Explore the vast range of titles available.

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects’ arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

In this paper, we present a multivariate approach to analyze multi-channel event-related potential (ERP) data using randomization statistics. The MATLAB-based open source toolbox (Ragu) provides, among other methods, a test for topographic consistency, a topographic analysis of variance, t-mapping and microstate analyses. Up to two within-subject factors and one between-subject factor, each with an open number of levels, can be defined and analyzed in Ragu. Ragu analyses include all sensor signals and no a-priori models have to be applied during the analyses. Additionally, periods of significant effects can be controlled for multiple testing using global overall statistics over time. Here, we introduce the different alternatives to apply Ragu, based on a step by step analysis of an example study. This example study examined the neural activity in response to semantic unexpected sentence endings in exchange students at the beginning of their stay and after staying in a foreign-language country for 5 months.

Epi proColon® is a new blood-based colorectal cancer (CRC) screening test designed to determine the methylation status of a promoter region of the SEPT9 (septin 9) gene in cell-free DNA isolated from plasma. We describe the analytical and clinical performance of the test. Analytical performance at 4 testing laboratories included determination of limit of detection, precision, and reproducibility of the SEPT9 test. Clinical performance was evaluated in a prospective study by use of samples (n = 1544) from subjects enrolled in the PRESEPT clinical trial. Results were analyzed by comparison with colonoscopy, the reference standard. The limit of detection for methylated SEPT9 DNA was 7.8 pg/mL (95% CI 6-11 pg/mL) corresponding to <2 genome copies of methylated SEPT9 per milliliter of plasma. In the prospective clinical trial, sensitivity for all stages of CRC was 68% (95% CI 53%-80%) and for stage I-III CRC, 64% (48%-77%). Adjusted specificity, on the basis of negative colonoscopy findings, was 80.0% (78%-82%). The Epi proColon test is a simple, real-time PCR-based assay for the detection of methylated SEPT9 DNA in blood that may provide a noninvasive CRC screening alternative for people noncompliant with current CRC screening guidelines.

fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.

•EEG microstate parameters are strongly related to vigilance levels and can predict them.•We find that vigilance Granger-causes changes in parameters of microstates.•Duration and occurrence of EEG microstates are differentially modulated by vigilance level. The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

We present a simple and effective method to test whether an event consistently activates a set of brain electric sources across repeated measurements of event-related scalp field data. These repeated measurements can be single trials, single subject ERPs, or ERPs from different studies. The method considers all sensors simultaneously, but can be applied separately to each time frame or frequency band of the data. This allows limiting the analysis to time periods and frequency bands where there is positive evidence of a consistent relation between the event and some brain electric sources. The test may therefore avoid false conclusions about the data resulting from an inadequate selection of the analysis window and bandpass filter, and permit the exploration of alternate hypotheses when group/condition differences are observed in evoked field data. The test will be called topographic consistency test (TCT). The statistical inference is based on simple randomization techniques. Apart form the methodological introduction, the paper contains a series of simulations testing the statistical power of the method as function of number of sensors and observations, a sample analysis of EEG potentials related to self-initiated finger movements, and Matlab source code to facilitate the implementation. Furthermore a series of measures to control for multiple testing are introduced and applied to the sample data.

Cognitive task performance differs considerably between individuals. Besides cognitive capacities, attention might be a source of such differences. The individual's EEG alpha frequency (IAF) is a putative marker of the subject's state of arousal and attention, and was found to be associated with task performance and cognitive capacities. However, little is known about the metabolic substrate (i.e. the network) underlying IAF. Here we aimed to identify this network. Correlation of IAF with regional Cerebral Blood Flow (rCBF) in fifteen young healthy subjects revealed a network of brain areas that are associated with the modulation of attention and preparedness for external input, which are relevant for task execution. We hypothesize that subjects with higher IAF have pre-activated task-relevant networks and thus are both more efficient in the task-execution, and show a reduced fMRI-BOLD response to the stimulus, not because the absolute amount of activation is smaller, but because the additional activation by processing of external input is limited due to the higher baseline.

•We create lifespan Riemannian qEEG norms for cross-spectral tensors.•EEG from 1564 subjects provided by 9 countries, 12 devices, and 14 studies were used.•We demonstrate qEEG “batch effects”, providing harmonization methods to remove them.•Multinational harmonized z-scores increase diagnostic accuracy of brain dysfunction.•Data and software are available for norm and individual z-scores calculation. This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG “batch effects” and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Knowledge about brain source localizations for religious states of mind is still limited. Previous studies have usually not set a direct emphasis on experience. The present study investigated the phenomenon of religious experience using inverse solution calculations, and it is one of the first to measure the dimension of experience directly. A total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. Using a bar slider, the participants continuously rated how strongly they sensed God’s presence at any given moment. A selection of songs helped to induce the desired experience. Measurements were made using EEG with 64 electrodes and inverse solutions were calculated with sLORETA. We appropriated two mutually compatible hypotheses from the literature pertaining to religious experiences: the executive inhibition hypothesis (reformulated as the frontal relaxation hypothesis) and the temporal involvement hypothesis. Our results did not yield any information about the frontal areas; however, they indicated that the right temporal cortex appeared to be involved during the experience.

Why do people take longer to associate the word “love” with outgroup words (incongruent condition) than with ingroup words (congruent condition)? Despite the widespread use of the implicit association test (IAT), it has remained unclear whether this IAT effect is due to additional mental processes in the incongruent condition, or due to longer duration of the same processes. Here, we addressed this previously insoluble issue by assessing the spatiotemporal evolution of brain electrical activity in 83 participants. From stimulus presentation until response production, we identified seven processes. Crucially, all seven processes occurred in the same temporal sequence in both conditions, but participants needed more time to perform one early occurring process (perceptual processing) and one late occurring process (implementing cognitive control to select the motor response) in the incongruent compared with the congruent condition. We also found that the latter process contributed to individual differences in implicit bias. These results advance understanding of the neural mechanics of response time differences in the IAT: They speak against theories that explain the IAT effect as due to additional processes in the incongruent condition and speak in favor of theories that assume a longer duration of specific processes in the incongruent condition. More broadly, our data analysis approach illustrates the potential of electrical neuroimaging to illuminate the temporal organization of mental processes involved in social cognition.