Correlation dimension of the human sleep electroencephalogram

Fractal Neurodynamics

The neuronal ongoing electrical activity in the brain network, the neurodynamics, reflects the structure and functionality of generating neuronal pools. The activity of neurons due to their excitatory and inhibitory projections is associated with specific brain functions. Here, the purpose was to investigate if the local ongoing electrical activity exhibits its characteristic spectral and fractal features in wakefulness and sleep across and within subjects. Moreover, we aimed to show that measures typical of complex systems catch physiological features missed by linear spectral analyses. For this study, we concentrated on the evaluation of the power spectral density (PSD) and Higuchi fractal dimension (HFD) measures. Relevant clinical impact of the specific features of neurodynamics identification stands primarily in the potential of classifying cortical parcels according to their neurodynamics as well as enhancing the effectiveness of neuromodulation interventions to cure symptoms secondary to neuronal activity unbalances.

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

Estimating the dimensionality of the manifold underlying multi-electrode neural recordings

It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

The effect of photic stimulation alone and in combination with sleep deprivation after a seizure-like event - reappraisal by using linear and nonlinear EEG methods

ObjectivesThe present study aimed to compare the effectiveness of different provocation tests used for the study of the 'susceptibility to seizure' by quantitative electroencephalography (EEG) analysis.MethodsEight subjects with a history of a seizure-like disturbed consciousness participated in this preliminary study. A routine EEG was carried out with photic stimulation (eyes closed and after eyes open) at the beginning of the investigation. Some days later, a sleep-deprived EEG was recorded with the same protocol. Selected epochs (in eyes closed condition) after the stimulations were analysed with Point(wise) Correlation Dimension (PD2i) and Synchronization Likelihood (SL) methods. The results were compared to those obtained by similar analysis of the resting state (control) epochs with Wilcoxon Signed Rank Test (p ≤ 0.05).ResultsIn our study, significantly lower grand mean PD2i and higher delta SL values were found in sleep-deprived state after stimulation with eyes closed compared to the control. Our results indicated a lower-dimensional, hypersynchronous state of the brain as a consequence of these combined provocations.DiscussionThis may correspond to a possible 'preictal' state of the brain. Accordingly, it is suggested that photic stimulation together with sleep deprivation seems to be more effective in provocation - especially when the stimulation was made with eyes closed.

Using Nonlinear Dynamics and Multivariate Statistics to Analyze EEG Signals of Insomniacs with the Intervention of Superficial Acupuncture

Objective

As a noninvasive and nonpharmacological therapeutic approach, superficial acupuncture (SA) is a special method of acupuncture. In this study, using nonlinear dynamics and multivariate statistics, we studied the electroencephalography (EEG) of primary insomnia under SA intervention to investigate how brain regions change.

Method

This study included 30 adults with primary insomnia. They underwent superficial acupuncture at the Shangen acupoint. The EEG signals were collected for 10 minutes at each state, including the resting state, the intervention state, and the postintervention state. The data were conducted using nonlinear dynamics (including approximate entropy (ApEn) and correlation dimension (CD)) and multivariate statistics.

Result

The repeated-measures ANOVA results showed that both ApEn and CD values were not significantly different at the three states (p > 0.05). The paired t-test results showed that the ApEn values of electrodes O2 (the right occipital lobe) at the postintervention state have decreased, compared with the resting state (p < 0.05), and no difference was detected in CD (p > 0.05). The cluster analysis results of ApEn showed that patients' EEG has changed from the right prefrontal lobe (electrode Fp2) to the right posterior temporal lobe (electrode T6) and finally to the right occipital lobe (electrode O2), before, during, and after the SA intervention. In addition, the factor analysis results of CD revealed that patients' EEG of all brain regions except for the occipital lobes has changed to the frontal lobes and anterior temporal and frontal lobes from pre- to postintervention.

Conclusion

SA activated the corresponding brain regions and reduced the complexity of the brain involved. It is feasible to use nonlinear dynamics analysis and multivariate statistics to examine the effects of SA on the human brain.

Evidence of a pharmacodynamic EEG profile in rats following clonidine administration using a nonlinear analysis

BACKGROUND

Changes caused by clonidine in rodent electroencephalograms (EEG) have been reported with some inconsistency. For this reason, a pre-clinical study was conducted in order to confirm previous findings with both a standard spectral analysis and a sleep stage scoring procedure. In addition, a nonlinear technique for analysing the time-varying signals was implemented to compare its performance against conventional approaches.

RESULTS

The nonlinear method succeeds in quantifying all dose-related responses from the data set relying solely on the EEG trace.

CONCLUSIONS

Nonlinear approaches can deliver a suitable alternative to the sleep-stage scoring methods commonly used for drug effect detection.

Remote monitoring of electroencephalogram, electrocardiogram, and behavior during controlled atmosphere stunning in broilers: implications for welfare

This study examined the welfare implications of euthanizing broilers with 3 gas mixtures relevant to the commercial application of controlled atmosphere stunning (CAS). Birds were implanted/equipped with electrodes to measure brain activity (electroencephalogram, EEG) and heart rate. These signals were recorded using a purpose-built telemetry-logging system, small enough to be worn by each bird in a spandex backpack. The birds were euthanized in a scaled-down CAS apparatus consisting of a conveyor belt passing through 2 compartments. Three gas environments were applied (8 birds per treatment): 1) anoxia (N(2) with <2% residual O(2), in both compartments), 2) hypercapnic anoxia (N(2) with 30% CO(2) and <2% residual O(2), in both compartments), and 3) a 2-phase approach with a hypercapnic hyperoxygenated anesthetic phase (40% CO(2), 30% O(2), and 30% N(2), in the first compartment, 80 s) followed by a second euthanasia phase (80% CO(2) in air, in the second compartment). All 3 CAS approaches effectively achieved nonrecovery states, and time to loss of consciousness for each bird was determined by visual determination of isoelectric EEG and by calculation of the correlation dimension of the EEG. Hypercapnic anoxia resulted in rapid unconsciousness and death; both anoxic treatments were associated with early onset prolonged wing flapping and sustained tonic convulsions as displayed in the electrophysiological recordings. These responses were seen in the period when consciousness remained a possibility. Hypercapnic hyperoxygenation (the 2-phase approach) was associated with respiratory disruption, but this treatment eliminated initial clonic convulsions in the stunning process, and tonic convulsions were not seen. These results suggest that the presence of O(2) in the first stage of CAS is associated with an absence of potentially distressing behavioral responses. The respiratory discomfort associated with hypercapnic hyperoxygenation is an issue. We propose that this may be compensated by a more gradual induction to unconsciousness, which eliminates the impact of other potentially negative experiences.

Physiological and behavioural responses of broilers to controlled atmosphere stunning: implications for welfare

Controlled atmosphere (gas) stunning (CAS) has the potential to improve the welfare of poultry at slaughter but there is a lack of consensus about which gas mixtures are most humane. The aim of this study was to evaluate the welfare consequences of different gas stunning approaches. Individual broilers were exposed to gas mixtures capable of inducing unconsciousness and euthanasia while their behavioural, cardiac, respiratory and neurophysiological responses were measured simultaneously. The approaches investigated included anoxia (N2 or Ar with < 2% residual O2), hypercapnic anoxia (30% CO2 in Ar, 40% CO2 in N2) and a biphasic method (40% CO2, 30% O2, 30% N2 for 60 s followed by 80% CO2 in air). Evaluation of the welfare implications of each approach centred on the likelihood of them inducing negative states or experiences during the conscious phase. Hypercapnic mixtures were associated with strong respiratory responses, while anoxic mixtures induced vigorous wing flapping. Electroencephalogram analysis using the correlation dimension (a non-linear measure of complexity) suggested that anoxic wing flapping occurred during periods in which a form of consciousness could not be excluded. Hypercapnic hyperoxygenation (biphasic approach) exacerbated respiratory responses but eliminated the possibility of vigorous behavioural responses occurring during a conscious phase. The relative importance of respiratory discomfort versus the potential to induce significant distress due to convulsive wing flapping and associated trauma is a matter for debate. We argue that respiratory discomfort is unpleasant but may be preferable to the risk of vigorous wing flapping and associated injury while conscious in poultry during CAS.

Effect of ethanol on human sleep EEG using correlation dimension analysis

Our study was designed to investigate the influence of alcohol on sleep using the correlation dimension (D2) analysis. Polysomnography (PSG) was performed in 10 adult human males during a baseline night (BL-N) and an ethanol (0.8 g/kg body weight) night (Et-N). The mean D2 values during the Et-N and BL-N decreased significantly from wakefulness to stages 1, 2, and 3+4 of nonrapid eye movement (non-REM) sleep, and increased during REM sleep. The mean D2 of the sleep electroencephalogram (EEG) during stage 2 during the Et-N was significantly higher than during BL-N. In addition, the mean D2 values of the sleep EEG for the second, third and fourth sleep cycles during the Et-N were significantly higher than during the BL-N. These significant differences between BL-N and Et-N were not recognized by spectral and visual analyses. Our results suggest that D2 is a potentially useful parameter for quantitative analysis of the effect of ethanol on sleep EEGs throughout the entire night.

Human sleep EEG analysis using the correlation dimension

Sleep electroencephalograms (EEG) were analyzed by non-linear analysis. Polysomnography (PSG) of nine healthy male subjects was analyzed and the correlation dimension (D2) was calculated. The D2 characterizes the dynamics of the sleep EEG, estimates the degrees of freedom, and describes the complexity of the signal. The mean D2 decreased from the awake stage to stages 1, 2, 3 and 4 increased during rapid eye movement (REM) sleep. The D2 during each REM sleep stage were high and those during each slow wave sleep stage were low, respectively, for each sleep cycle. The mean D2 of the sleep EEG in the second half of the night was significantly higher than those in the first half of the night. Significant changes were also observed during sleep stage 2, but were not seen during REM sleep and sleep stages 3 and 4. The D2 may be a useful method in the analysis of the entire sleep EEG.