Journal: Frontiers in Computational Neuroscience

Article Title: Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

doi: 10.3389/fncom.2016.00001

Figure Lengend Snippet: The reconfiguration of brain network structure and dynamics during sevoflurane anesthesia . For the alpha band, the changes of (A) the topography of node degree, (B) betweenness centrality, (C) peak frequency (Hz), (D) global efficiency, and (E) modularity are presented across five states: baseline, transition to unconsciousness (Trans UN ), unconscious state (UCS), transition to consciousness (Trans CON ), and recovery. The disruption and recovery of posterior hub structures, as defined by higher node degree and betweenness centrality, is obvious with loss and recovery of consciousness (node degree, p < 0.05; BC, p < 0.01). An increase of peak frequency in the unconscious state is also observed ( p < 0.05). In addition, the reduced global efficiency and increased modularity in UCS are significant. Error bars indicate standard deviation for seven subjects. Significance level using ANOVA: *** p < 0.001.

Article Snippet: We performed the Kruskal-Wallis test (“kruskalwallis.m,” Matlab statistical toolbox), which is a nonparameteric version of a classical one-way ANOVA with Tukey's multiple comparisons (“multcompare.m” with alpha = 0.05 and ctype = “tukey-kramer”) for node degree, BC, and peak frequency, taking the average of EEG channels in the anterior (prefrontal and frontal) and posterior (occipital) brain regions of each subject.

Techniques: Disruption, Standard Deviation

Journal: Frontiers in Computational Neuroscience

Article Title: Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

doi: 10.3389/fncom.2016.00001

Figure Lengend Snippet: Change of the correlation between node degree and frequency across states . The peak frequencies of alpha power for high (red line) and low (blue line) degree nodes are compared in (A) baseline and (B) UCS for a subject as an example. The high degree node has a larger peak frequency in UCS, whereas there is no significant dependency of peak frequency on node degree in baseline. The range of peak frequencies is also wider in UCS. (C) The correlation between average node degree and average peak frequency changes significantly across states. The node degree and peak frequency for a node is expressed as average values over seven subjects. There is a positive correlation between degree and peak frequency in UCS (Spearman coefficient of 0.473, p < 0.001), while no correlation appears in baseline (Spearman coefficient of −0.024, p = 0.86) and recovery (Spearman coefficient of −0.022, p = 0.87) states of consciousness. However, the transitional states showed relatively small negative correlations. The results suggest that the brain network in the UCS is in a condition primed for abrupt state transition, whereas the other states are in a condition conducive to gradual state transition.

Article Snippet: We performed the Kruskal-Wallis test (“kruskalwallis.m,” Matlab statistical toolbox), which is a nonparameteric version of a classical one-way ANOVA with Tukey's multiple comparisons (“multcompare.m” with alpha = 0.05 and ctype = “tukey-kramer”) for node degree, BC, and peak frequency, taking the average of EEG channels in the anterior (prefrontal and frontal) and posterior (occipital) brain regions of each subject.

Techniques:

Journal: Frontiers in Computational Neuroscience

Article Title: Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

doi: 10.3389/fncom.2016.00001

Figure Lengend Snippet: The network topology and topography of alpha peak frequency associated with ES change across states for one subject as an example . (A) The time evolution of anesthetic concentration (brown line) and responsiveness (green line) over the whole experimental period for a single subject. For the same subject, (B) both the disassortativity (blue line) and frequency difference (red line) are variable over time, showing a correlation with the behavioral profiles. (C) The average difference of coupling strength and (D) the average difference of peak frequency among all nodes are presented to show the overall spatial distributions. The selected five EEG epochs for each subject were averaged over seven subjects. In (C) , the large difference of connection strength among specific EEG channels in baseline are reduced such that all connection strengths become more homogenous in UCS. They are restored in recovery. On the contrary, (D) shows that the relatively small difference of peak frequency among EEG channels is significantly increased in UCS. The overall changes of connection strength and frequency difference are significant across states. For this display, the EEG channels are grouped in six brain regions (P, prefrontal; F, frontal; C, central; T, temporal; P, parietal; and O, occipital).

Article Snippet: We performed the Kruskal-Wallis test (“kruskalwallis.m,” Matlab statistical toolbox), which is a nonparameteric version of a classical one-way ANOVA with Tukey's multiple comparisons (“multcompare.m” with alpha = 0.05 and ctype = “tukey-kramer”) for node degree, BC, and peak frequency, taking the average of EEG channels in the anterior (prefrontal and frontal) and posterior (occipital) brain regions of each subject.

Techniques: Concentration Assay