Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis

Micheloyannis, Sifis; Pachou, Ellie; Stam, Cornelis J.; Vourkas, Michael; Erimaki, Sophia; Tsirka, Vasso

doi:10.1016/j.neulet.2006.04.006

Cited by 250 publications

(167 citation statements)

References 29 publications

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“…In specialist literature, several papers may be found where the small-world architecture was detected [47][48][49], as well as in other papers quoted in [5]. However, in some cases, small-world features were barely distinguishable from randomness [49,50].…”

Section: Discussionmentioning

confidence: 93%

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Application of directed transfer function and network formalism for the assessment of functional connectivity in working memory task

Blinowska

Kamiński

Brzezicka

et al. 2013

Phil. Trans. R. Soc. A.

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The dynamic pattern of functional connectivity during a working memory task was investigated by means of the short-time directed transfer function. A clear-cut picture of transmissions was observed with the main centres of propagation located in the frontal and parietal regions, in agreement with imaging studies and neurophysiological hypotheses concerning the mechanisms of working memory. The study of the time evolution revealed that most of the time short-range interactions prevailed, whereas the communication between the main centres of activity occurred more sparsely and changed dynamically in time. The patterns of connectivity were quantified by means of a network formalism based on assortative mixing-an approach novel in the field of brain networks study. By means of application of the above method, we have demonstrated the existence of a modular structure of brain networks. The strength of interaction inside the modules was higher than between modules. The obtained results are compatible with theories concerning metabolic energy saving and efficient wiring in the brain, which showed that preferred organization includes modular structure with dense connectivity inside the modules and more sparse connections between the modules.

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Section: Discussionmentioning

confidence: 93%

“…In the study of Micheloyannis et al [50], EEG was recorded during a working memory task for better-and less-educated people. It was reported that for better-educated people small-world properties were not prominent and the connectivity pattern was close to random.…”

Section: Discussionmentioning

confidence: 99%

Application of directed transfer function and network formalism for the assessment of functional connectivity in working memory task

Blinowska

Kamiński

Brzezicka

et al. 2013

Phil. Trans. R. Soc. A.

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“…Given the limited amount of prior data on small-world properties of human brain functional networks measured by using MEG (or electroencephalography) (12,17), our hypothetical predictions were modest. We expected to find evidence for small-world brain functional networks in MEG data recorded in both behavioral states.…”

mentioning

confidence: 83%

Adaptive reconfiguration of fractal small-world human brain functional networks

Bassett

Meyer‐Lindenberg

Achard

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

741

554

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Brain function depends on adaptive self-organization of largescale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequencyspecific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a fingertapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by smallworld properties at all six wavelet scales considered, corresponding approximately to classical ␦ (low and high), , ␣, ␤, and ␥ frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2-37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency ␥ network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both ␤ and ␥ networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters. magnetoencephalography ͉ wavelet ͉ graph theory ͉ connectivity ͉ binding C oherent or correlated oscillation of large-scale, distributed neural networks is widely regarded as an important physiological substrate for motor, perceptual and cognitive representations in the brain (1, 2). The topological description of brain networks promises quantitative insight into functionally relevant parameters because their topology strongly influences their dynamic properties such as speed and specialization of information processing, learning, and robustness against pathological attack by disease (3).The topology of networks can range from entirely random to fully ordered (a lattice). In this spectrum, small-world topology is characteristic of complex networks that demonstrate both clustered or cliquish interconnectivity within groups of nodes sharing many nearest neighbors in common (like regular lattices), and a short path length between any two nodes in the network (like random graphs) (3). This is an attractive configuration, in principle, for the anatomical and functional architecture of the brain, because small-world networks are known to optimize information transfer, increase the rate of learning, and support both segregated and...

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“…In the past few years, studies of the patterns of FC (coherence or correlation) among cortical regions using different modern brain imaging techniques, including magnetocencephalography (Stam 2004), electroencelography (Micheloyannis et al 2006b) and fMRI (Eguiluz et al 2005;Salvador et al 2005;Achard et al 2006), have demonstrated that large-scale human brain networks exhibit efficient small-world architecture. These findings support the presence of a rapid real-time integration of information across segregated sensory brain regions (Sporns and Zwi 2004), conferring resilience against pathological attack (Achard et al 2006) and maximizing efficiency at a minimal cost for effective information processing between different brain regions (Achard and Bullmore 2007).…”

Section: Topological Properties Of the Brain Networkmentioning

confidence: 99%

Spontaneous brain activity observed with functional magnetic resonance imaging as a potential biomarker in neuropsychiatric disorders

et al. 2010

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As functional magnetic resonance imaging (fMRI) studies have yielded increasing amounts of information about the brain's spontaneous activity, they have revealed fMRI's potential to locate changes in brain hemodynamics that are associated with neuropsychiatric disorders. In this paper, we review studies that support the notion that changes in brain spontaneous activity observed by fMRI can be used as potential biomarkers for diagnosis and treatment evaluation in neuropsychiatric disorders. We first review the methods used to study spontaneous activity from the perspectives of (1) the properties of local spontaneous activity, (2) the spatial pattern of spontaneous activity, and (3) the topological properties of brain networks. We also summarize the major findings associated with major neuropsychiatric disorders obtained using these methods. Then we review the pilot studies that have used spontaneous activity to discriminate patients from normal controls. Finally, we discuss current challenges and potential research directions to further elucidate the clinical use of spontaneous brain activity in neuropsychiatric disorders.

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Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis

Cited by 250 publications

References 29 publications

Application of directed transfer function and network formalism for the assessment of functional connectivity in working memory task

Application of directed transfer function and network formalism for the assessment of functional connectivity in working memory task

Adaptive reconfiguration of fractal small-world human brain functional networks

Spontaneous brain activity observed with functional magnetic resonance imaging as a potential biomarker in neuropsychiatric disorders

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