Brain activity during meditation in first-time meditators.

AIM OF THE STUDY: The electrophysiological correlates of meditation states in both short and long-term meditators have been increasingly documented; however, little is known about the brain activity associated with first-time meditation experiences. The goal of this study was to investigate the electrophysiological correlates of a single guided mindfulness meditation session in subjects with no previous meditation experience. MATERIALS AND METHODS: We analyzed electroencephalogram (EEG) changes in signal power, hemispheric asymmetry, and information flow between EEG channels, in 16 healthy subjects who were new to meditation practice. RESULTS: Our results show that information flow decreases in the theta (4-8 Hz) and alpha ranges (8-13 Hz) during mindfulness meditation exercise as compared to control: a passive listening condition. These changes are accompanied by a general trend in the decrease of alpha power over the whole scalp. One possible interpretation of these results is that there is an increased level of alertness/vigilance associated with the meditation task rather than reaching the target state. CONCLUSIONS: Our study expands on the existing body of knowledge concerning neural oscillations during breathing meditation practice by showing that in participants with no previous meditation training, EEG correlates are different from the electrophysiological signatures of mindfulness meditation found in studies of more advanced practitioners.

Mindfulness-based cognitive therapy reduces clinical symptoms, but do not change frontal alpha asymmetry in people with major depression disorder.

OBJECTIVES: Mindfulness-based cognitive therapy (MBCT) has demonstrated to be successful in the reduction of relapse rates in patients with recurrent major depressive disorder (MDD). Little is known if MBCT is effective for treating individuals with current MDD episode and about underlying psychophysiological mechanisms of symptoms reduction. The aim of the present study was to assess effects of MBCT on depressed individuals in terms of reduction of depressive and anxiety symptoms and to evaluate if this therapeutic improvement would be reflected on neurophysiological level by shift in frontal alpha asymmetry (FAA). PARTICIPANTS: We studied 20 individuals with current MDD. DESIGN: Participants were randomly assigned either to waiting list or 8-week MBCT. Before and after the treatment we have assessed depression, anxiety, and FAA in resting-state electroencephalogram (EEG) - an indicator of approach vs. withdrawal-related response dispositions and a vulnerability factor of MDD. RESULTS: In line with previous findings, reduction of depressive and anxiety symptoms, but no change in mean values of FAA in MBCT group was found. CONCLUSIONS: These results provide a support for the beneficial effects of MBCT in current MDD treatment, however, they do not support the hypothesis on alpha asymmetry change as a neural correlate of MDD improvement.

Asynchronous BCI based on motor imagery with automated calibration and neurofeedback training.

A new multiclass brain-computer interface (BCI) based on the modulation of sensorimotor oscillations by imagining movements is described. By the application of advanced signal processing tools, statistics and machine learning, this BCI system offers: 1) asynchronous mode of operation, 2) automatic selection of user-dependent parameters based on an initial calibration, 3) incremental update of the classifier parameters from feedback data. The signal classification uses spatially filtered signals and is based on spectral power estimation computed in individualized frequency bands, which are automatically identified by a specially tailored AR-based model. Relevant features are chosen by a criterion based on Mutual Information. Final recognition of motor imagery is effectuated by a multinomial logistic regression classifier. This BCI system was evaluated in two studies. In the first study, five participants trained the ability to imagine movements of the right hand, left hand and feet in response to visual cues. The accuracy of the classifier was evaluated across four training sessions with feedback. The second study assessed the information transfer rate (ITR) of the BCI in an asynchronous application. The subjects' task was to navigate a cursor along a computer rendered 2-D maze. A peak information transfer rate of 8.0 bit/min was achieved. Five subjects performed with a mean ITR of 4.5 bit/min and an accuracy of 74.84%. These results demonstrate that the use of automated interfaces to reduce complexity for the intended operator (outside the laboratory) is indeed possible. The signal processing and classifier source code embedded in BCI2000 is available from https://www.brain-project.org/downloads.html.

Event-related desynchronization and synchronization in evoked K-complexes.

K-complexes - phenomena occurring in sleep EEG - pose severe challenges in terms of detection as well as finding their physiological origin. In this study, K-complexes (KCs) were evoked by auditory stimuli delivered during sleep. The use of evoked KCs enables testing the sleeping nervous system under good experimental control. This paradigm allowed us to adopt into the KC studies a method of signal analysis that provides time-frequency maps of statistically significant changes in signal energy density. Our results indicate that KCs and sleep spindles may be organized by a slow oscillation. Accordingly, KCs might be evoked only if the stimulus occurs in a certain phase of the slow oscillation. We also observed middle-latency evoked responses following auditory stimulation in the last sleep cycle. This effect was revealed only by the time-frequency maps and was not visible in standard averages.