Resting EEG Research Articles

No Frequency-Specific Effect of Transcranial Random Noise Stimulation on Resting EEG.

Transcranial random noise stimulation (tRNS) is a form of noninvasive transcranial electrical stimulation that applies alternating current in various randomized frequencies to the cortex, thereby improving cognitive functioning in multiple domains. However, the precise mechanism of tRNS, as well as its impact on human electroencephalography (EEG), remains unclear. This is partly because most studies have used tRNS in conjunction with a cognitive task, making it difficult to tease apart whether the observed changes in EEG are a result of tRNS, the cognitive task, or their interaction. Forty-nine healthy individuals participated in this study and were randomly assigned to active tRNS (n = 24) and sham (n = 25) groups. tRNS was delivered for 20 minutes over Fp1/Fp2 and Oz. Resting-state EEG data were collected before and after either tRNS or sham stimulation. Cluster-based permutation tests using FieldTrip revealed no frequency-specific effect of tRNS on resting-state EEG data across four frequency bands (theta, alpha, beta, gamma). These observations suggest that tRNS itself does not target or alter specific EEG frequencies. Rather, tRNS most likely interacts with the cognitive task/activity at hand to produce an observable difference in post-tRNS EEG. Positive tRNS-EEG findings from previous studies are also likely to have resulted from the interactive and cognitive activity-dependent nature of tRNS.

The Functional Aspects of Resting EEG Microstates: A Systematic Review.

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.

Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects.

Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings.

Analyzing Brain Connectivity Patterns in Participants at Music and Rest State Using EEG Signals

One of the most widely used methods for monitoring human brains is the brain computer interface. Cognitive neuroscience is a recently developed field of research that aims to comprehend how the brain reacts to various inputs. When compared to typically developing controls (TDC), intellectual and developmental disorder (IDD) is a result of altered brain connections, and the activity of the brain is examined using frequency bands. Electroencephalography (EEG), which analyses brain activity and used to compares IDD and TDC brain waves to identify anomalies. Participants are prone to experiment with rest and music states and EEG signal were recorded for analysis.

A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data.

Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. As a result, BD-depressed episode (BD-DE) is often misdiagnosed as MDD, and inappropriate therapy is given. Electroencephalography (EEG) has been widely studied as a potential source of biomarkers to differentiate these disorders. Previous attempts using machine learning (ML) methods have delivered insufficient sensitivity and specificity for clinical use, likely as a consequence of the small training set size, and inadequate ML methodology. We hope to overcome these limitations by employing a training dataset of resting-state EEG from 71 MDD and 71 BD patients. We introduce a robust 3 steps ML technique: 1) a multi-step preprocessing method is used to improve the quality of the EEG signal 2) symbolic transfer entropy (STE), which is an effective connectivity measure, is applied to the resultant EEG signals 3) the ML algorithm uses the extracted STE features to distinguish MDD from BD patients. Clinical Relevance--- The accuracy of our algorithm, derived from a large sample of patients, suggests that this method may hold significant promise as a clinical tool. The proposed method delivered total accuracy, sensitivity, and specificity of 84.9%, 83.4%, and 87.1%, respectively.

Non-zero mean alpha oscillations revealed with computational model and empirical data.

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

Exposure to Depression Memes on Social Media Increases Depressive Mood and It Is Moderated by Self-Regulation: Evidence From Self-Report and Resting EEG Assessments.

This study aimed to investigate the effects of depression memes, spread mainly on social media, on depressive mood, and the moderating role of self-regulation based on self-report and electrophysiological (resting EEG frontal alpha asymmetry) assessments. We conducted a semi-online crossover study; first, we collected brain activity data from healthy young adults (n = 32) who were subsequently provided a link to the online experiment. Each participant participated in both the neutral and meme conditions. We also evaluated their level of depressive mood immediately before and after exposure to the stimuli. We further conducted a series of linear mixed effects model analyses and found that depression memes contributed to an increase in depressive symptoms. Specifically, lack of emotional clarity, difficulties in goal-directed behaviors in emotional distress, and impulse control difficulties were linked to greater depressive mood in the case of exposure to depression memes compared with neutral images. However, time interactions were insignificant. These results mainly indicate the centrality of behavioral problems during times of emotional distress caused by depression memes. Lastly, although frontal alpha asymmetry did not predict a change in depressive mood or significantly differ across conditions, lower inhibitory control may result in increased processing of depression memes as negative stimuli. This result is consistent with our self-report results (e.g., impulsivity) as well as other related studies in the literature. However, further research is needed to verify these frontal alpha asymmetry results.

Classification of Patients with Alzheimer's Disease and Dementia with Lewy Bodies using Resting EEG Selected Features at Sensor and Source Levels: A Proof-of-Concept Study.

Early differentiation between Alzheimer's disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.

Developmental Dysphonetic Dyslexia Differs From Nonspecific Reading Delay. Evidence From Resting EEG Effective Connectivity at the Sources

We consider the controller synthesis problem for stochastic, continuous-state, nonlinear systems against ω-regular specifications. We synthesize a symbolic controller that ensures almost sure (qualitative) satisfaction of the specification. The problem reduces, after some automata-theoretic constructions, to computing the almost sure winning region—the largest set of states from which a parity condition can be satisfied with probability 1 (on a possibly hybrid state space). While characterizing the exact almost sure winning region is still open for the considered system class, we propose an algorithm for obtaining a tight under-approximation of this set. The heart of our approach is a technique to symbolically compute this under-approximation via a finite-state abstraction as a 21/2-player parity game. Our abstraction procedure uses only the support of the probabilistic evolution; it does not use precise numerical transition probabilities. We have implemented our approach and evaluated it on the nonlinear model of the perturbed Dubins vehicle.

Neuronal biomarkers of Parkinson's disease are present in healthy aging

The prevalence of Parkinson's disease (PD) increases with aging and both processes share similar cellular mechanisms and alterations in the dopaminergic system. Yet it remains to be investigated whether aging can also demonstrate electrophysiological neuronal signatures typically associated with PD. Previous work has shown that phase-amplitude coupling (PAC) between the phase of beta oscillations and the amplitude of gamma oscillations as well as beta bursts features can serve as electrophysiological biomarkers for PD. Here we hypothesize that these metrics are also present in apparently healthy elderly subjects. Using resting state multichannel EEG measurements, we show that PAC between beta oscillation and broadband gamma activity (50–150 Hz) is elevated in a group of elderly (59–77 years) compared to young volunteers (20–35 years) without PD. Importantly, the increase of PAC is statistically significant even after ruling out confounds relating to changes in spectral power and non-sinusoidal shape of beta oscillation. Moreover, a trend for a higher percentage of longer beta bursts (> 0.2 s) along with the increase in their incidence rate is also observed for elderly subjects. Using inverse modeling, we further show that elevated PAC and longer beta bursts are most pronounced in the sensorimotor areas. Moreover, we show that PAC and longer beta bursts might reflect distinct mechanisms, since their spatial patterns only partially overlap and the correlation between them is weak. Taken together, our findings provide novel evidence that electrophysiological biomarkers of PD may already occur in apparently healthy elderly subjects. We hypothesize that PAC and beta bursts characteristics in aging might reflect a pre-clinical state of PD and suggest their predictive value to be tested in prospective longitudinal studies.

Abnormal resting state EEG power spectral in patients with tinnitus on different sides of the ear

Tinnitus is a symptom of sound perception in the absence of external sound stimulation, and most patients have unilateral tinnitus, with more tinnitus on the left than on the right. In this paper, the resting EEG data of 10 patients with binaural tinnitus, 10 patients with left tinnitus and 10 healthy controls were collected, and two 10-s segments of resting EEG data were intercepted from each group for approximately 10 min. The absolute and relative power was calculated by fast Fourier transform. The results showed that more frequency bands were significantly different in the left tinnitus patients compared to the bilateral tinnitus patients and the right tinnitus patients, and where significant differences were demonstrated, the absolute and relative power was higher in the Delta and Theta bands of the tinnitus patients than in the control group. In conclusion, these results compare the resting state EEG power spectrum abnormalities in patients with tinnitus on different sides with healthy controls from an electroencephalographic perspective, and are expected to be used as an evaluation criterion in the future to provide a more refined and precise treatment plan for patients with tinnitus on different sides and to improve the efficiency of treatment.

A Study on Resting EEG Effective Connectivity Difference before and after Neurofeedback for Children with ADHD

Altered functional networks in attention deficit/hyperactivity disorder (ADHD) have been frequently reported, but effective connectivity has hardly been studied. Especially the differences of effective connectivity in children with ADHD after receiving neurofeedback (NF) training have been merely reported. Therefore, this study aimed to explore the effective networks of ADHD and the positive influence of NF on the effective networks. Electroencephalogram (EEG) data were recorded from 22 children with ADHD (including data from children pretraining and posttraining) and 15 age-matched healthy controls during an eyes-closed resting state. Phase transfer entropy (PTE) was used to construct the effective connectivity. The topological properties of networks and flow gain were measured separately in four bands (delta, theta, alpha, and beta). Results revealed the following: pretraining children with ADHD manifested a higher clustering coefficient and lower characteristic path length in the delta band than healthy controls; weakened anterior-to-posterior flow gain in the delta band, strengthened posterior-to-anterior flow gain in the alpha band and strengthened anterior-to-posterior flow gain in the beta band were observed in pretraining children with ADHD; The topological properties and flow gain in posttraining children with ADHD were close to those of healthy controls. Moreover, parent’s SWAN presented significant improvements of ADHD symptoms after NF. Our findings revealed that the effective connectivity of ADHD was altered and that NF could improve the brain function of ADHD. The present study provided the first evidence that children with ADHD differed from healthy children in phase-based effective connectivity and that NF could reduce the differences.

The spontaneous brain activity of disgust: Perspective from resting state fMRI and resting state EEG

In recent years, more and more studies on disgust have shown the association between disgust and various psychopathologies. Revealing the spontaneous brain activity patterns associated with disgust sensitivity from the perspective of individual differences will give us an insight into the neurologic nature of disgust and its psychopathological vulnerability. Here, we used two modal brain imaging techniques (resting fMRI and resting EEG) to reveal spontaneous brain activity patterns closely related to disgust sensitivity. The amplitude of low-frequency fluctuation results showed that disgust sensitivity is negatively correlated with the spontaneous activity of the right cerebellum crus II and positively correlated with the spontaneous activity of the right superior frontal cortex, which are inhibition-related brain regions. Furthermore, the microstate results of rest EEG indicated that the corrected duration, occurrence rate, and contribution of Class C, which is related to the anterior default mode network and is considered to be related to subjective representation of one’ own body by combining interoceptive information with affective salience, were significantly positively correlated with the disgust sensitivity level. This data-driven approach provides the first evidence on the intrinsic brain features of disgust sensitivity based on two resting-state brain modalities. The results represent an initial effort to uncover the neurological basis of disgust sensitivity and its connection to psychopathology.

Sex differences in resting EEG in healthy young adults

Resting EEG, measured in eyes-closed (EC) and eyes-open (EO) states, can provide insight into behavioural differences between groups. Surprisingly, differences in resting EEG between females and males have not been investigated systematically in previous literature. The present study utilised the four traditional EEG bands to confirm their baseline EC topographies and reactivity (EO minus EC) across groups, to clarify topographical differences between sexes, and to confirm alpha as a measure of arousal. Participants were eighty healthy young adults (40 female), with a mean age of 20.4 (range 18–26) years. Continuous resting EEG was recorded from 30 scalp sites during three 2-minute conditions (EO1, EC, EO2), and EOG-corrected. Data from each condition were divided into 60 sequential 2-second epochs. Accepted artefact-free epochs were Fourier Transformed, and absolute amplitudes in the delta (0.5–3.5 Hz), theta (4.0–7.5 Hz), alpha (8.0–13.0 Hz), and beta (13.5–29.5 Hz) bands were calculated. Across groups in EC, significant topographical differences were found between the band amplitudes, broadly compatible with previous reports. Females had greater overall amplitudes in delta, alpha and beta, enhanced midline activity in theta, and parietal and midline activity in the alpha and beta bands. From EC to EO, reactivity was apparent across the bands as significant reductions, particularly in the parietal region. For females compared to males, the reduction in parietal midline delta and theta, parietal alpha and parietal midline beta was significantly larger. Additionally, across groups, alpha activity was confirmed as an inverse measure of arousal. These findings indicate significant differences in neuronal activity between young adult females and males, and help our interpretation of alpha changes.

Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (14.4 Hz); whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.

Alterations of Resting EEG in Hallucinating and Nonhallucinating Schizophrenia Patients.

Auditory hallucinations (AHs) are a common symptom of schizophrenia and contribute significantly to disease burden. Research on schizophrenia and AHs is limited and fails to adequately address the effect of AHs on resting EEG in patients with schizophrenia. This study assessed changes in frequency bands (delta, theta, alpha, beta) of resting EEG taken from hallucinating patients (n = 12), nonhallucinating patients (n = 11), and healthy controls (n = 12). Delta and theta activity were unaffected by AHs but differed between patients with schizophrenia and healthy controls. Alpha activity was affected by AHs: nonhallucinators had more alpha activity than hallucinators and healthy controls. Additionally, beta activity was inversely related to trait measures of AHs. These findings contribute to the literature of resting eyes closed EEG recordings of schizophrenia and AHs, and indicate the role of delta, theta, alpha, and beta as markers for schizophrenia and auditory hallucinations.

BIOELECTRIC ACTIVITY OF THE BRAIN IN PUPILS WITH DIFFERENT ACADEMIC SUCCESS. REPORT 1. CHILDREN WITH DIFFERENT ACADEMIC SUCCESS AS REPRESENTATIVES OF THE AGE NORM

Background. Studying the bioelectric activity of the brain allows scientists to get a picture of brain functioning, including cognitive activity. The sensitivity of this method allows identifying and explaining subtle differences between children with different academic success, representatives of the age norm. However, additional research is needed to develop and substantiate measures of psychological and pedagogical support for schoolchildren for increasing their academic success. Aim. The paper aims to study rest and cognitive load EEG data in 12-year-old children and assess the compliance of these data with the age norm. Materials and methods. Twelve-year-old children participated in the study, both males (n = 25) and females (n = 26). The EEG was recorded using the CONAN-m equipment, and the electrodes were mounted according to a 10–20 system. The study was carried out under four experimental conditions: at rest and under cognitive load, which consisted of solving three types of tasks (verbal-logical, arithmetic, and spatial rotation of a figure). EEG spectral characteristics were evaluated depending on the recording conditions. Results. It was found that predominant alpha-range frequency in females was from 9.6 to 9.8 Hz, in boys it was slightly higher (about 9.9–10.1 Hz). Response to cognitive load was slightly different depending on the type of task. In general, it consisted of a slight increase in theta and beta ranges and a decrease in bioelectric activity in the alpha range. In males, an asymmetric, cross-type activation was found with a more pronounced alpha rhythm depression in the left frontal, temporal and right occipital regions. Conclusion: In general, the examined sample corresponded to the age norm according to rest and cognitive load EEG.

Perturbation-evoked potentials can be classified from single-trial EEG

Objective. Loss of balance control can have serious consequences on interaction between humans and machines as well as the general well-being of humans. Perceived balance perturbations are always accompanied by a specific cortical activation, the so-called perturbation-evoked potential (PEP). In this study, we investigate the possibility to classify PEPs from ongoing EEG. Approach. Fifteen healthy subjects were exposed to seated whole-body perturbations. Each participant performed 120 trials; they were rapidly tilted to the right and left, 60 times respectively. Main results. We achieved classification accuracies of more than 85% between PEPs and rest EEG using a window-based classification approach. Different window lengths and electrode layouts were compared. We were able to achieve excellent classification performance (87.6 ± 8.0% accuracy) by using a short window length of 200 ms and a minimal electrode layout consisting of only the Cz electrode. The peak classification accuracy coincides in time with the strongest component of PEPs, called N1. Significance. We showed that PEPs can be discriminated against ongoing EEG with high accuracy. These findings can contribute to the development of a system that can detect balance perturbations online.

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression-An EEG Study on Rest-Stimulus Interaction.

Major depressive disorder (MDD) is a complex psychiatric disorder characterized by changes in both resting state and stimulus-evoked activity. Whether resting state changes are carried over to stimulus-evoked activity, however, is unclear. We conducted a combined rest (3 min) and task (three-stimulus auditory oddball paradigm) EEG study in n=28 acute depressed MDD patients, comparing them with n=25 healthy participants. Our focus was on the temporal dynamics of both resting state and stimulus-evoked activity for which reason we measured peak frequency (PF), coefficient of variation (CV), Lempel-Ziv complexity (LZC), and trial-to-trial variability (TTV). Our main findings are: i) atypical temporal dynamics in resting state, specifically in the alpha and theta bands as measured by peak frequency (PF), coefficient of variation (CV) and power; ii) decreased reactivity to external deviant stimuli as measured by decreased changes in stimulus-evoked variance and complexity—TTV, LZC, and power and frequency sliding (FS and PS); iii) correlation of stimulus related measures (TTV, LZC, PS, and FS) with resting state measures. Together, our findings show that resting state dynamics alone are atypical in MDD and, even more important, strongly shapes the dynamics of subsequent stimulus-evoked activity. We thus conclude that MDD can be characterized by an atypical temporal dynamic of its rest–stimulus interaction; that, in turn, makes it difficult for depressed patients to react to relevant stimuli such as the deviant tone in our paradigm.