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Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features.

ABSTRACT: This study was based on the contemporary proposal that distinct states of consciousness are quantifiable by neural complexity and critical dynamics. To test this hypothesis, it was aimed at comparing the electrophysiological correlates of three meditation conditions using nonlinear techniques from the complexity and criticality framework as well as power spectral density. Thirty participants highly proficient in meditation were measured with 64-channel electroencephalography (EEG) during one session consisting of a task-free baseline resting (eyes closed and eyes open), a reading condition, and three meditation conditions (thoughtless emptiness, presence monitoring, and focused attention). The data were analyzed applying analytical tools from criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), complexity measures (multiscale entropy, Higuchi's fractal dimension), and power spectral density. Task conditions were contrasted, and effect sizes were compared. Partial least square regression and receiver operating characteristics analysis were applied to determine the discrimination accuracy of each measure. Compared to resting with eyes closed, the meditation categories emptiness and focused attention showed higher values of entropy and fractal dimension. Long-range temporal correlations were declined in all meditation conditions. The critical exponent yielded the lowest values for focused attention and reading. The highest discrimination accuracy was found for the gamma band (0.83-0.98), the global power spectral density (0.78-0.96), and the sample entropy (0.86-0.90). Electrophysiological correlates of distinct meditation states were identified and the relationship between nonlinear complexity, critical brain dynamics, and spectral features was determined. The meditation states could be discriminated with nonlinear measures and quantified by the degree of neuronal complexity, long-range temporal correlations, and power law distributions in neuronal avalanches.

SUBMITTER: Walter N

PROVIDER: S-EPMC9319002 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPreexisting factors such as age and cognitive performance can influence the electroencephalogram (EEG) during general anesthesia. Specifically, spectral EEG power is lower in elderly, compared to younger, subjects. Here, the authors investigate age-related changes in EEG architecture in patients undergoing general anesthesia through a detailed examination of spectral and entropic measures.MethodsThe authors retrospectively studied 180 frontal EEG recordings from patients undergoing general anesthesia, induced with propofol/fentanyl and maintained by sevoflurane at the Waikato Hospital in Hamilton, New Zealand. The authors calculated power spectral density and normalized power spectral density, the entropic measures approximate and permutation entropy, as well as the beta ratio and spectral entropy as exemplary parameters used in current monitoring systems from segments of EEG obtained before the onset of surgery (i.e., with no noxious stimulation).ResultsThe oldest quartile of patients had significantly lower 1/f characteristics (P < 0.001; area under the receiver operating characteristics curve, 0.84 [0.76 0.92]), indicative of a more uniform distribution of spectral power. Analysis of the normalized power spectral density revealed no significant impact of age on relative alpha (P = 0.693; area under the receiver operating characteristics curve, 0.52 [0.41 0.63]) and a significant but weak effect on relative beta power (P = 0.041; area under the receiver operating characteristics curve, 0.62 [0.52 0.73]). Using entropic parameters, the authors found a significant age-related change toward a more irregular and unpredictable EEG (permutation entropy: P < 0.001, area under the receiver operating characteristics curve, 0.81 [0.71 0.90]; approximate entropy: P < 0.001; area under the receiver operating characteristics curve, 0.76 [0.66 0.85]). With approximate entropy, the authors could also detect an age-induced change in alpha-band activity (P = 0.002; area under the receiver operating characteristics curve, 0.69 [0.60 78]).ConclusionsLike the sleep literature, spectral and entropic EEG features under general anesthesia change with age revealing a shift toward a faster, more irregular, oscillatory composition of the EEG in older patients. Age-related changes in neurophysiological activity may underlie these findings however the contribution of age-related changes in filtering properties or the signal to noise ratio must also be considered. Regardless, most current EEG technology used to guide anesthetic management focus on spectral features, and improvements to these devices might involve integration of entropic features of the raw EEG.

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Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features.

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