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Microstates (MS), the fingerprints of the momentarily and time-varying states of the brain derived from electroencephalography (EEG), are associated with the resting state networks (RSNs). However, using MS fluctuations along different EEG frequency bands to model the functional MRI (fMRI) signal has not been investigated so far, or elucidated the role of the thalamus as a fundamental gateway and a putative key structure in cortical functional networks. Therefore, in the current study, we used MS predictors in standard frequency bands to predict blood oxygenation level dependent (BOLD) signal fluctuations. We discovered that multivariate modeling of BOLD-fMRI using six EEG-MS classes in eight frequency bands strongly correlated with thalamic areas and large-scale cortical networks. Thalamic nuclei exhibited distinct patterns of correlations for individual MS that were associated with specific EEG frequency bands. Anterior and ventral thalamic nuclei were sensitive to the beta frequency band, medial nuclei were sensitive to both alpha and beta frequency bands, and posterior nuclei such as the pulvinar were sensitive to delta and theta frequency bands. These results demonstrate that EEG-MS informed fMRI can elucidate thalamic activity not directly observable by EEG, which may be highly relevant to understand the rapid formation of thalamocortical networks., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Introduction: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN)., Methods: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps., Results: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls., Conclusions: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling., Significance: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

Objective: There are relevant links between resting-state fMRI networks, EEG microstate classes and psychopathological alterations in mental disorders associated with frontal lobe dysfunction. We hypothesized that a certain microstate class, labeled C and correlated with the salience network, was impaired early in frontotemporal dementia (FTD), and that microstate class D, correlated with the frontoparietal network, was impaired in schizophrenia., Methods: We measured resting EEG microstate parameters in patients with mild FTD (n = 18), schizophrenia (n = 20), mild Alzheimer's disease (AD; n = 19) and age-matched controls (old n = 19, young n = 18) to investigate neuronal dynamics at the whole-brain level., Results: The duration of class C was significantly shorter in FTD than in controls and AD, and the duration of class D was significantly shorter in schizophrenia than in controls, FTD and AD. Transition analysis showed a reversed sequence of activation of classes C and D in FTD and schizophrenia patients compared with that in controls, with controls preferring transitions from C to D, and patients preferring D to C., Conclusion: The duration and sequence of EEG microstates reflect specific aberrations of frontal lobe functions in FTD and schizophrenia., Significance: This study highlights the importance of subsecond brain dynamics for understanding of psychiatric disorders., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Developmental venous anomalies (DVAs) are associated with epileptic seizures; however, the role of DVA in the epileptogenesis is still not established. Simultaneous interictal electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) recordings provide supplementary information to electroclinical data about the epileptic generators, and thus aid in the differentiation of clinically equivocal epilepsy syndromes. The main objective of our study was to characterize the epileptic network in a patient with DVA and epilepsy by simultaneous EEG/fMRI recordings. A 17-year-old woman with recently emerging generalized tonic-clonic seizures, and atypical generalized discharges, was investigated using simultaneous EEG/fMRI at the university hospital. Previous high-resolution MRI showed no structural abnormalities, except a DVA in the right frontal operculum. Interictal EEG recordings showed atypical generalized discharges, corresponding to positive focal blood oxygen level dependent (BOLD) correlates in the right frontal operculum, a region drained by the DVA. Additionally, widespread cortical bilateral negative BOLD correlates in the frontal and parietal lobes were delineated, resembling a generalized epileptic network. The EEG/fMRI recordings support a right frontal lobe epilepsy, originating in the vicinity of the DVA, propagating rapidly to both frontal and parietal lobes, as expressed on the scalp EEG by secondary bilateral synchrony. The DVA may be causative of focal epilepsies in cases where no concomitant epileptogenic lesions can be detected. Advanced imaging techniques, such as simultaneous EEG/fMRI, may thus aid in the differentiation of clinically equivocal epilepsy syndromes.

In Alzheimer's disease (AD) patients, episodic memory impairments are apparent, yet semantic memory difficulties are also observed. While the episodic pathology has been thoroughly studied, the neurophysiological mechanisms of the semantic impairments remain obscure. Semantic dementia (SD) is characterized by isolated semantic memory deficits. The present study aimed to find an early marker of mild AD and SD by employing a semantic priming paradigm during electroencephalogram recordings. Event-related potentials (ERP) of early (P1, N1) and late (N400) word processing stages were obtained to measure semantic memory functions. Separately, baseline cerebral blood flow (CBF) was acquired with arterial spin labeling. Thus, the analysis focused on linear regressions of CBF with ERP topographical similarity indices in order to find the brain structures that showed altered baseline functionality associated with deviant ERPs. All participant groups showed semantic priming in their reaction times. Furthermore, decreased CBF in the temporal lobes was associated with abnormal N400 topography. No significant CBF clusters were found for the early ERPs. Taken together, the neurophysiological results suggested that the automatic spread of activation during semantic word processing was preserved in mild dementia, while controlled access to the words was impaired. These findings suggested that N400-topography alterations might be a potential marker for the detection of early dementia. Such a marker could be beneficial for differential diagnosis due to its low cost and non-invasive application as well as its relationship with semantic memory dysfunctions that are closely associated to the cortical deterioration in regions crucial for semantic word processing.

Objective: Gamma zero-lag phase synchronization has been measured in the animal brain during visual binding. Human scalp EEG studies used a phase locking factor (trial-to-trial phase-shift consistency) or gamma amplitude to measure binding but did not analyze common-phase signals so far. This study introduces a method to identify networks oscillating with near zero-lag phase synchronization in human subjects., Methods: We presented unpredictably moving face parts (NOFACE) which - during some periods - produced a complete schematic face (FACE). The amount of zero-lag phase synchronization was measured using global field synchronization (GFS). GFS provides global information on the amount of instantaneous coincidences in specific frequencies throughout the brain., Results: Gamma GFS was increased during the FACE condition. To localize the underlying areas, we correlated gamma GFS with simultaneously recorded BOLD responses. Positive correlates comprised the bilateral middle fusiform gyrus and the left precuneus., Conclusions: These areas may form a network of areas transiently synchronized during face integration, including face-specific as well as binding-specific regions and regions for visual processing in general., Significance: Thus, the amount of zero-lag phase synchronization between remote regions of the human visual system can be measured with simultaneously acquired EEG/fMRI., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Combined EEG/fMRI recordings offer a promising opportunity to detect brain areas with altered BOLD signal during interictal epileptic discharges (IEDs). These areas are likely to represent the irritative zone, which is itself a reflection of the epileptogenic zone. This paper reports on the imaging findings using independent component analysis (ICA) to continuously quantify epileptiform activity in simultaneously acquired EEG and fMRI. Using ICA derived factors coding for the epileptic activity takes into account that epileptic activity is continuously fluctuating with each spike differing in amplitude, duration and maybe topography, including subthreshold epileptic activity besides clear IEDs and may thus increase the sensitivity and statistical power of combined EEG/fMRI in epilepsy. Twenty patients with different types of focal and generalized epilepsy syndromes were investigated. ICA separated epileptiform activity from normal physiological brain activity and artifacts. In 16/20 patients, BOLD correlates of epileptic activity matched the EEG sources, the clinical semiology, and, if present, the structural lesions. In clinically equivocal cases, the BOLD correlates aided to attribute proper diagnosis of the underlying epilepsy syndrome. Furthermore, in one patient with temporal lobe epilepsy, BOLD correlates of rhythmic delta activity could be employed to delineate the affected hippocampus. Compared to BOLD correlates of manually identified IEDs, the sensitivity was improved from 50% (10/20) to 80%. The ICA EEG/fMRI approach is a safe, non-invasive and easily applicable technique, which can be used to identify regions with altered hemodynamic effects related to IEDs as well as intermittent rhythmic discharges in different types of epilepsy.

Electroencephalography (EEG) is an established measure in the field of brain resting state with a range of quantitative methods (qEEG) that yield unique information about neuronal activation and synchronization. Meanwhile, in the last decade, functional magnetic resonance imaging (fMRI) studies have revealed the existence of more than a dozen resting state networks (RSNs), and combined qEEG and fMRI have allowed us to gain understanding about the relationship of qEEG and fMRI-RSNs. However, the overall picture is less clear because there is no a priori hypothesis about which EEG features correspond well to fMRI-RSNs. We reviewed the associations of several types of qEEG features to four RSNs considered as neurocognitive systems central for higher brain processes: the default mode network, dorsal and ventral frontoparietal networks, and the salience network. We could identify 12 papers correlating qEEG and RSNs in adult human subjects and employing a simultaneous design under a no-task resting state condition. A systematic overview investigates which qEEG features replicably relate to the chosen RSNs. This review article leads to the conclusion that spatially delimited θ and whole/local α may be the most promising measures, but the time domain methods add important additional information. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Background: 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. Methodology/Principal Findings: 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. Conclusions/Significance: 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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Electroencephalography (EEG)-based brain–computer interfaces (BCIs) for motor rehabilitation aim to "close the loop" between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants' performance on the BCI was similar between display conditions, the participants' reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens. [ABSTRACT FROM AUTHOR]