Data-driven derivation of natural EEG frequency components: An optimised example assessing resting EEG in healthy ageing

Linear mixed-effect models for correlated response to process electroencephalogram recordings

A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them. The mean of permutation Lempel-Ziv complexity and permutation Shanon entropy used simultaneously for each of the four states are distinguishable from each other. However, used separately, the differences between permutation Lempel-Ziv complexity or permutation Shanon entropy of some states were not statistically significant. This shows that the joint use of both metrics provides more information than the separate use of each of them. Despite their wide use in medicine, LMEMs have not been commonly applied to simultaneously model metrics that quantify EEG signals. Modeling EEGs using a model that characterizes more than one response variable and their possible correlations represents a new way of analyzing EEG data in neuroscience.

Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation

Introduction

Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited. Magnetoencephalography (MEG) is known to facilitate a higher spatial resolution of brain source images. This study aimed to image post-tPBM effects in brain space based on both MEG and EEG measurements across the entire human brain.

Methods

MEG and EEG scans were concurrently acquired for 6 min before and after 8-min of tPBM delivered using a 1,064-nm laser on the right forehead of 25 healthy participants. Group-level changes in both the MEG and EEG power spectral density with respect to the baseline (pre-tPBM) were quantified and averaged within each frequency band in the sensor space. Constrained modeling was used to generate MEG and EEG source images of post-tPBM, followed by cluster-based permutation analysis for family wise error correction (p < 0.05).

Results

The 8-min tPBM enabled significant increases in alpha (8-12 Hz) and beta (13-30 Hz) powers across multiple cortical regions, as confirmed by MEG and EEG source images. Moreover, tPBM-enhanced oscillations in the beta band were located not only near the stimulation site but also in remote cerebral regions, including the frontal, parietal, and occipital regions, particularly on the ipsilateral side.

Discussion

MEG and EEG results shown in this study demonstrated that tPBM modulates neurophysiological activity locally and in distant cortical areas. The EEG topographies reported in this study were consistent with previous observations. This study is the first to present MEG and EEG evidence of the electrophysiological effects of tPBM in the brain space, supporting the potential utility of tPBM in treating neurological diseases through the modulation of brain oscillations.

Microstate dynamics and spectral components as markers of persistent and remittent attention-deficit/hyperactivity disorder

OBJECTIVE

We leveraged microstate characteristics and power features to examine temporal and spectral deviations underlying persistent and remittent attention-deficit/hyperactivity disorder (ADHD).

METHODS

50 young adults with childhood ADHD (28 persisters, 22 remitters) and 28 demographically similar healthy controls (HC) were compared on microstates features and frequency principal components (f-PCs) of eye-closed resting state. Support vector machine model with sequential forward selection (SVM-SFS) was utilized to discriminate three groups.

RESULTS

Four microstates and four comparable f-PCs were identified. Compared to HC, ADHD persisters showed prolonged duration in microstate C, elevated power of the delta component (D), and compromised amplitude of the two alpha components (A1 and A2). Remitters showed increased duration and coverage of microstate C, together with decreased activity of D, relatively intact amplitude of A1, and amplitude reduction in A2. The SVM-SFS algorithm achieved an accuracy of 93.59% in classifying persisters, remitters and controls. The most discriminative features selected were those exhibiting group differences.

CONCLUSIONS

We found widespread anomalies in ADHD persisters in brain dynamics and intrinsic EEG components. Meanwhile, the neural features in remitters exhibited multiple patterns.

SIGNIFICANCE

This study underlines the use of microstate dynamics and spectral components as potential markers of persistent and remittent ADHD.

Frontocentral delta-beta amplitude coupling in endometriosis-related chronic pelvic pain

OBJECTIVE

Endometriosis is associated with neuroplastic changes in cognitive control and pain processing networks. This was the first study to assess eyes-closed resting electroencephalogram (EEG) oscillatory amplitudes in women with endometriosis compared to healthy controls, and explore the relationship with chronic pelvic pain.

METHODS

Women with endometriosis-related chronic pelvic pain and individually age-matched pain-free controls (N = 20 per group) documented pelvic pain for 28 days before having continuous EEG recorded during a 2 min eyes closed resting state. Natural frequency components were extracted for each group using frequency principal components analysis. Corresponding components were assessed for group differences and correlated with pain scores.

RESULTS

Relative to controls, the endometriosis group had greater component amplitudes in delta (0.5 Hz) and beta (∼28 Hz), and reduced alpha (∼10 Hz). Delta and beta amplitudes were positively associated with pain severity, but only beta maintained this association after delta-beta amplitude coupling was controlled.

CONCLUSIONS

Enhanced resting delta and beta amplitudes were seen in women with endometriosis experiencing chronic pelvic pain. This delta-beta coupling varied with pelvic pain severity, perhaps reflecting altered cholinergic tone and/or stress reactivity.

SIGNIFICANCE

Endometriosis-related changes in central pain processing demonstrate a distinct neuronal oscillatory signature detectable at rest.

The Effects of Concentrative Meditation on the Electroencephalogram in Novice Meditators

Following investigations into the benefits of meditation on psychological health and well-being, research is now seeking to understand the mechanisms underlying these outcomes. This study aimed to identify natural alpha and theta frequency components during eyes-closed resting and concentrative meditation states and examined their differences within and between two testing sessions. Novice meditators had their EEG recorded during eyes-closed resting and concentrative meditation conditions, before and after engaging in a brief daily concentrative meditation practice for approximately one-month. Separate frequency Principal Components Analyses (f-PCA) yielded four spectral components of interest, congruent between both conditions and sessions: Delta-Theta-Alpha, Low Alpha, High Alpha, and Alpha-Beta. While all four components showed some increase in the meditation condition at the second session, only Low Alpha (∼9.5-10.0 Hz) showed similar increases while resting. These findings support the use of f-PCA as a novel method of data analysis in the investigation of psychophysiological states in meditation.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Intrinsic phase-amplitude coupling on multiple spatial scales during the loss and recovery of consciousness

BACKGROUND

Recent studies have demonstrated that changes in brain information processing during anesthetic-induced loss of consciousness (LOC) might be influenced by phase-amplitude coupling (PAC) in electroencephalogram (EEG). However, most anesthesia research on PAC typically focuses on delta and alpha oscillations. Studies of spatial-frequency characteristics by PAC for EEG may yield additional insights into understanding the impaired information processing under anesthesia unconsciousness and provide potential improvements in anesthesia monitoring.

OBJECTIVE

Considering different frequency bands of EEG represent neural activities on different spatial scales, we hypothesized that functional coupling simultaneously appears in multiple frequency bands and specific brain regions during anesthesia unconsciousness. In this paper, PAC analysis on whole-brain EEG besides delta and alpha oscillations was investigated to understand the influence of multiple cross-frequency coordination coupling on information processing during the loss and recovery of consciousness.

METHOD

EEG data from fifteen patients without cognitive diseases (7 males/8 females, aged 43.8 ± 13.4 years, weighing 63.3 ± 14.9 kilograms) undergoing lower limb surgery and sevoflurane anesthesia was recorded. To investigate the spatial-frequency characteristics of EEG source signals during loss and recovery of consciousness, the time-resolved PAC (tPAC) was calculated to reflect cross-frequency coordination in different frequency bands (delta, theta, alpha, beta, gamma) and different functional regions (Visual, Limbic, Dorsal attention, Ventral attention, Default, Somatomotor, Control, Salience networks). Furthermore, different patterns (peak-max and trough-max) of PAC were examined by constructing phase-amplitude histograms using phase bins to investigate the different information processing during LOC. The multivariate analysis of variance (MANOVA) and trend analysis were used for statistical analysis.

RESULTS

Theta-alpha and alpha-beta PAC were observed during sevoflurane-induced LOC, which significantly changed during loss and recovery of consciousness (F_4,70 = 16.553, p < 0.001 for theta-alpha PAC and F_4,70 = 12.446, p < 0.001 for alpha-beta PAC, MANOVA test). Simultaneously, PAC was distributed in specific functional regions, i.e., Visual, Limbic, Default, Somatomotor, etc. Furthermore, peak-max patterns of theta-alpha PAC were observed while alpha-beta PAC showed trough-max patterns and vice versa.

CONCLUSION

Theta-alpha and alpha-beta PAC observed in specific brain regions represent information processing on multiple spatial scales, and the opposite patterns of PAC indicate opposite information processing on multiple spatial scales during LOC. Our study demonstrates the regulation of local-global information processing during sevoflurane-induced LOC. It suggests the utility of evaluating the balance of functional integration and segregation in monitoring anesthetized states.

Data-Driven EEG Theta and Alpha Components Are Associated with Subjective Experience during Resting State

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. However, the relationship between the ongoing experience of mind wandering and the individual biological signal is still unclear. We aim to estimate associations between subjective experiences measured with the Amsterdam Resting-State Questionnaire and data-driven components of an electroencephalogram extracted by frequency principal component analysis (f-PCA). Five minutes of resting multichannel EEG was recorded in 226 participants and six EEG data-driven components were extracted—three components in the alpha range (peaking at 9, 10.5, and 11.5 Hz) and one each in the delta (peaking at 0.5 Hz), theta (peaking at 5.5 Hz) and beta (peaking at 17 Hz) ranges. Bayesian Pearson’s correlation revealed a positive association between the individual loadings of the theta component and ratings for Sleepiness (r = 0.200, BF10 = 7.676), while the individual loadings on one of the alpha components correlated positively with scores for Comfort (r = 0.198, BF10 = 7.115). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

Behavioral tests for evaluating the characteristics of brain diseases in rodent models: Optimal choices for improved outcomes (Review)

Behavioral assessment is the dominant approach for evaluating whether animal models of brain diseases can successfully mimic the clinical characteristics of diseases. At present, most research regarding brain diseases involves the use of rodent models. While studies have reported numerous methods of behavioral assessments in rodent models of brain diseases, each with different principles, procedures, and assessment criteria, only few reviews have focused on characterizing and differentiating these methods based on applications for which they are most appropriate. Therefore, in the present review, the representative behavioral tests in rodent models of brain diseases were compared and differentiated, aiming to provide convenience for researchers in selecting the optimal methods for their studies.

A tutorial on the use of temporal principal component analysis in developmental ERP research – opportunities and challenges

Developmental researchers are often interested in event-related potentials (ERPs). Data-analytic approaches based on the observed ERP suffer from major problems such as arbitrary definition of analysis time windows and regions of interest and the observed ERP being a mixture of latent underlying components. Temporal principal component analysis (PCA) can reduce these problems. However, its application in developmental research comes with the unique challenge that the component structure differs between age groups (so-called measurement non-invariance). Separate PCAs for the groups can cope with this challenge. We demonstrate how to make results from separate PCAs accessible for inferential statistics by re-scaling to original units. This tutorial enables readers with a focus on developmental research to conduct a PCA-based ERP analysis of amplitude differences. We explain the benefits of a PCA-based approach, introduce the PCA model and demonstrate its application to a developmental research question using real-data from a child and an adult group (code and data openly available). Finally, we discuss how to cope with typical challenges during the analysis and name potential limitations such as suboptimal decomposition results, data-driven analysis decisions and latency shifts.

Electrophysiological correlates of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism

The brain-derived neurotrophic factor (BDNF) protein is essential for neuronal development. Val66Met (rs6265) is a functional polymorphism at codon 66 of the BDNF gene that affects neuroplasticity and has been associated with cognition, brain structure and function. The aim of this study was to clarify the relationship between BDNF Val66Met polymorphism and neuronal oscillatory activity, using the electroencephalogram (EEG), in a normative cohort. Neurotypical (N = 92) young adults were genotyped for the BDNF Val66Met polymorphism and had eyes open resting-state EEG recorded for four minutes. Focal increases in right fronto-parietal delta, and decreases in alpha-1 and right hemispheric alpha-2 amplitudes were observed for the Met/Met genotype group compared to Val/Val and Val/Met groups. Stronger frontal topographies were demonstrated for beta-1 and beta-2 in the Val/Met group versus the Val/Val group. Findings highlight BDNF Val66Met genotypic differences in EEG spectral amplitudes, with increased cortical excitability implications for Met allele carriers.