COMPLEXITY ANALYSIS OF DENSE ARRAY EEG SIGNAL REVEALS SEX DIFFERENCE

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences

BACKGROUND

The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84-96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope-the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt-to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated.

RESULTS

We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets.

CONCLUSIONS

Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.

Sex differences in resting EEG in healthy young adults

Resting EEG, measured in eyes-closed (EC) and eyes-open (EO) states, can provide insight into behavioural differences between groups. Surprisingly, differences in resting EEG between females and males have not been investigated systematically in previous literature. The present study utilised the four traditional EEG bands to confirm their baseline EC topographies and reactivity (EO minus EC) across groups, to clarify topographical differences between sexes, and to confirm alpha as a measure of arousal. Participants were eighty healthy young adults (40 female), with a mean age of 20.4 (range 18-26) years. Continuous resting EEG was recorded from 30 scalp sites during three 2-minute conditions (EO1, EC, EO2), and EOG-corrected. Data from each condition were divided into 60 sequential 2-second epochs. Accepted artefact-free epochs were Fourier Transformed, and absolute amplitudes in the delta (0.5-3.5 Hz), theta (4.0-7.5 Hz), alpha (8.0-13.0 Hz), and beta (13.5-29.5 Hz) bands were calculated. Across groups in EC, significant topographical differences were found between the band amplitudes, broadly compatible with previous reports. Females had greater overall amplitudes in delta, alpha and beta, enhanced midline activity in theta, and parietal and midline activity in the alpha and beta bands. From EC to EO, reactivity was apparent across the bands as significant reductions, particularly in the parietal region. For females compared to males, the reduction in parietal midline delta and theta, parietal alpha and parietal midline beta was significantly larger. Additionally, across groups, alpha activity was confirmed as an inverse measure of arousal. These findings indicate significant differences in neuronal activity between young adult females and males, and help our interpretation of alpha changes.

The Effect of Prostration (Sajdah) on the Prefrontal Brain Activity: A Pilot Study

Introduction

"Sajdah", a prostration position, is part of Muslim daily prayers. It seems to have several effects on the brain and heart function. This study aimed to investigate the prefrontal brain activity after 10 seconds of Sajdah in the direction of Qibla (the direction that a Muslim prays) while putting the forehead on the ground.

Methods

Three women and two men participated in this pilot study. Linear (absolute and relative power of θ (4-8Hz), α 1 (8-10 Hz), α 2 (10-12 Hz), β 1 (12-16 Hz), β 2 (16-20 Hz), β 3 (20-30 Hz), γ 1 (30-40 Hz), γ 2 (40-50 Hz) and non-linear features (approximate entropy, Katz fractal dimension, Petrosian fractal dimension, spectral entropy, and sample entropy) from Fps channel were calculated.

Results

The relative β to γ band, approximate and sample entropy, Petrosian fractal dimension and mean of amplitude decreased in open eye state in women. While θ to γ bands in the closed eye state decreased after Sajdah in women. The absolute γ bands in closed eye state and relative β band in open eye state increased after Sajdah in men.

Conclusion

The pilot study showed that 10 seconds of Sajdah has effects on brain activity and sometimes showed the opposite effect on genders.

Sex Differences in the Complexity of Healthy Older Adults' Magnetoencephalograms

The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently introduced non-linear signal processing algorithm based on symbolic dynamics, was used to evaluate the complexity of MEG signals in source space. PLZC was estimated in a broad band of frequencies (2–45 Hz), as well as in narrow bands (i.e., theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz)) in a sample of 98 healthy elderly subjects (49 males, 49 female) aged 65–80 (average age of 72.71 ± 4.22 for males and 72.67 ± 4.21 for females). PLZC was significantly higher for females than males in the high beta band at posterior brain regions including the precuneus, and the parietal and occipital cortices. Further statistical analyses showed that higher complexity values over highly overlapping regions than the ones mentioned above were associated with larger hippocampal volumes only in females. These results suggest that sex differences in healthy aging can be identified from the analysis of magnetoencephalograms with novel signal processing methods.

Brain oscillatory complexity across the life span

OBJECTIVE

Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span.

METHOD

This study examines changes in resting-state magnetoencephalogram (MEG) complexity - quantified with the Lempel-Ziv complexity (LZC) algorithm - due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years.

RESULTS

A significant quadratic (curvilinear) relationship (p<0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life. Once that peak was crossed, complexity values slowly decreased until late senescence. Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p<0.05).

CONCLUSIONS

These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm.

SIGNIFICANCE

Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age.

No difference indicated in electroencephalographic power spectral analysis in 3- and 6-month-old infants fed soy- or milk-based formula

Increasing concern has been recently raised on the possible effects of soy-derived phyto-oestrogens on the development of cognitive functions in infants. However, limited studies have been conducted to date, and no data have been made available for determining whether infant soy formula can affect normal development of the human brain. We compared electroencephalographic (EEG) spectral power derived from high-density recordings of infants fed milk-based or soy formula (46 fed milk-based formula and 39 fed soy formula) at 3 and 6 months of age. The spectral parameters included absolute power, relative power and spectral edge frequency (SEF) at 85%, 90% and 95% levels. The frequency domain contained four bands (0.1-3, 3-6, 6-9 and 9-12 Hz). EEG signals were collected from eight brain areas in each hemisphere. The results showed that the highest spectral power was mainly distributed in the low-frequency bands and was predominant in the frontal and anterior temporal areas. None of the spectral variables significantly differed between the soy- and milk-fed infants (anova, all P > 0.2). However, significant effects were indicated on the SEFs for factors of sex, age and brain area (all P < 0.01). Hemispheric differences in the absolute and relative power were also indicated. Our results suggest that the EEG power spectral development of soy-fed infants does not differ from that of infants fed milk-based formula. In addition, EEG spectral development appears more advanced in female than in male infants at 6 months.

Annual variations of quantitative EEG in patients with chronic epilepsy

PURPOSE

Annual changes in powers of the close-eyed EEG were measured over 12 months in 12 patients with epilepsy.

MATERIAL AND METHODS

The present study included 12 patients aged 14.6+/-2.7 years with chronic epilepsy, suffering for 6-14 years. EEGs were recorded every month between 9 and 11 a.m. Five patients had generalized seizures and 7 partial complex seizures secondarily generalized. Visual analysis of EEG was performed before the quantitative assessment. The signals were recorded using a set of 14 (F3, F4, F7, F8, C3, C4, P3, P4, 01, 02, T3, T4, T5, T6) scalp electrodes. For each patient, 20 artifact-free EEG epochs, each of 2 s. duration were selected for spectral analysis to calculate spectral power. The sampling frequency was 240 Hz. Frequencies below 1 Hz and above 70 Hz were eliminated by digital filtering. The channels were recorded relative to a vertex reference. A fast Fourier transformation algorithm of signal processing was used to obtain the power spectrum of each lead. Absolute power spectrum was calculated within 4 frequency bands: delta (1-3.99 Hz), theta (4-7.99 Hz), alpha (8-12.99 Hz), and beta (13-30.99 Hz).

RESULTS

We found the abnormalities to be predominantly focal in EEG. None of the EEG recordings were normal, and the power spectra differed over the year. A statistically significant increase of delta and theta bands in May and a decrease of alpha bands in September was found. We did not find positive correlations between the changes in the quantitative EEG analysis and the number of seizures in patients.

CONCLUSIONS

These findings suggest the annual variations of EEG in patients with chronic epilepsy. Furthermore studies are needed to clarify the annual variations of EEG among healthy volunteers.