Topographic electrophysiological signatures of FMRI Resting State Networks

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.

Renal artery assessment with nonenhanced steady-state free precession versus contrast-enhanced MR angiography

PURPOSE: To prospectively assess the diagnostic accuracy of nonenhanced three-dimensional (3D) steady-state free precession (SSFP) magnetic resonance (MR) angiography for detection of renal artery stenosis (RAS), with breath-hold contrast material-enhanced MR angiography performed as the reference standard. MATERIALS AND METHODS: The study was local ethics committee approved; all patients gave written informed consent. Fifty-three patients (30 male, 23 female; mean age, 58 years) with arterial hypertension and suspected of having RAS were examined with 1.5-T 3D SSFP renal MR angiography. Stenosis grade, maximal visible vessel length, and subjective image quality were compared. Sensitivity, specificity, accuracy, and negative predictive value (NPV) were calculated on artery-by-artery and patient-by-patient bases. The significance of the results was assessed with the paired two-sided t test for continuous variables and with the marginal homogeneity test for categorical variables. Cohen kappa statistics were used to estimate interobserver agreement. RESULTS: One hundred eight renal arteries with 20 significant (>or=50%) stenoses were detected with contrast-enhanced MR angiography. At artery-by-artery analysis, sensitivity, specificity, accuracy, and NPV of nonenhanced SSFP MR angiography for RAS detection were 100%, 93%, 94%, and 100%, respectively, for observer 1 and 95%, 95%, 95%, and 99%, respectively, for observer 2. Corresponding patient-by-patient values were 100%, 92%, 94%, and 100%, respectively, for observer 1 and 100%, 95%, 96%, and 100%, respectively, for observer 2. Overestimation of stenosis grade with SSFP MR angiography resulted in six and four false-positive findings for readers 1 and 2, respectively. Mean maximal visible lengths of the renal arteries were 69.9 mm at contrast-enhanced MR angiography and 61.1 mm at SSFP MR angiography (P<.001). Both techniques yielded good to excellent image quality. CONCLUSION: Slab-selective inversion-prepared 3D SSFP MR angiography had high sensitivity, specificity, accuracy, and NPV for RAS detection, without the need for contrast material. However, RAS severity was overestimated in some patients.

Shifted Coupling of EEG Driving Frequencies and fMRI Resting State Networks in Schizophrenia Spectrum Disorders

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.

The brain’s default state interacts with working memory processes in a complex and load-dependent manner

Recently, many studies about a network active during rest and deactivated during tasks emerged in the literature: the default mode network (DMN). Spatial and temporal DMN features are important markers for psychiatric diseases. Another prominent indicator of cognitive functioning, yielding information about the mental condition in health and disease, is working memory (WM) processing. In EEG studies, frontal-midline theta power has been shown to increase with load during WM retention in healthy subjects. From these findings, the conclusion can be drawn that an increase in resting state DMN activity may go along with an increase in theta power in high-load WM conditions. We followed this hypothesis in a study on 17 healthy subjects performing a visual Sternberg WM task. The DMN was obtained by a BOLD-ICA approach and its dynamics represented by the percent-strength during pre-stimulus periods. DMN dynamics were temporally correlated with EEG theta spectral power from retention intervals. This so-called covariance mapping yielded the spatial distribution of the theta EEG fluctuations associated with the dynamics of the DMN. In line with previous findings, theta power was increased at frontal-midline electrodes in high- versus low-load conditions during early WM retention. However, load-dependent correlations of DMN with theta power resulted in primarily positive correlations in low-load conditions, while during high-load conditions negative correlations of DMN activity and theta power were observed at frontal-midline electrodes. This DMN-dependent load effect reached significance during later retention. Our results show a complex and load-dependent interaction of pre-stimulus DMN activity and theta power during retention, varying over the course of the retention period. Since both, WM performance and DMN activity, are markers of mental health, our results could be important for further investigations of psychiatric populations.

The brain’s pre-stimulus default state interacts with working memory in a load-dependent manner

Recently, multiple studies showed that spatial and temporal features of a task-negative default mode network (DMN) (Greicius et al., 2003) are important markers for psychiatric diseases (Balsters et al., 2013). Another prominent indicator of cognitive functioning, yielding information about the mental condition in health and disease, is working memory (WM) processing. In EEG and MEG studies, frontal-midline theta power has been shown to increase with load during WM retention in healthy subjects (Brookes et al., 2011). Negative correlations between DMN activity and theta amplitude have been found during resting state (Jann et al., 2010) as well as during WM (Michels et al., 2010). Likewise, WM training resulted in higher resting state theta power as well as increased small-worldness of the resting brain (Langer et al., 2013). Further, increased fMRI connectivity between nodes of the DMN correlated with better WM performance (Hampson et al., 2006). Hence, the brain’s default state might influence it’s functioning during task. We therefore hypothesized correlations between pre-stimulus DMN activity and EEG-theta power during WM maintenance, depending on the WM load. 17 healthy subjects performed a Sternberg WM task while being measured simultaneously with EEG and fMRI. Data was recorded within a multicenter-study: 12 subjects were measured in Zurich with a 64-channels MR-compatible system (Brain Products) in a 3T Philips scanner, 5 subjects with a 96-channel MR-compatible system (Brain Products) in a 3T Siemens Scanner in Bern. The DMN components was obtained by a group BOLD-ICA approach over the full task duration (figure 1). The subject-wise dynamics were obtained by back-reconstructed onto each subject’s fMRI data and normalized to percent signal change values. The single trial pre-stimulus-DMN activation was then temporally correlated with the single trial EEG-theta (3-8 Hz) spectral power during retention intervals. This so-called covariance mapping (Jann et al., 2010) yielded the spatial distribution of the theta EEG fluctuations during retention associated with the dynamics of the pre-stimulus DMN. In line with previous findings, theta power was increased at frontal-midline electrodes in high- versus low-load conditions during early WM retention (figure 2). However, correlations of DMN with theta power resulted in primarily positive correlations in low-load conditions, while during high-load conditions negative correlations of DMN activity and theta power were observed at frontal-midline electrodes. This DMN-dependent load effect reached significance in the middle of the retention period (TANOVA, p<0.05) (figure 3). Our results show a complex and load-dependent interaction of pre-stimulus DMN activity and theta power during retention, varying over time. While at a more global, load-independent view pre-stimulus DMN activity correlated positively with theta power during retention, the correlation was inversed during certain time windows in high-load trials, meaning that in trials with enhanced pre-stimulus DMN activity theta power decreases during retention. Since both WM performance and DMN activity are markers of mental health our results could be important for further investigations of psychiatric populations.

Assessing the function of the fronto-parietal attention network: Insights from resting-state fMRI and the attentional network test

In the recent past, various intrinsic connectivity networks (ICN) have been identified in the resting brain. It has been hypothesized that the fronto-parietal ICN is involved in attentional processes. Evidence for this claim stems from task-related activation studies that show a joint activation of the implicated brain regions during tasks that require sustained attention. In this study, we used functional magnetic resonance imaging (fMRI) to demonstrate that functional connectivity within the fronto-parietal network at rest directly relates to attention. We applied graph theory to functional connectivity data from multiple regions of interest and tested for associations with behavioral measures of attention as provided by the attentional network test (ANT), which we acquired in a separate session outside the MRI environment. We found robust statistical associations with centrality measures of global and local connectivity of nodes within the network with the alerting and executive control subfunctions of attention. The results provide further evidence for the functional significance of ICN and the hypothesized role of the fronto-parietal attention network. Hum Brain Mapp , 2013. © 2013 Wiley Periodicals, Inc.

Observation of an Excited B±c Meson State with the ATLAS Detector

A search for excited states of the B ± c meson is performed using 4.9  fb −1 of 7 TeV and 19.2  fb −1 of 8 TeV pp collision data collected by the ATLAS experiment at the LHC. A new state is observed through its hadronic transition to the ground state, with the latter detected in the decay B ± c →J/ψπ ± . The state appears in the m(B ± c π + π − )−m(B ± c )−2m(π ± ) mass difference distribution with a significance of 5.2 standard deviations. The mass of the observed state is 6842±4±5  MeV , where the first error is statistical and the second is systematic. The mass and decay of this state are consistent with expectations for the second S -wave state of the B ± c meson, B ± c (2S) .

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.

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.