Increased Sample Entropy in EEGs During the Functional Rehabilitation of an Injured Brain

AOP report: Development of an adverse outcome pathway for deposition of energy leading to learning and memory impairment

Understanding radiation-induced non-cancer effects on the central nervous system (CNS) is essential for the risk assessment of medical (e.g., radiotherapy) and occupational (e.g., nuclear workers and astronauts) exposures. Herein, the adverse outcome pathway (AOP) approach was used to consolidate relevant studies in the area of cognitive decline for identification of research gaps, countermeasure development, and for eventual use in risk assessments. AOPs are an analytical construct describing critical events to an adverse outcome (AO) in a simplified form beginning with a molecular initiating event (MIE). An AOP was constructed utilizing mechanistic information to build empirical support for the key event relationships (KERs) between the MIE of deposition of energy to the AO of learning and memory impairment through multiple key events (KEs). The evidence for the AOP was acquired through a documented scoping review of the literature. In this AOP, the MIE is connected to the AO via six KEs: increased oxidative stress, increased deoxyribonucleic acid (DNA) strand breaks, altered stress response signaling, tissue resident cell activation, increased pro-inflammatory mediators, and abnormal neural remodeling that encompasses atypical structural and functional alterations of neural cells and surrounding environment. Deposition of energy directly leads to oxidative stress, increased DNA strand breaks, an increase of pro-inflammatory mediators and tissue resident cell activation. These KEs, which are themselves interconnected, can lead to abnormal neural remodeling impacting learning and memory processes. Identified knowledge gaps include improving quantitative understanding of the AOP across several KERs and additional testing of proposed modulating factors through experimental work. Broadly, it is envisioned that the outcome of these efforts could be extended to other cognitive disorders and complement ongoing work by international radiation governing bodies in their review of the system of radiological protection.

Impairment of inhibitory control due to repetitive subconcussions from indirect brain impacts: Evidence from event-related potentials and resting-state EEG complexity in parachuters

The present study aims to investigate the unknown relationship between inhibitory control and repetitive subconcussion induced by the indirect brain impacts. We enrolled 28 parachuters exposed to repetitive subconcussion (SC) and 27 matched health controls (HC). Parachuters who have completed at least 70 actual parachuting (71-112 times) and at least 1500 simulated platform jumps (1500-4500 times) were included in the SC group. The SC group had a reduced accuracy rate in both the Stroop congruent and incongruent conditions. Larger N2 and N450 amplitudes were elicited in the frontal regions of the SC group, which indicate compensatory adaptations to the deficit in conflict monitoring. The reduced frontal resting-state EEG complexity in full-band (1-40 Hz) may demonstrate the frontal structural damage following the indirect brain impacts of repetitive subconcussion. Pearson correlation analysis showed that in the SC group, the frontal beta-band sample entropy values are positively correlated with the accuracy rate of the Stroop incongruent condition, suggesting the frontal beta-band sample entropy values may serve as potential electrophysiological markers of impaired inhibitory control after indirectly repetitive brain impacts. This study provides the robust evidence that repetitive subconcussion resulting from indirect brain impacts may lead to impairment of inhibitory control.

Sample Entropy Computation on Signals with Missing Values

Sample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes problematic. Preprocessing techniques, such as deletion or interpolation, can be employed as a solution, producing time series without missing or invalid values. While deletion ignores missing values, interpolation replaces them using approximations based on neighboring points. This paper proposes a novel approach for the computation of sample entropy when values are considered as missing or invalid. The proposed algorithm accommodates points in the m-dimensional space and handles them there. A theoretical and experimental comparison of the proposed algorithm with deletion and interpolation demonstrates several advantages over these other two approaches. Notably, the deviation of the expected sample entropy value for the proposed methodology consistently proves to be lowest one.

Spectral Time-Varying Pattern Causality and Its Application

In this paper, a new method based on delayed pattern causality is proposed, called spectral time-varying pattern causality. Specifically, this method uses symbolic dynamics and phase space reconstruction to infer causality, systematically quantifies the causal relationship between different frequency components of the signal, and the generated spectrum provides a rich information representation of the time-varying potential causality. The causal intensity at different times is quantified by a sliding window, providing a dynamic perspective for the study of causality in complex systems. Through the simulation data, we verified the effectiveness of the method and its robustness to noise, and then applied it to physiological data to compare the differences in coupling between electrodes in different brain regions between normal and Parkinson's patients in the resting state. The study of causality in complex systems provides a new perspective to better capture the latent and elusive dynamic structures.

Evaluating EEG complexity metrics as biomarkers for depression

Nonlinear EEG analysis offers the potential for both increased diagnostic accuracy and deeper mechanistic understanding of psychopathology. EEG complexity measures have previously been shown to positively correlate with clinical depression. In this study, resting state EEG recordings were taken across multiple sessions and days with both eyes open and eyes closed conditions from a total of 306 subjects, 62 of which were in a current depressive episode, and 81 of which had a history of diagnosed depression but were not currently depressed. Three different EEG montages (mastoids, average, and Laplacian) were also computed. Higuchi fractal dimension (HFD) and sample entropy (SampEn) were calculated for each unique condition. The complexity metrics showed high internal consistency within session and high stability across days. Higher complexity was found in open-eye recordings compared to closed eyes. The predicted correlation between complexity and depression was not found. However, an unexpected sex effect was observed, in which males and females exhibited different topographic patterns of complexity.

Spatio-Temporal Dynamics of Entropy in EEGS during Music Stimulation of Alzheimer's Disease Patients with Different Degrees of Dementia

Music has become a common adjunctive treatment for Alzheimer’s disease (AD) in recent years. Because Alzheimer’s disease can be classified into different degrees of dementia according to its severity (mild, moderate, severe), this study is to investigate whether there are differences in brain response to music stimulation in AD patients with different degrees of dementia. Seventeen patients with mild-to-moderate dementia, sixteen patients with severe dementia, and sixteen healthy elderly participants were selected as experimental subjects. The nonlinear characteristics of electroencephalogram (EEG) signals were extracted from 64-channel EEG signals acquired before, during, and after music stimulation. The results showed the following. (1) At the temporal level, both at the whole brain area and sub-brain area levels, the EEG responses of the mild-to-moderate patients showed statistical differences from those of the severe patients (p < 0.05). The nonlinear characteristics during music stimulus, including permutation entropy (PmEn), sample entropy (SampEn), and Lempel−Ziv complexity (LZC), were significantly higher in both mild-to-moderate patients and healthy controls compared to pre-stimulation, while it was significantly lower in severe patients. (2) At the spatial level, the EEG responses of the mild-to-moderate patients and the severe patients showed statistical differences (p < 0.05), showing that as the degree of dementia progressed, fewer pairs of EEG characteristic showed significant differences among brain regions under music stimulation. In this paper, we found that AD patients with different degrees of dementia had different EEG responses to music stimulation. Our study provides a possible explanation for this discrepancy in terms of the pathological progression of AD and music cognitive hierarchy theory. Our study has adjunctive implications for clinical music therapy in AD., potentially allowing for more targeted treatment. Meanwhile, the variations in the brains of Alzheimer’s patients in response to music stimulation might be a model for investigating the neural mechanism of music perception.

The Impact of the COVID-19 Pandemic on the Unpredictable Dynamics of the Cryptocurrency Market

The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.

Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury

Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain.

Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli

BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles. OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain. METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content). RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals. CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: a pilot study

BACKGROUND

Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment.

METHODS

The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks' treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment.

RESULTS

For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of δ1 band (0.5 - 2 Hz), accompanied by a significant decrease in the RE of β2 band (20 - 30 Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices.

CONCLUSIONS

The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

Analysis of Streamflow Complexity Based on Entropies in the Weihe River Basin, China

The study on the complexity of streamflow has guiding significance for hydrologic simulation, hydrologic prediction, water resources planning and management. Utilizing monthly streamflow data from four hydrologic control stations in the mainstream of the Weihe River in China, the methods of approximate entropy, sample entropy, two-dimensional entropy and fuzzy entropy are introduced into hydrology research to investigate the spatial distribution and dynamic change in streamflow complexity. The results indicate that the complexity of the streamflow has spatial differences in the Weihe River watershed, exhibiting an increasing tendency along the Weihe mainstream, except at the Linjiacun station, which may be attributed to the elevated anthropogenic influence. Employing sliding entropies, the variation points of the streamflow time series at the Weijiabu station were identified in 1968, 1993 and 2003, and those at the Linjiacun station, Xianyang station and Huaxian station occurred in 1971, 1993 and 2003. In the verification of the above points, the minimum value of t-test is 3.7514, and that of Brown-Forsythe is 7.0307, far exceeding the significance level of 95%. Also, the cumulative anomaly can detect two variation points. The t-test, Brown-Forsythe test and cumulative anomaly test strengthen the conclusion regarding the availability of entropies for identifying the streamflow variability. The results lead us to conclude that four entropies have good application effects in the complexity analysis of the streamflow time series. Moreover, two-dimensional entropy and fuzzy entropy, which have been rarely used in hydrology research before, demonstrate better continuity and relative consistency, are more suitable for short and noisy hydrologic time series and more effectively identify the streamflow complexity. The results could be very useful in identifying variation points in the streamflow time series.