Short-term meditation induces changes in brain resting EEG theta networks

A Randomized Controlled Trial of Mindfulness Meditation Combined With BrainLink Intelligent Biofeedback Instrument on Pancreatic Cancer Patients Under Chemotherapy

BACKGROUND

Adjuvant chemotherapy can promote the 5-year overall survival rate of pancreatic cancer (PC) patients to 16%-21%. However, the negative emotions of patients, such as anxiety, are usually omitted. Moreover, their disease burden concentrates on pain symptoms, seriously affecting the quality of life of patients. How to improve the negative emotions of PC patients, alleviate their pain, and ameliorate their quality of life has become an urgent issue.

OBJECTIVE

To explore the effect of mindfulness meditation (MM) combined with the BrainLink intelligent biofeedback instrument on the anxiety, pain, brain fatigue, and quality of life of PC patients under chemotherapy.

METHOD

A total of 145 PC patients under chemotherapy were admitted to the Department of Hepatobiliary Surgery of the Second Affiliated Hospital of Army Military Medical University from January 2022 to March 2024 and were incorporated as study objects. They were divided into a control group (n = 72) and a test group (n = 73) according to the random number table. The control group received routine nursing treatment; the experimental group was treated with MM combined with an intelligent biofeedback instrument. The general information, anxiety (SAS), pain (VAS), EEG signal data (degrees of fatigue, concentration, and relaxation and EEG waves), and quality of life (EORTC QLQ-C30) of the two groups before intervention and 4th, 8th, and 12th weeks after intervention were compared.

RESULT

There is no significant difference in baseline data and pathological features between the two groups. After the 4th, 8th, and 12th weeks of intervention, the SAS values of the experimental group are remarkably lower than those of the control group (p < 0.05). In the 4th, 8th, and 12th weeks of intervention, the VAS scores of the experimental group are apparently lower than those of the control group (p < 0.05). In the 4th, 8th, and 12th weeks of intervention, the score of the quality of life (EORTC QLQ-C30) in the experimental group is significantly higher than that in the control group (p < 0.05). In the 4th, 8th, and 12th weeks, compared with the control group, the experimental group patients showed significant improvement in brain fatigue relief, concentration, and relaxation levels (p < 0.05), while the experimental group's brain α and θ. The wave showed an upward trend (p < 0.05).

CONCLUSION

MM combined with the BrainLink intelligent biofeedback instrument can significantly mitigate the anxiety, pain severity, and brain fatigue relief degree of PC patients under chemotherapy, thereby promoting their quality of life. It provides a novel intervention for the psychosomatic health of PC patients under chemotherapy.

Mobile brain imaging in butoh dancers: from rehearsals to public performance

BACKGROUND

Dissecting the neurobiology of dance would shed light on a complex, yet ubiquitous, form of human communication. In this experiment, we sought to study, via mobile electroencephalography (EEG), the brain activity of five experienced dancers while dancing butoh, a postmodern dance that originated in Japan.

RESULTS

We report the experimental design, methods, and practical execution of a highly interdisciplinary project that required the collaboration of dancers, engineers, neuroscientists, musicians, and multimedia artists, among others. We explain in detail how we technically validated all our EEG procedures (e.g., via impedance value monitoring) and minimized potential artifacts in our recordings (e.g., via electrooculography and inertial measurement units). We also describe the engineering details and hardware that enabled us to achieve synchronization between signals recorded at different sampling frequencies, along with a signal preprocessing and denoising pipeline that we used for data re-sampling and power line noise removal. As our experiment culminated in a live performance, where we generated a real-time visualization of the dancers' interbrain synchrony on a screen via an artistic brain-computer interface, we outline all the methodology (e.g., filtering, time-windows, equation) we used for online bispectrum estimations. Additionally, we provide access to all the raw EEG data and codes we used in our recordings. We, lastly, discuss how we envision that the data could be used to address several hypotheses, such as that of interbrain synchrony or the motor theory of vocal learning.

CONCLUSIONS

Being, to our knowledge, the first study to report synchronous and simultaneous recording from five dancers, we expect that our findings will inform future art-science collaborations, as well as dance-movement therapies.

Same, Same but Different? A Multi-Method Review of the Processes Underlying Executive Control

Attention, working memory, and executive control are commonly considered distinct cognitive functions with important reciprocal interactions. Yet, longstanding evidence from lesion studies has demonstrated both overlap and dissociation in their behavioural expression and anatomical underpinnings, suggesting that a lower dimensional framework could be employed to further identify processes supporting goal-directed behaviour. Here, we describe the anatomical and functional correspondence between attention, working memory, and executive control by providing an overview of cognitive models, as well as recent data from lesion studies, invasive and non-invasive multimodal neuroimaging and brain stimulation. We emphasize the benefits of considering converging evidence from multiple methodologies centred on the identification of brain mechanisms supporting goal-driven behaviour. We propose that expanding on this approach should enable the construction of a comprehensive anatomo-functional framework with testable new hypotheses, and aid clinical neuroscience to intervene on impairments of executive functions.

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.

Electrophysiological effects of smartphone notifications on cognitive control following a brief mindfulness induction

Smartphone use is nearly ubiquitous, with 93% of adults among economically developed countries, including the United States, Canada, Israel, and South Korea owning a smartphone (Taylor & Silver, 2019). Multiple studies have demonstrated the distracting effects of smartphone notifications on behavioral measures of cognition. Fewer studies have examined the effects of notifications on neural activity underlying higher-level cognitive processes or behavioral inductions to reduce smartphone-related distraction. Using EEG spectral frequency power densities, we assessed the effects of smartphone notifications (vs. control trials) on engagement of attentional shifting processes involved in cognitive control during a Navon Letter visual oddball task. Participants were randomly assigned to a brief mindfulness induction (N = 44) or a neutral narration control condition (N = 43). Overall, participants had lower theta-band power, but higher alpha- and beta-band power densities on target letter trials preceded by smartphone notifications. Additionally, participants in the mindfulness (vs. control) condition had a larger attention shifting oddball assessed via theta power density and theta/beta ratio (TBR) values-reflecting increased engagement of cognitive control-particularly on smartphone notification (vs. control) trials. Altogether, these results provide evidence supporting the idea that smartphone notifications can decrease activity of neural correlates of cognitive control, and offer the promise of a brief mindfulness induction to buffer against the effects of smartphone notifications on cognitive control. The findings indicate a need for further research on mindfulness inductiosn as a means to reduce potential distraction caused by smartphones.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Introduction

This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP).

Methods

We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario.

Results

For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively.

Conclusion

Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

Eliciting brain waves of people with cognitive impairment during meditation exercises using portable electroencephalography in a smart-home environment: a pilot study

Objectives

Meditation imparts relaxation and constitutes an important non-pharmacological intervention for people with cognitive impairment. Moreover, EEG has been widely used as a tool for detecting brain changes even at the early stages of Alzheimer's Disease (AD). The current study investigates the effect of meditation practices on the human brain across the AD spectrum by using a novel portable EEG headband in a smart-home environment.

Methods

Forty (40) people (13 Healthy Controls-HC, 14 with Subjective Cognitive Decline-SCD and 13 with Mild Cognitive Impairment-MCI) participated practicing Mindfulness Based Stress Reduction (Session 2-MBSR) and a novel adaptation of the Kirtan Kriya meditation to the Greek culture setting (Session 3-KK), while a Resting State (RS) condition was undertaken at baseline and follow-up (Session 1-RS Baseline and Session 4-RS Follow-Up). The signals were recorded by using the Muse EEG device and brain waves were computed (alpha, theta, gamma, and beta).

Results

Analysis was conducted on four-electrodes (AF7, AF8, TP9, and TP10). Statistical analysis included the Kruskal-Wallis (KW) nonparametric analysis of variance. The results revealed that both states of MBSR and KK lead to a marked difference in the brain's activation patterns across people at different cognitive states. Wilcoxon Signed-ranks test indicated for HC that theta waves at TP9, TP10 and AF7, AF8 in Session 3-KK were statistically significantly reduced compared to Session 1-RS Z = -2.271, p = 0.023, Z = -3.110, p = 0.002 and Z = -2.341, p = 0.019, Z = -2.132, p = 0.033, respectively.

Conclusion

The results showed the potential of the parameters used between the various groups (HC, SCD, and MCI) as well as between the two meditation sessions (MBSR and KK) in discriminating early cognitive decline and brain alterations in a smart-home environment without medical support.

The architecture of functional brain network modulated by driving during adverse weather conditions

Traffic accidents caused by adverse weather conditions have attracted the attention of many countries. Previous studies have focused on the driver's response in a particular situation under foggy conditions, but little is known about the functional brain network (FBN) topology that is modulated by driving in foggy weather, especially when the vehicle encounters cars in the opposite lane. An experiment consisting of two driving tasks is designed and conducted using sixteen participants. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase-locking value (PLV). Based on this, a PLV-weighted network is subsequently generated. The clustering coefficient (C) and the characteristic path length (L) are adopted as measures for the graph analysis. Statistical analyses are performed on graph-derived metrics. The major finding is that the PLV is significantly increased in the delta, theta and beta frequency bands while driving in foggy weather. Additionally, for the brain network topology metric, compared with driving in clear weather, significant increases are observed (driving in foggy weather) in the clustering coefficient for alpha and beta frequency bands and the characteristic path length for all frequency bands considered in this work. Driving in foggy weather would regulate FBN reorganization in different frequency bands. Our findings also suggest that the effects of adverse weather conditions on functional brain networks with a trend toward a more economic but less efficient architecture. Graph theory analysis may be a beneficial tool to further understand the neural mechanisms of driving in adverse weather conditions, which in turn may help to reduce the occurrence of road traffic accidents to some extent.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09825-y.

A study of the cognitive process of pedestrian avoidance behavior based on synchronous EEG and eye movement detection

Pedestrian avoidance behavior often occurs in underground public spaces that connect urban rail transit and commercial complexes. This study proposes a co-monitoring method based on eye movement and electroencephalogram (EEG) to study pedestrian avoidance behavior in a real environment, taking the underground public space of the commercial complex of the Luoxiong Road railway station in Wuhan City as an experimental site. It is found that pedestrian avoidance behavior is influenced by both personal and environmental factors. The pedestrian avoidance behavior is a comprehensive response to the evaded person and the current environment. The personal factors mainly affect the pedestrian avoidance mode, while the environmental factors mainly affect the frequency of avoidance behavior. Avoidance patterns are related to the tendency of Chinese pedestrians to walk right, and the frequency of avoidance behavior is related to the complexity of the intersection of pedestrian walking routes within the environment, so avoidance behavior can be reduced by using spaces with good spatial connectivity in the design of underground public spaces. These findings provide theoretical support and data supplement for future environmental design optimization of underground public spaces.

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study

Background Meditation is a mental practice with health benefits and may increase activity in the prefrontal cortex of the brain. Heartfulness meditation (HM) is a modified form of rajyoga meditation supported by a unique feature called "yogic transmission." This feasibility study aimed to explore the effect of HM on electroencephalogram (EEG) connectivity parameters of long-term meditators (LTM), short-term meditators (STM), and non-meditators (NM) with an application of machine learning models and determining classifier methods that can effectively discriminate between the groups. Materials and methods EEG data were collected from 34 participants. The functional connectivity parameters, correlation coefficient, clustering coefficient, shortest path, and phase locking value were utilized as a feature vector for classification. To evaluate the various states of HM practice, the categorization was done between (LTM, NM) and (STM, NM) using a multitude of machine learning classifiers. Results The classifier's performances were evaluated based on accuracy using 10-fold cross-validation. The results showed that the accuracy of machine learning models ranges from 84% to 100% while classifying LTM and NM, and accuracy from 80% to 93% while classifying STM and NM. It was found that decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers performed better than linear discriminant analysis and logistic regression. Conclusion This is the first study to our knowledge employing machine learning for the classification among HM meditators and NM The results indicated that machine learning classifiers with EEG functional connectivity as a feature vector could be a viable marker for accessing meditation ability.

A Pilot Study Examining the Effects of Music Training on Attention in Children with Fetal Alcohol Spectrum Disorders (FASD)

Prior studies indicate differences in brain volume and neurophysiological responses of musicians relative to non-musicians. These differences are observed in the sensory, motor, parietal, and frontal cortex. Children with a fetal alcohol spectrum disorder (FASD) experience deficits in auditory, motor, and executive function domains. Therefore, we hypothesized that short-term music training in children with an FASD due to prenatal alcohol exposure may improve brain function. Children (N = 20) with an FASD were randomized to participate in either five weeks of piano training or to a control group. Selective attention was evaluated approximately seven weeks apart (pre-/post-music training or control intervention), examining longitudinal effects using the Attention Networks Test (ANT), a well-established paradigm designed to evaluate attention and inhibitory control, while recording EEG. There was a significant group by pre-/post-intervention interaction for the P250 ms peak of the event-related potential and for theta (4-7 Hz) power in the 100-300 ms time window in response to the congruent condition when the flanking stimuli were oriented congruently with the central target stimulus in fronto-central midline channels from Cz to Fz. A trend for improved reaction time at the second assessment was observed for the music trained group only. These results support the hypothesis that music training changes the neural indices of attention as assessed by the ANT in children with an FASD. This study should be extended to evaluate the effects of music training relative to a more closely matched active control and determine whether additional improvements emerge with longer term music training.

Decision Making as a Learned Skill in Mice and Humans

Attention is a necessary component in many forms of human and animal learning. Numerous studies have described how attention and memory interact when confronted with a choice point during skill learning. In both animal and human studies, pathways have been found that connect the executive and orienting networks of attention to the hippocampus. The anterior cingulate cortex, part of the executive attention network, is linked to the hippocampus via the nucleus reuniens of the thalamus. The parietal cortex, part of the orienting attention network, accesses the hippocampus via the entorhinal cortex. These studies have led to specific predictions concerning the functional role of each pathway in connecting the cortex to the hippocampus. Here, we review some of the predictions arising from these studies. We then discuss potential methods for manipulating the two pathways and assessing the directionality of their functional connection using viral expression techniques in mice. New studies may allow testing of a behavioral model specifying how the two pathways work together during skill learning.

Neural dynamics of mindfulness meditation and hypnosis explored with intracranial EEG: A feasibility study

PURPOSE

Intracranial electroencephalography (iEEG) offers a unique window on brain dynamics with excellent temporal and spatial resolution and is less prone to recording artefacts than surface EEG. This study used a within-subject design to explore the feasibility to compare iEEG data during mind wandering, mindfulness meditation and hypnosis.

RESULTS

Three patients who had iEEG for clinical monitoring and who were new to mindfulness meditation and hypnosis were able to enter these states. We found non-specific and wide-spread amplitude modulations. Data-driven connectivity analysis revealed widespread connectivity patterns that were common across the three conditions. These were predominant in the low frequencies (delta, theta and alpha) and characterised by positively correlated activity. Connectivity patterns that were unique to the three conditions predominated in the gamma band, one third of the correlations in these patterns were negative.

CONCLUSIONS

This study is the first to support the feasibility of a direct comparison of the neural correlates of mindfulness meditation and hypnosis using iEEG. These modulations may reflect the complex interplay between different known brain networks, and warrant further functional investigations in particular in the gamma band.

Determinants of bilingualism predict dynamic changes in resting state EEG oscillations

This study uses resting state EEG data from 103 bilinguals to understand how determinants of bilingualism may reshape the mind/brain. Participants completed the LSBQ, which quantifies language use and crucially the division of labor of dual-language use in diverse activities and settings over the lifespan. We hypothesized correlations between the degree of active bilingualism with power of neural oscillations in specific frequency bands. Moreover, we anticipated levels of mean coherence (connectivity between brain regions) to vary by degree of bilingual language experience. Results demonstrated effects of Age of L2/2L1 onset on high beta and gamma powers. Higher usage of the non-societal language at home and society modulated indices of functional connectivity in theta, alpha and gamma frequencies. Results add to the emerging literature on the neuromodulatory effects of bilingualism for rs-EEG, and are in line with claims that bilingualism effects are modulated by degree of engagement with dual-language experiential factors.

EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review

OBJECTIVES

The present investigation is to study the impact of yoga and meditation on Brain waves concerning physical and mental health. There are mainly three stages (steps) in the brain wave classification:(i) preprocessing, ii) feature extraction, and iii) classification. This work provides a review of interpretation methods of Brain signals (Electroencephalogram (EEG)) EEG during yoga and meditation. Past research has revealed significant mental and physical advantages with yoga and meditation.

METHODS

The research topic reviewed focused on the machine learning strategies applied for the interpretation of brain waves. In addressing the research questions highlighted earlier in the general introduction, we conducted a systematic search of articles from targeted scientific and journal online databases that included PubMed, Web of Science, IEEE Xplore Digital Library (IEEE), and Arxiv databases based on their relevance to the research questions and domain topic. The survey topic is relatively nascent, and therefore, the scope of the search period was limited to the 20-year timeline that was deemed representative of the research topic under investigation. The literature search was based on the keywords "EEG", "yoga*" and "meditation*". The key phrases were concatenated using Boolean expressions and applied to search through the selected online databases yielding a total of 120 articles. The online databases were selected based on the relevancy of content with the research title, research questions, and the domain application. The literature review search, process, and classification were carefully conducted guided by two defined measures; 1.) Inclusion criteria; and 2.) Exclusion criteria. These measures define the criteria for searching and extracting relevant articles relating to the research title and domain of interest.

RESULTS

Our literature search and review indicate a broad spectrum of neural mechanics under a variety of meditation styles have been investigated. A detailed analysis of various mental states using Zen, CHAN, mindfulness, TM, Rajayoga, Kundalini, Yoga, and other meditation styles have been described by means of EEG bands. Classification of mental states using KNN, SVM, Random forest, Fuzzy logic, neural networks, Convolutional Neural Networks has been described. Superior research is still required to classify the EEG signatures corresponding to different mental states.

CONCLUSIONS

Yoga practice may be an effective adjunctive treatment for a clinical and aging population. Advanced research can examine the effects of specific branches of yoga on a designated clinical grouping. Yoga and meditation increased overall healthy brain activity.

Baseline Power of Theta Oscillations Predicts Mystical-Type Experiences Induced by DMT in a Natural Setting

N,N-Dimethyltryptamine (DMT) is a classic psychedelic capable of inducing short-lasting but profound changes in consciousness. As with other psychedelics, the experience induced by DMT strongly depends upon contextual factors, yet the neurobiological determinants of this variability remain unknown. The present study investigated changes in neural oscillations elicited by inhaled DMT, and whether baseline electroencephalography (EEG) recordings could predict the subjective effects reported by the participants. Healthy volunteers (N = 35) were measured with EEG before and during the acute effects of DMT consumed in a natural setting. Source-localized neural oscillations were correlated with the results of multiple questionnaires employed to assess the subjective effects of the drug. DMT resulted in a marked reduction of alpha and beta oscillations, and increased posterior spectral power in the delta, theta and gamma bands. The power of fronto-temporal theta oscillations was inversely correlated with scales indexing feelings of unity and transcendence, which are an integral part of the phenomenology of mystical-type experiences. The robustness of these results was supported using a machine learning model for regression trained and tested following a cross-validation procedure. These results are consistent with the observation that the state of mind prior to consuming a psychedelic drug influences the ensuing subjective experience of the user. They also suggest that baseline EEG screenings before administration of a serotonergic psychedelic could be useful to estimate the likelihood of inducing mystical-type experiences, previously linked to sustained positive effects in well-being and improved outcome of therapeutic interventions.

Increasing the amplitude of intrinsic theta in the human brain

In a mouse study we found increased myelination of pathways surrounding the anterior cingulate cortex (ACC) following stimulation near the theta rhythm (4-8 Hz), and evidence that this change in connectivity reduced behavioral anxiety. We cannot use the optogenetic methods with humans that were used in our mouse studies. This paper examines whether it is possible to enhance intrinsic theta amplitudes in humans using less invasive methods. The first experiment compares electrical, auditory and biofeedback as methods for increasing intrinsic theta rhythm amplitudes in the Anterior Cingulate Cortex (ACC). These methods are used alone or in conjunction with a task designed to activate the same area. The results favor using electrical stimulation in conjunction with a task targeting this region. Stimulating the ACC increases intrinsic theta more in this area than in a control area distant from the site of stimulation, suggesting some degree of localization of the stimulation. In Experiment 2, we employed electrical stimulation with the electrodes common to each person, or with electrodes selected from an individual head model. We targeted the ACC or Motor Cortex (PMC). At baseline, intrinsic theta is higher in the ACC than the PMC. In both areas, theta can be increased in amplitude by electrical stimulation plus task. In the PMC, theta levels during stimulation plus task are not significantly higher than during task alone. There is no significant difference between generic and individual electrodes. We discuss steps needed to determine whether we can use the electrical stimulation + task to improve the connectivity of white matter in different brain areas.

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.

Biobehavioral utility of mindfulness-based art therapy: Neurobiological underpinnings and mental health impacts

Mindfulness-based art therapy (MBAT) combines mindfulness practices with art therapy to promote health, wellness, and adaptive responses to stress. Mindfulness-based stress reduction programs have been demonstrated to reduce anxiety symptoms in people with generalized anxiety disorder and serious medical conditions such as cancer. However, the benefits of MBAT specifically—not just in clinical anxiety disorders, but also for routine day-to-day anxiousness—have received little attention until recently. In this mini-review, we summarize how several established mindfulness-based interventions affect mental health with a specific focus on MBAT. We also describe and evaluate data indicating involvement of specific neural circuits and neuroendocrine markers of stress and/or anxiety. Lastly, we suggest areas of focus for future rigorous research on MBAT interventions and propose multiple biobehavioral and physiological mechanisms through which therapeutic benefits may be achieved.

Impact statement

Recent studies demonstrate the therapeutic potential of intensive MBAT and other mindfulness-based interventions for severe health conditions including cancer, heart disease, and anxiety, with effectiveness that rivals and sometimes even exceeds cognitive behavior therapy. MBAT-based approaches appear to be poised to mature into large-scale, cost-effective therapies for mental health disorders and symptoms comorbid with other serious health issues. However, the field currently suffers from inconsistent deployment protocols and needs additional validation data—at the behavioral, neuroendocrine, and neural levels—in order to establish best practices.

EEG signals during mouth breathing in a working memory task

Background: Continuous mouth breathing results not only morphological deformations but also poor learning outcomes. However, there were few studies that observed correlations between mouth breathing and cognition. This study aimed at investigating the changes in brain activity during mouth breathing while the participant simultaneously performed a cognitive task using electroencephalography (EEG).Methods: Twenty subjects participated in this study, and EEG electrodes (32 channels, 250-Hz sampling rate) were placed on their scalp. Brain waves during a resting state and n-back tasks (0-back and 2-back) and physiological parameters such as SpO2, ETCO2, and the airway respiratory rate were measured. The pre-processed EEG signals were analyzed based on their frequencies as delta waves (0.5 ∼ 4 Hz), theta waves (4 ∼ 8 Hz), alpha waves (8 ∼ 13 Hz), beta waves (13 ∼ 30 Hz) and gamma waves (30 ∼ 50 Hz) using fast Fourier transform (FFT).Results: When compared with nose breathing, theta and alpha powers were lower during mouth breathing at rest and alpha wave presented low power at 0-back and 2-back tasks. Furthermore, beta and gamma waves exhibited low powers at 2-back task. However, the behavioral results (accuracy and response time) have no significant difference between two breathing methods (mouth and nose). Mouth breathing showed different brain activity patterns, compared to nose breathing, and these changes are related to cognitive regions.Conclusion: The reason for this change seems to relate to the decreased oxygen saturation during mouth breathing, suggesting that when cognitive abilities are required, mouth breathing can act as one of the variables that cause different outcomes in brain activities.

Anxiety and Brain Networks of Attentional Control

Advances in the study of brain networks can be applied to our understanding of anxiety disorders (eg, generalized anxiety, obsessive-compulsive, and posttraumatic stress disorders) to enable us to create targeted treatments. These disorders have in common an inability to control thoughts, emotions, and behaviors related to a perceived threat. Here we review animal and human imaging studies that have revealed separate brain networks related to various negative emotions. Research has supported the idea that brain networks of attention serve to control emotion networks as well as the thoughts and behaviors related to them. We discuss how attention networks can modulate both positive and negative affect. Disorders arise from both abnormal activation of negative affect and a lack of attentional control. Training attention has been one way to foster improved attentional control. We review attention training studies as well as efforts to generally improve attention networks through stimulation in self-regulation.

Frontal theta activity and white matter plasticity following mindfulness meditation

Both brain alpha and theta power have been examined in the mindfulness meditation literature and suggested as key biological signatures that potentially facilitate a successful meditative state. However, the exact role of how alpha and theta waves contribute to the initiation and maintenance of a meditative state remains elusive. In this perspective paper, we discuss the role of frontal midline theta (FMθ) activity in brain white matter plasticity following mindfulness meditation. In accordance with the previous studies in humans, we propose that FMθ activity indexes the control needed to maintain the meditation state; whereas alpha activity is related to the preparation needed to achieve the meditative state. Without enough mental preparation, one often struggles with and has difficulty achieving a meditative state. Animal work provides further evidence supporting the hypothesis that mindfulness meditation induces white matter changes through increasing FMθ activity. These studies shed light on how to effectively enhance brain plasticity through mindfulness meditation.

Mindfulness Training: Can It Create Superheroes?

With the emergence of the science of heroism there now exists both theoretical and empirical literature on the characteristics of our everyday hero. We seek to expand this inquiry and ask what could be the causes and conditions of a superhero. To address this we investigate the origins of mindfulness, Buddhist psychology and the assertion that its practitioners who have attained expertise in mindfulness practices can develop supernormal capabilities. Examining first their foundational eight "jhana" states (levels of attention) and the six consequent "abhinnas" (siddhis or special abilities) that arise from such mental mastery, we then explore any evidence that mindfulness practices have unfolded the supernormal potential of its practitioners. We found a growing base of empirical literature suggesting some practitioners exhibit indicators of enhanced functioning including elevated physical health and resistance to disease, increased immunity to aging and improved cognitive processing, greater resilience and fearlessness, more self-less and pro-social behaviors, some control over normally autonomic responses, and possibly some paranormal functionality. These improvements in normal human functioning provide some evidence that there are practices that develop these abilities, and as such we might want to consider adopting them to develop this capability. There are however insufficient studies of expert meditators and more research of adepts is called for that explores the relationship between levels of attentional skill and increases in functionality. We propose in search of the superhero, that if conventional mindfulness training can already augment mental and physical capabilities, a more serious inquiry and translation of its advanced methods into mainstream psychological theory is warranted.

Illuminating the Neural Circuits Underlying Orienting of Attention

Human neuroimaging has revealed brain networks involving frontal and parietal cortical areas as well as subcortical areas, including the superior colliculus and pulvinar, which are involved in orienting to sensory stimuli. Because accumulating evidence points to similarities between both overt and covert orienting in humans and other animals, we propose that it is now feasible, using animal models, to move beyond these large-scale networks to address the local networks and cell types that mediate orienting of attention. In this opinion piece, we discuss optogenetic and related methods for testing the pathways involved, and obstacles to carrying out such tests in rodent and monkey populations.

Temperament and brain networks of attention

The attention networks of the human brain are important control systems that develop from infancy into adulthood. While they are common to everyone, they differ in efficiency, forming the basis of individual differences in attention. We have developed methods for measuring the efficiency of these networks in older children and adults and have also examined their development from infancy. During infancy the alerting and orienting networks are dominant in control of the infant's actions, but later an executive network dominates. Each network has been associated with its main neuromodulator and these have led to associations with genes related to that network neuromodulator. The links between parent reports of their child's effortful control and the executive attention network allow us to associate molecular mechanisms to fundamental behavioural outcomes.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.

Age-Related Alterations in Electroencephalography Connectivity and Network Topology During n-Back Working Memory Task

The study of the healthy brain in elders, especially age-associated alterations in cognition, is important to understand the deficits created by Alzheimer's disease (AD), which imposes a tremendous burden on individuals, families, and society. Although, the changes in synaptic connectivity and reorganization of brain networks that accompany aging are gradually becoming understood, little is known about how normal aging affects brain inter-regional synchronization and functional networks when items are held in working memory (WM). According to the classic Sternberg WM paradigm, we recorded multichannel electroencephalography (EEG) from healthy adults (young and senior) in three different conditions, i.e., the resting state, 0-back (control) task, and 2-back task. The phase lag index (PLI) between EEG channels was computed and then weighted and undirected network was constructed based on the PLI matrix. The effects of aging on network topology were examined using a brain connectivity toolbox. The results showed that age-related alteration was more prominent when the 2-back task was engaged, especially in the theta band. For the younger adults, the WM task evoked a significant increase in the clustering coefficient of the beta-band functional connectivity network, which was absent in the older adults. Furthermore, significant correlations were observed between the behavioral performance of WM and EEG metrics in the theta and gamma bands, suggesting the potential use of those measures as biomarkers for the evaluation of cognitive training, for instance. Taken together, our findings shed further light on the underlying mechanism of WM in physiological aging and suggest that different EEG frequencies appear to have distinct functional correlates in cognitive aging. Analysis of inter-regional synchronization and topological characteristics based on graph theory is thus an appropriate way to explore natural age-related changes in the human brain.

Effects of an Online Mind-Body Training Program on the Default Mode Network: An EEG Functional Connectivity Study

Online mind-body training (MBT) programs can improve the psychological capabilities of practitioners. Although there has been a lot of effort to understand the neural mechanisms underlying the therapeutic effects of meditation, little is known about changes in electroencephalographic (EEG) functional connectivity that accompany mind-body training. The present study aimed to investigate how an online MBT program alters EEG functional connectivity in the default mode network (DMN). We assessed a group of healthcare providers, including 14 females who participated in the 4-week MBT program and 15 females who underwent a 4-week of waiting period. EEG data and information about psychological states were obtained at baseline and 4 weeks. The result was that the intervention group showed significant reductions in anxiety and trait anger that were accompanied by increased global DMN network strengths in the theta and alpha (but not beta and delta) frequency bands; these changes were not observed in the control group. Other variables including state anger, positive and negative affect, and self-esteem have not been changed over time in both groups. These findings suggest that practicing the mind-body training could have a relevance to the functional differences in network related to stress and anxiety reaction.

A Novel Method of Segmentation and Classification for Meditation in Health Care Systems

Meditation improves positivity in behavioral as well as psychological changes, which are brought elucidated by knowing neuro-physiological consequences of meditation. In the field of cognitive science, neuroscience and physiological research, Electroencephalogram (EEG) is extensively utilized. The primary tasks of EEG signal analysis is to identify the noisy signal as well as enormous data that create signal processing and subsequent analysis. Beforehand any analysis of the EEG signal, the obtained raw signal must be preprocessed for eliminating undesirable artifacts as well as horrible noise. With the aim of resolving this issue, in this research, raw signals are preprocessed with the help of Band-Pass Filter (BPF) for noise removal method. Instead, in adaptive Sliding Window with Fuzzy C Means Clustering (SW-FCM) segmentation is presented, which precisely as well as automatically segments the signals. So as to analyze the accuracy, five features such as electroencephalography alpha spectrum, frequency of the main peak, Amplitude of the main peak, Higher Order Crossing (HOC), and wavelet features are used as the evaluating variables. Lastly to assess the meditation experience with Fuzzy Kernel least square Support Vector Machine (FKLSSVM) classifier, the presented method with a cross-sectional analysis is utilized. These two classifiers are utilized for meditation experience classification by utilizing an individual feature vector values from equivalent EEG signals. The dataset samples are gathered from Open source Brain-Computer Interface (Open BCI) platform. Outcomes attained are matched up for diverse techniques for identifying as well as for classifying signal segments features using MATLAB. Presented classifiers of the meditation process validate quick interpretation methods that differentiate meditation experience and valuable performance related to artificial approaches for the big-data analysis.

Rehabilitating the brain through meditation and electrical stimulation

This paper is a review of our recent studies and ideas related to the neuropsychological issues that Robert Rafal and I worked together to understand attention and hopefully improve it in a variety of patients. Rehabilitation is also a goal of my current research to determine if non invasive stimuli can improve white matter in humans. We have found that fractional anisotropy (FA) is improved in pathways surrounding the anterior cingulate cortex (ACC) following two week to four weeks of meditation training. We hypothesized that the frontal theta increased following meditation training might be a cause of the improved connectivity. This was confirmed by a mouse study using optogenetics to impose theta rhythms in the ACC. We have evidence that electrical stimulation while performing a task that activates the ACC can also increase theta. We plan studies to determine whether two to four weeks of stimulation can improve FA in pathways surrounding the anterior cingulate.

Changes in white matter in mice resulting from low-frequency brain stimulation

Recent reports have begun to elucidate mechanisms by which learning and experience produce white matter changes in the brain. We previously reported changes in white matter surrounding the anterior cingulate cortex in humans after 2-4 weeks of meditation training. We further found that low-frequency optogenetic stimulation of the anterior cingulate in mice increased time spent in the light in a light/dark box paradigm, suggesting decreased anxiety similar to what is observed following meditation training. Here, we investigated the impact of this stimulation at the cellular level. We found that laser stimulation in the range of 1-8 Hz results in changes to subcortical white matter projection fibers in the corpus callosum. Specifically, stimulation resulted in increased oligodendrocyte proliferation, accompanied by a decrease in the g-ratio within the corpus callosum underlying the anterior cingulate cortex. These results suggest that low-frequency stimulation can result in activity-dependent remodeling of myelin, giving rise to enhanced connectivity and altered behavior.

Mind over Matter: Using Technology to Improve Wellness

Wellness-the balance of mental, emotional, spiritual, and physical health for the overall health benefit of the individual-is a growing concern, particularly for professionals in the medical field. Although wellness is usually viewed in opposition to technology, more digital devices and mobile applications are emerging to support wellness for health consumers. This wellness technology seeks to improve the overall health of the user through increasing calm and decreasing stress. This column will explain what wellness technology is, concerns over its widespread application, examples of wellness devices on the market currently, and an overview of where it is being applied in libraries.

Meditation is associated with increased brain network integration

INTRODUCTION

This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration.

METHODS

Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4-8 Hz), alpha (8-13 Hz) and lower beta (13-20 Hz) frequency bands.

RESULTS

Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P = 0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P = 0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P = 0.016 and P = 0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands.

CONCLUSION

These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

How changes in white matter might underlie improved reaction time due to practice

Why does training on a task reduce the reaction time for performing it? New research points to changes in white matter pathways as one likely mechanism. These pathways connect remote brain areas involved in performing the task. Genetic variations may be involved in individual differences in the extent of this improvement. If white matter change is involved in improved reaction time with training, it may point the way toward understanding where and how generalization occurs. We examine the hypothesis that brain pathways shared by different tasks may result in improved performance of cognitive tasks remote from the training.

EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges

Meditation advances positivity but how these behavioral and psychological changes are brought can be explained by understanding neurophysiological effects of meditation. In this paper, a broad spectrum of neural mechanics under a variety of meditation styles has been reviewed. The overall aim of this study is to review existing scientific studies and future challenges on meditation effects based on changing EEG brainwave patterns. Albeit the existing researches evidenced the hold for efficacy of meditation in relieving anxiety and depression and producing psychological well-being, more rigorous studies are required with better design, considering client variables like personality characteristics to avoid negative effects, randomized controlled trials, and large sample sizes. A bigger number of clinical trials that concentrate on the use of meditation are required. Also, the controversial subject of epileptiform EEG changes and other adverse effects during meditation has been raised.

Time course of conflict processing modulated by brief meditation training

Resolving conflict is a pivotal self-control ability for human adaptation and survival. Although some studies reported meditation may affect conflict resolution, the neural mechanisms are poorly understood. We conducted a fully randomized 5 h trial of one form of mindfulness meditation—integrative body-mind training (IBMT) in comparison to a relaxation training control. During the Stroop word-color task, IBMT group produced faster resolution of conflict, a smaller N2 and an earlier and larger P3 component of the event-related brain potentials. These results indicate that brief meditation training induces a brain state that improves the resolution of conflict.

What it means to be Zen: marked modulations of local and interareal synchronization during open monitoring meditation

Experienced meditators are able to voluntarily modulate their state of consciousness and attention. In the present study, we took advantage of this ability and studied brain activity related to the shift of mental state. Electrophysiological activity, i.e. EEG, was recorded from 11 subjects with varying degrees of meditation experience during Zen meditation (a form of open monitoring meditation) and during non-meditation rest. On a behavioral level, mindfulness scores were assessed using the Mindfulness Attention and Awareness Scale (MAAS). Analysis of EEG source power revealed the so far unreported finding that MAAS scores significantly correlated with gamma power (30-250Hz), particularly high-frequency gamma (100-245Hz), during meditation. High levels of mindfulness were related to increased high-frequency gamma, for example, in the cingulate cortex and somatosensory cortices. Further, we analyzed the relationship between connectivity during meditation and self-reported mindfulness (MAAS). We found a correlation between graph measures in the 160-170Hz range and MAAS scores. Higher levels of mindfulness were related to lower small worldedness as well as global and local clustering in paracentral, insular, and thalamic regions during meditation. In sum, the present study shows significant relationships of mindfulness and brain activity during meditation indicated by measures of oscillatory power and graph theoretical measures. The most prominent effects occur in brain structures crucially involved in processes of awareness and attention, which also show structural changes in short- and long-term meditators, suggesting continuative alterations in the meditating brain. Overall, our study reveals strong changes in ongoing oscillatory activity as well as connectivity patterns that appear to be sensitive to the psychological state changes induced by Zen meditation.

Mechanisms of white matter change induced by meditation training

Training can induce changes in specific brain networks and changes in brain state. In both cases it has been found that the efficiency of white matter as measured by diffusion tensor imaging is increased, often after only a few hours of training. In this paper we consider a plausible molecular mechanism for how state change produced by meditation might lead to white matter change. According to this hypothesis frontal theta induced by meditation produces a molecular cascade that increases myelin and improves connectivity.