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.

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.

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.

Partial Autoinformation to Characterize Symbolic Sequences

An information-theoretic approach to numerically determine the Markov order of discrete stochastic processes defined over a finite state space is introduced. To measure statistical dependencies between different time points of symbolic time series, two information-theoretic measures are proposed. The first measure is time-lagged mutual information between the random variables X_n and X_n+k, representing the values of the process at time points n and n + k, respectively. The measure will be termed autoinformation, in analogy to the autocorrelation function for metric time series, but using Shannon entropy rather than linear correlation. This measure is complemented by the conditional mutual information between X_n and X_n+k, removing the influence of the intermediate values X_n+k−1, …, X_n+1. The second measure is termed partial autoinformation, in analogy to the partial autocorrelation function (PACF) in metric time series analysis. Mathematical relations with known quantities such as the entropy rate and active information storage are established. Both measures are applied to a number of examples, ranging from theoretical Markov and non-Markov processes with known stochastic properties, to models from statistical physics, and finally, to a discrete transform of an EEG data set. The combination of autoinformation and partial autoinformation yields important insights into the temporal structure of the data in all test cases. For first- and higher-order Markov processes, partial autoinformation correctly identifies the order parameter, but also suggests extended, non-Markovian effects in the examples that lack the Markov property. For three hidden Markov models (HMMs), the underlying Markov order is found. The combination of both quantities may be used as an early step in the analysis of experimental, non-metric time series and can be employed to discover higher-order Markov dependencies, non-Markovianity and periodicities in symbolic time series.

EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

We analyse statistical and information-theoretical properties of EEG microstate sequences, as seen through the lens of five different clustering algorithms. Microstate sequences are computed for n = 20 resting state EEG recordings during wakeful rest. The input for all clustering algorithms is the set of EEG topographic maps obtained at local maxima of the spatial variance. This data set is processed by two classical microstate clustering algorithms (1) atomize and agglomerate hierarchical clustering (AAHC) and (2) a modified K-means algorithm, as well as by (3) K-medoids, (4) principal component analysis (PCA) and (5) fast independent component analysis (Fast-ICA). Using this technique, EEG topographies can be substituted with microstate labels by competitive fitting based on spatial correlation, resulting in a symbolic, non-metric time series, the microstate sequence. Microstate topographies and symbolic time series are further analyzed statistically, including static and dynamic properties. Static properties, which do not contain information about temporal dependencies of the microstate sequence include the maximum similarity of microstate maps within and between the tested clustering algorithms, the global explained variance and the Shannon entropy of the microstate sequences. Dynamic properties are sensitive to temporal correlations between the symbols and include the mixing time of the microstate transition matrix, the entropy rate of the microstate sequences and the location of the first local maximum of the autoinformation function. We also test the Markov property of microstate sequences, the time stationarity of the transition matrix and detect periodicities by means of time-lagged mutual information. Finally, possible long-range correlations of microstate sequences are assessed via Hurst exponent estimation. We find that while static properties partially reflect properties of the clustering algorithms, information-theoretical quantities are largely invariant with respect to the clustering method used. As each clustering algorithm has its own profile of computational speed, ease of implementation, determinism vs. stochasticity and theoretical underpinnings, our results convey a positive message concerning the free choice of method and the comparability of results obtained from different algorithms. The invariance of these quantities implies that the tested properties are algorithm-independent, inherent features of resting state EEG derived microstate sequences.

Information-Theoretical Analysis of EEG Microstate Sequences in Python

We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A-D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

Mutual information identifies spurious Hurst phenomena in resting state EEG and fMRI data

Long-range memory in time series is often quantified by the Hurst exponent H, a measure of the signal's variance across several time scales. We analyze neurophysiological time series from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state experiments with two standard Hurst exponent estimators and with the time-lagged mutual information function applied to discretized versions of the signals. A confidence interval for the mutual information function is obtained from surrogate Markov processes with equilibrium distribution and transition matrix identical to the underlying signal. For EEG signals, we construct an additional mutual information confidence interval from a short-range correlated, tenth-order autoregressive model. We reproduce the previously described Hurst phenomenon (H>0.5) in the analytical amplitude of alpha frequency band oscillations, in EEG microstate sequences, and in fMRI signals, but we show that the Hurst phenomenon occurs without long-range memory in the information-theoretical sense. We find that the mutual information function of neurophysiological data behaves differently from fractional Gaussian noise (fGn), for which the Hurst phenomenon is a sufficient condition to prove long-range memory. Two other well-characterized, short-range correlated stochastic processes (Ornstein-Uhlenbeck, Cox-Ingersoll-Ross) also yield H>0.5, whereas their mutual information functions lie within the Markovian confidence intervals, similar to neural signals. In these processes, which do not have long-range memory by construction, a spurious Hurst phenomenon occurs due to slow relaxation times and heteroscedasticity (time-varying conditional variance). In summary, we find that mutual information correctly distinguishes long-range from short-range dependence in the theoretical and experimental cases discussed. Our results also suggest that the stationary fGn process is not sufficient to describe neural data, which seem to belong to a more general class of stochastic processes, in which multiscale variance effects produce Hurst phenomena without long-range dependence. In our experimental data, the Hurst phenomenon and long-range memory appear as different system properties that should be estimated and interpreted independently.

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].

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].

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.

Suppress to Forget: The Effect of a Mindfulness-Based Strategy during an Emotional Item-Directed Forgetting Paradigm

Forgetting is a common phenomenon in everyday life. Although it often has negative connotations, forgetting is an important adaptive mechanism to avoid loading the memory storage with irrelevant information. A very important aspect of forgetting is its interaction with emotion. Affective events are often granted special and priority treatment over neutral ones with regards to memory storage. As a consequence, emotional information is more resistant to extinction than neutral information. It has been suggested that intentional forgetting serves as a mechanism to cope with unwanted or disruptive emotional memories and the main goal of this study was to assess forgetting of emotional auditory material using the item-method directed forgetting (DF) paradigm using a forgetting strategy based on mindfulness as a means to enhance DF. Contrary to our prediction, the mindfulness-based strategy not only did not improve DF but reduced it for neutral material. These results suggest that an interaction between processes such as response inhibition and attention is required for intentional forgetting to succeed.

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.

Transcranial ultrasound to detect elevated intracranial pressure: comparison of septum pellucidum undulations and optic nerve sheath diameter

Two ultrasound tests that can be used to assess increased intracranial pressure (ICP) at the bedside are described. In outpatients receiving lumbar puncture and in intensive care patients with invasive ICP monitoring, we measured the optic nerve sheath diameter (ONSD) with transbulbar B-mode sonography and septum pellucidum undulation (SPU) induced by repeated passive head rotation with transtemporal M-mode sonography. We assessed the sensitivity and specificity of ONSD and SPU in the prediction of ICP >20 cm H2O. For ONSD, sensitivity was 53% and specificity 100% (n = 35, p < 0.001). The sensitivity of the SPU test was 75% and the specificity 100% (n = 32, p < 0.001). Although the SPU test may not feasible in some patients, it has high sensitivity and specificity comparable to those of ONSD measurement. The SPU test and ONSD may be useful alternatives to fundoscopy in clinical routine, preferably in combination.

Exact stochastic simulation of a calcium microdomain reveals the impact of Ca²⁺ 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.

Microdomain calcium fluctuations as a colored noise process

Calcium ions play a key role in subcellular signaling as localized transients of the intracellular calcium concentration modify the activity of ion channels, enzymes and transcription factors, among others. The intracellular calcium concentration is inherently noisy, as diffusion, the transient binding to and dissociation from buffer molecules and stochastically gating calcium channels contribute to the fluctuations of the local copy number of Ca²⁺ ions. We study the properties of the fluctuating calcium concentration in sub-femtoliter volumes using an exact stochastic simulation algorithm and approximations to the exact stochastic solution. It is shown that the time course of the local calcium concentration represents a colored noise process whose autocorrelation time is a function of buffer kinetics and diffusion constants. Using the chemical Langevin description and the excess buffer approximation of the process, fast approximative algorithms and theoretical connections to the Ornstein-Uhlenbeck process are obtained. In a generic example, we show how calcium noise can couple to the dynamics of a single variable moving in a double-well potential, leading to a colored noise induced transition. Our work shows how a multitude of intracellular signaling pathways may be influenced by the inherent stochasticity of calcium signals, a key messenger in virtually any cell type, and how the calcium signal can be implemented efficiently in cellular signaling models.

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.

Dynamic BOLD functional connectivity in humans and its electrophysiological correlates

Neural oscillations subserve many human perceptual and cognitive operations. Accordingly, brain functional connectivity is not static in time, but fluctuates dynamically following the synchronization and desynchronization of neural populations. This dynamic functional connectivity has recently been demonstrated in spontaneous fluctuations of the Blood Oxygen Level-Dependent (BOLD) signal, measured with functional Magnetic Resonance Imaging (fMRI). We analyzed temporal fluctuations in BOLD connectivity and their electrophysiological correlates, by means of long (≈50 min) joint electroencephalographic (EEG) and fMRI recordings obtained from two populations: 15 awake subjects and 13 subjects undergoing vigilance transitions. We identified positive and negative correlations between EEG spectral power (extracted from electrodes covering different scalp regions) and fMRI BOLD connectivity in a network of 90 cortical and subcortical regions (with millimeter spatial resolution). In particular, increased alpha (8-12 Hz) and beta (15-30 Hz) power were related to decreased functional connectivity, whereas gamma (30-60 Hz) power correlated positively with BOLD connectivity between specific brain regions. These patterns were altered for subjects undergoing vigilance changes, with slower oscillations being correlated with functional connectivity increases. Dynamic BOLD functional connectivity was reflected in the fluctuations of graph theoretical indices of network structure, with changes in frontal and central alpha power correlating with average path length. Our results strongly suggest that fluctuations of BOLD functional connectivity have a neurophysiological origin. Positive correlations with gamma can be interpreted as facilitating increased BOLD connectivity needed to integrate brain regions for cognitive performance. Negative correlations with alpha suggest a temporary functional weakening of local and long-range connectivity, associated with an idling state.

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.

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.

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.

Second harmonic generation microscopy probes different states of motor protein interaction in myofibrils

The second harmonic generation (SHG) signal intensity sourced from skeletal muscle myosin II strongly depends on the polarization of the incident laser beam relative to the muscle fiber axis. This dependence is related to the second-order susceptibility χ((2)), which can be described by a single component ratio γ under generally assumed symmetries. We precisely extracted γ from SHG polarization dependence curves with an extended focal field model. In murine myofibrillar preparations, we have found two distinct polarization dependencies: With the actomyosin system in the rigor state, γ(rig) has a mean value of γ(rig) = 0.52 (SD = 0.04, n = 55); in a relaxed state where myosin is not bound to actin, γ(rel) has a mean value of γ(rel) = 0.24 (SD = 0.07, n = 70). We observed a similar value in an activated state where the myosin power stroke was pharmacologically inhibited using N-benzyl-p-toluene sulfonamide. In summary, different actomyosin states can be visualized noninvasively with SHG microscopy. Specifically, SHG even allows us to distinguish different actin-bound states of myosin II using γ as a parameter.

Ultra-rapid activation and deactivation of store-operated Ca(2+) entry in skeletal muscle

Skeletal muscle is highly specialized for the rapid delivery of Ca(2+) to the contractile apparatus during excitation-contraction coupling (EC coupling). Previous studies have shown the presence of a relatively fast-activated store-operated Ca(2+) entry (SOCE) mechanism (<1s) to be present in skeletal muscle, unlike the situation occurring in non-excitable cells. We simultaneously imaged [Ca(2+)] in the t-system and cytoplasm in mechanically skinned fibers during SR Ca(2+) release and observed both cell-wide Ca(2+) release and Ca(2+) waves. SOCE activation followed cell-wide Ca(2+) release from high sarcoplasmic reticulum (SR) [Ca(2+)] ([Ca(2+)](SR)) by seconds, consistent with depletion of [Ca(2+)](SR) to an absolute threshold for SOCE and an unformed SOCE complex at high [Ca(2+)](SR). Ca(2+) waves occurred at low [Ca(2+)](SR), close to the threshold for SOCE, minimizing the time between Ca(2+) release and Ca(2+) influx. Local activation of SOCE during Ca(2+) waves occurred in approximately 27ms following local initiation of SR depletion indicating a steep relationship between [Ca(2+)](SR) and SOCE activation. Most of this delay was due to slow release of Ca(2+) from SR, leaving only milliseconds at most for the activation of Ca(2+) entry following store depletion. SOCE was also observed to deactivate effectively instantly during store refilling at low [Ca(2+)](SR). These rapid kinetics of SOCE persisted as subsequent Ca(2+) waves propagated along the fiber. Thus we show for the first time millisecond activation and deactivation of SOCE during low amplitude [Ca(2+)](SR) oscillations at low [Ca(2+)](SR). To account for the observed Ca(2+) movements we propose the SOCE complex forms during the progressive depletion of [Ca(2+)](SR) prior to reaching the activation threshold of SOCE and this complex remains stable at low [Ca(2+)](SR).

Upregulation of store-operated Ca2+ entry in dystrophic mdx mouse muscle

Store-operated Ca(2+) entry (SOCE) is an important mechanism in virtually all cells. In adult skeletal muscle, this mechanism is highly specialized for the rapid delivery of Ca(2+) from the transverse tubule into the junctional cleft during periods of depleting Ca(2+) release. In dystrophic muscle fibers, SOCE may be a source of Ca(2+) overload, leading to cell necrosis. However, this possibility is yet to be examined in an adult fiber during Ca(2+) release. To examine this, Ca(2+) in the tubular system and cytoplasm were simultaneously imaged during direct release of Ca(2+) from sarcoplasmic reticulum (SR) in skeletal muscle fibers from healthy (wild-type, WT) and dystrophic mdx mouse. The mdx fibers were found to have normal activation and deactivation properties of SOCE. However, a depression of the cytoplasmic Ca(2+) transient in mdx compared with WT fibers was observed, as was a shift in the SOCE activation and deactivation thresholds to higher SR Ca(2+) concentrations ([Ca(2+)](SR)). The shift in SOCE activation and deactivation thresholds was accompanied by an approximately threefold increase in STIM1 and Orai1 proteins in dystrophic muscle. While the mdx fibers can introduce more Ca(2+) into the fiber for an equivalent depletion of [Ca(2+)](SR) via SOCE, it remains unclear whether this is deleterious.

New factors contributing to dynamic calcium regulation in the skeletal muscle triad-a crowded place

Skeletal muscle is a highly organized tissue that has to be optimized for fast signalling events conveying electrical excitation to contractile response. The site of electro-chemico-mechanical coupling is the skeletal muscle triad where two membrane systems, the extracellular t-tubules and the intracellular sarcoplasmic reticulum, come into very close contact. Structure fits function here and the signalling proteins DHPR and RyR1 were the first to be discovered to bridge this gap in a conformational coupling arrangement. Since then, however, new proteins and more signalling cascades have been identified just in the last decade, adding more diversity and fine tuning to the regulation of excitation-contraction coupling (ECC) and control over Ca2+ store content. The concept of Ca2+ entry into working skeletal muscle has become attractive again with the experimental evidence summarized in this review. Store-operated Ca2+ entry (SOCE), excitation-coupled Ca2+ entry (ECCE), action-potential-activated Ca2+ current (APACC), and retrograde EC-coupling (ECC) are new concepts additional to the conventional orthograde ECC; they have provided fascinating new insights into muscle physiology. In this review, we discuss the discovery of these pathways, their potential roles, and the signalling proteins involved that show that the triad may become a crowded place in time.

Motor protein function in skeletal muscle-a multiple scale approach to contractility

We present an approach to skeletal muscle contractility and its regulation over different scales ranging from biomechanical studies in intact muscle fibers down to the motility and interaction of single motor protein molecules. At each scale, shortening velocities as a measure for weak cross-bridge cycling rates are extracted and compared. Experimental approaches include transmitted light microscopy, second harmonic generation imaging of contracting myofibrils, and fluorescence microscopy of single molecule motility. Each method yields image sequences that are analyzed with automated image processing algorithms to extract the contraction velocity. Using this approach, we show how to isolate the contribution of the motor proteins actin and myosin and their modulation by regulatory proteins from the concerted action of electro-mechanical activation on a more complex cellular scale. The advantage of this approach is that averaged contraction velocities can be determined on the different scales ranging from isolated motor proteins to sarcomere levels in myofibrils and myofibril arrays within the cellular architecture. Our results show that maximum shortening velocities during in situ electrical activation of sarcomere contraction in intact single muscle cells can substantially deviate from sliding velocities obtained in oriented in vitro motility assays of isolated motor proteins showing that biophysical contraction kinetics not simply translate linearly between contractility scales. To adequately resolve the very fast initial mechanical activation kinetics of shortening at each scale, it was necessary to implement high-speed imaging techniques. In the case of intact fibers and single molecule motility, we achieved a major increase in temporal resolution up to frame rates of 200-1000 fps using CMOS image sensor technology. The data we obtained at this unprecedented temporal resolution and the parameters extracted can be used to validate results obtained from computational models of motor protein interaction and skeletal muscle contractility in health and muscle disease. Our approach is feasible to explain the possible underlying mechanisms that contribute to different shortening velocities at different scales and complexities.

Velocity distributions of single F-actin trajectories from a fluorescence image series using trajectory reconstruction and optical flow mapping

We present an approach for the computation of single-object velocity statistics in a noisy fluorescence image series. The algorithm is applied to molecular imaging data from an in vitro actin-myosin motility assay. We compare the relative efficiency of wavelet and curvelet transform denoising in terms of noise reduction and object restoration. It is shown that while both algorithms reduce background noise efficiently, curvelet denoising restores the curved edges of actin filaments more reliably. Noncrossing spatiotemporal actin trajectories are unambiguously identified using a novel segmentation scheme that locally combines the information of 2-D and 3-D segmentation. Finally, the optical flow vector field for the image sequence is computed via the 3-D structure tensor and mapped to the segmented trajectories. Using single-trajectory statistics, the global velocity distribution extracted from an image sequence is decomposed into the contributions of individual trajectories. The technique is further used to analyze the distribution of the x and y components of the velocity vectors separately, and it is shown that directed actin motion is found in myosin extracts from single skeletal muscle fibers. The presented approach may prove helpful to identify actin filament subpopulations and to analyze actin-myosin interaction kinetics under biochemical regulation.

Resting membrane potentials recorded on-site in intact skeletal muscles from deep sea fish (Sigmops gracile) salvaged from depths up to 1.000 m

The effect of elevated ambient pressures in deep sea fish residing at certain bottom depths or even covering different depth levels during migration is poorly understood. Elevated pressures are known to influence membrane properties of various excitable tissues in many species. Reliable results on membrane properties require freshly isolated living cells and short decompression times. During a scientific cruise south of Japan, deep sea fish were sampled from depths up to 1.000 m by using the intelligent operative net sampling system IONESS. On-site electrophysiological recordings of resting membrane potentials were performed in freshly isolated skeletal muscles from Sigmops gracile. Experiments were conducted at various extracellular K+ concentrations to derive relative membrane ion permeabilities and estimate intracellular K+ concentrations [K+]i in the muscles studied. With increasing sampling depth, a tendency for depolarized resting membrane potentials was observed. This could be explained by an increase in relative Na+ over K+ resting membrane permeabilities. Fish samples from deeper sites also had larger [K+]i values compared with shallower sites. This study represents a first approach to perform sophisticated physiological live-cell experiments on board a fully operating ship. These data are expected to more realistically reflect the physiological state of biological preparations residing in the deep sea.

Modulation of angiogenesis by dithiolethione-modified NSAIDs and valproic acid

BACKGROUND AND PURPOSE

Angiogenesis involves multiple signaling pathways that must be considered when developing agents to modulate pathological angiogenesis. Because both cyclooxygenase inhibitors and dithioles have demonstrated anti-angiogenic properties, we investigated the activities of a new class of anti-inflammatory drugs containing dithiolethione moieties (S-NSAIDs) and S-valproate.

EXPERIMENTAL APPROACH

Anti-angiogenic activities of S-NSAIDS, S-valproate, and the respective parent compounds were assessed using umbilical vein endothelial cells, muscle and tumor tissue explant angiogenesis assays, and developmental angiogenesis in Fli:EGFP transgenic zebrafish embryos.

KEY RESULTS

Dithiolethione derivatives of diclofenac, valproate, and sulindac inhibited endothelial cell proliferation and induced Ser(78) phosphorylation of hsp27, a known molecular target of anti-angiogenic signaling. The parent drugs lacked this activity, but dithiolethiones were active at comparable concentrations. Although dithiolethiones can potentially release hydrogen sulphide, NaSH did not reproduce some activities of the S-NSAIDs, indicating that the dithioles regulate angiogenesis through mechanisms other than release of H(2)S. In contrast to the parent drugs, S-NSAIDs, S-valproate, NaSH, and dithiolethiones were potent inhibitors of angiogenic responses in muscle and HT29 tumor explants assessed by 3-dimensional collagen matrix assays. Dithiolethiones and valproic acid were also potent inhibitors of developmental angiogenesis in zebrafish embryos, but the S-NSAIDs, remarkably, lacked this activity.

CONCLUSIONS AND IMPLICATION

S-NSAIDs and S-valproate have potent anti-angiogenic activities mediated by their dithiole moieties. The novel properties of S-NSAIDs and S-valproate to inhibit pathological versus developmental angiogenesis suggest that these agents may have a role in cancer treatment.

'In situ' high pressure confocal Ca(2+)-fluorescence microscopy in skeletal muscle: a new method to study pressure limits in mammalian cells

We combined 'in situ' high pressure microscopy with confocal laser scanning microscopy to directly study Ca2+ homeostasis in intact mammalian (murine) skeletal muscle fibres during high pressure exposure up to 35 MPa. Cytosolic Fluo-4 and mitochondrial Rhod-2 Ca2+ fluorescence were simultaneously monitored. To separate changes in Ca2+ and direct/indirect effects of pressure on the dye, experiments in permeabilized ('skinned') muscle fibres were performed at a fixed Ca2+ concentration. Normalized Fluo-4 fluorescence sharply declined up to 10 MPa but showed a plateau between 10 MPa and -35 MPa. In the intact fibre, Fluo-4 fluorescence exponentially decreased during pressurization to 35 MPa with a pressure constant of pi-5 MPa whereas mitochondrial Rhod-2 fluorescence exponentially increased with a four-fold larger pi. Holding the pressure at 35 MPa almost did not change Fluo-4 fluorescence. However, Rhod-2 fluorescence started to decrease after -40 min. Upon decompression, Rhod-2 and Fluo-4 fluorescence increased exponentially with similar pi. However, initial Fluo-4 fluorescence values were not restored. Our results are in agreement with pressure induced Ca2+ leakage from the sarcoplasmic reticulum. Ca2+ might then be taken up in large amounts by mitochondria preventing cytosolic increase in Ca2+. Prolonged pressure applications (-40 min at 35 MPa) seem to destabilize mitochondrial function with release of Ca2+ from mitochondria back into the cytosol and eventually mechanical activation resulting in irreversible contractures. The pressure induced disturbance of Ca2+ homeostasis might have important implications for the pressure exposure limits and/or dive profiles of deep sea mammals.

Preclinical Evaluation of the Breast Cancer Cell-Binding Peptide, p160

PURPOSE

Selective delivery of drugs into the target tissue is expected to result in high drug concentrations in the tissue of interest and therefore enhanced drug efficacy. To develop a peptide-based radiopharmaceutical, we investigated the properties of a peptide with affinity for human breast cancer, which has been selected through phage display.

EXPERIMENTAL DESIGN

The bioactivity of the p160 peptide (VPWMEPAYQRFL) was evaluated in vitro and in vivo. The specific binding to human breast cancer MDA-MB-435 cells was confirmed in competition experiments. Internalization of the peptide was investigated with confocal microscopy. Furthermore, the biodistribution of (131)I-labeled p160 was studied in tumor-bearing mice. In vivo stability was evaluated at different periods after tracer administration using high-performance liquid chromatography analysis.

RESULTS

The binding of (125)I-labeled p160 was inhibited up to 95% by the unlabeled peptide with an IC(50) value of 0.6 micromol/L. In addition, 40% of the total bound activity was found to be internalized into the human breast cancer cells. Although a rapid degradation was seen, biodistribution studies in nude mice showed a higher uptake in tumor than in most of the organs. Perfusion of the animals caused a reduction of the radioligand accumulation in the healthy tissues, whereas the tumor uptake remained constant. A comparison of [(131)I]p160 with a (131)I-labeled Arg-Gly-Asp peptide revealed a higher tumor-to-organ ratio for [(131)I]p160.

CONCLUSIONS

p160 has properties that make it an attractive carrier for tumor imaging and the intracellular delivery of isotopes or chemotherapeutic drugs.