Algorithmic complexity as an index of cortical function in awake and pentobarbital-anesthetized rats

EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience

In a recent electroencephalography (EEG) sleep study inspired by complexity theories of consciousness, we found that multi-channel signal diversity progressively decreased from wakefulness to slow wave sleep, but failed to find any significant difference between dreaming and non-dreaming awakenings within the same sleep stage (NREM2). However, we did find that multi-channel Lempel-Ziv complexity (LZC) measured over the posterior cortex increased with more perceptual ratings of NREM2 dream experience along a thought-perceptual axis. In this follow-up study, we re-tested our previous findings, using a slightly different approach. Partial sleep-deprivation was followed by evening sleep experiments, with repeated awakenings and immediate dream reports. Participants reported whether they had been dreaming, and were asked to rate how diverse, vivid, perceptual, and thought-like the contents of their dreams were. High density (64 channel) EEG was recorded throughout the experiment, and mean single-channel LZC was calculated for each 30 s sleep epoch. LZC progressively decreased with depth of non-REM sleep. Surprisingly, estimated marginal mean LZC was slightly higher for NREM1 than for wakefulness, but the difference did not remain significant after adjusting for multiple comparisons. We found no significant difference in LZC between dream and non-dream awakenings, nor any significant relationship between LZC and subjective ratings of dream experience, within the same sleep stage (NREM2). The failure to reproduce our own previous finding of a positive correlation between posterior LZC and more perceptual dream experiences, or to find any other correlation between brain signal complexity and subjective experience within NREM2 sleep, raises the question of whether EEG LZC is really a reliable correlate of richness of experience as such, within the same sleep stage.

EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience

Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents.

Exploration of Lower Frequency EEG Dynamics and Cortical Alpha Asymmetry in Long-term Rajyoga Meditators

BACKGROUND

Rajyoga meditation is taught by Prajapita Brahmakumaris World Spiritual University (Brahmakumaris) and has been followed by more than one million followers across the globe. However, rare studies were conducted on physiological aspects of rajyoga meditation using electroencephalography (EEG). Band power and cortical asymmetry were not studied with Rajyoga meditators.

AIMS

This study aims to investigate the effect of regular meditation practice on EEG brain dynamics in low-frequency bands of long-term Rajyoga meditators.

SETTINGS AND DESIGN

Subjects were matched for age in both groups. Lower frequency EEG bands were analyzed in resting and during meditation.

MATERIALS AND METHODS

Twenty-one male long-term meditators (LTMs) and same number of controls were selected to participate in study as par inclusion criteria. Semi high-density EEG was recorded before and during meditation in LTM group and resting in control group. The main outcome of the study was spectral power of alpha and theta bands and cortical (hemispherical) asymmetry calculated using band power.

STATISTICAL ANALYSIS

One-way ANOVA was performed to find the significant difference between EEG spectral properties of groups. Pearson's Chi-square test was used to find difference among demographics data.

RESULTS

Results reveal high-band power in alpha and theta spectra in meditators. Cortical asymmetry calculated through EEG power was also found to be high in frontal as well as parietal channels. However, no correlation was seen between the experience of meditation (years, hours) practice and EEG indices.

CONCLUSION

Overall findings indicate contribution of smaller frequencies (alpha and theta) while maintaining meditative experience. This suggests a positive impact of meditation on frontal and parietal areas of brain, involved in the processes of regulation of selective and sustained attention as well as provide evidence about their involvement in emotion and cognitive processing.

Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity

To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer's disease (AD), power spectrum density (PSD) and Lempel-Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel-Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.

Analyze the dynamic features of rat EEG using wavelet entropy

Wavelet entropy (WE), a new method of complexity measure for non-stationary signals, was used to investigate the dynamic features of rat EEGs under three vigilance states. The EEGs of the freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the wavelet entropy curves were calculated as a function of time. The results showed that there were significant differences among the average WEs of EEGs recorded under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement (REM) sleep. The changes of WE had different relationships with the four power components under different states. Moreover, there was evident rhythm in EEG WEs of SWS sleep for most experimental rats, which indicated a reciprocal relationship between slow waves and sleep spindles in the micro-states of SWS sleep. Therefore, WE can be used not only to distinguish the long-term changes in EEG complexity, but also to reveal the short-term changes in EEG micro-state.

An effective correlation dimension and burst suppression ratio of the EEG in rat. Correlation with sevoflurane induced anaesthetic depth

BACKGROUND AND OBJECTIVE

Anaesthesiologists need parameters that measure the depth of anaesthesia. In the context of this need, the present study investigated in rats how two variables from the electroencephalogram, the burst suppression ratio and effective correlation dimension correlated with a measure of anaesthetic depth as measured in the strength of a noxious withdrawal reflex.

METHODS

Eight rats were exposed to different inspiratory concentrations of sevoflurane, each rat in two separate experiments. In the first experiment, spontaneously breathing animals could move freely and no painful stimuli were applied. In the second experiment, in mechanically ventilated restrained anaesthetized rats, the withdrawal reflex was measured every 80 s. In both experiments the electroencephalogram was continuously recorded. The concentration in the effector compartment was estimated using a first order two compartment model. Correlation dimension was computed following the Grassberger/Procaccia/Takens approach with optimized parameter settings to achieve maximum sensitivity to anaesthetic drug effects and enable real-time computation. The Hill, equation was fitted to the data, describing the effect as a function of sevoflurane concentration.

RESULTS

Good correlations of Depth of Anaesthesia with correlation dimension as well as burst suppression ratio were established in both types of experiments. Arousal by noxious stimuli decreased burst suppression ratio and increased correlation dimension. The effective sevoflurane concentration associated with 50% of the maximum effect (C50) was higher in experiment II (stimulation) than in experiment I (no stimulation): i.e. for correlation dimension 2.18% vs. 0.60% and for burst suppression ratio 3.07% vs. 1.73%. The slope factors were: gammaCD = 4.15 vs. gammaCD = 1.73 and gammaBSR = 5.2 vs. gammaBSR = 5.4. Correlation dimension and burst suppression ratio both correlated with the strength of the withdrawal reflex with correlation coefficients of 0.46 and 0.66 respectively (P < 0.001).

CONCLUSIONS

Both correlation dimension and burst suppression ratio are related to anaesthetic depth and are affected by noxious stimuli. The relationship between anaesthetic depth and burst suppression ratio is confirmed and the potential of correlation dimension is demonstrated.

A simple and effective process for noise reduction of multichannel cortical field potential recordings in freely moving rats

Simple and useful steps, i.e. placing a grounded plate under the recording chamber as well as using multiple reference electrodes, are introduced here for obtaining reliable low-noise recordings of brain activity in freely moving rats. A general circuit model was built to analyze the electrical interference of both single-grounded and two-reference ground-free recording configurations. In both simulated and realistic conditions under two recording states, 60-Hz magnitude was in the microvolt range. Moreover, the noise was significantly reduced by shortening the distance between the subject and the grounded plate under the recording chamber. Furthermore, in chronically implanted rats, average 60-Hz interference of multichannel electroencephalograms of two-reference ground-free recordings (3.74 +/- 0.18 microV) was significantly smaller than that of the single-grounded condition (9.03 +/- 1.98 microV). Thus, we demonstrated that a lower-noise recording can be achieved by a two-reference configuration and a closely-placed metal grounded plate in an open-field circumstance. As compared to the use of a Faraday cage, this simple procedure is of benefit for long-term behavioral tracking with a video camera and for pharmacological experiments.

The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior

Symbolic measures of complexity provide a quantitative characterization of the sequential structure of symbol sequences. Promising results from the application of these methods to the analysis of electroencephalographic (EEG) and event-related brain potential (ERP) activity have been reported. Symbolic measures used thus far have two limitations, however. First, because the value of complexity increases with the length of the message, it is difficult to compare signals of different epoch lengths. Second, these symbolic measures do not generalize easily to the multichannel case. We address these issues in studies in which both single and multichannel EEGs were analyzed using measures of signal complexity and algorithmic redundancy, the latter being defined as a sequence-sensitive generalization of Shannon's redundancy. Using a binary partition of EEG activity about the median, redundancy was shown to be insensitive to the size of the data set while being sensitive to changes in the subject's behavioral state (eyes open vs. eyes closed). The covariance complexity, calculated from the singular value spectrum of a multichannel signal, was also found to be sensitive to changes in behavioral state. Statistical separations between the eyes open and eyes closed conditions were found to decrease following removal of the 8- to 12-Hz content in the EEG, but still remained statistically significant. Use of symbolic measures in multivariate signal classification is described.

A low-noise flexible integrated system for recording and analysis of multiple electrical signals during sleep-wake states in rats

A low-noise flexible system for the simultaneous recording and analysis of several electrical signals (EEG, ECG, EMG, and diaphragm EMG) from the same rat was constructed for studying changes in physiological functions during the sleep-wake cycle. The hardware in the system includes a multichannel amplifier, a video camera, a timer code generator, and a PC. A miniature buffer headstage with high-input impedance connected to a 6-channel amplifier was developed. All electrical activities devoid of 60 Hz interference could be consistently recorded by our low-cost amplifier with no shielding treatment. The analytical software was established in the LabVIEW environment and consisted of three major frames: temporal, spectral, and nonlinear analyses. These analytical tools demonstrated several distinct utilities. For example, the sleep-wake states could be successfully distinguished by combining temporal and spectral analyses. An obvious theta rhythm during rapid-eye-movement sleep (REMS) was recorded from parietal to occipital cortical areas but not from the frontal area. In addition, two types of sleep apnea with/without cardiac arrhythmias were observed under REMS condition. Moreover, the evoked potentials of the primary somatosensory cortex elicited by innocuous electrical pulses were modulated by vigilant states, especially under a slow-wave sleep state. These results show that our system delivers high-quality signals and is suitable for sleep investigations. The system can be easily expanded by combining other recording devices, like a plethysmograph. This compact system can also be easily modified and applied to other related physiological or pharmacological studies.