Within and Between Subject Spectral Fingerprints of EEG-Microstate Parameters.

Early reports have claimed that EEG microstate features (e.g. their mean duration or percent of time covered) are largely independent from EEG spectra. This has meanwhile been questioned for conceptual and empirical reasons, but so far, EEG spectral power map correlates of microstate features have not been reported. We present the results of such analyses, conducted both within and between subjects, and report patterns of systematic changes in local EEG spectral amplitude associated with the mean duration, frequency of occurrence and relative contribution of particular microstate classes. The combination of EEG microstate analysis with spectral analysis may therefore be helpful to come to a deeper understanding of local patterns of activation and inhibition associated with particular microstate classes

Electrocorticographic Activation Patterns of Electroencephalographic Microstates.

Electroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5 min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates

EEG MICROSTATES ANALYSIS IN PATIENTS WITH EPILEPSY

Analysis of EEG microstates is a promising topographical method that is currently being studied for diagnosis of neuro-psychiatric diseases such as schizophrenia, dementia, etc. The aim of our study is to describe the possibility of using the microstate analysis of electroencephalographic recordings (EEG) for examination of the epileptic activity. The EEG recordings were measured on patients with epilepsy and on control subjects (with no epileptic pathology) in the system 10 - 20. The data are analysed in average montage and filtered with bandpass from 0.5 to 30.0 Hz. We calculate the global field power (GFP) curve to extract microstates from the EEG recordings. We take local maxima (peaks) of GFP curve to create amplitude topographic maps. The microstate 1 seems to have higher occurrence for the non-epileptic controls than the patients with epilepsy. The duration of the microstate 4 seems to be higher in the epileptic patients than the non-epileptic controls. We have found that there is a significant difference in the duration, occurrence and contribution of the amplitude topographic maps between the non-epileptic controls and the patients with epilepsy

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

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth

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

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript