EEG Mu Rhythm in Typical and Atypical Development

Electroencephalography (EEG) is an effective, efficient, and noninvasive method of assessing and recording brain activity. Given the excellent temporal resolution, EEG can be used to examine the neural response related to specific behaviors, states, or external stimuli. An example of this utility is the assessment of the mirror neuron system (MNS) in humans through the examination of the EEG mu rhythm. The EEG mu rhythm, oscillatory activity in the 8-12 Hz frequency range recorded from centrally located electrodes, is suppressed when an individual executes, or simply observes, goal directed actions. As such, it has been proposed to reflect activity of the MNS. It has been theorized that dysfunction in the mirror neuron system (MNS) plays a contributing role in the social deficits of autism spectrum disorder (ASD). The MNS can then be noninvasively examined in clinical populations by using EEG mu rhythm attenuation as an index for its activity. The described protocol provides an avenue to examine social cognitive functions theoretically linked to the MNS in individuals with typical and atypical development, such as ASD.      

Designing matrix models for fluorescence energy transfer between moving donors and acceptors.

A recipe is given for designing theoretical models for donor-acceptor systems in which fluorescence energy transfer and motion takes place simultaneously. This recipe is based on the idea that a system exhibiting both motion and fluorescence energy transfer can be modeled by specifying a number of "states" and the rates of transitions between them. A state in this context is a set of specific coordinates and conditions that describe the system at a certain moment in time. As time goes on, the coordinates and conditions for the system change, and this evolution can be described as a series of transitions from one state to the next. The recipe is applied to a number of example systems in which the donors and/or acceptors undergo either rotational or translational motion. In each example, fluorescence intensities and anisotropies for the donor and acceptor are calculated from solutions of eigensystems. The proposed method allows for analyzing time-resolved fluorescence energy transfer data without restrictive assumptions for motional averaging regimes and the orientation factor. It is shown that the fluorescence quantities depend on the size of the motional step (i.e., on the number of states), only if fluorescence energy transfer occurs. This finding indicates that fluorescence energy transfer studies may reveal whether the dynamics of a system (e.g., a protein) is better described in terms of transitions between a relatively small number of discrete states (jumping) or a large number of dense states (diffusion).     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

Universality in the brain while listening to music.

The human brain, which is one of the most complex organic systems, involves billions of interacting physiological and chemical processes that give rise to experimentally observed neuroelectrical activity, which is called an electroencephalogram (EEG). The presence of non-stationarity and intermittency render standard available methods unsuitable for detecting hidden dynamical patterns in the EEG. In this paper, a method that is suitable for non-stationary signals and preserving the phase characteristics and that combines wavelet and Hilbert transforms was applied to multivariate EEG signals from human subjects at rest as well as in different cognitive states: listening to music, listening to text and performing spatial imagination. It was found that, if suitably rescaled, the gamma band EEG over distributed brain areas while listening to music can be described by a universal and homogeneous scaling, whereas this homogeneity in scale is reduced at resting conditions and also during listening to text and performing spatial imagination. The degree of universality is characterized by a Kullback-Leibler divergence measure. By statistical surrogate analysis, nonlinear phase interaction was found to play an important role in exhibiting universality among multiple cortical regions.     

Multiscale analysis of reaction networks

In most natural sciences there is currently the insight that it is necessary to bridge gaps between different processes which can be observed on different scales. This is especially true in the field of chemical reactions where the different abilities to form bonds between different types of atoms and molecules create much of the properties we experience in our everyday life, especially in all biological activity. There are essentially two types of processes related to biochemical reaction networks, the interactions among molecules and interactions involving their conformational changes, so in a sense, their internal state. The first type of processes can be conveniently approximated by the so-called mass-action kinetics, but this is not necessarily so for the second kind: here molecular states do not define any kind of density or concentration. In this paper, we demonstrate the necessity to study reaction networks in a stochastic formulation for which we can construct a coherent approximation in terms of specific space–time scales and the number of particles. The continuum limit procedure naturally creates equations of Fokker–Planck type where the evolution of the concentration occurs on a slower time scale when compared to the evolution of the conformational changes, for example triggered by binding or unbinding events with other (typically smaller) molecules. We apply the asymptotic theory to derive the effective, i.e. macroscopic dynamics of a general biochemical reaction system. The theory can also be applied to other processes where entities can be described by finitely many internal states, with changes of states occurring by arrival of other entities described by a birth–death process.     

Character-state space versus rate of evolution in phylogenetic inference

With only four alternative character states, parallelisms and reversals are expected to occur frequently when using nucleotide characters for phylogenetic inference. Greater available character‐state space has been described as one of the advantages of third codon positions relative to first and second codon positions, as well as amino acids relative to nucleotides. We used simulations to quantify how character‐state space and rate of evolution relate to one another, and how this relationship is affected by differences in: tree topology, branch lengths, rate heterogeneity among sites, probability of change among states, and frequency of character states. Specifically, we examined how inferred tree lengths, consistency and retention indices, and accuracy of phylogenetic inference are affected. Our results indicate that the relatively small increases in the character‐state space evident in empirical data matrices can provide enormous benefits for the accuracy of phylogenetic inference. This advantage may become more pronounced with unequal probabilities of change among states. Although increased character‐state space greatly improved the accuracy of topology inference, improvements in the estimation of the correct tree length were less apparent. Accuracy and inferred tree length improved most when character‐state space increased initially; further increases provided more modest improvements. © The Willi Hennig Society 2004.     

Adaptive method for EEG spectrum component decomposition

A new computerized method for EEG rhythms extraction is proposed as a development of the idea of adjustable boundaries of frequency components that was put forward in previous investigations. Principle component analysis of the correlation matrix of EEG spectra with subsequent rotation of factor solutions was used for decomposition of a spectrum into physically meaningful spectral components. The method was tested on EEG of 14 healthy subjects recorded in 17 functional waking states. Fourteen independent spectral components in the spectral range from 0 to 100 Hz were extracted and their frequency boundaries were consistent with the current knowledge on frequency components of EEG oscillations. Main advantage of the described method is the adjustable estimation of EEG frequency oscillators taking into account characteristic properties of individual EEGs. Possible area of application might be the correct evaluation of spectral power of the EEG rhythms, EEG coherence and other spectral characteristics in clinical and experimental research, studies of the frequency characteristics of the EEG rhythms in different human functional states, changes in frequency characteristics of the EEG rhythms during maturation and in mental pathology.     

Striking HIV-1 Entry by Targeting HIV-1 gp41. But,Where Should We Target?

HIV-1 gp41 facilitates the viral fusion through a conformational switch involving the association of three C-terminal helices along the conserved hydrophobic grooves of three N-terminal helices coiled-coil. The control of these structural rearrangements is thought to be central to HIV-1 entry and, therefore, different strategies of intervention are being developed. Herewith, we describe a procedure to simulate the folding of an HIV-1 gp41 simplified model. This procedure is based on the construction of plausible conformational pathways, which describe protein transition between non-fusogenic and fusogenic conformations. The calculation of the paths started with 100 molecular dynamics simulations of the non-fusogenic conformation, which were found to converge to different intermediate states. Those presenting defined criteria were selected for separate targeted molecular dynamics simulations, subjected to a force constant imposing a movement towards the gp41 fusogenic conformation. Despite significant diversity, a preferred sequence of events emerged when the simulations were analyzed in terms of the formation, breakage and evolution of the contacts. We pointed out 29 residues as the most relevant for the movement of gp41; also, 2696 possible interactions were reduced to only 48 major interactions, which reveals the efficiency of the method. The analysis of the evolution of the main interactions lead to the detection of four main behaviors for those contacts: stable, increasing, decreasing and repulsive interactions. Altogether, these results suggest a specific small cavity of the HIV-1 gp41 hydrophobic groove as the preferred target to small molecules.     

Dioxygen and nitric oxide pathways and affinity to the catalytic site of rubredoxin:oxygen oxidoreductase from Desulfovibrio gigas

Rubredoxin:oxygen oxidoreductase (ROO) is the terminal oxidase of a soluble electron transfer chain found in Desulfovibrio gigas. This protein belongs to the flavodiiron family and was initially described as an oxygen reductase, converting this substrate to water and acting as an oxygen-detoxifying system. However, more recent studies evidenced also the ability for this protein to act as a nitric oxide reductase, suggesting an alternative physiological role. To clarify the apparent bifunctional nature of this protein, we performed molecular dynamics simulations of the protein, in different redox states, together with O₂ and NO molecules in aqueous solution. The two small molecules were parameterized using free-energy calculations of the hydration process. With these simulations we were able to identify specific protein paths that allow the diffusion of both these molecules through the protein towards the catalytic centers. Also, we have tried to characterize the preference of ROO towards the presence of O₂ and/or NO at the active site. By using free-energy simulations, we did not find any significant preference for ROO to accommodate both O₂ and NO. Also, from our molecular dynamics simulations we were able to identify similar diffusion profiles for both O₂ and NO molecules. These two conclusions are in good agreement with previous experimental works stating that ROO is able to catalyze both O₂ and NO. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Calculation of excess free energy of molecular solids comprised of flexible molecules using Einstein molecule method

Knowledge of free energies of crystalline solid phases is pivotal in determination of phase equilibria; however, their computations using molecular simulations are quite challenging. In this paper, we apply the Einstein molecule method to compute the excess free energies of crystalline molecular solids comprising of molecules described by flexible models. This work can be viewed as an extension of the method described in Aragones et al. [J Chem Phys. 2013;139:a034104]. In this article, we present our calculations of the free energies of ice-Ih described by q-TIP4P/F model and Orcinol Form I described by the OPLS-AA forcefield. The free energies computed for ice-Ih are in agreement with previous reported results, and those of Orcinol are shown to be thermodynamically consistent. We demonstrate that the efficiency of our calculations compares favourably with existing methods. The method described here can be easily implemented in popular publicly available molecular dynamics packages without any modification to their existing source codes.     

Generalized-ensemble algorithms for molecular simulations of biopolymers.

In complex systems with many degrees of freedom such as peptides and proteins, there exists a huge number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped in states of these energy local minima. A simulation in generalized ensemble performs a random walk in potential energy space and can overcome this difficulty. From only one simulation run, one can obtain canonical-ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review uses of the generalized-ensemble algorithms in biomolecular systems. Three well-known methods, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are given. We then present three new generalized-ensemble algorithms that combine the merits of the above methods. The effectiveness of the methods for molecular simulations in the protein folding problem is tested with short peptide systems.     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

Insights into panicoid inflorescence evolution

Inflorescence forms can be described by different combinatorial patterns of meristem fates (indeterminate versus determinate). In theory, the model predicts that any combination is possible. Whether this is true for grasses is unknown. In this paper, the subfamily Panicoideae s.s. (panicoid grasses) was chosen as the model group to investigate this aspect of grass inflorescence evolution. We have studied the inflorescence morphology of 201 species to complement information available in the literature. We have identified the most recurrent inflorescence types and character states among panicoids. Using multivariate approaches, we have indentified correlations among different inflorescence character states. By phylogenetic reconstruction methods we have inferred the patterns of panicoid inflorescence evolution. Our results demonstrate that not all theoretical combinatorial patterns of variation are found in panicoids. The fact that each panicoid lineage has a unique pattern of inflorescence evolution adds an evolutionary component to combinatorial model.     

Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network

In this paper, a new approach based on eigen-systems pseudo-spectral estimation methods, namely Eigenvector (EV) and MUSIC, and Multiple Layer Perceptron (MLP) neural network is introduced. In this approach, the calculated EEG (electroencephalogram) spectrum is divided into smaller frequency sub-bands. Then, a set of features, {maximum, entropy, average, standard deviation, mobility}, are extracted from these sub-bands. Next, incorporating a set of the EEG time domain features {standard deviation, complexity measure} with the spectral feature set, a feature vector is formed. The feature vector is then fetched into a MLP neural network to classify the signal into the following three states: normal (healthy), epileptic patient signal in a seizure-free interval (inter-ictal), and epileptic patient signal in a full seizure interval (ictal). The experimental results show that the classification of the EEG signals maybe achieved with approximately 97.5% accuracy and the variance of 0.095% using an available public EEG signals database. The results are among the best reported methods for classifying the three states aforementioned. This is a high speed with high accuracy as well as low misclassifying rate method so it can make the practical and real-time detection of this chronic disease feasible.     

Translation of EEG spatial filters from resting to motor imagery using independent component analysis

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters.     

Covariation of GC content and the silent site substitution rate in rodents: implications for methodology and for the evolution of isochores

Many attempts to test selectionist and neutralist models employ estimates of synonymous (Ks) and non-synonymous (Ka) substitution rates of orthologous genes. For example, a stronger Ka-Ks correlation than expected under neutrality has been argued to indicate a role for selection and the absence of a Ks-GC4 correlation has been argued to be inconsistent with neutral models for isochore evolution. However, both of these results, we have shown previously, are sensitive to the method by which Ka and Ks are estimated. Using a maximum likelihood (ML) estimator (GY94) we found a positive correlation between Ks and GC4 and only a weak correlation between Ka and Ks, lower than expected under neutral expectations. This ML method is computationally slow. Recently, a new ad hoc approximation of this ML method has been provided (YN00). This is effectively an extension of Li's protocol but that also allows for codon usage bias. This method is computationally near-instantaneous and therefore potentially of great utility for analysis of large datasets. Here we ask whether this method might have such applicability. To this end we ask whether it too recovers the two unusual results. We report that when the ML and earlier ad hoc methods disagree, YN00 recovers the results described by the ML methods, i.e. a positive correlation between GC4 and Ks and only a weak correlation between Ks and Ka. If the ML method can be trusted, then YN00 can also be considered an adequately reliable method for analysis of large datasets. Assuming this to be so we also analyze further the patterns. We show, for example, that the positive correlation between GC4 and Ks is probably in part a mutational bias, there being more methyl induced CpG-->TpG mutations in GC rich regions. As regards the evolution of isochores, it seems inappropriate to use the claimed lack of a correlation between GC and Ks as definitive evidence either against or for any model. If the positive correlation is real then, we argue, this is hard to reconcile with the biased gene conversion model for isochore formation as this predicts a negative correlation.     

Gene perturbation and intervention in probabilistic Boolean networks

MOTIVATION: A major objective of gene regulatory network modeling, in addition to gaining a deeper understanding of genetic regulation and control, is the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. We consider the general question of the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene perturbation as well as intervention in order to elicit desired network behavior. RESULTS: Using a recently introduced class of models, called Probabilistic Boolean Networks (PBNs), this paper develops a model for random gene perturbations and derives an explicit formula for the transition probabilities in the new PBN model. This result provides a building block for performing simulations and deriving other results concerning network dynamics. An example is provided to show how the gene perturbation model can be used to compute long-term influences of genes on other genes. Following this, the problem of intervention is addressed via the development of several computational tools based on first-passage times in Markov chains. The consequence is a methodology for finding the best gene with which to intervene in order to most likely achieve desirable network behavior. The ideas are illustrated with several examples in which the goal is to induce the network to transition into a desired state, or set of states. The corresponding issue of avoiding undesirable states is also addressed. Finally, the paper turns to the important problem of assessing the effect of gene perturbations on long-run network behavior. A bound on the steady-state probabilities is derived in terms of the perturbation probability. The result demonstrates that states of the network that are more 'easily reachable' from other states are more stable in the presence of gene perturbations. Consequently, these are hypothesized to correspond to cellular functional states. AVAILABILITY: A library of functions written in MATLAB for simulating PBNs, constructing state-transition matrices, computing steady-state distributions, computing influences, modeling random gene perturbations, and finding optimal intervention targets, as described in this paper, is available on request from is@ieee.org.