The functional significance of EEG microstates-Associations with modalities of thinking

The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, attention reorientation, and subjective interoceptive-autonomic processing, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions concerned particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond, the associations of microstate classes A and B with visual and verbal processing, respectively and microstate class D with interoceptive-autonomic processing, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts, as well as interoceptive-autonomic processing. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

EEG marker of inhibitory brain activity correlates with resting-state cerebral blood flow in the reward system in major depression

Frontal alpha band asymmetry (FAA) is a marker of altered reward processing in major depressive disorder (MDD), associated with reduced approach behavior and withdrawal. However, its association with brain metabolism remains unclear. The aim of this study is to investigate FAA and its correlation with resting – state cerebral blood flow (rCBF). We hypothesized an association of FAA with regional rCBF in brain regions relevant for reward processing and motivated behavior, such as the striatum. We enrolled 20 patients and 19 healthy subjects. FAA scores and rCBF were quantified with the use of EEG and arterial spin labeling. Correlations of the two were evaluated, as well as the association with FAA and psychometric assessments of motivated behavior and anhedonia. Patients showed a left – lateralized pattern of frontal alpha activity and a correlation of FAA lateralization with subscores of Hamilton Depression Rating Scale linked to motivated behavior. An association of rCBF and FAA scores was found in clusters in the dorsolateral prefrontal cortex bilaterally (patients) and in the left medial frontal gyrus, in the right caudate head and in the right inferior parietal lobule (whole group). No correlations were found in healthy controls. Higher inhibitory right – lateralized alpha power was associated with lower rCBF values in prefrontal and striatal regions, predominantly in the right hemisphere, which are involved in the processing of motivated behavior and reward. Inhibitory brain activity in the reward system may contribute to some of the motivational problems observed in MDD.

Studying functional brain networks with concurrent transcranical alternating current stimulation and EEG

Recently transcranial electric stimulation (tES) has been widely used as a mean to modulate brain activity. The modulatory effects of tES have been studied with the excitability of primary motor cortex. However, tES effects are not limited to the site of stimulation but extended to other brain areas, suggesting a need for the study of functional brain networks. Transcranial alternating current stimulation (tACS) applies sinusoidal current at a specified frequency, presumably modulating brain activity in a frequency-specific manner. At a behavioural level, tACS has been confirmed to modulate behaviour, but its neurophysiological effects are still elusive. In addition, neural oscillations are considered to reflect rhythmic changes in transmission efficacy across brain networks, suggesting that tACS would provide a mean to modulate brain networks. To study neurophysiological effects of tACS, we have been developing a methodological framework by combining transcranial magnetic stimulation (TMS), EEG and tACS. We have developed the optimized concurrent tACS-EEG recording protocol and powerful artefact removal method that allow us to study neurophysiological effects of tACS. We also established the concurrent tACS-TMS-EEG recording to study brain network connectivity while introducing extrinsic oscillatory activity by tACS. We show that tACS modulate brain activity in a phase-dependent manner. Our methodological advancement will open an opportunity to study causal role of oscillatory brain activity in neural transmissions in cortical brain networks.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

OBJECTIVE Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Discovering EEG resting state alterations of Semantic Dementia

Objective Diagnosis of semantic dementia relies on cost-intensive MRI or PET, although resting EEG markers of other dementias have been reported. Yet the view still holds that resting EEG in patients with semantic dementia is normal. However, studies using increasingly sophisticated EEG analysis methods have demonstrated that slightest alterations of functional brain states can be detected. Methods We analyzed the common four resting EEG microstates (A, B, C, and D) of 8 patients with semantic dementia in comparison with 8 healthy controls and 8 patients with Alzheimer’s disease. Results Topographical differences between the groups were found in microstate classes B and C, while microstate classes A and D were comparable. The data showed that the semantic dementia group had a peculiar microstate E, but the commonly found microstate C was lacking. Furthermore, the presence of microstate E was significantly correlated with lower MMSE and language scores. Conclusion Alterations in resting EEG can be found in semantic dementia. Topographical shifts in microstate C might be related to semantic memory deficits. Significance This is the first study that discovered resting state EEG abnormality in semantic dementia. The notion that resting EEG in this dementia subtype is normal has to be revised.

Evidence for a perception memory continuum: an EEG study in a healthy population

In line with current memory theories of a perception-memory continuum along the ventral visual pathway, there is evidence that the specific profile of enhanced memory in special populations (e.g. synaesthesia) is based on increased perceptual sensitivity. The main goal of this study was to test in a more general population, if increased perceptual sensitivity is indeed associated with enhanced memory performance. We measured ERPs in response to simple perceptual stimuli biasing either the ventral or the dorsal route and established if perceptual sensitivity in response to ventrally (but not dorsally) processed stimuli is associated with visual short term memory performance in a change detection task. Preliminary results confirm the hypothesis and strengthen the assumption of a perceptual-memory-continuum.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

Brain rhythms alterations and their effect in functional networks: A simultaneous EEG/fMRI study in Fixation-off epilepsy

Fixation-off sensitivity (FOS) denotes the forms of EEG abnormalities, which are elicited by elimination of central vision or fixation. The phenomenon seems to depend on variables that modulate the alpha rhythm, however, the cerebral mechanisms underlying FOS remain unclear [1]. The scarce previous fMRI findings related to FOS have shown activation in extrastriate cortex [2] and also in frontal areas [3][4]. On the other hand, simultaneous EEG-fMRI technique has been used to assess the relationship between spontaneous power fluctuations of electrical rhythms and associated fMRI signal modulations. These studies have identified that lateral frontoparietal networks show a negative correlation with alpha band in healthy subjects. This neuroanatomical pattern is related to attentional processes and cognitive resources. Moreover, a sub-beta band (17-23 Hz) has been identified with posterior cingulate, temporoparietal junction and dorso-medial prefrontal cortex activations, which correspond to the DMN [5][6].