Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep.

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms.

EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.

Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations.

Microstate sequences summarize the changing voltage patterns measured by electroencephalography, using a clustering approach to reduce the high dimensionality of the underlying data. A common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and to interpolate the microstate fit in between. In this study, we investigate how the anesthetic propofol affects microstate sequence periodicity and predictability, and how these metrics are changed by interpolation. We performed two frequency analyses on microstate sequences, one based on time-lagged mutual information, the other based on Fourier transform methodology, and quantified the effects of interpolation. Resting-state microstate sequences had a 20 Hz frequency peak related to dominant 10 Hz (alpha) rhythms, and the Fourier approach demonstrated that all five microstate classes followed this frequency. The 20 Hz periodicity was reversibly attenuated under moderate propofol sedation, as shown by mutual information and Fourier analysis. Characteristic microstate frequencies could only be observed in non-interpolated microstate sequences and were masked by smoothing effects of interpolation. Information-theoretic analysis revealed faster microstate dynamics and larger entropy rates under propofol, whereas Shannon entropy did not change significantly. In moderate sedation, active information storage decreased for non-interpolated sequences. Signatures of non-equilibrium dynamics were observed in non-interpolated sequences, but no changes were observed between sedation levels. All changes occurred while subjects were able to perform an auditory perception task. In summary, we show that low dose propofol reversibly increases the randomness of microstate sequences and attenuates microstate oscillations without correlation to cognitive task performance. Microstate dynamics between GFP peaks reflect physiological processes that are not accessible in interpolated sequences.

Simulation Strategies for Calcium Microdomains and Calcium Noise.

In this article, we present an overview of simulation strategies in the context of subcellular domains where calcium-dependent signaling plays an important role. The presentation follows the spatial and temporal scales involved and represented by each algorithm. As an exemplary cell type, we will mainly cite work done on striated muscle cells, i.e. skeletal and cardiac muscle. For these cells, a wealth of ultrastructural, biophysical and electrophysiological data is at hand. Moreover, these cells also express ubiquitous signaling pathways as they are found in many other cell types and thus, the generalization of the methods and results presented here is straightforward.The models considered comprise the basic calcium signaling machinery as found in most excitable cell types including Ca2+ ions, diffusible and stationary buffer systems, and calcium regulated calcium release channels. Simulation strategies can be differentiated in stochastic and deterministic algorithms. Historically, deterministic approaches based on the macroscopic reaction rate equations were the first models considered. As experimental methods elucidated highly localized Ca2+ signaling events occurring in femtoliter volumes, stochastic methods were increasingly considered. However, detailed simulations of single molecule trajectories are rarely performed as the computational cost implied is too large. On the mesoscopic level, Gillespie's algorithm is extensively used in the systems biology community and with increasing frequency also in models of microdomain calcium signaling. To increase computational speed, fast approximations were derived from Gillespie's exact algorithm, most notably the chemical Langevin equation and the τ-leap algorithm. Finally, in order to integrate deterministic and stochastic effects in multiscale simulations, hybrid algorithms are increasingly used. These include stochastic models of ion channels combined with deterministic descriptions of the calcium buffering and diffusion system on the one hand, and algorithms that switch between deterministic and stochastic simulation steps in a context-dependent manner on the other. The basic assumptions of the listed methods as well as implementation schemes are given in the text. We conclude with a perspective on possible future developments of the field.

The challenge of forgetting: Neurobiological mechanisms of auditory directed forgetting.

Directed forgetting (DF) is considered an adaptive mechanism to cope with unwanted memories. Understanding it is crucial to develop treatments for disorders in which thought control is an issue. With an item-method DF paradigm in an auditory form, the underlying neurocognitive processes that support auditory DF were investigated. Subjects were asked to perform multi-modal encoding of word-stimuli before knowing whether to remember or forget each word. Using functional magnetic resonance imaging, we found that DF is subserved by a right frontal-parietal-cingulate network. Both qualitative and quantitative analyses of the activation of this network show converging evidence suggesting that DF is a complex process in which active inhibition, attentional switching, and working memory are needed to manipulate both unwanted and preferred items. These results indicate that DF is a complex inhibitory mechanism which requires the crucial involvement of brain areas outside prefrontal regions to operate over attentional and working memory processes. Hum Brain Mapp 39:249-263, 2018. © 2017 Wiley Periodicals, Inc.

Personalized translational epilepsy research - Novel approaches and future perspectives: Part II: Experimental and translational approaches.

Despite the availability of more than 15 new "antiepileptic drugs", the proportion of patients with pharmacoresistant epilepsy has remained constant at about 20-30%. Furthermore, no disease-modifying treatments shown to prevent the development of epilepsy following an initial precipitating brain injury or to reverse established epilepsy have been identified to date. This is likely in part due to the polyetiologic nature of epilepsy, which in turn requires personalized medicine approaches. Recent advances in imaging, pathology, genetics, and epigenetics have led to new pathophysiological concepts and the identification of monogenic causes of epilepsy. In the context of these advances, the First International Symposium on Personalized Translational Epilepsy Research (1st ISymPTER) was held in Frankfurt on September 8, 2016, to discuss novel approaches and future perspectives for personalized translational research. These included new developments and ideas in a range of experimental and clinical areas such as deep phenotyping, quantitative brain imaging, EEG/MEG-based analysis of network dysfunction, tissue-based translational studies, innate immunity mechanisms, microRNA as treatment targets, functional characterization of genetic variants in human cell models and rodent organotypic slice cultures, personalized treatment approaches for monogenic epilepsies, blood-brain barrier dysfunction, therapeutic focal tissue modification, computational modeling for target and biomarker identification, and cost analysis in (monogenic) disease and its treatment. This report on the meeting proceedings is aimed at stimulating much needed investments of time and resources in personalized translational epilepsy research. This Part II includes the experimental and translational approaches and a discussion of the future perspectives, while the diagnostic methods, EEG network analysis, biomarkers, and personalized treatment approaches were addressed in Part I [1].

Personalized translational epilepsy research - Novel approaches and future perspectives: Part I: Clinical and network analysis approaches.

Despite the availability of more than 15 new "antiepileptic drugs", the proportion of patients with pharmacoresistant epilepsy has remained constant at about 20-30%. Furthermore, no disease-modifying treatments shown to prevent the development of epilepsy following an initial precipitating brain injury or to reverse established epilepsy have been identified to date. This is likely in part due to the polyetiologic nature of epilepsy, which in turn requires personalized medicine approaches. Recent advances in imaging, pathology, genetics and epigenetics have led to new pathophysiological concepts and the identification of monogenic causes of epilepsy. In the context of these advances, the First International Symposium on Personalized Translational Epilepsy Research (1st ISymPTER) was held in Frankfurt on September 8, 2016, to discuss novel approaches and future perspectives for personalized translational research. These included new developments and ideas in a range of experimental and clinical areas such as deep phenotyping, quantitative brain imaging, EEG/MEG-based analysis of network dysfunction, tissue-based translational studies, innate immunity mechanisms, microRNA as treatment targets, functional characterization of genetic variants in human cell models and rodent organotypic slice cultures, personalized treatment approaches for monogenic epilepsies, blood-brain barrier dysfunction, therapeutic focal tissue modification, computational modeling for target and biomarker identification, and cost analysis in (monogenic) disease and its treatment. This report on the meeting proceedings is aimed at stimulating much needed investments of time and resources in personalized translational epilepsy research. Part I includes the clinical phenotyping and diagnostic methods, EEG network-analysis, biomarkers, and personalized treatment approaches. In Part II, experimental and translational approaches will be discussed (Bauer et al., 2017) [1].

Dopaminergic Modulation of Cognitive Preparation for Overt Reading: Evidence from the Study of Genetic Polymorphisms.

Choosing and implementing the rules for contextually adequate behavior depends on frontostriatal interactions. Observations in Parkinson's disease and pharmacological manipulations of dopamine transmission suggest that these corticobasal loops are modulated by dopamine. To determine, therefore, the physiological contributions of dopamine to task-rule-related processing, we performed a cue-target fMRI reading paradigm in 71 healthy participants and investigated the effects of COMT Val158Met, DAT1 VNTR 9/10, and DRD2/ANKK1 polymorphisms. The DRD2/ANKK1 polymorphism did not affect results. Intermediate prefrontal dopamine concentrations in COMT Val158Met heterozygotes facilitated preparatory interactions between the mesial prefrontal cortex and the left striatum during preparation for overt reading. To our knowledge, this is the first report of an inverted U-shaped curve modulation of cognition-related brain activity by prefrontal dopamine levels. In contrast, a linear effect of COMT Val158Met and DAT1 VNTR 9/10 polymorphisms on preparatory activity in the left inferior frontal gyrus pointed to a negative interaction between tonic lateral prefrontal and phasic subcortical dopamine. The COMT Val158Met polymorphism affected also feedforward and feedback processing in the sensorimotor speech system. Our results suggest that dopamine modulates corticobasal interactions on both the cortical and subcortical level but differently depending on the specific cognitive subprocesses involved.

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep.

The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as long-term memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest.

Exact stochastic simulation of a calcium microdomain reveals the impact of Ca²âº fluctuations on IP3R gating.

In this study, we numerically analyzed the nonlinear Ca(2+)-dependent gating dynamics of a single, nonconducting inositol 1,4,5-trisphosphate receptor (IP3R) channel, using an exact and fully stochastic simulation algorithm that includes channel gating, Ca(2+) buffering, and Ca(2+) diffusion. The IP3R is a ubiquitous intracellular Ca(2+) release channel that plays an important role in the formation of complex spatiotemporal Ca(2+) signals such as waves and oscillations. Dynamic subfemtoliter Ca(2+) microdomains reveal low copy numbers of Ca(2+) ions, buffer molecules, and IP3Rs, and stochastic fluctuations arising from molecular interactions and diffusion do not average out. In contrast to models treating calcium dynamics deterministically, the stochastic approach accounts for this molecular noise. We varied Ca(2+) diffusion coefficients and buffer reaction rates to tune the autocorrelation properties of Ca(2+) noise and found a distinct relation between the autocorrelation time τac, the mean channel open and close times, and the resulting IP3R open probability PO. We observed an increased PO for shorter noise autocorrelation times, caused by increasing channel open times and decreasing close times. In a pure diffusion model the effects become apparent at elevated calcium concentrations, e.g., at [Ca(2+)] = 25 µM, τac = 0.082 ms, the IP3R open probability increased by ≈20% and mean open times increased by ≈4 ms, compared to a zero noise model. We identified the inactivating Ca(2+) binding site of IP3R subunits as the primarily noise-susceptible element of the De Young and Keizer model. Short Ca(2+) noise autocorrelation times decrease the probability of Ca(2+) association and consequently increase IPvR activity. These results suggest a functional role of local calcium noise properties on calcium-regulated target molecules such as the ubiquitous IP3R. This finding may stimulate novel experimental approaches analyzing the role of calcium noise properties on microdomain behavior.

Narcoleptic Patients Show Fragmented EEG-Microstructure During Early NREM Sleep.

Narcolepsy is a chronic disorder of the sleep-wake cycle with pathological shifts between sleep stages. These abrupt shifts are induced by a sleep-regulating flip-flop mechanism which is destabilized in narcolepsy without obvious alterations in EEG oscillations. Here, we focus on the question whether the pathology of narcolepsy is reflected in EEG microstate patterns. 30 channel awake and NREM sleep EEGs of 12 narcoleptic patients and 32 healthy subjects were analyzed. Fitting back the dominant amplitude topography maps into the EEG led to a temporal sequence of maps. Mean microstate duration, ratio total time (RTT), global explained variance (GEV) and transition probability of each map were compared between both groups. Nine patients reached N1, 5 N2 and only 4 N3. All healthy subjects reached at least N2, 19 also N3. Four dominant maps could be found during wakefulness and all NREM- sleep stages in healthy subjects. During N3, narcolepsy patients showed an additional fifth map. The mean microstate duration was significantly shorter in narcoleptic patients than controls, most prominent in deep sleep. Single maps' GEV and RTT were also altered in narcolepsy. Being aware of the limitation of our low sample size, narcolepsy patients showed wake-like features during sleep as reflected in shorter microstate durations. These microstructural EEG alterations might reflect the intrusion of brain states characteristic of wakefulness into sleep and an instability of the sleep-regulating flip-flop mechanism resulting not only in pathological switches between REM- and NREM-sleep but also within NREM sleep itself, which may lead to a microstructural fragmentation of the EEG.

IL-1α reversibly inhibits skeletal muscle ryanodine receptor. a novel mechanism for critical illness myopathy?

Critical illness myopathies in patients with sepsis or sustained mechanical ventilation prolong intensive care treatment and threaten both patients and health budgets; no specific therapy is available. Underlying pathophysiological mechanisms are still patchy. We characterized IL-1α action on muscle performance in "skinned" muscle fibers using force transducers and confocal Ca(2+) fluorescence microscopy for force/Ca(2+) transients and Ca(2+) sparks. Association of IL-1α with sarcoplasmic reticulum (SR) release channel, ryanodine receptor (RyR) 1, was investigated with coimmunoprecipitation and confocal immunofluorescence colocalization. Membrane integrity was studied in single, intact fibers challenged with IL-1α. IL-1α reversibly stabilized Mg(2+) inhibition of Ca(2+) release. Low Mg(2+)-induced force and Ca(2+) transients were reversibly abolished by IL-1α. At normal Mg(2+), IL-1α reversibly increased caffeine-induced force and Ca(2+) transients. IL-1α reduced SR Ca(2+) leak via RyR1, as judged by (1) increased SR Ca(2+) retention, (2) increased IL-1α force transients being reproduced by 25 µM tetracaine, and (3) reduced Ca(2+) spark frequencies by IL-1α or tetracaine. Coimmunoprecipitation confirmed RyR1/IL-1 association. RyR1/IL-1 immunofluorescence patterns perfectly colocalized. Long-term, 8-hour IL-1α challenge of intact muscle fibers compromised membrane integrity in approximately 50% of fibers, and confirmed intracellular IL-1α deposition. IL-1α exerts a novel, specific, and reversible interaction mechanism with the skeletal muscle RyR1 macromolecular release complex without the need to act via its membrane IL-1 receptor, as IL-1R membrane expression levels were not detectable in Western blots or immunostaining of single fibers. We present a potential explanation of how the inflammatory mediator, IL-1α, may contribute to muscle weakness in critical illness.

Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle.

Large-scale brain functional networks (measured with functional magnetic resonance imaging, fMRI) are organized into separated but interacting modules, an architecture supporting the integration of distinct dynamical processes. In this work we study how the aforementioned modular architecture changes with the progressive loss of vigilance occurring in the descent to deep sleep and we examine the relationship between the ensuing slow electroencephalographic rhythms and large-scale network modularity as measured with fMRI. Graph theoretical methods are used to analyze functional connectivity graphs obtained from fifty-five participants at wakefulness, light and deep sleep. Network modularity (a measure of functional segregation) was found to increase during deeper sleep stages but not in light sleep. By endowing functional networks with dynamical properties, we found a direct link between increased electroencephalographic (EEG) delta power (1-4 Hz) and a breakdown of inter-modular connectivity. Both EEG slowing and increased network modularity were found to quickly decrease during awakenings from deep sleep to wakefulness, in a highly coordinated fashion. Studying the modular structure itself by means of a permutation test, we revealed different module memberships when deep sleep was compared to wakefulness. Analysis of node roles in the modular structure revealed an increase in the number of locally well-connected nodes and a decrease in the number of globally well-connected hubs, which hinders interactions between separated functional modules. Our results reveal a well-defined sequence of changes in brain modular organization occurring during the descent to sleep and establish a close parallel between modularity alterations in large-scale functional networks (accessible through whole brain fMRI recordings) and the slowing of scalp oscillations (visible on EEG). The observed re-arrangement of connectivity might play an important role in the processes underlying loss of vigilance and sensory awareness during deep sleep.

Probing the Contribution of Vertical Processing Layers of the Retina to White-Noise Electrical Stimulation Responses.

Optimal stimulus parameters for epiretinal prostheses have been investigated by analyzing retinal ganglion cell (RGC) spiking responses to white-noise electrical stimulation, through a spike-triggered average (STA) analysis technique. However, it is currently unknown as to activation of which retinal cells contribute to features of the STA. We conducted whole-cell patch clamping recordings in ON and OFF RGCs in response to white-noise epiretinal electrical stimulation by using different inhibitors of synaptic transmission in a healthy retina. An mGluR6 agonist, L-AP4, was firstly used to selectively block the output of photoreceptors (PRs) to ON bipolar cells (BCs). We subsequently fully blocked all synaptic inputs to RGCs using a combination of pharmacological agents. Our data shows that PRs dominate the ability of ON RGCs to integrate electrical pulses and form a unique STA shape, while BCs do not contribute in any way. In addition, our results demonstrate that the ability of OFF RGCs to integrate pulses is consistently impaired after blocking the PR to ON BC pathway. We hypothesise that the mechanisms underlying this co-effect are related to the narrow field AII amacrine cells connecting ON and OFF pathways.Clinical Relevance-Recent retinal studies recorded mirror-inverted STAs in ON and OFF retinal pathways, thus raising the possibility of designing a stimulation approach that can differentially activate ON and OFF pathways with electrical stimulation. However, the detailed contribution of three major retinal cell layers in forming characteristic STAs is still unclear. It is of great clinical relevance to investigate the isolated contribution of PRs to the electrically driven STA since PRs progressively degenerate in the course of retinal disease.

Automatic sleep staging using fMRI functional connectivity data.

Recent EEG-fMRI studies have shown that different stages of sleep are associated with changes in both brain activity and functional connectivity. These results raise the concern that lack of vigilance measures in resting state experiments may introduce confounds and contamination due to subjects falling asleep inside the scanner. In this study we present a method to perform automatic sleep staging using only fMRI functional connectivity data, thus providing vigilance information while circumventing the technical demands of simultaneous recording of EEG, the gold standard for sleep scoring. The features to classify are the linear correlation values between 20 cortical regions identified using independent component analysis and two regions in the bilateral thalamus. The method is based on the construction of binary support vector machine classifiers discriminating between all pairs of sleep stages and the subsequent combination of them into multiclass classifiers. Different multiclass schemes and kernels are explored. After parameter optimization through 5-fold cross validation we achieve accuracies over 0.8 in the binary problem with functional connectivities obtained for epochs as short as 60s. The multiclass classifier generalizes well to two independent datasets (accuracies over 0.8 in both sets) and can be efficiently applied to any dataset using a sliding window procedure. Modeling vigilance states in resting state analysis will avoid confounded inferences and facilitate the study of vigilance states themselves. We thus consider the method introduced in this study a novel and practical contribution for monitoring vigilance levels inside an MRI scanner without the need of extra recordings other than fMRI BOLD signals.

To wake or not to wake? The two-sided nature of the human K-complex.

Sleep fosters performance but likewise renders creatures insensitive to environmental threat. The brain balances between sleep promotion and protection during light sleep. One associated electrophysiological hallmark is the K-complex (KC), the sleep promoting versus arousal inducing role of which is under debate. We examined 37 subjects using EEG-combined fMRI and found KC-associated positive BOLD signal changes in subcortical (brainstem, thalamus), sensory and motor, midline and regions which form part of the default mode network, and negative changes in the anterior insula. Connectivity analysis revealed the primary auditory cortex as the first region to be influenced during the KC and that midline regions activated successively from front to back in association with the sleep protecting part of the KC. Our findings support thalamic involvement in KC mediation and an association of KCs with subcortical arousal mechanisms: activations in sensory areas suggest the existence of low level information processing during KC limited by anterior insula disengagement suggesting a two-sided nature of the KC: it embodies an arousal with subsequent sleep-guarding counteraction that might on the one hand serve periodical monitoring of the environment with basic information processing and on the other hand protect the continuity of sleep and thus its restoring effect.

EEG microstates of wakefulness and NREM sleep.

EEG-microstates exploit spatio-temporal EEG features to characterize the spontaneous EEG as a sequence of a finite number of quasi-stable scalp potential field maps. So far, EEG-microstates have been studied mainly in wakeful rest and are thought to correspond to functionally relevant brain-states. Four typical microstate maps have been identified and labeled arbitrarily with the letters A, B, C and D. We addressed the question whether EEG-microstate features are altered in different stages of NREM sleep compared to wakefulness. 32-channel EEG of 32 subjects in relaxed wakefulness and NREM sleep was analyzed using a clustering algorithm, identifying the most dominant amplitude topography maps typical of each vigilance state. Fitting back these maps into the sleep-scored EEG resulted in a temporal sequence of maps for each sleep stage. All 32 subjects reached sleep stage N2, 19 also N3, for at least 1 min and 45 s. As in wakeful rest we found four microstate maps to be optimal in all NREM sleep stages. The wake maps were highly similar to those described in the literature for wakefulness. The sleep stage specific map topographies of N1 and N3 sleep showed a variable but overall relatively high degree of spatial correlation to the wake maps (Mean: N1 92%; N3 87%). The N2 maps were the least similar to wake (mean: 83%). Mean duration, total time covered, global explained variance and transition probabilities per subject, map and sleep stage were very similar in wake and N1. In wake, N1 and N3, microstate map C was most dominant w.r.t. global explained variance and temporal presence (ratio total time), whereas in N2 microstate map B was most prominent. In N3, the mean duration of all microstate maps increased significantly, expressed also as an increase in transition probabilities of all maps to themselves in N3. This duration increase was partly--but not entirely--explained by the occurrence of slow waves in the EEG. The persistence of exactly four main microstate classes in all NREM sleep stages might speak in favor of an in principle maintained large scale spatial brain organization from wakeful rest to NREM sleep. In N1 and N3 sleep, despite spectral EEG differences, the microstate maps and characteristics were surprisingly close to wakefulness. This supports the notion that EEG microstates might reflect a large scale resting state network architecture similar to preserved fMRI resting state connectivity. We speculate that the incisive functional alterations which can be observed during the transition to deep sleep might be driven by changes in the level and timing of activity within this architecture.

Simulation strategies for calcium microdomains and calcium-regulated calcium channels.

In this article, we present an overview of simulation strategies in the context of subcellular domains where calcium-dependent signaling plays an important role. The presentation follows the spatial and temporal scales involved and represented by each algorithm. As an exemplary cell type, we will mainly cite work done on striated muscle cells, i.e. skeletal and cardiac muscle. For these cells, a wealth of ultrastructural, biophysical and electrophysiological data is at hand. Moreover, these cells also express ubiquitous signaling pathways as they are found in many other cell types and thus, the generalization of the methods and results presented here is straightforward.The models considered comprise the basic calcium signaling machinery as found in most excitable cell types including Ca(2+) ions, diffusible and stationary buffer systems, and calcium regulated calcium release channels. Simulation strategies can be differentiated in stochastic and deterministic algorithms. Historically, deterministic approaches based on the macroscopic reaction rate equations were the first models considered. As experimental methods elucidated highly localized Ca(2+) signaling events occurring in femtoliter volumes, stochastic methods were increasingly considered. However, detailed simulations of single molecule trajectories are rarely performed as the computational cost implied is too large. On the mesoscopic level, Gillespie's algorithm is extensively used in the systems biology community and with increasing frequency also in models of microdomain calcium signaling. To increase computational speed, fast approximations were derived from Gillespie's exact algorithm, most notably the chemical Langevin equation and the τ-leap algorithm. Finally, in order to integrate deterministic and stochastic effects in multiscale simulations, hybrid algorithms are increasingly used. These include stochastic models of ion channels combined with deterministic descriptions of the calcium buffering and diffusion system on the one hand, and algorithms that switch between deterministic and stochastic simulation steps in a context-dependent manner on the other. The basic assumptions of the listed methods as well as implementation schemes are given in the text. We conclude with a perspective on possible future developments of the field.

Resting state fMRI reveals increased subthalamic nucleus-motor cortex connectivity in Parkinson's disease.

Parkinson's disease (PD) is associated with abnormal hypersynchronicity in basal ganglia-thalamo-cortical loops. The clinical effectiveness of subthalamic nucleus (STN) high frequency stimulation indicates a crucial role of this nucleus within the affected motor networks in PD. Here we investigate alterations in the functional connectivity (FC) profile of the STN using resting state BOLD correlations on a voxel-by-voxel basis in functional magnetic resonance imaging (fMRI). We compared early stage PD patients (n=31) during the medication-off state with healthy controls (n=44). The analysis revealed increased FC between the STN and cortical motor areas (BA 4 and 6) in PD patients in accordance with electrophysiological studies. Moreover, FC analysis of the primary motor cortex (M1) hand area revealed that the FC increase was primarily found in the STN area within the basal ganglia. These findings are in good agreement with recent experimental data, suggesting that an increased STN-motor cortex synchronicity mediated via the so called hyperdirect motor cortex-subthalamic pathway might play a fundamental role in the pathophysiology of PD. An additional subgroup analysis was performed according to the presence (n=16) or absence (n=15) of tremor in patients. Compared to healthy controls tremor patients showed increased STN FC specifically in the hand area of M1 and the primary sensory cortex. In non-tremor patients, increased FC values were also found between the STN and midline cortical motor areas including the SMA. Taken together our results underline the importance of the STN as a key node for the modulation of BG-cortical motor network activity in PD patients.

Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks.

Design is a ubiquitous, complex, and open-ended creation behaviour that triggers creativity. The brain dynamics underlying design is unclear, since a design process consists of many basic cognitive behaviours, such as problem understanding, idea generation, idea analysis, idea evaluation, and idea evolution. In this present study, we simulated the design process in a loosely controlled setting, aiming to quantify the design-related cognitive workload and control, identify EEG-defined large-scale brain networks, and uncover their temporal dynamics. The effectiveness of this loosely controlled setting was tested through comparing the results with validated findings available in the literature. Task-related power (TRP) analysis of delta, theta, alpha and beta frequency bands revealed that idea generation was associated with the highest cognitive workload and lowest cognitive control, compared to other design activities in the experiment, including problem understanding, idea evaluation, and self-rating. EEG microstate analysis supported this finding as microstate class C, being negatively associated with the cognitive control network, was the most prevalent in idea generation. Furthermore, EEG microstate sequence analysis demonstrated that idea generation was consistently associated with the shortest temporal correlation times concerning finite entropy rate, autoinformation function, and Hurst exponent. This finding suggests that during idea generation the interplay of functional brain networks is less restricted and the brain has more degrees of freedom in choosing the next network configuration than during other design activities. Taken together, the TRP and EEG microstate results lead to the conclusion that idea generation is associated with the highest cognitive workload and lowest cognitive control during open-ended creation task.