Abstract
It has long been known that electroencephalogram (EEG) signals generate chaotic strange attractors and the shape of these attractors correlate with depth of anesthesia. We applied chaos analysis to frontal cortical and hippocampal micro-EEG signals from implanted microelectrodes (layer 4 and CA1, respectively). Rats were taken to and from loss of righting reflex (LORR) with isoflurane and behavioral measures were compared to attractor shape. Resting EEG signals at LORR differed markedly from awake signals, more similar to slow wave sleep signals, and easily discerned in raw recordings (high amplitude slow waves), and in fast Fourier transform analysis (FFT; increased delta power), in good agreement with previous studies. EEG activation stimulated by turning rats on their side, to test righting, produced signals quite similar to awake resting state EEG signals. That is, the high amplitude slow wave activity changed to low amplitude fast activity that lasted for several seconds, before returning to slow wave activity. This occurred regardless of whether the rat was able to right itself, or not. Testing paw pinch and tail clamp responses produced similar EEG activations, even from deep anesthesia when burst suppression dominated the spontaneous EEG. Chaotic attractor shape was far better at discerning between these awake-like signals, at loss of responses, than was FFT analysis. Comparisons are provided between FFT and chaos analysis of EEG during awake walking, slow wave sleep, and isoflurane-induced effects at several depths of anesthesia. Attractors readily discriminated between natural sleep and isoflurane-induced “delta” activity. Chaotic attractor shapes changed gradually through the transition from awake to LORR, indicating that this was not an on/off like transition, but rather a point along a continuum of brain states.
Highlights
Analysis of electroencephalogram (EEG) recordings have been used to characterize anesthetic effects at various concentrationrelated depths, from sedation through loss of recall, loss of consciousness, and full surgical immobility
Barbiturates, and propofol produce a stereotypic pattern of EEG changes, with high amplitude slow wave activity seen during sedation and loss of consciousness, and transitioning to burst suppression patterns at surgical levels of anesthesia (Clark et al, 1973; MacIver et al, 1996; Pilge et al, 2014)
EEG SIGNALS CORRELATED WITH BEHAVIOR For both the frontal cortex and hippocampal EEG signals there was a good correlation between ongoing behavior and signal appearance, in agreement with previous studies (Bland and Oddie, 2001)
Summary
Analysis of electroencephalogram (EEG) recordings have been used to characterize anesthetic effects at various concentrationrelated depths, from sedation through loss of recall, loss of consciousness, and full surgical immobility. Measures based on Fourier, entropy, coherence, and/or bispectral transforms have proven useful in the design of commercially available anesthetic depth monitors These monitors have been shown to achieve accuracies/congruencies of only ∼ 85–95%, far below a level that is needed to prevent intraoperative awareness (Niedhart et al, 2006; Hrelec et al, 2010). It has long been known that EEG signals can generate chaotic strange attractors and that the shape of these attractors correlate with depth of anesthesia (Watt and Hameroff, 1988; Walling and Hicks, 2006) One of these studies compared frequency domain measures [FFT: (Walling and Hicks, 2006)], with chaos analysis of anesthetic-induced changes in EEG signals, but the attractor density was too sparse for detailed analysis. Loss of righting reflex is a commonly used surrogate endpoint measure in rodents for loss of consciousness in humans (Frank and Jhamandas, 1970)
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