Intensity and coherence of motifs in weighted complex networks

Onnela, Jukka Pekka; Saramäki, Jari; Kertész, János; Kaski, Kimmo

doi:10.1103/physreve.71.065103

Cited by 922 publications

(771 citation statements)

References 27 publications

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“…These fractions at each node are then averaged over the network to give the overall network CC. Each node's CC can also be weighted by the product of the relevant edge weights to obtain a weighted version of this measure [57,39]. This weighted CC is what is calculated in our study.…”

Section: Network Analysismentioning

confidence: 99%

Structural network analysis of brain development in young preterm neonates

et al. 2014

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Preterm infants develop differently than those born at term and are at higher risk of brain pathology. Thus, an understanding of their development is of particular importance. Diffusion tensor imaging (DTI) of preterm infants offers a window into brain development at a very early age, an age at which that development is not yet fully understood. Recent works have used DTI to analyze structural connectome of the brain scans using network analysis. These studies have shown that, even from infancy, the brain exhibits small-world properties. Here we examine a cohort of 47 normal preterm neonates (i.e., without brain injury and with normal neurodevelopment at 18 months of age) scanned between 27 and 45 weeks post-menstrual age to further the understanding of how the structural connectome develops. We use fullbrain tractography to find white matter tracts between the 90 cortical and sub-cortical regions defined in the University of North Carolina Chapel Hill neonatal atlas. We then analyze the resulting connectomes and explore the differences between weighting edges by tract count versus fractional anisotropy. We observe that the brain networks in preterm infants, much like infants born at term, show high efficiency and clustering measures across a range of network scales. Further, the development of many individual region-pair connections, particularly in the frontal and occipital lobes, is significantly correlated with age. Finally, we observe that the preterm infant connectome remains highly efficient yet becomes more clustered across this age range, leading to a significant increase in its small-world structure.

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Section: Network Analysismentioning

confidence: 99%

Structural network analysis of brain development in young preterm neonates

et al. 2014

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“…To evaluate network topologies for these regions, we chose the graph theoretical measure of node strength. Strength reflects how functionally connected a given region is to other regions in the analyzed network 54, 55…”

Section: Methodsmentioning

confidence: 99%

Reduced brain connectivity and mental flexibility in mild traumatic brain injury

Pang

Dunkley

Doesburg

et al. 2015

Ann Clin Transl Neurol

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ObjectiveA mild traumatic brain injury (mTBI), or concussion, has known neuropsychological sequelae, and neuroimaging shows disturbed brain connectivity during the resting state. We hypothesized that task‐based functional connectivity measures, using magnetoencephalography (MEG), would better link the neurobiological underpinnings of cognitive deficits to specific brain damage.MethodsWe used a mental flexibility task in the MEG and compared brain connectivity between adults with and without mTBI.ResultsAffected individuals showed significant reductions in connectivity. When challenged with a more difficult task, these individuals were not able to “boost” their connectivity, and as such, showed deterioration in performance.InterpretationWe discuss these findings in the context of limitations in cognitive reserve as a consequence of a mTBI.

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“…Weighted functional networks were analyzed and compared based on five different measures of network connectivity, namely the modularity M (Newman, 2006;Rubinov & Sporns, 2010), clustering coefficient C (Onnela et al, 2005;Rubinov & Sporns, 2010), characteristic path length L (Rubinov & Sporns, 2010), edge density D and the mean edge-weight W . We have chosen these measures, because they signify seizure characteristics that have previously been reported in the literature (Kramer et al, 2008;Schindler et al, 2008;Kramer et al, 2010).…”

Section: Analysis and Comparison Of Functional Networkmentioning

confidence: 99%

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

2015

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Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20 − 30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g. in the context of therapeutical brain stimulation.In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct Graphical models (so called Chow-Liu(CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient(CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature.Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones.Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

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Intensity and coherence of motifs in weighted complex networks

Cited by 922 publications

References 27 publications

Structural network analysis of brain development in young preterm neonates

Structural network analysis of brain development in young preterm neonates

Reduced brain connectivity and mental flexibility in mild traumatic brain injury

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

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