Mindfulness Training is Associated with Changes in Alpha-Theta Cross-Frequency Dynamics During Meditation

Data Availability: All data are available at the Open Science Framework: https://osf.io/y23k8/?view_only=74193001fd39435b853b6a0b641d1e72Objectives: Previous literature suggests that cross-frequency phase synchronization is a mechanism by which information is transmitted and coordinated in the brain. Since cross-frequency phase synchronization is only strictly possible when two oscillators form a harmonic frequency ratio (e.g., 2:1, 3:1), a recent theory posited that interactions between brain oscillations at different frequencies are facilitated/hindered by the transient occurrence of harmonic/non-harmonic cross-frequency arrangements. In this line, recent evidence has shown that 2:1 harmonic relationships between alpha (8–14 Hz) and theta (4–8 Hz) rhythms are reduced during meditative states in experienced practitioners. In the present study, we investigated whether mindfulness training in novices is associated with longitudinal changes in alpha-theta cross-frequency dynamics during meditation practice. Methods: Thirty-six participants (mean age = 30.3; 2 men) underwent an 8-week mindfulness training program based on the mindfulness-based stress reduction (MBSR) syllabus and electroencephalography (EEG) recordings (64 electrodes) were performed during a guided meditation before and after the training. Results: Mindfulness training compliance (quantified by minutes of attendance and practice at home) was significantly correlated to decreased 3:1 harmonicity and cross-frequency phase synchrony between alpha and theta rhythms during meditation. Conclusions: Mindfulness training in novices was shown to be associated with a reduction in alpha-theta cross-frequency coupling during meditation. EEG parameters based on alpha-theta cross-frequency dynamics may be adequate for quantifying and/or facilitating mindfulness meditation training.Branco Weiss fellowship of the Society in Science–ETH Zurich, by Grants from the Flanders Fund for Scientific Research (FWO projects KAN 1506716N and G079017N); the Far East Organization

Electrophysiological Correlates of Naturally Occurring Thought Patterns

Humans engage in a continuous flow of thoughts throughout the day. These thoughts change depending on the context in which they occur and correspond with unique patterns of connectivity within and between neural networks. Notably, less is known about the electrophysiological signatures of these thought patterns. To address this question, this study examined the interplay between thought patterns and electrophysiological activity in internally and externally oriented contexts. Forty-one participants were asked to attend internally to their own thoughts (thought focus condition) and externally to a set of videos (video focus condition), during which they were asked to report various dimensions of their ongoing thoughts. We implemented principal component analysis on the ratings of these multiple thought dimensions and identified three thought patterns (representing co-occurring thought dimensions): present external thought, goal-oriented future thoughts, and freely moving external positive thoughts. We found that these three thought patterns differentially associated with the experimental conditions and EEG measures. Present external thought was more closely associated with the video focus condition and showed increased frontal alpha and posterior alpha. Goal-oriented future thoughts increased during the thought versus video focus condition but was not significantly linked to any EEG measures. Freely moving external positive thoughts were more strongly associated with the video focus condition and showed decreased frontal alpha activity. Taken together, our results highlight the complex relationship between thought patterns and electrophysiological activity in different contexts

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed. Keywords: Cognition; Electrophysiology; Event-related-potentials; Neural oscillations; Neural synchronisation; Neuromodulatio

ON THE REPRESENTATION OF SPATIAL AND TEMPORAL STRUCTURES: EFFECTS IN HUMAN VISUOSPATIAL WORKING MEMORY

In a diverse range of environments, each replete with unique physical phenomena, humans are capable of acting and achieving with volition. To do so we capitalize upon structures that exist in the physical world, rapidly drawing associations and forming conceptual relationships between items and occurrences. In this dissertation work, I examine how structures in the domains of space and time impact the representations of information that we form and hold in working memory, in the service of goal-driven behavior. Three key findings arise from the studies I present herein. First, representation of spatial structures in working memory is supported by oscillatory neural activity that differs between individuals based upon biological sex. The peak of posterior alpha frequency oscillatory activity is modulated in support of visuospatial representation maintenance more so in females than males. Among males but not females, successful representation of relative spatial structure is positively tied to an individual’s peak frequency of alpha oscillatory activity. Second, the interaction of spatial and temporal structures across perceptual modalities impacts representation in working memory. Shared temporal structure between a stream of visual targets and a stream of sounds promotes representation of the spatial structure of those sounds. This integration of perceptual information occurs whether helpful or harmful, differentially impacting performance. Third, the representation of spatial information in working memory is impacted by a particular form of temporal structure — rhythm. The presence of rhythmic versus arrhythmic temporal structure within a visuospatial stream does not increase the precision of working memory representation, but rather increases the speed with which representations may be formed. Rhythmic structure spontaneously and consistently facilitates working memory performance. Arrhythmic structure may hinder temporal processing but can be behaviorally compensated for with the application of controlled attention to the temporal domain. A novel paradigm, designed and utilized to study effects of rhythmic temporal structure upon visuospatial working memory is described

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena