Temporal dynamics of the default mode network characterise meditation induced alterations in consciousness

Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network

EEG Microstate Dynamics Associated with Dream-Like Experiences During the Transition to Sleep.

Consciousness always requires some representational content; that is, one can only be conscious about something. However, the presence of conscious experience (awareness) alone does not determine whether its content is in line with the external and physical world. Dreams, apart from certain forms of hallucinations, typically consist of non-veridical percepts, which are not recognized as false, but rather considered real. This type of experiences have been described as a state of dissociation between phenomenal and reflective awareness. Interestingly, during the transition to sleep, reflective awareness seems to break down before phenomenal awareness as conscious experience does not immediately fade with reduced wakefulness but is rather characterized by the occurrence of uncontrolled thinking and perceptual images, together with a reduced ability to recognize the internal origin of the experience. Relative deactivation of the frontoparietal and preserved activity in parieto-occipital networks has been suggested to account for dream-like experiences during the transition to sleep. We tested this hypothesis by investigating subjective reports of conscious experience and large-scale brain networks using EEG microstates in 45 healthy young subjects during the transition to sleep. We observed an inverse relationship between cognitive effects and physiological activation; dream-like experiences were associated with an increased presence of a microstate with sources in the superior and middle frontal gyrus and precuneus. Additionally, the presence of a microstate associated with higher-order visual areas was decreased. The observed inverse relationship might therefore indicate a disengagement of cognitive control systems that is mediated by specific, inhibitory EEG microstates

Tracking brain dynamics across transitions of consciousness

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings

EEG Microstates in Social and Affective Neuroscience.

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience

Normative Intercorrelations Between EEG Microstate Characteristics.

EEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions of underlying networks, interpret and relate results, and generate new hypotheses. Here, we present a systematic analysis of intercorrelations between EEG microstate characteristics in a large sample representative of western working populations (n = 583). Notably, we find that microstate duration is a general characteristic that varies across microstate types. Further, microstate A and B show mutual reinforcement, indicating a relationship between auditory and visual sensory processing at rest. Microstate C appears to play a special role, as it is associated with longer durations of all other microstate types and increased global field power, suggesting a relationship of these parameters with the anterior default mode network. All findings could be confirmed using independent EEG recordings from a retest-session (n = 542)

The association of psychotic disorders, dopaminergic agents and resting-state EEG/MEG functional connectivity

Psychotic disorders are complex and heterogeneous mental disorders with low recovery rates despite a great amount of research on the topic. Various hypotheses exist as to the etiology of psychotic disorders. Amongst these, the dopamine hypothesis and the dysconnectivity hypothesis have been the most enduring in the last six decades. Little is known on how the dopamine and the dysconnectivity hypothesis are associated. The overarching research question of this thesis is to investigate this knowledge gap. Resting-state magneto- and electroencephalography (MEG, EEG) were chosen as non-invasive measurement modalities of dysconnectivity at the source and sensor level of the brain in publication 1. Parameters of resting-state EEG microstate classes A-D were used as a global analysis method of functional connectivity at the sensor level of the brain in publications 2 and 3. The first research question focused on finding systematic evidence on the association of the two hypotheses and was addressed by means of a systematic review (publication 1) of 20 studies published since 2000. Based on the review, no definite conclusion on the association of antipsychotic medication (that mainly acts on the dopamine system) and source- and sensor-level EEG/MEG functional connectivity could be drawn. The second research question focused on whether differences in parameters of resting-state EEG microstate classes A-D are associated to antipsychotic medication. It was addressed by a study (publication 2) that compared 19-channel clinical EEG recordings of medicated (mFEP, n = 17) and medication-naïve (untreated; uFEP, n = 30) patients with first-episode psychotic disorders (FEP). The study results revealed significant decrease of microstate class A and significant increase of microstate class B to differentiate mFEP from uFEP. The third research question focused on whether differences in parameters of resting-state EEG microstate classes A-D are associated with psychosis illness progression and transition to psychosis in FEP and ultra-high-risk (UHR) patients. It was addressed by a study (publication 3) that found significantly increased microstate class A to differentiate a combined group of medication-naïve FEP (n = 29) and UHR patients (n = 54) together from healthy controls (HC, n = 25); significantly decreased microstate class B to differentiate FEP from all UHR patients combined; and significantly decreased microstate class D to differentiate UHR-T patients with (n = 20) from UHR-NT patients without (n = 34) later transition to psychotic disorders using 19-channel EEG recordings. In conclusion across all three publications, an association between the dopamine and the dysconnectivity hypothesis could be demonstrated by means of resting-state EEG microstates assessed in publication 2 and 3. No definite conclusion could be drawn by the systematic review (publication 1). More studies with longitudinal designs are needed to rule-out between-subject differences, track response trajectories, pre-post effects of antipsychotic medication and their association with dysconnectivity. With increased effort, resting-state EEG microstates could contribute to establishing a robust biomarker in a multi- domain approach in order to inform clinicians for the diagnosis, treatment and outcome prediction of psychotic disorders

Cortical monitoring of cardiac activity during rapid eye movement sleep: the heartbeat evoked potential in phasic and tonic rapid-eye-movement microstates

The project was supported by the Hungarian Scientific Research Fund (NKFI FK 128100 and K 128117) of the National Research, Development and Innovation Office, as well as by the Higher Education Institutional Excellence Program of the Ministry of Human Capacities in Hungary, within the framework of the Neurology thematic program of the Semmelweis University. This project has also received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska–Curie grant (agreement No. 801505). PP was supported by a project from the Spanish Ministry of Science, Innovation and Universities (PGC2018-096655-A-I00). The study was supported by ELTE Thematic Excellence Programme 2020 TKP2020-IKA-05 provided by National Research, Development and Innovation Office.Sleep is a fundamental physiological state that facilitates neural recovery during periods of attenuated sensory processing. On the other hand, mammalian sleep is also characterized by the interplay between periods of increased sleep depth and environmental alertness. Whereas the heterogeneity of microstates during non-rapid-eye-movement (NREM) sleep was extensively studied in the last decades, transient microstates during rapid-eye-movement (REM) sleep received less attention. REM sleep features two distinct microstates: phasic and tonic. Previous studies indicate that sensory processing is largely diminished during phasic REM periods, whereas environmental alertness is partially reinstated when the brain switches into tonic REM sleep. Here, we investigated interoceptive processing as quantified by the heartbeat evoked potential (HEP) during REM microstates. We contrasted the HEPs of phasic and tonic REM periods using two separate databases that included the nighttime polysomnographic recordings of healthy young individuals (N = 20 and N = 19). We find a differential HEP modulation of a late HEP component (after 500 ms post-R-peak) between tonic and phasic REM. Moreover, the late tonic HEP component resembled the HEP found in resting wakefulness. Our results indicate that interoception with respect to cardiac signals is not uniform across REM microstates, and suggest that interoceptive processing is partially reinstated during tonic REM periods. The analyses of the HEP during REM sleep may shed new light on the organization and putative function of REM microstates.Hungarian Scientific Research Fund (NKFI FK 128100 and K 128117)Higher Education Institutional Excellence Program of the Ministry of Human Capacities in HungaryEuropean Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska–Curie grant (agreement No. 801505)Spanish Ministry of Science, Innovation and Universities (PGC2018-096655-A-I00)ELTE Thematic Excellence Programme 2020 TKP2020-IKA-05 National Research, Development and Innovation Offic

On the Reliability of the EEG Microstate Approach.

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach