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The utility of quantitative EEG in early detection of cerebral ischemia is still underappreciated in clinical practice. We present a case of aneurysmal subarachnoid hemorrhage complicated by vasospasm as detected by the cerebral angiogram. The patient was being monitored on electroencephalogram. It showed early signs of cerebral ischemia represented by decline in the Alpha-Delta-Ratio (ADR) and the Relative-Alpha-Variability (RAV). Surprisingly, the RAV changes preceded the ADR changes. This is a significant finding that can also apply to early reocclusion or reperfusion injuries after mechanical thrombectomy.

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Objective: To evaluate the role of combining relative alpha variability and electroencephalogram (EEG) reactivity to predict the prognosis of hypoxic-ischemic encephalopathy(HIE) in adult patients. Methods: A total of 28 adult patients with HIE admitted to general intensive care unit at Xiangya Hospital in Central South University were enrolled in this observational study from January2016 to April 2017. These patients with body temperature over 35℃ after 72-hour admission could be continuously monitored at least 12 hours byEEG.At the same time,each patient was assessed for EEG reactivity.Then we analyzed the correlation between EEG reactivity, relative alpha variability and clinical prognosis. Results: EEG reactivity was elicited in 15/28 patients, among whom 12 patients had a good outcome. While in the other 13 patients, EEG reactivity was not elicited, among whom only 3 patients had a good outcome. As to the results ofrelative alpha variability,11/13 patients with degree 3-4were of good prognosis; while only 3/15 patients with degree 1-2 were of good prognosis. Glasgow coma scale(GCS), EEG reactivity, and relative alpha variability were correlated with clinical outcome(χ(2)=5.073,9.073,-3.626, respectively,all P<0.05). The sensitivity of GCS, EEG reactivity, and relative alpha variability to predict the poor prognosis were 69.2%, 76.9%, 84.6%, respectively. The specificity were 73.3%, 80.0%, 73.3%, respectively. The consistency rates were 71.4%, 78.6%, 78.6%, respectively. The positive predictive values were 69.2%, 76.9%, 73.3%, respectively. The negative predictive values were 73.3%, 80.0%, 84.6%, respectively. More importantly, the accuracy of the relative alpha variability combined with EEG reactivity for the prediction of poor prognosis was much higher with the positive predictive value of 90.0%,the specificity of 93.3%, the sensitivity of 69.2%, the consistency rate of 82.1%,and the negative predictive values of 77.8%. Conclusions: The combination of relative alpha variability and EEG reactivityis reliable to predict clinical outcome of patients with HIE.

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Objective To evaluate the role of combining relative alpha variability and electroencephalogram (EEG) reactivity to predict the prognosis of hypoxic?ischemic encephalopathy(HIE) in adult patients. Methods A total of 28 adult patients with HIE admitted to general intensive care unit at Xiangya Hospital in Central South University were enrolled in this observational study from January2016 to April 2017. These patients with body temperature over 35℃after 72?hour admission could be continuously monitored at least 12 hours byEEG.At the same time,each patient was assessed for EEG reactivity.Then we analyzed the correlation between EEG reactivity, relative alpha variability and clinical prognosis. Results EEG reactivity was elicited in 15/28 patients, among whom 12 patients had a good outcome. While in the other 13 patients, EEG reactivity was not elicited, among whom only 3 patients had a good outcome. As to the results ofrelative alpha variability,11/13 patients with degree 3?4were of good prognosis; while only 3/15 patients with degree 1?2 were of good prognosis. Glasgow coma scale(GCS), EEG reactivity, and relative alpha variability were correlated with clinical outcome(χ2=5.073,9.073,-3.626, respectively,all P<0.05). The sensitivity of GCS, EEG reactivity,and relative alpha variability to predict the poor prognosis were 69.2%, 76.9%, 84.6%, respectively. The specificity were 73.3%, 80.0%, 73.3%, respectively. The consistency rates were 71.4%, 78.6%, 78.6%, respectively. The positive predictive values were 69.2%, 76.9%, 73.3%, respectively. The negative predictive values were 73.3%, 80.0%, 84.6%, respectively. More importantly, the accuracy of the relative alpha variability combined with EEG reactivity for the prediction of poor prognosis was much higher with the positive predictive value of 90.0%,the specificity of 93.3%,the sensitivity of 69.2%, the consistency rate of 82.1%,and the negative predictive values of 77.8%. Conclusions The combination of relative alpha variability and EEG reactivityis reliable to predict clinical outcome of patients with HIE.

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Temporal stability of ecosystem functioning increases the predictability and reliability of ecosystem services, and understanding the drivers of stability across spatial scales is important for land management and policy decisions. We used species-level abundance data from 62 plant communities across five continents to assess mechanisms of temporal stability across spatial scales. We assessed how asynchrony (i.e. different units responding dissimilarly through time) of species and local communities stabilised metacommunity ecosystem function. Asynchrony of species increased stability of local communities, and asynchrony among local communities enhanced metacommunity stability by a wide range of magnitudes (1-315%); this range was positively correlated with the size of the metacommunity. Additionally, asynchronous responses among local communities were linked with species' populations fluctuating asynchronously across space, perhaps stemming from physical and/or competitive differences among local communities. Accordingly, we suggest spatial heterogeneity should be a major focus for maintaining the stability of ecosystem services at larger spatial scales.

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