Resting-State Electroencephalography (EEG) Functional Connectivity Analysis / 대한소아신경학회지

Advances in network science and computer engineering have enabled brain connectivity analysis using clinical big data such as brain magnetic resonance imaging (MRI), electroencephalography (EEG), or magnetoencephalography (MEG). Resting-state functional connectivity analysis aims to reveal the characteristics of functional brain network in various diseases and normal brain maturation using resting-state EEG. Simplified sequence of resting-state functional connectivity analysis methods will be reviewed in this article. The outcomes from EEG resting-state connectivity analysis are comprised of connectivity itself of the specific condition and the network topology measure which describe the characteristics of specific connectivity. An increasing number of studies report the differences in the functional connection itself, global network measures including segregation (connectedness), integration (efficiency), and importance of specific nodes (centrality or node degree). Several issues that are relevant in the resting-state connectivity analysis are obtaining good quality EEG for analysis, consideration of particular features of EEG signal, understanding different types of association measures, and statistics for comparison of connectivities. Well-designed and carefully analyzed EEG resting-state connectivity analysis can provide useful information for patient care in pediatric neurology.

Disease association and inter-connectivity analysis of human brain specific co-expressed functional modules

BACKGROUND: In the recent studies, it is suggested that the analysis of transcriptomic change of functional modules instead of individual genes would be more effective for system-wide identification of cellular functions. This could also provide a new possibility for the better understanding of difference between human and chimpanzee. RESULTS: In this study, we analyzed to find molecular characteristics of human brain functions from the difference of transcriptome between human and chimpanzee's brain using the functional module-centric co-expression analysis. We performed analysis of brain disease association and systems-level connectivity of species-specific co-expressed functional modules. CONCLUSIONS: Throughout the analyses, we found human-specific functional modules and significant overlap between their genes in known brain disease genes, suggesting that human brain disorder could be mediated by the perturbation of modular activities emerged in human brain specialization. In addition, the human-specific modules having neurobiological functions exhibited higher networking than other functional modules. This finding suggests that the expression of neural functions are more connected than other functions, and the resulting high-order brain functions could be identified as a result of consolidated inter-modular gene activities. Our result also showed that the functional module based transcriptome analysis has a potential to expand molecular understanding of high-order complex functions like cognitive abilities and brain disorders.