Significance Anesthesiologists reversibly manipulate the brain function of nearly 60,000 patients each day, but brain-state monitoring is not an accepted practice in anesthesia care because markers that reliably track changes in level of consciousness under general anesthesia have yet to be identified. We found specific behavioral and electrophysiological changes that mark the transition between consciousness and unconsciousness induced by propofol, one of the most commonly used anesthetic drugs. Our results provide insights into the mechanisms of propofol-induced unconsciousness and establish EEG signatures of this brain state that could be used to monitor the brain activity of patients receiving general anesthesia.

The widely used electroencephalogram-based indices for depth-of-anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in Part I, we review the biophysics of the electroencephalogram, and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine and ketamine; and four inhaled anesthetics: sevoflurane, isoflurane, desflurane and nitrous oxide. Later in Part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically-based paradigm for brain-state monitoring of patients receiving anesthesia care.

These profound age-related changes in the EEG are consistent with known neurobiological and neuroanatomical changes that occur during typical ageing. Commercial EEG-based depth-of-anaesthesia indices do not account for age and are therefore likely to be inaccurate in elderly patients. In contrast, monitoring the unprocessed EEG and its spectrogram can account for age and individual patient characteristics.

Background The neural mechanisms of anesthetic vapors have not been studied in depth. However, modeling and experimental studies on the intravenous anesthetic propofol indicate that potentiation of γ-Aminobutyric acid receptors leads to a state of thalamocortical synchrony, observed as coherent frontal alpha oscillations, associated with unconsciousness. Sevoflurane, an ether derivative, also potentiates γ-Aminobutyric acid receptors. However, in humans, sevoflurane-induced coherent frontal alpha oscillations have not been well detailed. Methods To study the electroencephalogram dynamics induced by sevoflurane, we identified age and gender matched patients in which sevoflurane (n = 30) or propofol (n = 30) were used as the sole agent for maintenance of general anesthesia during routine surgery. We compared the electroencephalogram signatures of sevoflurane to propofol using time-varying spectral and coherence methods. Results Sevoflurane general anesthesia is characterized by alpha oscillations with maximum power and coherence at ~10 Hz, (mean±std; peak power, 4.3dB ± 3.5; peak coherence, 0.73 ± 0.1). These alpha oscillations are similar to those observed during propofol general anesthesia, which also has maximum power and coherence at ~10 Hz (peak power, 2.1dB ± 4.3; peak coherence, 0.71 ± 0.1). However, sevoflurane also exhibited a distinct theta coherence signature (peak frequency, 4.9Hz ± 0.6; peak coherence, 0.58 ± 0.1). Slow oscillations were observed in both cases, with no significant difference in power or coherence. Conclusion Our results indicate that sevoflurane, like propofol, induces coherent frontal alpha oscillations and slow oscillations in humans to sustain the anesthesia-induced unconscious state. These results suggest a shared molecular and systems-level mechanism for the unconscious state induced by these drugs.

Background Electroencephalogram patterns observed during sedation with dexmedetomidine appear similar to those observed during general anesthesia with propofol. This is evident with the occurrence of slow (0.1–1 Hz), delta (1–4 Hz), propofol-induced alpha (8–12 Hz), and dexmedetomidine-induced spindle (12–16 Hz) oscillations. However, these drugs have different molecular mechanisms and behavioral properties, and are likely accompanied by distinguishing neural circuit dynamics. Methods We measured 64-channel electroencephalogram under dexmedetomidine (n = 9) and propofol (n = 8) in healthy volunteers, 18–36 years of age. We administered dexmedetomidine with a 1mcg/kg loading bolus over 10 minutes, followed by a 0.7mcg/kg/hr infusion. For propofol, we used a computer controlled infusion to target the effect-site concentration gradually from and 0 µg/mL to 5 µg/mL. Volunteers listened to auditory stimuli and responded by button-press to determine unconsciousness. We analyzed the electroencephalogram using multitaper spectral and coherence analysis. Results Dexmedetomidine was characterized by spindles with maximum power and coherence at ~13 Hz, (mean±std; power, −10.8dB±3.6; coherence, 0.8±0.08), while propofol was characterized with frontal alpha oscillations with peak frequency at ~11 Hz (power, 1.1dB±4.5; coherence, 0.9±0.05). Notably, slow oscillation power during a general anesthetic state under propofol (power, 13.2dB±2.4) was much larger than during sedative states under both propofol (power, −2.5dB±3.5) and dexmedetomidine (power, −0.4dB±3.1). Conclusion Our results indicate that dexmedetomidine and propofol place patients into different brain states, and suggests that propofol enables a deeper state of unconsciousness by inducing large amplitude slow oscillations that produce prolonged states of neuronal silence.

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.

Objectives Switching from maintenance of general anesthesia with an ether anesthetic to maintenance with high-dose (concentration > 50% and total gas flow rate > 4 liters per minute) nitrous oxide is a common practice used to facilitate emergence from general anesthesia. The transition from the ether anesthetic to nitrous oxide is associated with a switch in the putative mechanisms and sites of anesthetic action. We investigated whether there is an electroencephalogram (EEG) marker of this transition. Methods We retrospectively studied the ether anesthetic to nitrous oxide transition in 19 patients with EEG monitoring receiving sevoflurane, oxygen and air for general anesthesia maintenance. Results Following the transition to nitrous oxide, the alpha (8 to 12 Hz) oscillations associated with sevoflurane dissipated within 3 to 12 minutes (median 6 minutes) and were replaced by highly coherent large-amplitude slow-delta (0.1 to 4 Hz) oscillations that persisted for 2 to 12 minutes (median 3 minutes). Conclusions Administration of high-dose nitrous oxide is associated with transient, large amplitude slow-delta oscillations. Significance We postulate that these slow-delta oscillations may result from nitrous oxide-induced blockade of major excitatory inputs (NMDA glutamate projections) from the brainstem (parabrachial nucleus and medial pontine reticular formation) to the thalamus and cortex. This EEG signature of high-dose nitrous oxide may offer new insights into brain states during general anesthesia.

This manuscript concerns the application of infrared birefringence imaging ͑IBI͒ to quantify macroscopic and microscopic internal stresses in multicrystalline silicon ͑mc-Si͒ solar cell materials. We review progress to date, and advance four closely related topics. ͑1͒ We present a method to decouple macroscopic thermally-induced residual stresses and microscopic bulk defect related stresses. In contrast to previous reports, thermally-induced residual stresses in wafer-sized samples are generally found to be less than 5 MPa, while defect-related stresses can be several times larger. ͑2͒ We describe the unique IR birefringence signatures, including stress magnitudes and directions, of common microdefects in mc-Si solar cell materials including: ␤-SiC and ␤-Si 3 N 4 microdefects, twin bands, nontwin grain boundaries, and dislocation bands. In certain defects, local stresses up to 40 MPa can be present. ͑3͒ We relate observed stresses to other topics of interest in solar cell manufacturing, including transition metal precipitation, wafer mechanical strength, and minority carrier lifetime. ͑4͒ We discuss the potential of IBI as a quality-control technique in industrial solar cell manufacturing.