The analysis of the EEG

Examining the effects of biofield therapy through simultaneous assessment of electrophysiological and cellular outcomes

In this case study, a self-described biofield therapy (BT) practitioner (participant) took part in multiple (n = 60) treatment and control (non-treatment) sessions under double-blind conditions. During the treatment phases, the participant provided BT treatment at a distance of about 12 inches from the cells, alternating with rest phases where no such efforts were made. Human pancreatic cancer cell activity was assessed using three markers - cytoskeleton changes (tubulin and β-actin) and Ca2+ uptake. The study examined changes in the participant's physiological parameters including electroencephalogram (EEG) and heart rate measures during the treatment of: (1) live cells and (2) either dead cells or medium only with no cells (control group). Changes in cellular outcomes and if there was an association between the participant's physiological parameters and cellular outcomes were examined. The experimental setup was a 2 × 2 design, contrasting cell type (live vs. control) against session type (treatment vs. non-treatment). Parallel sham-treated control cells were examined for changes in the cell parameters over time while controlling for the presence of a person in front of the cells mimicking the distance and movements of the participant. The participant's physiological data, including 64-channel EEG and heart rate, were continuously monitored throughout these sessions. We observed significant (p < 0.01) spectral changes in the participant's EEG during BT treatment in all frequency bands of interest, as well as in heart rate variability (HRV) (RMSSD measure; p < 0.01). We also observed significant differences in beta and gamma EEG and HRV (pNN50 measure) when the participant treated live but not control cells (p = 0.02). However, no interaction between treatment and cell type (live vs. dead cells/medium-no cells) was observed. We observed Ca2+ uptake increased over time during both BT and sham treatment, but the increase was significantly less for the BT group relative to the sham-treatment controls (p = 0.03). When using Granger causality to assess causal directional associations between cell markers and participant's physiological parameters, EEG measurements showed significant bidirectional causal effects with cell metrics, especially β-actin and intracellular Ca2+ levels (p < 0.000001). These outcomes suggest a complex relationship between physiological responses and cellular effects during BT treatment sessions. Given the study's limitations, follow-up investigations are warranted.

Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.

Biomarkers for prognostic functional recovery poststroke: A narrative review

Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.

A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment

EEG experiments yield high-dimensional event-related potential (ERP) data in response to repeatedly presented stimuli throughout the experiment. Changes in the high-dimensional ERP signal throughout the duration of an experiment (longitudinally) is the main quantity of interest in learning paradigms, where they represent the learning dynamics. Typical analysis, which can be performed in the time or the frequency domain, average the ERP waveform across all trials, leading to the loss of the potentially valuable longitudinal information in the data. Longitudinal time-frequency transformation of ERP (LTFT-ERP) is proposed to retain information from both the time and frequency domains, offering distinct but complementary information on the underlying cognitive processes evoked, while still retaining the longitudinal dynamics in the ERP waveforms. LTFT-ERP begins by time-frequency transformations of the ERP data, collected across subjects, electrodes, conditions and trials throughout the duration of the experiment, followed by a data driven multidimensional principal components analysis (PCA) approach for dimension reduction. Following projection of the data onto leading directions of variation in the time and frequency domains, longitudinal learning dynamics are modeled within a mixed effects modeling framework. Applications to a learning paradigm in autism depict distinct learning patterns throughout the experiment among children diagnosed with Autism Spectrum Disorder and their typically developing peers. LTFT-ERP time-frequency joint transformations are shown to bring an additional level of specificity to interpretations of the longitudinal learning patterns related to underlying cognitive processes, which is lacking in single domain analysis (in the time or the frequency domain only). Simulation studies show the efficacy of the proposed methodology.

Bayesian inference for stationary points in gaussian process regression models for event-related potentials analysis

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, which also produces an estimate of the function. We use Gaussian processes as a flexible prior for the underlying function and impose derivative constraints to control the function's shape via conditioning. We develop an inferential strategy that intentionally restricts estimation to the case of at least one stationary point, bypassing possible mis-specifications in the number of stationary points and avoiding the varying dimension problem that often brings in computational complexity. We illustrate the proposed methods using simulations and then apply the method to the estimation of event-related potentials (ERP) derived from electroencephalography (EEG) signals. We show how the proposed method automatically identifies characteristic components and their latencies at the individual level, which avoids the excessive averaging across subjects which is routinely done in the field to obtain smooth curves. By applying this approach to EEG data collected from younger and older adults during a speech perception task, we are able to demonstrate how the time course of speech perception processes changes with age. This article is protected by copyright. All rights reserved.

A multi-dimensional functional principal components analysis of EEG data

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.

Identifying longitudinal trends within EEG experiments

Differential brain response to sensory stimuli is very small (a few microvolts) compared to the overall magnitude of spontaneous electroencephalogram (EEG), yielding a low signal-to-noise ratio (SNR) in studies of event-related potentials (ERP). To cope with this phenomenon, stimuli are applied repeatedly and the ERP signals arising from the individual trials are averaged at the subject level. This results in loss of information about potentially important changes in the magnitude and form of ERP signals over the course of the experiment. In this article, we develop a meta-preprocessing step utilizing a moving average of ERP across sliding trial windows, to capture such longitudinal trends. We embed this procedure in a weighted linear mixed effects model to describe longitudinal trends in features such as ERP peak amplitude and latency across trials while adjusting for the inherent heteroskedasticity created at the meta-preprocessing step. The proposed unified framework, including the meta-processing and the weighted linear mixed effects modeling steps, is referred to as MAP-ERP (moving-averaged-processed ERP). We perform simulation studies to assess the performance of MAP-ERP in reconstructing existing longitudinal trends and apply MAP-ERP to data from young children with autism spectrum disorder (ASD) and their typically developing counterparts to examine differences in patterns of implicit learning, providing novel insights about the mechanisms underlying social and/or cognitive deficits in this disorder.

The impact of melancholia versus non-melancholia on resting-state, EEG alpha asymmetry: electrophysiological evidence for depression heterogeneity

While depression has been associated with relatively greater right than left frontal cortical activity - a neurophysiological marker reflecting greater activation of the withdrawal system - contradictory findings have been reported. It was hypothesised that melancholia would be associated with relative right frontal activation, in comparison to non-melancholia and controls. We collected 2-min of resting-state, eyes closed, electroencephalographic activity from a total of 237 participants including 117 patients with major depressive disorder (57 with melancholia, 60 with non-melancholia) and 120 healthy controls. In contrast to hypotheses, patients with non-melancholia displayed relative left frontal activation in comparison to controls and those with melancholia. These findings were associated with a small to moderate effect size (Cohen's d=0.30-0.34). Critically, patients with melancholic subtype did not differ from controls despite increased severity - relative to those with non-melancholia - on clinical measures. These results may reflect an increase in approach tendencies in patients with non-melancholia including reassurance seeking, anger or irritable aggression. Findings highlight the need for further research on the heterogeneity MDD.

Application of modern tests for stationarity to single-trial MEG data: transferring powerful statistical tools from econometrics to neuroscience

Stationarity is a crucial yet rarely questioned assumption in the analysis of time series of magneto- (MEG) or electroencephalography (EEG). One key drawback of the commonly used tests for stationarity of encephalographic time series is the fact that conclusions on stationarity are only indirectly inferred either from the Gaussianity (e.g. the Shapiro-Wilk test or Kolmogorov-Smirnov test) or the randomness of the time series and the absence of trend using very simple time-series models (e.g. the sign and trend tests by Bendat and Piersol). We present a novel approach to the analysis of the stationarity of MEG and EEG time series by applying modern statistical methods which were specifically developed in econometrics to verify the hypothesis that a time series is stationary. We report our findings of the application of three different tests of stationarity--the Kwiatkowski-Phillips-Schmidt-Schin (KPSS) test for trend or mean stationarity, the Phillips-Perron (PP) test for the presence of a unit root and the White test for homoscedasticity--on an illustrative set of MEG data. For five stimulation sessions, we found already for short epochs of duration of 250 and 500 ms that, although the majority of the studied epochs of single MEG trials were usually mean-stationary (KPSS test and PP test), they were classified as nonstationary due to their heteroscedasticity (White test). We also observed that the presence of external auditory stimulation did not significantly affect the findings regarding the stationarity of the data. We conclude that the combination of these tests allows a refined analysis of the stationarity of MEG and EEG time series.

The effects of a poor night sleep on mood, cognitive, autonomic and electrophysiological measures

Sustained sleep problems such as insomnia have been shown to be detrimental to health. This study examines the less understood, finer grained effects of a single bad night's sleep on mood, cognitive, autonomic and electrophysiological functions. We assessed 338 individuals who had no symptoms of a clinical sleep disorder. Of these, 226 individuals had six or more hours sleep and 112 individuals had less than six hours sleep prior to an assessment of mood, cognition, autonomic and electrophysiological functioning. Individuals in the relatively "bad night" sleep group had higher depression, anxiety, and stress scores and reported significantly poorer overall wellbeing. They made more errors on simple cognitive tasks while more complex task components were unaffected. They also had an increase in heart rate and EEG alpha and beta power at rest. Participants in this study had no symptoms of a clinical sleep disorder, however the effects of a poor night sleep on measures of mood, cognition, autonomic and electrophysiological function were similar, but less severe than those reported in insomnia patients. The integrative profile of measures reported here point to an increase in physiological arousal and sub-optimal cognition, following a poor night's sleep.

The coincidence between late non-phase-locked gamma synchronization response and saccadic eye movements

The event-related response in the gamma (30-45 Hz) frequency band was studied in healthy subjects (n=45) viewing sequentially presented pictures from the International Affective Picture System. The distinct non-phase-locked gamma response was obtained in characteristic time window (200-400 ms) with clear-cut centro-parietal location. The strong coincidence between induced gamma oscillations and saccadic eye movements was revealed. We suggest that saccade-related gamma increase is another manifestation of the phenomenon known as presaccadic spike potential, which is commonly registered over parietal scalp leads at 10-20 ms prior to saccade onset. It is hypothesized that late non-phase-locked gamma synchronization mainly reflects activity of a system responsible for attentional tuning and motor planning/execution of saccadic eye movements.

Modelling and exploring human sleep with event history analysis

In this paper we propose the use of statistical models of event history analysis for investigating human sleep. These models provide appropriate tools for statistical evaluation when sleep data are recorded continuously over time or on a fine time grid, and are classified into sleep stages such as REM and nonREM as defined by Rechtschaffen and Kales (1968). In contrast to conventional statistical procedures, event history analysis makes full use of the information contained in sleep data, and can therefore provide new insights into non-stationary properties of sleep. Probabilities of or intensities for transitions between sleep stages are the basic quantities for characterising sleep processes. The statistical methods of event history analysis aim at modelling and estimating these intensities as functions of time, taking into account individual sleep history and assessing the influence of factors of interest, such as hormonal secretion. In this study we suggest the use of non-parametric approaches to reveal unknown functional forms of transition intensities and to explore time-varying and non-stationary effects. We then apply these techniques in a study of 30 healthy male volunteers to assess the mean population intensity and the effects of plasma cortisol concentration on the transition between selected sleep stages as well as the influence of elapsed time in a current REM period on the intensity for a transition to nonREM. The most interesting findings are that (a) the intensity of the nonREM-to-REM transitions after sleep onset in young men shows a periodicity which is similar to that of nonREM/REM cycles; (b) 30-45 min after sleep onset, young men reveal a great propensity to pass from light sleep (stages 1 or 2) into slow-wave sleep (SWS) (stages 3 or 4); (c) high cortisol levels imposed additional impulses on the transition intensity of (i) wake to sleep around 2 h after sleep onset, (ii) nonREM to REM around 6 h later, (iii) stage 1 or stage 2 sleep to SWS around 2, 4 and 6 h later and (iv) SWS to stage 1 or stage 2 sleep about 2 h later. Moreover, high cortisol concentrations at the beginning of REM periods favoured the change to nonREM sleep, whereas later their influence on a nonREM change became weak and weaker. As sleep data are also available as event-oriented data in many studies in sleep research, event history analysis applied additionally to conventional statistical procedures, such as regression analysis or analysis of variance, could help to acquire more information and knowledge about the mechanisms behind the sleep process.