectivity with similar signature to other neurological diseases, indications of alterations in the default-mode-network. Our findings are consistent with previously described connectivity abnormalities in neuroimaging studies confirming that dystonia is a network disorder.

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Measures for brain connectivity analysis: nodes centrality and their invariant patterns

Cited by 4 publications

References 46 publications

Complexity, Chaos and Fluctuations

Complexity, Chaos and Fluctuations

Connective Core Structures in Cognitive Networks: The Role of Hubs

Brain connectivity in patients with dystonia during motor tasks

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