Meditation training modulates brain electric microstates and felt states of awareness

Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches

Throughout the brief history of contemplative neuroscience, electroencephalography (EEG) has been a valuable and enduring methodology used to elucidate the neural correlates and mechanisms of mindfulness. In this review, we provide a reminder that longevity should not be conflated with obsoletion and that EEG continues to offer exceptional promise for addressing key questions and challenges that pervade the field today. Toward this end, we first outline the unique advantages of EEG from a research strategy and experimental design perspective, then highlight an array of new sophisticated data analytic approaches and translational paradigms. Along the way, we provide illustrative examples from our own work and the broader literature to showcase how these innovations can be leveraged to spark new insights and stimulate progress across both basic science and translational applications of mindfulness. Ultimately, we argue that EEG still has much to contribute to contemplative neuroscience, and we hope to solicit the interest of other investigators to make full use of its capabilities in service of maximizing its potential within the field.

EEG microstates during resting-state and dissociative events in patients with psychogenic non-epileptic seizures

OBJECTIVE

Previous neuroimaging studies indicate complex network alterations in psychogenic non-epileptic seizures (PNES) patients, but brain activity modifications occurring during PNES remain unexplored. This study aimed to analyze EEG microstate metrics in PNES patients both during events and resting state to investigate the neurophysiological changes underlying these dissociative events.

METHODS

We recruited 22 PNES patients and 24 age- and sex-matched healthy controls. Inclusion criteria included a history of PNES and at least one recorded akinetic PNES during video-EEG.

RESULTS

A two-way repeated-measures ANOVA revealed significant class*condition interactions for microstate duration (p = 0.017), coverage (p = 0.012), and global field power (GFP) (p = 0.008). Post-hoc FDR-adjusted paired t-tests showed a significant decrease in microstate C duration (p = 0.036), coverage (p = 0.04), and GFP (p = 0.036) during PNES events compared to resting state. Mann-Whitney U tests showed significantly higher microstate C duration in PNES patients during resting state compared to controls (p = 0.009).

CONCLUSIONS

Microstate C, previously associated with the default mode network, showed increased duration during resting state and decreased representation during PNES, suggesting a prominent shift in neural activity dynamics within this network during dissociative events.

SIGNIFICANCE

These findings provide insights into neurophysiological changes occurring during PNES, suggestingan interplay between pathological and adaptive mechanisms in their pathophysiology.

Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners

Background

Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG).

Methods

Expert Vipassana meditators (n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information.

Results

We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths.

Conclusions

This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices.

Attention demands modulate brain electrical microstates and mental fatigue induced by simulated flight tasks

Objective.Prolonged engagement in tasks with varying attention demands is thought to elicit distinct forms of mental fatigue, potentially indicating variations in neural activity. This study aimed to investigate the association between mental fatigue and changes in electroencephalogram microstate dynamics during tasks with varying attention demands.Approach.In the present study, we employed a 2 × 2 repeated measures ANOVA to analyze the temporal parameters of four distinct microstates (A, B, C, and D) across different levels of attention demands (high vs. low) and mental fatigue (high vs. low) within a controlled flight simulation task involving 17 college students.Main results.Significant variations in mean durations were observed, with microstates A and B exhibiting shorter durations under high fatigue during low attention demands, while their durations increased under high attention demands. Microstate C showed increased occurrences with high fatigue under low attention demands and decreased occurrences under high attention demands. The duration and occurrence of the microstates exhibited different trends throughout the course of mental fatigue, potentially reflecting distinct fatigue-related processes.Significance.These findings establish a link between different types of mental fatigue and microstate dynamics, contributing to a comprehensive understanding of the neural processing mechanisms underlying mental fatigue.

Mindfulness Training in High-Demand Cohorts Alters Resting-State Electroencephalography: An Exploratory Investigation of Individual Alpha Frequency, Aperiodic 1/f Activity, and Microstates

Background

Mindfulness training (MT) programs have demonstrated utility as cognitive training tools, but there is little consensus on the neurophysiological processes that may underlie its benefits. It has been posited that intrinsic brain activity recorded at rest reflects the functional connectivity of large-scale brain networks and may provide insight into neuroplastic changes that support MT. In the current study, we indexed changes in several resting-state electroencephalography (EEG) parameters to investigate the neurophysiological underpinnings of MT.

Methods

Resting-state EEG data were collected from active-duty U.S. military personnel (N = 80) at 2 testing sessions: before (time [T] 1) and after (T2) engaging in an 8-week MT or active comparison intervention (positivity training). We examined longitudinal and/or groupwise differences in several EEG parameters through parameterization of power spectra (individual alpha frequency and 1/f activity) and microstate analysis.

Results

While no significant group × time differences were observed in individual alpha frequency, significant group × time effects were observed in several EEG parameters from T1 to T2. Compared with MT, positivity training was associated with a steepening of the 1/f slope and higher 1/f intercepts together with decreased duration and increased global field power of microstates.

Conclusions

Taken together, these results suggest that the effects of interventions may be differentiated in resting-state brain activity in a sample of military personnel. Such findings provide insight into the neural underpinnings of MT-related brain changes, but more research is required to elucidate how these may relate to task-related neural and performance changes with MT and whether results generalize to other mindfulness interventions in alternative cohorts and contexts.

EEG Microstates in Social and Affective Neuroscience

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience.

EEG Microstates as Markers for Cognitive Impairments in Fragile X Syndrome

Fragile X syndrome (FXS) is one of the most common inherited causes of intellectual disabilities. While there is currently no cure for FXS, EEG is considered an important method to investigate the pathophysiology and evaluate behavioral and cognitive treatments. We conducted EEG microstate analysis to investigate resting brain dynamics in FXS participants. Resting-state recordings from 70 FXS participants and 71 chronological age-matched typically developing control (TDC) participants were used to derive microstates via modified k-means clustering. The occurrence, mean global field power (GFP), and global explained variance (GEV) of microstate C were significantly higher in the FXS group compared to the TDC group. The mean GFP was significantly negatively correlated with non-verbal IQ (NVIQ) in the FXS group, where lower NVIQ scores were associated with greater GFP. In addition, the occurrence, mean duration, mean GFP, and GEV of microstate D were significantly greater in the FXS group than the TDC group. The mean GFP and occurrence of microstate D were also correlated with individual alpha frequencies in the FXS group, where lower IAF frequencies accompanied greater microstate GFP and occurrence. Alterations in microstates C and D may be related to the two well-established cognitive characteristics of FXS, intellectual disabilities and attention impairments, suggesting that microstate parameters could serve as markers to study cognitive impairments and evaluate treatment outcomes in this population. Slowing of the alpha peak frequency and its correlation to microstate D parameters may suggest changes in thalamocortical dynamics in FXS, which could be specifically related to attention control. (250 words).

EEG changes induced by meditative practices: State and trait effects in healthy subjects and in patients with epilepsy

The effect of meditation on brain activity has been the topic of many studies in healthy subjects and in patients suffering from chronic diseases. These effects are either explored during meditation practice (state effects) or as a longer-term result of meditation training during the resting-state (trait). The topic of this article is to first review these findings by focusing on electroencephalography (EEG) changes in healthy subjects with or without experience in meditation. Modifications in EEG baseline rhythms, functional connectivity and advanced nonlinear parameters are discussed in regard to feasibility in clinical applications. Secondly, we provide a state-of-the-art of studies that proposed meditative practices as a complementary therapy in patients with epilepsy, in whom anxiety and depressive symptoms are prevalent. In these studies, the effects of standardized meditation programs including elements of traditional meditation practices such as mindfulness, loving-kindness and compassion are explored both at the level of psychological functioning and on the occurrence of seizures. Lastly, preliminary results are given regarding our ongoing study, the aim of which is to quantify the effects of a mindfulness self-compassion (MSC) practice on interictal and ictal epileptic activity. Feasibility, difficulties, and prospects of this study are discussed.

Electroencephalography microstates highlight specific mindfulness traits

The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Evidence for modulation of EEG microstates by mental workload levels and task types

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Revealing subgroup-specific mechanisms of change via moderated mediation: A meditation intervention example

OBJECTIVE

Effective psychosocial interventions exist for numerous mental health conditions. However, despite decades of research, limited progress has been made in clarifying the mechanisms that account for their beneficial effects. We know that many treatments work, but we know relatively little about why they work. Mechanisms of change may be obscured due to prior research collapsing across heterogeneous subgroups of patients with differing underlying mechanisms of response. Studies identifying baseline individual characteristics that predict differential response (i.e., moderation) may inform research on why (i.e., mediation) a particular subgroup has better outcomes to an intervention via tests of moderated mediation.

METHOD

In a recent randomized controlled trial comparing a 4-week meditation app with a control condition in school system employees (N = 662), we previously developed a "Personalized Advantage Index" (PAI) using baseline characteristics, which identified a subgroup of individuals who derived relatively greater benefit from meditation training. Here, we tested whether the effect of mindfulness acquisition in mediating group differences in outcome was moderated by PAI scores.

RESULTS

A significant index of moderated mediation (IMM = 1.22, 95% CI [0.30, 2.33]) revealed that the effect of mindfulness acquisition in mediating group differences in outcome was only significant among those individuals with PAI scores predicting relatively greater benefit from the meditation app.

CONCLUSIONS

Subgroups of individuals may differ meaningfully in the mechanisms that mediate their response to an intervention. Considering subgroup-specific mediators may accelerate progress on clarifying mechanisms of change underlying psychosocial interventions and may help inform which specific interventions are most beneficial for whom. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Changes in Mental Health and EEG Biomarkers of Undergraduates Under Different Patterns of Mindfulness

The effects of short-term mindfulness are associated with the different patterns (autonomic, audio guided, or experienced and certified mindfulness instructor guided mindfulness). However, robust evidence for reported the impacts of different patterns of mindfulness on mental health and EEG biomarkers of undergraduates is currently lacking. Therefore, we aimed to test the hypotheses that mindfulness training for undergraduates would improve mental health, and increase alpha power over frontal region and theta power over midline region at the single electrode level. We also describe the distinction among frequency bands patterns in different sites of frontal and midline regions. 70 participants were enrolled and assigned to either 5-day mindfulness or a waiting list group. Subjective questionnaires measured mental health and other psychological indicators, and brain activity was recorded during various EEG tasks before and after the intervention. The 5-day mindfulness training improved trait mindfulness, especially observing (p = 0.001, d = 0.96) and nonreactivity (p = 0.03, d = 0.56), sleep quality (p = 0.001, d = 0.91), and social support (p = 0.001, d = 0.95) while not in affect. Meanwhile, the expected increase in the alpha power of frontal sites (p < 0.017, d > 0.84) at the single electrode level was confirmed by the current data rather than the theta. Interestingly, the alteration of low-beta power over the single electrode of the midline (p < 0.05, d > 0.71) was difference between groups. Short-term mindfulness improves practitioners' mental health, and the potentially electrophysiological biomarkers of mindfulness on neuron oscillations were alpha activity over frontal sites and low-beta activity over midline sites.

The Electroencephalographic Brainwave Spectrum, Mindful Meditation, and Awareness: Hypothesis

It is hypothesized that being mindfully aware is a spontaneous state of being. It is imbued with joy, peace and happiness. Such a state is periodically revealed during restful attentiveness or presence. It is also associated with spontaneous brain alpha activity of 8-13 Hz. In deep nonrapid eye movement sleep, there is synchronous delta activity at a coherent frequency of 0.1 Hz. Both of these brainwave ground states are spontaneous, calm and effortless. When any physical or mental effort is made, the alpha rhythm is desynchronized, and it is superposed by faster brain waves of beta (13-30 Hz) and gamma frequencies (30-150 Hz). This is associated with a stream of dualistic conscious experiences with contents. During deep sleep, delta activity is superposed by beta and gamma activity with microarousals resulting in dream experiences. During effortless, meditative awareness, the whole family of alpha rhythm is synchronized including (a) Occipital-parietal alpha with visual clarity, formless color, and the absence of visual imagery (b) Frontal eye-field alpha with relatively motionless eyes, and the absence of voluntary actions or plans to move the eyes in some direction, along with nonactive working memory, (c) Somatosensory alpha or Mu rhythm from the somatic motor-sensory cortex with the resultant stillness of the body including head, face, larynx, spine, hands and legs, (d) Mid-temporal auditory alpha with vocal quietness and internal verbal silence (Maunam) with a feeling of spontaneous silence and serenity, (e) Cingulate and precuneus alpha resulting in freedom from autobiographical memories and the sense of agency or ego. The insular cortex serves as a gatekeeper, a hierarchical controller to switch between conscious engagement or disengagement from the internal or the external world. It switches between the default mode network and the executive frontoparietal networks, between the sequential and the parallel modes of functioning. Mindful consciousness is local and dualistic, whereas mindful awareness is nonlocal and nondual.

Event-related microstate dynamics represents working memory performance

In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics. To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50-300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50-300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.

Application of Meditation Relaxation Training and Rosenthal Effect in Patients with Adenoidectomy

Objective

This study aims to explore the application effect of meditation relaxation training and the Rosenthal effect in patients with adenoidectomy.

Methods

This study included 94 children who underwent adenoidectomy in our hospital from April 2020 to May 2022 and were divided into a study group and a control group. The control group was given routine care, and the study group was given meditation relaxation training and the Rosenthal effect on the basis of the control group. The negative emotions, treatment compliance, complication rates, and nursing satisfaction of children's family members before and after the intervention were compared between the two groups.

Results

The results of this study showed that after the intervention, the CDI and SCARED scores of the children in the study group were significantly lower than those in the control group. The treatment compliance in the study group was significantly higher than that in the control group, and the incidence of complications was significantly lower than that in the control group.

Conclusion

The intervention of meditation relaxation training and the Rosenthal effect on children with adenoidectomy can relieve their negative emotions, improve treatment compliance, reduce the incidence of complications, and the children's family members are more satisfied.

Shifting Baselines: Longitudinal Reductions in EEG Beta Band Power Characterize Resting Brain Activity with Intensive Meditation

Objectives

A core assumption of meditation training is that cognitive capacities developed during formal practice will transfer to other contexts or activities as expertise develops over time. This implies that meditation training might influence domain-general neurocognitive systems, the spontaneous activity of which should be reflected in the dynamics of the resting brain. Previous research has demonstrated that 3 months of meditation training led to reductions in EEG beta band power during mindfulness of breathing practice. The current study extends these findings to ask whether concomitant shifts in power are observed during 2 min of eyes closed rest, when participants are not explicitly engaged in formal meditation.

Methods

Experienced meditation practitioners were randomly assigned to practice 3 months of focused attention meditation in a residential retreat, or to serve as waitlist controls. The waitlist controls later completed their own 3-month retreat. Permutation-based cluster analysis of 88-channel resting EEG data was used to test for spectral changes in spontaneous brain activity over the course of the retreats.

Results

Longitudinal reductions in EEG power in the beta frequency range were identified and replicated across the two independent training periods. Less robust reductions were also observed in the high alpha frequency range, and in individual peak alpha frequency. These changes closely mirror those previously observed during formal mindfulness of breathing meditation practice.

Conclusions

These findings suggest that the neurocognitive effects of meditation training can extend beyond the bounds of formal practice, influencing the spontaneous activity of the resting brain. Rather than serving as an invariant baseline, resting states might carry meaningful training-related effects, blurring the line between state and trait change.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12671-022-01974-9.

EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

Evidence suggests that the stream of consciousness is parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new implementation of a method to estimate the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive "U-shape" that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

EEG microstates suggest atypical resting-state network activity in high-functioning children and adolescents with Autism Spectrum Development

EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handedness-matched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies. The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD. This article is protected by copyright. All rights reserved.

Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach

Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF₁₀ > 10); remaining correlations showed substantial evidence (10 > BF₁₀ > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

Meditation training modulates brain electric microstates and felt states of awareness

Meditation practice is believed to foster states of mindful awareness and mental quiescence in everyday life. If so, then the cultivation of these qualities with training ought to leave its imprint on the activity of intrinsic functional brain networks. In an intensive longitudinal study, we investigated associations between meditation practitioners' experiences of felt mindful awareness and changes in the spontaneous electrophysiological dynamics of functional brain networks. Experienced meditators were randomly assigned to complete 3 months of full‐time training in focused‐attention meditation (during an initial intervention) or to serve as waiting‐list controls and receive training second (during a later intervention). We collected broadband electroencephalogram (EEG) during rest at the beginning, middle, and end of the two training periods. Using a data‐driven approach, we segmented the EEG into a time series of transient microstate intervals based on clustering of topographic voltage patterns. Participants also provided daily reports of felt mindful awareness and mental quiescence, and reported daily on four experiential qualities of their meditation practice during training. We found that meditation training led to increases in mindful qualities of awareness, which corroborate contemplative accounts of deepening mental calm and attentional focus. We also observed reductions in the strength and duration of EEG microstates across both interventions. Importantly, changes in the dynamic sequencing of microstates were associated with daily increases in felt attentiveness and serenity during training. Our results connect shifts in subjective qualities of meditative experience with the large‐scale dynamics of whole brain functional EEG networks at rest.