A systematic review of the neurophysiology of mindfulness on EEG oscillations

Mindfulness meditation has been purported as a beneficial practice for wellbeing. It would be expected that the neurophysiology of mindfulness would reflect this impact on wellbeing. However, investigations of the effect of mindfulness have generated mixed reports of increases, decreases, as well as no differences in EEG oscillations in comparison with a resting state and a variety of tasks. We have performed systematic review of EEG studies of mindfulness meditation in order to determine any common effects and to identify factors which may impact on the effects. Databases were reviewed from 1966 to August 2015. Eligibility criteria included empirical quantitative analyses of mindfulness meditation practice and EEG measurements acquired in relation to practice. A total of 56 papers met the eligibility criteria and were included in the systematic review, consisting of a total 1,715 subjects: 1,358 healthy individuals and 357 individuals with psychiatric diagnoses. Studies were principally examined for power outcomes in each bandwidth, in particular the power differentials between mindfulness and the control state, as well as outcomes relating to hemispheric asymmetry and event-related potentials. The systematic review revealed that mindfulness was most commonly associated with enhanced alpha and theta power as compared to an eyes closed resting state, although such outcomes were not uniformly reported. No consistent patterns were observed with respect to beta, delta and gamma bandwidths. In summary, mindfulness is associated with increased alpha and theta power in both healthy individuals and in patient groups. This co-presence of elevated alpha and theta may signify a state of relaxed alertness which is conducive to mental health

Other race effect on amygdala response during affective facial processing in major depression

Objective: The other race effect, also known as own race bias, refers to the enhanced ability to recognize faces belonging to one’s own race relative to faces from another race. The other race effect is associated with increased amygdala response in healthy individuals. The amygdala is a key node in emotion processing which shows impaired functioning in depression and has been proposed to be a marker of depressive state. We investigated the impact of the other race effect on amygdala responses in depression. Methods: Participants were 30 individuals with major depression (mean age39.4 years) and 23 healthy individuals (mean age: 38.8 years) recruited from the community. Participants were Asian, Black/African American and Caucasian. During a functional MRI scan, participants viewed Caucasian faces which displayed a range of sad expressions. A region of interest analysis of left and right amygdala responses was performed. Results: Increased bilateral amygdala responses were observed in response to the Caucasian face stimuli in participants who were Asian or Black/African American as compared to Caucasian participants in both healthy individuals and individuals with major depression. There was no significant group by race interaction effect. Conclusions: Increased amygdala responses associated with the other race effect were evident in both individuals with major depression and in healthy participants. Increased amygdala responses with the other race effect is a potential confound of the neural correlates of facial processing in healthy participants and in mental health disorders. The implications of the other race effect on impairments in interpersonal functioning in depression require further investigation

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

Acceptability of home-based transcranial direct current stimulation (tDCS) in major depression:a qualitative analysis of individual experiences

Purpose The purpose of this paper was to gain a qualitative view of the participant experience of using home-based transcranial direct current stimulation (tDCS). Acceptability impacts patient preference, treatment adherence and outcomes. However, acceptability is usually assessed by rates of attrition, while multifaceted constructs are not reflected or given meaningful interpretations. tDCS is a novel non-invasive brain stimulation that is a potential treatment for major depressive disorder (MDD). Most studies have provided tDCS in a research centre. As tDCS is portable, the authors developed a home-based treatment protocol that was associated with clinical improvements that were maintained in the long term. Design/methodology/approach The authors examined the acceptability of home-based tDCS treatment in MDD through questionnaires and individual interviews at three timepoints: baseline, at a six-week course of treatment, and at six-month follow-up. Twenty-six participants (19 women) with MDD in a current depressive episode of at least moderate severity were enrolled. tDCS was provided in a bifrontal montage with real-time remote supervision by video conference at each session. A thematic analysis was conducted of the individual interviews. Findings Thematic analysis revealed four main themes: effectiveness, side effects, time commitment and support, feeling held and contained. The themes reflected the high acceptability of tDCS treatment, whereas the theme of feeling contained might be specific to this protocol. Originality/value Qualitative analysis methods and individual interviews generated novel insights into the acceptability of tDCS as a potential treatment for MDD. Feelings of containment might be specific to the present protocol, which consisted of real-time supervision at each session. Meaningful interpretation can provide context to a complex construct, which will aid in understanding and clinical applications

Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques

Stress can be described as an alteration in our body that can cause strain emotionally, physically, or psychologically. It is a reaction from our body to something that demands attention or exertion. It can be caused by various reasons depending on the physical or mental activity of the body. Commuting on a regular basis also acts as a source of stress. This research aims to explore the physiological effects of the commute with an application of a machine-learning algorithm. The data used in this research is collected from 45 healthy participants who commute to work on a regular basis. A multimodal dataset containing medical data like biosignals (heart rate, blood pressure, and EEG signal) plus responses obtained from the questionnaire PANAS. Evaluation is based on the performance metrics that include confusion matrix, ROC/AUC, and classification accuracy of the model. In this research, several machine learning algorithms are applied to design a model which can predict the effect of a commute. The results obtained from this research suggest that whether the interval of commute was small or large, there was a significant rise in stress levels including the bio-signals (electroencephalogram, blood pressure and heart rate) after the commute. The results obtained from the employed machine learning algorithms predict that heart rate difference before and after commute will correlate with EEG signals in participants who have self-reported to be stress after the commute. The random forest algorithm gave a very promising result with an accuracy of 91%, while the KNN and the SVM showed the accuracy of 78% and 80% respectively.</p