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EEG-based graph metrics of brain networks during cognitive task performance and at restстатья
- Авторы: Maslennikova E., Feklicheva I., Zakharov I., Ismatullina V., Adamovich T., Malykh S., Chipeeva N.
- Журнал: IBRO Reports
- Том: 6
- Номер: Supplement
- Год издания: 2019
- Первая страница: 345
- DOI: 10.1016/j.ibror.2019.07.1769
- Аннотация: One of the promising approaches to study functionally integrated relationship between spatially separated brain regions is graph theory analysis which can be used to describe global brain dynamics. The graph characteristics of the brain networks during resting state have been shown to be stable for the individual (Finn et al., 2015, Lui et al., 2019). At the same time, the connectivity metrics has been shown to change during task performance (Arbabshirani, 2012, Mennes et al., 2010). In the present study we investigated the difference in several EEG-based graph metrics during resting-state and sustained attention (Flanker task). We have chosen EEG due to its high temporal resolution. The sample consisted of 23 healthy right-handed participants aged 18 to 25 years (13 women).We have found that the metrics related to path lengths within the network (characteristic path length, diameter, closeness centrality) are significantly lower during task performance comparing to resting state (p < 0.01). We didn’t find any significant differences in the modularity and clustering coefficients of the networks. To sum up, we have found that the brain networks at rest has longer pathways which may be related to communication with more distant areas, whereas tasks performance is associated with more local connections. The results are discussed according to the network approach to understand communication dynamics within the brain
- Добавил в систему: Малых Сергей Борисович