Cortical and autonomic responses during staged Taoist meditation: Two distinct meditation strategies

Advanced and long-term meditation and the autonomic nervous system: A review and synthesis

Meditation has become prominent in both clinical and non-clinical applications for its effects on psychological and physical well-being. Long-term meditators, who have dedicated extensive time to their practice, present a unique opportunity to explore the effects of prolonged meditation training on the autonomic nervous system. Research has reported concomitant activation of both sympathetic (aroused) and parasympathetic (relaxed) branches of the autonomic nervous system during some forms of meditation, leading to the term 'relaxed alertness.' However, findings are not consistent, with reports of both sympathetic and parasympathetic activation, sympathetic-only, parasympathetic-only, or temporally variable activations, depending on several factors. This review synthesizes these heterogeneous and seemingly inconsistent results in relation to three explanatory factors: (1) specific classification of style or type of meditation; (2) specific definition of the level of expertise of the meditators; and (3) intra-individual variations within a given meditation practice. When these factors are considered, convergent and meaningful patterns emerge, allowing for a shift from the broad notion of 'long-term' meditation to a more precise characterization of 'advanced' meditation, highlighting skills, states, and stages of mastery developed over time. Our synthesis is particularly useful for understanding both long-term and advanced meditation, as it reveals specific heart rate variability patterns, including very low and low-frequency spectral power peaks, along with cardiac and respiratory coupling. Better characterization of the role of the autonomic nervous system in the context of advanced meditation promises to inform improved meditation training, including training assisted by technology, toward more impactful outcomes.

Multimodal neurophenomenology of advanced concentration absorption meditation: An intensively sampled case study of Jhana

Using a combination of fMRI, EEG, and phenomenology ratings, we examined the neurophenomenology of advanced concentrative absorption meditation, namely jhanas (ACAM-J), in a practitioner with over 23,000 h of meditation practice. Our study shows that ACAM-J states induce reliable changes in conscious experience and that these experiences are related to neural activity. Using resting-state fMRI functional connectivity, we found that ACAM-J is associated with decreased within-network modularity, increased global functional connectivity (GFC), and desegregation of the default mode and visual networks. Compared to control tasks, the ACAM-J were also related to widespread decreases in broadband EEG oscillatory power and increases in Lempel-Ziv complexity (LZ, a measure of brain entropy). Some fMRI findings varied by the control task used, while EEG results remained consistent, emphasizing both shared and unique neural features of ACAM-J. These differences in fMRI and EEG-measured neurophysiological properties correlated with specific changes in phenomenology - and especially with ACAM-J-induced states of bliss - enriching our understanding of these advanced meditative states. Our results show that advanced meditation practices markedly dysregulate high-level brain systems via practices of enhanced attention to sensations, corroborating recent neurocognitive theories of meditation as the deconstruction of the brain's cortical hierarchy. Overall, our results suggest that ACAM-J is associated with the modulation of large-scale brain networks in both fMRI and EEG, with potential implications for understanding the mechanisms of deep concentration practices and their effects on subjective experience.

Short-term meditation training alters brain activity and sympathetic responses at rest, but not during meditation

Although more people are engaging in meditation practices that require specialized training, few studies address the issues associated with nervous activity pattern changes brought about by such training. For beginners, it remains unclear how much practice is needed before objective physiological changes can be detected, whether or not they are similar across the novices and what are the optimal strategies to track these changes. To clarify these questions we recruited individuals with no prior meditation experience. The experimental group underwent an eight-week Taoist meditation course administered by a professional, while the control group listened to audiobooks. Both groups participated in audio-guided, 34-min long meditation sessions before and after the 8-week long intervention. Their EEG, photoplethysmogram, respiration, and skin conductance were recorded during the mediation and resting state periods. Compared to the control group, the experimental group exhibited band-specific topically organized changes of the resting state brain activity and heart rate variability associated with sympathetic system activation. Importantly, no significant changes were found during the meditation process prior and post the 8-week training in either of the groups. The absence of notable changes in CNS and ANS activity indicators during meditation sessions, for both the experimental and control groups, casts doubt on the effectiveness of wearable biofeedback devices in meditation practice. This finding redirects focus to the importance of monitoring resting state activity to evaluate progress in beginner meditators. Also, 16 h of training is not enough for forming individual objectively different strategies manifested during the meditation sessions. Our results contributed to the development of tools to objectively monitor the progress in novice meditators and the choice of the relevant monitoring strategies. According to our findings, in order to track early changes brought about by the meditation practice it is preferable to monitor brain activity outside the actual meditation sessions.

Math on cortex-enhanced delta phase synchrony in math experts during long and complex math demonstrations

Neural oscillations are important for working memory and reasoning and they are modulated during cognitively challenging tasks, like mathematics. Previous work has examined local cortical synchrony on theta (4-8 Hz) and alpha (8-13 Hz) bands over frontal and parietal electrodes during short mathematical tasks when sitting. However, it is unknown whether processing of long and complex math stimuli evokes inter-regional functional connectivity. We recorded cortical activity with EEG while math experts and novices watched long (13-68 seconds) and complex (bachelor-level) math demonstrations when sitting and standing. Fronto-parietal connectivity over the left hemisphere was stronger in math experts than novices reflected by enhanced delta (0.5-4 Hz) phase synchrony in experts. Processing of complex math tasks when standing extended the difference to right hemisphere, suggesting that other cognitive processes, such as maintenance of body balance when standing, may interfere with novice's internal concentration required during complex math tasks more than in experts. There were no groups differences in phase synchrony over theta or alpha frequencies. These results suggest that low-frequency oscillations modulate inter-regional connectivity during long and complex mathematical cognition and demonstrate one way in which the brain functions of math experts differ from those of novices: through enhanced fronto-parietal functional connectivity.

SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.

Pharmacological and non-pharmacological therapeutic interventions for the treatment of spinal cord injury-induced pain

Spinal cord injury (SCI) is a complex neurophysiological disorder, which can result in many long-term complications including changes in mobility, bowel and bladder function, cardiovascular function, and metabolism. In addition, most individuals with SCI experience some form of chronic pain, with one-third of these individuals rating their pain as severe and unrelenting. SCI-induced chronic pain is considered to be "high impact" and broadly affects a number of outcome measures, including daily activity, physical and cognitive function, mood, sleep, and overall quality of life. The majority of SCI pain patients suffer from pain that emanates from regions located below the level of injury. This pain is often rated as the most severe and the underlying mechanisms involve injury-induced plasticity along the entire neuraxis and within the peripheral nervous system. Unfortunately, current therapies for SCI-induced chronic pain lack universal efficacy. Pharmacological treatments, such as opioids, anticonvulsants, and antidepressants, have been shown to have limited success in promoting pain relief. In addition, these treatments are accompanied by many adverse events and safety issues that compound existing functional deficits in the spinally injured, such as gastrointestinal motility and respiration. Non-pharmacological treatments are safer alternatives that can be specifically tailored to the individual and used in tandem with pharmacological therapies if needed. This review describes existing non-pharmacological therapies that have been used to treat SCI-induced pain in both preclinical models and clinical populations. These include physical (i.e., exercise, acupuncture, and hyper- or hypothermia treatments), psychological (i.e., meditation and cognitive behavioral therapy), and dietary interventions (i.e., ketogenic and anti-inflammatory diet). Findings on the effectiveness of these interventions in reducing SCI-induced pain and improving quality of life are discussed. Overall, although studies suggest non-pharmacological treatments could be beneficial in reducing SCI-induced chronic pain, further research is needed. Additionally, because chronic pain, including SCI pain, is complex and has both emotional and physiological components, treatment should be multidisciplinary in nature and ideally tailored specifically to the patient.