Spectral content in a physiological dataset of finite size has the potential to produce spurious measures of coherence. This is especially true for electroencephalography (EEG) during general anesthesia because of the significant alteration of the power spectrum. In this study we quantitatively evaluated the genuine and spurious phase synchronization strength (PSS) of EEG during consciousness, general anesthesia, and recovery. A computational approach based on the randomized data method was used for evaluating genuine and spurious PSS. The validity of the method was tested with a simulated dataset. We applied this method to the EEG of normal subjects undergoing general anesthesia and investigated the finite size effects of EEG references, data length and spectral content on phase synchronization. The most influential factor for genuine PSS was the type of EEG reference; the most influential factor for spurious PSS was the spectral content. Genuine and spurious PSS showed characteristic temporal patterns for each frequency band across consciousness and anesthesia. Simultaneous measurement of both genuine and spurious PSS during general anesthesia is necessary in order to avoid incorrect interpretations regarding states of consciousness.
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"It is likely that the overall level of wakefulness is a consequence of the interactions among many brain regions rather than any specific feature of neuronal activity observed at any one region taken in isolation. Therefore, more recent efforts have been aimed at detecting decreases in arousal using connectivity measures based on spectral coherence as well as mutual information and phase relationships among brain activity recorded simultaneously at multiple locations (Imas et al., 2005; Cimenser et al., 2011; Lee et al., 2012). While this connectivity analysis does suggest that integration of information between different brain regions may be decreased when the level of wakefulness is reduced, it is not trivial to relate changes in connectivity to the changes in global dynamics of the brain. "
[Show abstract][Hide abstract] ABSTRACT: In this work we analyze electro-corticography (ECoG) recordings in human subjects during induction of anesthesia with propofol. We hypothesize that the decrease in responsiveness that defines the anesthetized state is concomitant with the stabilization of neuronal dynamics. To test this hypothesis, we performed a moving vector autoregressive analysis and quantified stability of neuronal dynamics using eigenmode decomposition of the autoregressive matrices, independently fitted to short sliding temporal windows. Consistent with the hypothesis we show that while the subject is awake, many modes of neuronal activity oscillations are found at the edge of instability. As the subject becomes anesthetized, we observe statistically significant increase in the stability of neuronal dynamics, most prominently observed for high frequency oscillations. Stabilization was not observed in phase randomized surrogates constructed to preserve the spectral signatures of each channel of neuronal activity. Thus, stability analysis offers a novel way of quantifying changes in neuronal activity that characterize loss of consciousness induced by general anesthetics.
Full-text · Article · Mar 2014 · Frontiers in Neural Circuits
[Show abstract][Hide abstract] ABSTRACT: General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network "backbones." We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks.