Large‐scale network organization of EEG functional connectivity in newborn infants

Tóth, Brigitta; Urbán, Gábor; Háden, Gábor P.; Molnár, Mihály; Török, Marianna; Stam, Cornelis J.; Winkler, István

doi:10.1002/hbm.23645

Cited by 60 publications

(67 citation statements)

References 73 publications

(143 reference statements)

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“…The smaller Diameter and larger Leaf Fraction in children compared to adults indicates that the topology of the functional brain networks becomes segregated via a transition from a star-like (centralised) configuration toward a more line-like (de-centralised) configuration during development. Such network topological change has been found in infants right after birth ( Toth et al, 2017 ) and continues up to 18 years of age ( Boersma et al, 2011 ). In addition, the observed larger Kappa in children compared to adults suggests a movement away from a scale-free network.…”

Section: Discussionmentioning

confidence: 93%

“…It follows then, that electrophysiological networks are expected to become increasingly segregated during childhood development. However, prior EEG studies have reported conflicting results, which include increasing segregation ( Boersma et al, 2011, 2013; Janssen et al, 2017; Toth et al, 2017 ), decreasing segregation ( Smit et al, 2016; Bathelt et al, 2013; Miskovic et al, 2015 ), or no changes with age ( Schafer et al, 2014 ). Discrepancy between developmental MRI-and electrophysiology-based network findings has been difficult to reconcile, partly due to the different spatial scales that functional networks have been examined at (sensor-level in most EEG versus cortical-level in fMRI studies).…”

Section: Introductionmentioning

confidence: 98%

“…We hypothesised that, based on the heuristic model of complex brain networks, the healthy brain develops from a more random and integrated structure towards a configuration that offers a balance between network integration and segregation during normative development ( Stam, 2014 ). Specifically, we predicted that: (1) functional networks become more segregated, shifting from a centralised network topology to a de-centralised configuration ( Boersma et al, 2013; Toth et al, 2017 ); (2) individual brain regions become more diverse in their connectedness, i.e., centrality of brain regions increases for hubs (e.g., regions in the default mode and the fronto-parietal areas), but decreases in non-hub regions (e.g., regions in the primary visual and auditory areas).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Topological segregation of functional networks increases in developing brains

Sowman

Brock

et al. 2018

Preprint

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Topological segregation of functional networks increases in developing brains

Sowman

Brock

et al. 2018

Preprint

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“…Although high-density EEG studies with more than 64 recording channels can display a highly spatial resolution of brain function and the number of electrodes or nodes can improve the GTA analysis, several recent studies with limited recording channels indicate that reliable results can also be obtained using standard 10/20 system with 19 EEG channels 12,13,[60][61][62][63] . In order to compute a functional brain network, we used coherence as a simple and well-studied EEG connectivity measure 4,12,14,[64][65][66][67] .…”

Section: -3 Eeg Connectivity and Adjacency Matrixmentioning

confidence: 99%

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

Ghaderi

Baltaretu

Andevari

et al. 2019

Preprint

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To characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or 2 closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations. Author SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network.Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks.However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches 3 are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks).We found that SGT measures can clearly distinguish between distinct staterelated brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of randomgenerated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

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“…One study evaluated the influence of these parameters on the neural connection of neonates 33 . The objective was to evaluate the organization of functional networks of large-scale phase synchronization in brains of newborns during silent sleep and to explore their relationship with gestational age and variables that characterize possible prenatal influences on brain maturation.…”

Section: Coherence or Geodesic Sensor Net Electroencephalogrammentioning

confidence: 99%

Comparison of conventional, amplitude-integrated and geodesic sensor net EEG used in premature neonates: a systematic review

Sousa

Gugelmin

Fernandes

et al. 2019

Arq. Neuro-Psiquiatr.

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The use of methods to evaluate cortical activity in neonates has great importance in modern medicine, as it allows the observation and evaluation of several clinical aspects, which guarantees that the health team has knowledge about possible intervention measures that may be necessary in the treatment of newborns. Objective: This systematic review aimed to compare the main technologies available for the evaluation of brain functions in neonates, among them: the conventional electroencephalogram (EEG), the amplitude-integrated electroencephalogram (aEEG) and the geodesic sensor net EEG. Methods: A search was conducted forarticles from national and international periodicals included in the Web of Science, LILACS, SciELO and Medline electronic databases. Results: The search found 39 among 155 articles of interest and the analyses indicated that, in the clinical environment, the use of both conventional EEG and aEEG is highly recommended, as the combination of their functions allows, for example, a greater number of subclinical seizures to be detected. Conversely, the use of a geodesic sensor net EEG could be of great value, as it allows a large amount of data to be analyzed. Conclusion: This analysis may be useful in studies and research related to diseases and symptoms, such as seizures, a current challenge for neonatal neuromonitoring, as well as aspects of neurological development and functional studies. However, despite many advances in technology, electroencephalography in preterm neonates remains a challenge worldwide and still requires more robust research and efforts towards the best clinical assistance in this extremely early stage of life.

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Large‐scale network organization of EEG functional connectivity in newborn infants

Cited by 60 publications

References 73 publications

Topological segregation of functional networks increases in developing brains

Topological segregation of functional networks increases in developing brains

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

Comparison of conventional, amplitude-integrated and geodesic sensor net EEG used in premature neonates: a systematic review

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