Neurophysiological Effects of Meditation Based on Evoked and Event Related Potential Recordings

Modulation of attention and stress with arousal: The mental and physical effects of riding a motorcycle

Existing theories suggest that moderate arousal improves selective attention, as would be expected in the context of competitive sports or sensation-seeking activities. Here we investigated how riding a motorcycle, an attention-demanding physical activity, affects sensory processing. To do so, we implemented the passive auditory oddball paradigm and measured the EEG response of participants as they rode a motorcycle, drove a car, and sat at rest. Specifically, we measured the N1 and mismatch negativity to auditory tones, as well as alpha power during periods of no tones. We investigated whether riding and driving modulated non-CNS metrics including heart rate and concentrations of the hormones epinephrine, cortisol, DHEA-S, and testosterone. While participants were riding, we found a decrease in N1 amplitude, increase in mismatch negativity, and decrease in relative alpha power, together suggesting enhancement of sensory processing and visual attention. Riding increased epinephrine levels, increased heart rate, and decreased the ratio of cortisol to DHEA-S. Together, these results suggest that riding increases focus, heightens the brain's passive monitoring of changes in the sensory environment, and alters HPA axis response. More generally, our findings suggest that selective attention and sensory monitoring seem to be separable neural processes.

Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.

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.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

State-trait influences of Vipassana meditation practice on P3 EEG dynamics

Several studies have demonstrated that meditation naïve subjects can, in just a few weeks, become proficient enough in meditation to show cognitive improvements accompanied with functional and structural changes in the brain. Would long-term exposure to qualitatively different levels of meditative training bring about differences in cognitive processing? Would meditation prior to task performance help separate out these differences? Could the nature of the task influence the findings related to cognitive enhancements? To address these questions, we evaluated cognitive functions in three groups of experienced Vipassana practitioners (Novices: n=22, Mean±SD meditation experience=989±595h; Senior practitioners: 21, 10,510±5313; Teachers: 16, 14,648±9623) who differed in terms of duration and quality of meditative practice. Specifically, we employed "ANGEL" a gamified multilevel oddball paradigm, to assess P3 event-related potentials (ERPs) and associated EEG dynamics-power spectra, event related spectral perturbations (ERSP) and inter-trial coherence (ITC). In order to elicit the state-trait influences of meditation, the cognitive task was performed after the participants had undergone an hour long traditional meditation session. All participants could perform the task well and the gross ERP waveforms were similar for the three groups. As hypothesized, we found distinct state-trait influences of meditation leading to graded differences in P3 EEG dynamics. Specifically, we found reduced theta synchrony, enhanced alpha de-synchrony and lesser theta-alpha coherence in the more proficient meditators. Post hoc analyses revealed several differences between the novice and teacher groups but not as many between novice and seniors suggesting that the senior meditators formed an intermediate group. Our study demonstrates that both quantity and quality of meditation influence EEG dynamics during cognitive processing and that meditation prior to a task can provide additional state-trait effects involved in meeting the specific cognitive demands.

Linking numbers to perceptions and experiences: Why we need transdisciplinary mixed-methods combining neurophysiological and qualitative data

Today, more and more problems that scientists need to tackle are complex problems. Many examples of these can be found in the health sciences, medicine and ecology. Typical features of complex problems are that they cannot be studied by one discipline and that they need to take into account subjective data as well as objective data. Two promising responses to deal with complex problems are Transdisciplinary and Mixed Method approaches. However, there is still a lacuna to fill, with transdisciplinary studies bridging the social sciences and biomedical sciences. More specifically, we need more and better studies that combine qualitative data about subjective experiences, perception and so on with objective, quantitative, neurophysiological data. We believe that the combination of qualitative and neurophysiological data is a good example of what we would like to call transdisciplinary mixed methods. In this article, we aim to explore the opportunities of transdisciplinary mixed-methods studies in which qualitative and neurophysiological data are used. We give a brief overview of what is characteristic for this kind of studies and illustrate this with examples; we point out strengths and limitations and propose an agenda for the future. We conclude that transdisciplinary mixed-methods studies in which qualitative and neurophysiological data are used have the potential to improve our knowledge about complex problems. A main obstacle seems to be that most scientists from the biomedical sciences are not familiar with the (qualitative) methods from the social sciences and vice versa. To end this ‘clash of paradigms’™, we urgently need to cultivate transdisciplinary thinking.

Electroencephalographic Resting-State Networks: Source Localization of Microstates

Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router," crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.

Dull to Social Acceptance Rather than Sensitivity to Social Ostracism in Interpersonal Interaction for Depression: Behavioral and Electrophysiological Evidence from Cyberball Tasks

Objectives: Impairments in interpersonal relationships in depression present as irritability, pessimism, and withdrawal, and play an important role in the onset and maintenance of the disorder. However, we know little about the neurological causes of this impaired interpersonal function. This study used the event-related brain potential (ERP) version of the Cyberball paradigm to investigate the emotions and neural activities in depressive patients during social inclusion and exclusion simultaneously to explore neuropsychological mechanisms.

Methods: Electrophysiological data were recorded when 27 depressed patients and 23 healthy controls (HCs) performed a virtual ball tossing game (Cyberball) during which the participants believed they were playing with two other co-players over the internet. The Cyberball paradigm included two other conditions; inclusion during which participants received the ball with the same probability as the other players to experience a feeling of acceptance, and exclusion during which the participants experienced a feeling of ostracism when the other two players threw the ball with each other. The Positive and Negative Affect Schedule (PANAS) was used as a baseline and after each block during the Cyberball to assess positive and negative effects. In addition, a brief Need-Threat Scale (NTS) was used to assess the fulfillment of basic needs of subjects after each block and 10 min after ostracism. Moreover, the relationship between the ERP data of depression and clinical symptoms was analyzed.

Results: Exclusion compared to inclusion Cyberball caused a decrease in positive affect and an increase in negative affect. The group differences were only found in the positive affect. Moreover, patients reported a lower level of basic needs than did HCs after social inclusion, but a similar level of basic needs after social exclusion. At the electrophysiological level, patients showed decreased P3 amplitudes compared to HCs in social inclusion, and P3 amplitudes were borderline negatively correlated with their scores of anhedonia symptoms.

Limitations: A limitation of our study was that the subjects' criteria were different.

Conclusions: The behavioral and electrophysiological results indicated that the interpersonal problems in depressive patients were mainly due to deficits in processing the pleasurable social stimuli rather than aversive social cues.

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.