Analysis of Amplitude Modulation of EEG Based on Holo-Hilbert Spectrum Analysis During General Anesthesia.

OBJECTIVE: The study aims to investigate the relationship between amplitude modulation (AM) of EEG and anesthesia depth during general anesthesia. METHODS: In this study, Holo-Hilbert spectrum analysis (HHSA) was used to decompose the multichannel EEG signals of 15 patients to obtain the spatial distribution of AM in the brain. Subsequently, HHSA was applied to the prefrontal EEG (Fp1) obtained during general anesthesia surgery in 15 and 34 patients, and the α-θ and α-δ regions of feature (ROFs) were defined in Holo-Hilbert spectrum (HHS) and three features were derived to quantify AM in ROFs. RESULTS: During anesthetized phase, an anteriorization of the spatial distribution of AMs of α-carrier in brain was observed, as well as AMs of α-θ and α-δ in the EEG of Fp1. The total power ([Formula: see text]), mean carrier frequency ([Formula: see text]) and mean amplitude frequency ([Formula: see text]) of AMs changed during different anesthesia states. CONCLUSION: HHSA can effectively analyze the cross-frequency coupling of EEG during anesthesia and the AM features may be applied to anesthesia monitoring. SIGNIFICANCE: The study provides a new perspective for the characterization of brain states during general anesthesia, which is of great significance for exploring new features of anesthesia monitoring.

An integrated information theory index using multichannel EEG for evaluating various states of consciousness under anesthesia.

BACKGROUND: The integrated information theory (IIT) of consciousness introduces a measure Φ to quantify consciousness in a physical system. Directly related to this, general anesthesia aims to induce reversible and safe loss of consciousness (LOC). We sought to propose an electroencephalogram (EEG)-based IIT index ΦEEG to evaluate various states of consciousness under general anesthesia. METHODS: Based on the definition of mutual information, we estimated the ΦEEG by maximizing the integrated information under various time lags. We used the binning method to cut the nonGaussian EEG data for estimating mutual information. We tested two EEG databases collected from propofol- (n=20) and sevoflurane-induced (n=15) anesthesia, and especially, we compared the ΦEEG of drowsy (n=7) and responsive participants (n=13) under propofol anesthesia. We compared the effectiveness of ΦEEG with the estimated bispectral index (eBIS). RESULTS: In all EEG frequency bands, we observed a negative correlation between ΦEEG and end-tidal sevoflurane concentration under sevoflurane-induced anesthesia (p<0.001,BF10>6000). Under propofol-induced anesthesia, drowsy participants in moderate sedation (6.96±0.26(mean±SD)) showed decreased alpha-band ΦEEG compared with baseline (7.40±0.53,p=0.016,BF10=3.58), no significant difference was observed for responsive participants. Oppositely, the responsive participants in moderate sedation (-5.32±0.38) showed decreased eBIS compared with baseline (-4.94±0.40,p=0.03,BF10=2.41). CONCLUSIONS: These findings may enable monitors of the anesthetic state that can distinguish consciousness and unconsciousness rather than the changes of anesthetic concentrations. The alpha-band ΦEEG is promising for deriving the gold standard for depth of anesthesia monitoring.

Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane.

Anesthetic-induced loss of consciousness (LOC) has been studied using functional connectivity (FC) and functional network analysis (FNA), manifested as fragmentation of the whole-brain functional network. However, how the fragmented brain networks reversibly recover during the recovery of consciousness (ROC) remains vague. This study aims to investigate the changes in brain network structure during ROC, to better understand the network fragmentation during anesthesia, thus providing insights into consciousness monitoring. We analyzed EEG data recorded from 15 individuals anesthetized by sevoflurane. By investigating the properties of functional networks generated using different brain atlases and performing community detection for functional networks, we explored the changes in brain network structure to understand how fragmented brain networks recover during the ROC. We observed an overall larger FC magnitude during LOC than in the conscious state. The ROC was accompanied by the increasing binary network efficiency, decreasing FC magnitude, and decreasing community similarity with the functional atlas. Furthermore, we observed a negative correlation between modularity and community number ( [Formula: see text] and , linear regression test), in which modularity increased and community number decreased during ROC. Our results show that a larger FC magnitude reveals excessive synchronization of neuronal activities during LOC. The increasing binary network efficiency, decreasing community number, and decreasing community similarity indicate the recovery of functional network integration. The increasing modularity implies the recovery of functional network segregation during ROC. The results suggest the limitation of FC magnitude and modularity in monitoring anesthetized states and the potential of integrated information theory to evaluate consciousness.

Dynamic alpha-gamma phase-amplitude coupling signatures during sevoflurane-induced loss and recovery of consciousness.

Phase-amplitude coupling (PAC) plays an important role in anesthetic-induced unconsciousness. The delta-alpha PAC signature during anesthetic-induced unconsciousness is gradually becoming known; however, the frequency dependence and spatial characteristics of PAC are still unclear. Multi-channel electroencephalography (EEG) was performed during the loss and recovery phases of consciousness in patients undergoing general anesthesia using sevoflurane. First, a spectral analysis was used to investigate the power change of the different frequency bands in the EEG signals. Second, PAC comodulogram analysis was performed to confirm the frequencies of the PAC phase drivers. Finally, to investigate the spatial characteristics of PAC, a novel PAC network was constructed using within- and cross-lead PAC, and a K-means clustering algorithm was used to identify PAC network patterns. Our results show that, in addition to the delta-alpha PAC, unconsciousness induced by sevoflurane was accompanied by spatial non-uniform alpha-gamma PAC in the cortical network, and dynamic PAC patterns between the anterior and posterior brain were observed during the unconscious phase. The dynamic transition of PAC network patterns indicates that brain states under sevoflurane-induced unconsciousness emerge from the regulation of functional integration and segregation instantiated by delta-alpha and alpha-gamma PAC.

Intrinsic phase-amplitude coupling on multiple spatial scales during the loss and recovery of consciousness.

BACKGROUND: Recent studies have demonstrated that changes in brain information processing during anesthetic-induced loss of consciousness (LOC) might be influenced by phase-amplitude coupling (PAC) in electroencephalogram (EEG). However, most anesthesia research on PAC typically focuses on delta and alpha oscillations. Studies of spatial-frequency characteristics by PAC for EEG may yield additional insights into understanding the impaired information processing under anesthesia unconsciousness and provide potential improvements in anesthesia monitoring. OBJECTIVE: Considering different frequency bands of EEG represent neural activities on different spatial scales, we hypothesized that functional coupling simultaneously appears in multiple frequency bands and specific brain regions during anesthesia unconsciousness. In this paper, PAC analysis on whole-brain EEG besides delta and alpha oscillations was investigated to understand the influence of multiple cross-frequency coordination coupling on information processing during the loss and recovery of consciousness. METHOD: EEG data from fifteen patients without cognitive diseases (7 males/8 females, aged 43.8 ± 13.4 years, weighing 63.3 ± 14.9 kilograms) undergoing lower limb surgery and sevoflurane anesthesia was recorded. To investigate the spatial-frequency characteristics of EEG source signals during loss and recovery of consciousness, the time-resolved PAC (tPAC) was calculated to reflect cross-frequency coordination in different frequency bands (delta, theta, alpha, beta, gamma) and different functional regions (Visual, Limbic, Dorsal attention, Ventral attention, Default, Somatomotor, Control, Salience networks). Furthermore, different patterns (peak-max and trough-max) of PAC were examined by constructing phase-amplitude histograms using phase bins to investigate the different information processing during LOC. The multivariate analysis of variance (MANOVA) and trend analysis were used for statistical analysis. RESULTS: Theta-alpha and alpha-beta PAC were observed during sevoflurane-induced LOC, which significantly changed during loss and recovery of consciousness (F4,70 = 16.553, p < 0.001 for theta-alpha PAC and F4,70 = 12.446, p < 0.001 for alpha-beta PAC, MANOVA test). Simultaneously, PAC was distributed in specific functional regions, i.e., Visual, Limbic, Default, Somatomotor, etc. Furthermore, peak-max patterns of theta-alpha PAC were observed while alpha-beta PAC showed trough-max patterns and vice versa. CONCLUSION: Theta-alpha and alpha-beta PAC observed in specific brain regions represent information processing on multiple spatial scales, and the opposite patterns of PAC indicate opposite information processing on multiple spatial scales during LOC. Our study demonstrates the regulation of local-global information processing during sevoflurane-induced LOC. It suggests the utility of evaluating the balance of functional integration and segregation in monitoring anesthetized states.

Cloning and expression of a thermostable keratinase gene from Thermoactinomyces sp. YT06 in Escherichia coli and characterization of purified recombinant enzymes.

The feather-degrading strain Thermoactinomyces sp. YT06 secretes an extracellular keratinolytic protease (KERTYT); however, the gene encoding this protease remains unknown. The kerT1 gene (1170 bp) encoding keratinase was cloned and expressed in Escherichia coli BL21(DE3). Purified recombinant keratinase (rKERTYT) was achieved at a yield of 39.16% and 65.27-fold purification with a specific activity of 1325 U/mg. It was shown that rKERTYT has many similarities to the native enzyme (KERTYT) by characterization of rKERTYT. The molecular weight of rKERTYT secreted by recombinant E. coli was approximately 28 kDa. The optimal temperature and the pH values of rKERTYT were 65 °C and 8.5, respectively, and the protein remained stable from 50 to 60 °C and pH 6-11. The keratinase was strongly inhibited by phenyl methane sulfonyl fluoride (PMSF), suggesting that it belongs to the serine protease family. It was significantly activated by Mn2+ and ß-mercaptoethanol (ß-Me). rKERTYT showed stability and retained over 80% activity with the existence of organic solvents such as acetone, methylbenzene and dimethyl sulfoxide. These findings indicated that rKERTYT will be a promising candidate for the enzymatic processing of keratinous wastes.

Comparison of characterization and microbial communities in rice straw- and wheat straw-based compost for Agaricus bisporus production.

Rice straw (RS) is an important raw material for the preparation of Agaricus bisporus compost in China. In this study, the characterization of composting process from RS and wheat straw (WS) was compared for mushroom production. The results showed that the temperature in RS compost increased rapidly compared with WS compost, and the carbon (C)/nitrogen (N) ratio decreased quickly. The microbial changes during the Phase I and Phase II composting process were monitored using denaturing gradient gel electrophoresis (DGGE) and phospholipid fatty acid (PLFA) analysis. Bacteria were the dominant species during the process of composting and the bacterial community structure dramatically changed during heap composting according to the DGGE results. The bacterial community diversity of RS compost was abundant compared with WS compost at stages 4-5, but no distinct difference was observed after the controlled tunnel Phase II process. The total amount of PLFAs of RS compost, as an indicator of microbial biomass, was higher than that of WS. Clustering by DGGE and principal component analysis of the PLFA compositions revealed that there were differences in both the microbial population and community structure between RS- and WS-based composts. Our data indicated that composting of RS resulted in improved degradation and assimilation of breakdown products by A. bisporus, and suggested that the RS compost was effective for sustaining A. bisporus mushroom growth as well as conventional WS compost.