Resting-state Electroencephalogram Research Articles

Functional embedding predicts the variability of neural activity

Neural activity is irregular and unpredictable, yet little is known about why this is the case and how this property relates to the functional architecture of the brain. Here we show that the variability of a region’s activity systematically varies according to its topological role in functional networks. We recorded the resting-state electroencephalogram (EEG) and constructed undirected graphs of functional networks. We measured the centrality of each node in terms of the number of connections it makes (degree), the ease with which the node can be reached from other nodes in the network (efficiency) and the tendency of the node to occupy a position on the shortest paths between other pairs of nodes in the network (betweenness). As a proxy for variability, we estimated the information content of neural activity using multiscale entropy analysis. We found that the rate at which information was generated was largely predicted by centrality. Namely, nodes with greater degree, betweenness, and efficiency were more likely to have high information content, while peripheral nodes had relatively low information content. These results suggest that the variability of regional activity reflects functional embedding.

Relation between resting EEG to cognitive performance and clinical symptoms in adults with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) in children is characterized by elevated levels of slow wave activity and reduced fast wave activity in resting-state electroencephalogram (EEG). In adults with ADHD, resting-state EEG findings are scarce and inconsistent. The present study examined whether the disparate findings are due EEG recording conditions (i.e., eyes-open vs. eyes-closed). A second goal of the current study was to assess relations between EEG spectral indices to performance measures obtained using a stop-signal task, and to behavioral ADHD symptoms. The present study included 24 adults with ADHD and 24 control adults. The EEG results showed a greater reduction in alpha power from eyes-closed to eyes-open (i.e., alpha attenuation) in ADHD compared to controls. In addition, theta/beta ratio was negatively correlated to the speed of responding to choice stimuli. These findings were interpreted vis-à-vis a biophysical model assuming that the hypo-arousal in ADHD is due to an overdrive of the nucleus coeruleus resulting in inhibitory activity of the thalamic reticular nucleus.

Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity

The brain is widely assumed to be a paradigmatic example of a complex, self-organizing system. As such, it should exhibit the classic hallmarks of nonlinearity, multistability, and "nondiffusivity" (large coherent fluctuations). Surprisingly, at least at the very large scale of neocortical dynamics, there is little empirical evidence to support this, and hence most computational and methodological frameworks for healthy brain activity have proceeded very reasonably from a purely linear and diffusive perspective. By studying the temporal fluctuations of power in human resting-state electroencephalograms, we show that, although these simple properties may hold true at some temporal scales, there is strong evidence for bistability and nondiffusivity in key brain rhythms. Bistability is manifest as nonclassic bursting between high- and low-amplitude modes in the alpha rhythm. Nondiffusivity is expressed through the irregular appearance of high amplitude "extremal" events in beta rhythm power fluctuations. The statistical robustness of these observations was confirmed through comparison with Gaussian-rendered phase-randomized surrogate data. Although there is a good conceptual framework for understanding bistability in cortical dynamics, the implications of the extremal events challenge existing frameworks for understanding large-scale brain systems.

Genetic and Disorder-Specific Aspects of Resting State EEG Abnormalities in Schizophrenia

We evaluated whether abnormal frequency composition of the resting state electroencephalogram (EEG) in schizophrenia was associated with genetic liability for the disorder by studying first-degree biological relatives of schizophrenia patients. The study included a data-driven method for defining EEG frequency components and determined the specificity of resting state EEG frequency abnormalities by assessing schizophrenia patients, bipolar disorder patients, and relatives of both patient groups. Schizophrenia patients and their relatives, but not bipolar patients or their relatives, exhibited increased high-frequency activity (beta) providing evidence for disturbances in resting state brain activity being specific to genetic liability for schizophrenia. Schizophrenia patients exhibited augmented low-frequency EEG activity (delta, theta), while bipolar disorder patients and the 2 groups of relatives generally failed to manifest similar low-frequency EEG abnormalities. The Val(158)Met polymorphism for the catechol-O-methyl transferase (COMT) gene was most strongly associated with delta and theta activity in schizophrenia patients. Met homozygote schizophrenia patients exhibited augmented activity for the 2 low-frequency bands compared with control subjects. Excessive high-frequency EEG activity over frontal brain regions may serve as an endophenotype that reflects cortical expression of genetic vulnerability for schizophrenia. Low-frequency resting state EEG anomalies in schizophrenia may relate to disorder-specific pathophysiology in schizophrenia and the influence of the COMT gene on tonic dopamanergic function.

P.6.013 EEG-effects of gustatory and olfactory stimuli in anorexia nervosa

This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of type 2 diabetes mellitus (T2DM) with or without amnestic mild cognitive impairment (aMCI).Permutation conditional mutual information (PCMI) was used to calculate the coupling strength and direction of rsEEG signals between different brain areas of 19 aMCI and 20 normal control (NC) with T2DM on 7 frequency bands: Delta, Theta, Alpha1, Alpha2, Beta1, Beta2 and Gamma. The difference in coupling strength or direction of rsEEG between two groups was calculated. The correlation between coupling strength or direction of rsEEG and score of different neuropsychology scales were also calculated.We have demonstrated that PCMI can calculate effectively the coupling strength and directionality of EEG signals between different brain regions. The significant difference in coupling strength and directionality of EEG signals was found between the patients of aMCI and NC with T2DM on different brain regions. There also existed significant correlation between sex or age and coupling strength or coupling directionality of EEG signals between a few different brain regions from all subjects.The coupling strength or directionality of EEG signals calculated by PCMI are significantly different between aMCI and NC with T2DM.These results showed that the coupling strength or directionality of EEG signals calculated by PCMI might be used as a biomarker in distinguishing the aMCI from NC with T2DM.

The Impact of Moderate Sleep Loss on Neurophysiologic Signals during Working-Memory Task Performance

This study examined how sleep loss affects neurophysiologic signals related to attention and working memory. Subjective sleepiness, resting-state electroencephalogram, and behavior and electroencephalogram during performance of working-memory tasks were recorded in a within-subject, repeated-measures design. Data collection occurred in a computerized laboratory setting. Sixteen healthy adults (mean age, 26 years; 8 female) Data from alert daytime baseline tests were compared with data from tests during a late-night, extended-wakefulness session that spanned up to 21 hours of sleep deprivation. Alertness measured both subjectively and electrophysiologically decreased monotonically with increasing sleep deprivation. A lack of alertness-related changes in electroencephalographic measures of the overall mental effort exerted during task execution indicated that participants attempted to maintain high levels of performance throughout the late-night tests. Despite such continued effort, responses became slower, more variable, and more error prone within 1 hour after participants' normal time of sleep onset. This behavior failure was accompanied by significant degradation of event-related brain potentials related to the transient focusing of attention. Moderate sleep loss compromises the function of neural circuits critical to subsecond attention allocation during working-memory tasks, even when an effort is made to maintain wakefulness and performance. Multivariate analyses indicate that combinations of working-memory-related behavior and neurophysiologic measures can be sensitive enough to permit reliable detection of such effects of sleep loss in individuals. Similar methods might prove useful for assessment of functional alertness in patients with sleep disorders.