Electroencephalography Functional Networks Reveal Global Effects of Methylphenidate in Youth with Attention Deficit/Hyperactivity Disorder

Rubinson, Mica; Horowitz, Itai; Naim-Feil, Jodie; Gothelf, Doron; Moses, Elisha; Levit‐Binnun, Nava

doi:10.1089/brain.2018.0630

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Clustering Coe Cient1

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“…Clustering coe cient is proposed to assess the ability of segregation in graph which is the most important measures in researching cognitive problem of brain [31]. The algorithm is shown as follow [22]:…”

Section: Clustering Coe Cientmentioning

confidence: 99%

Graph theoretical analysis based on EEG effective connectivity in ADHD children

Shen

Wen

et al. 2021

Preprint

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This paper reports a new method to identify the ADHD children using EEG signals and effective connectivity techniques. In this study, the original EEG data is pre-filtered and divided into Delta, Theta, Alpha and Beta bands. And then, the effective connectivity graphs are constructed by applying independent component analysis, multivariate regression model and phase slope index. The measures of clustering coefficient, nodal efficiency and degree centrality in graph theory are used to extract features from these graphs. Statistical analysis based on the standard error of the mean are employed to evaluate the graph theory measures in each frequency band. The results show a decreased average clustering coefficient in delta band for ADHD subjects. Also, in delta band, the ADHD subjects have increased nodal efficiency and degree centrality in left forehead part and decreased in forehead middle.

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Section: Clustering Coe Cientmentioning

confidence: 99%