Clappmeincke6348

This study highlights the high burden and mortality rates of candidemia in hospitals from Parana as well as the need to enhance antifungal stewardship program in the enrolled medical centers.

Two measures of cross-frequency coupling are phase-amplitude coupling (PAC) and bicoherence. Lithospermate B The estimation of PAC with meaningful bandwidth for the high-frequency amplitude is crucial in order to avoid misinterpretations. While recommendations on the bandwidth of PAC's amplitude component exist, there is no consensus yet. Theoretical relationships between PAC and bicoherence can provide insights on how to set PAC's filters.

To illustrate this, PAC estimated from simulated and empirical data are compared to the bispectrum. We used simulations replicated from earlier studies and empirical data from human electro-encephalography and rat local field potentials. PAC's amplitude component was estimated using a filter bandwidth with a ratio of (1) 21, (2) 11, or (3) 0.51 relative to the phase frequency.

For both simulated and empirical data, PAC was smeared over a broad frequency range and not present when the estimates comprised a 21- and 0.51-ratio, respectively. In contrast, the 11-ratio accurately avoids smearing and results in clear signals of cross-frequency coupling. Bicoherence estimates were found to be essentially identical to PAC calculated with the recommended frequency setting.

Earlier recommendations on filter settings of PAC lead to estimates which are smeared in the frequency domain, which makes it difficult to identify cross-frequency coupling of neural processes operating in narrow frequency bands.

We conclude that smearing of PAC estimates can be avoided with a different choice of filter settings by theoretically relating PAC to bicoherence.

We conclude that smearing of PAC estimates can be avoided with a different choice of filter settings by theoretically relating PAC to bicoherence.

Dynamic functional network connectivity (dFNC) summarizes associations among time-varying brain networks and is widely used for studying dynamics. However, most previous studies compute dFNC using temporal variability while spatial variability started receiving increasing attention. It is hence desirable to investigate spatial variability and the interaction between temporal and spatial variability.

We propose to use an adaptive variant of constrained independent vector analysis to simultaneously capture temporal and spatial variability, and introduce a goal-driven scheme for addressing a key challenge in dFNC analysis---determining the number of transient states. We apply our methods to resting-state functional magnetic resonance imaging data of schizophrenia patients (SZs) and healthy controls (HCs).

The results show spatial variability provides more features discriminative between groups than temporal variability. A comprehensive study of graph-theoretical (GT) metrics determines the optimal number of spatial states and suggests centrality as a key metric. Four networks yield significantly different levels of involvement in SZs and HCs. The high involvement of a component that relates to multiple distributed brain regions highlights dysconnectivity in SZ. One frontoparietal component and one frontal component demonstrate higher involvement in HCs, suggesting a more efficient cognitive control system relative to SZs.

Spatial variability is more informative than temporal variability. The proposed goal-driven scheme determines the optimal number of states in a more interpretable way by making use of discriminative features.

GT analysis is promising in dFNC analysis as it identifies distinctive transient spatial states of dFNC and reveals unique biomedical patterns in SZs.

GT analysis is promising in dFNC analysis as it identifies distinctive transient spatial states of dFNC and reveals unique biomedical patterns in SZs.

Electrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions.

We demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method - comparison scanning generalized eigendecomposition, csGED - harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED.

To validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band.

GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology.

We demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

We demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

Speed of performance improvements and the strength of memory consolidation in humans vary with movement expertise. Underlying neural mechanisms of behavioural differences between levels of movement expertise are so far unknown.

In this study, PET with [

F]fluorodeoxyglucose (FDG) was proposed as a powerful novel methodology to assess learning-related brain activity patterns during large non-restricted movements (ball throwing with a right hand). 24 male handball players ('Experts') and 24 male participants without handball experience ('Novices') performed visuomotor adaptations to prismatic glasses with or without strategic manoeuvres (i.e., explicit or implicit adaptation).

Regional changes in FDG uptake as a marker of neuronal activity, relative to a control condition, were assessed. Prismatic adaptation, in general, was associated with decreased occipital neuronal activity as a possible response to misleading visual information. In 'Experts', the adaptation was associated with altered neuronal activity in a network comprising the right parietal cortex and the left cerebellum.