Non-linear, non-invasive method for seizure anticipation in focal epilepsy

In this paper we discuss an approach, using methods of non-linear time series analysis applied to scalp electrode recordings, which is able to distinguish between epochs temporally distant from and just prior to, the onset of a seizure in patients with temporal lobe epilepsy. The method involves a comparison of recordings taken from electrodes adjacent to and remote from the site of ictal onset. In particular, we define a non-linear quantity which we call 'marginal predictability'. This quantity is computed using data from remote and from adjacent electrodes. We find that the difference between the marginal predictabilities computed for the remote and adjacent electrodes decreases several tens of minutes prior to seizure onset, compared to its value interictally.     

脑电图机电极与头皮接触阻抗的检测

脑电图机中电极与头皮接触的好坏对脑电波形质量有很大影响，本文利用AT89C51单片机实现电极与头皮接触阻抗的检测。还通过发光二极管给予医务人员对电极接触好坏直观的指示。     

Gaussian mixture clustering and imputation of microarray data

MOTIVATION: In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. RESULTS: Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.     

Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.     

Extraction of reproducible seizure patterns based on EEG scalp correlations

The objective of this work is to identify similarities in the spatio-temporal dynamics of epileptic seizures, record with scalp EEG. A comprehensive method is proposed and applied in EEG of the patients who suffer from temporal lobe epilepsy. The method is based on the computation of the time-varying degree of non linear correlation between scalp electrodes at seizure onset and during seizure spread, determined by a nonlinear regression analysis. The quantification and coding of these similarity relations allow the comparison between two epileptic networks. Results show that reproducible patterns may be extracted from different seizures of the same patient and confirm the existence of different subtypes of temporal lobe epilepsy.     

Multivariate Association and Dimension Reduction: A Generalization of Canonical Correlation Analysis

Summary In this article, we propose a new generalized index to recover relationships between two sets of random vectors by finding the vector projections that minimize an L ₂ distance between each projected vector and an unknown function of the other. The unknown functions are estimated using the Nadaraya–Watson smoother. Extensions to multiple sets and groups of multiple sets are also discussed, and a bootstrap procedure is developed to detect the number of significant relationships. All the proposed methods are assessed through extensive simulations and real data analyses. In particular, for environmental data from Los Angeles County, we apply our multiple‐set methodology to study relationships between mortality, weather, and pollutants vectors. Here, we detect existence of both linear and nonlinear relationships between the dimension‐reduced vectors, which are then used to build nonlinear time‐series regression models for the dimension‐reduced mortality vector. These findings also illustrate potential use of our method in many other applications. A comprehensive assessment of our methodologies along with their theoretical properties are given in a Web Appendix.     

Evolutionarily distinctive species often capture more phylogenetic diversity than expected

Evolutionary distinctiveness measures of how evolutionarily isolated a species is relative to other members of its clade. Recently, distinctiveness metrics that explicitly incorporate time have been proposed for conservation prioritization. However, we found that such measures differ qualitatively in how well they capture the total amount of evolution (termed phylogenetic diversity, or PD) represented by a set of species. We used simulation and simple graph theory to explore this relationship with reference to phylogenetic tree shape. Overall, the distinctiveness measures capture more PD on more unbalanced trees and on trees with many splits near the present. The rank order of performance was robust across tree shapes, with apportioning measures performing best and node-based measures performing worst. A sample of 50 ultrametric trees from the literature showed the same patterns. Taken together, this suggests that distinctiveness metrics may be a useful addition to other measures of value for conservation prioritization of species. The simplest measure, the age of a species, performed surprisingly well, suggesting that new measures that focus on tree shape near the tips may provide a transparent alternative to more complicated full-tree approaches.     

Studying Food Reward and Motivation in Humans

A key challenge in studying reward processing in humans is to go beyond subjective self-report measures and quantify different aspects of reward such as hedonics, motivation, and goal value in more objective ways. This is particularly relevant for the understanding of overeating and obesity as well as their potential treatments. In this paper are described a set of measures of food-related motivation using handgrip force as a motivational measure. These methods can be used to examine changes in food related motivation with metabolic (satiety) and pharmacological manipulations and can be used to evaluate interventions targeted at overeating and obesity. However to understand food-related decision making in the complex food environment it is essential to be able to ascertain the reward goal values that guide the decisions and behavioral choices that people make. These values are hidden but it is possible to ascertain them more objectively using metrics such as the willingness to pay and a method for this is described. Both these sets of methods provide quantitative measures of motivation and goal value that can be compared within and between individuals.     

An algorithmic and information-theoretic approach to multimetric index construction

The use of multimetric indices (MMIs), such as the widely used index of biological integrity (IBI), to measure, track, summarize and infer the overall impact of human disturbance on biological communities has been steadily growing in recent years. Initially, MMIs were developed for aquatic communities using pre-selected biological metrics as indicators of system integrity. As interest in these bioassessment tools has grown, so have the types of biological systems to which they are applied. For many ecosystem types the appropriate biological metrics to use as measures of biological integrity are not known a priori. As a result, a variety of ad hoc protocols for selecting metrics empirically has developed. However, the assumptions made by proposed protocols have not be explicitly described or justified, causing many investigators to call for a clear, repeatable methodology for developing empirically derived metrics and indices that can be applied to any biological system. An issue of particular importance that has not been sufficiently addressed is the way that individual metrics combine to produce an MMI that is a sensitive composite indicator of human disturbance. In this paper, we present and demonstrate an algorithm for constructing MMIs given a set of candidate metrics and a measure of human disturbance. The algorithm uses each metric to inform a candidate MMI, and then uses information-theoretic principles to select MMIs that capture the information in the multidimensional system response from among possible MMIs. Such an approach can be used to create purely empirical (data-based) MMIs or can, optionally, be influenced by expert opinion or biological theory through the use of a weighting vector to create value-weighted MMIs. We demonstrate the algorithm with simulated data to demonstrate the predictive capacity of the final MMIs and with real data from wetlands from Acadia and Rocky Mountain National Parks. For the Acadia wetland data, the algorithm identified 4 metrics that combined to produce a −0.88 correlation with the human disturbance index. When compared to other methods, we find this algorithmic approach resulted in MMIs that were more predictive and comprise fewer metrics.     

Validation of Network Communicability Metrics for the Analysis of Brain Structural Networks

Computational network analysis provides new methods to analyze the brain''s structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.     

The importance of nonlinear tissue modelling in finite element simulations of infant head impacts

Despite recent efforts on the development of finite element (FE) head models of infants, a model capable of capturing head responses under various impact scenarios has not been reported. This is hypothesized partially attributed to the use of simplified linear elastic models for soft tissues of suture, scalp and dura. Orthotropic elastic constants are yet to be determined to incorporate the direction-specific material properties of infant cranial bone due to grain fibres radiating from the ossification centres. We report here on our efforts in advancing the above-mentioned aspects in material modelling in infant head and further incorporate them into subject-specific FE head models of a newborn, 5- and 9-month-old infant. Each model is subjected to five impact tests (forehead, occiput, vertex, right and left parietal impacts) and two compression tests. The predicted global head impact responses of the acceleration–time impact curves and the force–deflection compression curves for different age groups agree well with the experimental data reported in the literature. In particular, the newly developed Ogden hyperelastic model for suture, together with the nonlinear modelling of scalp and dura mater, enables the models to achieve more realistic impact performance compared with linear elastic models. The proposed approach for obtaining age-dependent skull bone orthotropic material constants counts both an increase in stiffness and decrease in anisotropy in the skull bone—two essential biological growth parameters during early infancy. The profound deformation of infant head causes a large stretch at the interfaces between the skull bones and the suture, suggesting that infant skull fractures are likely to initiate from the interfaces; the impact angle has a profound influence on global head impact responses and the skull injury metrics for certain impact locations, especially true for a parietal impact.     

Nonlinear poisson equation for heterogeneous media

The Poisson equation is a widely accepted model for electrostatic analysis. However, the Poisson equation is derived based on electric polarizations in a linear, isotropic, and homogeneous dielectric medium. This article introduces a nonlinear Poisson equation to take into consideration of hyperpolarization effects due to intensive charges and possible nonlinear, anisotropic, and heterogeneous media. Variational principle is utilized to derive the nonlinear Poisson model from an electrostatic energy functional. To apply the proposed nonlinear Poisson equation for the solvation analysis, we also construct a nonpolar solvation energy functional based on the nonlinear Poisson equation by using the geometric measure theory. At a fixed temperature, the proposed nonlinear Poisson theory is extensively validated by the electrostatic analysis of the Kirkwood model and a set of 20 proteins, and the solvation analysis of a set of 17 small molecules whose experimental measurements are also available for a comparison. Moreover, the nonlinear Poisson equation is further applied to the solvation analysis of 21 compounds at different temperatures. Numerical results are compared to theoretical prediction, experimental measurements, and those obtained from other theoretical methods in the literature. A good agreement between our results and experimental data as well as theoretical results suggests that the proposed nonlinear Poisson model is a potentially useful model for electrostatic analysis involving hyperpolarization effects.     

一种统计复杂性测度及在心率变异信号分析中的应用

统计复杂性率量方法是作为结构或关联的一般性指示被提出来的。最近,Ｌｏｐｅｚ－Ｒｕｉｚ等提出一种称为ＣＬＭＣ的统计复杂性测度,满足在秩序和随机两个极端情况下测度为０的边界条件。Ｄａｖｉｄ详细研究了ＣＬＭＣ的特性,发现它既不是一个热力学集中变量也不是一个热力学扩张变量,并提出一种满足热力学扩张特性的补偿形式,但最后证明ＣＬＭＣ只是熵密度的普通解,不能作为结构测度。因此统计复杂性度量不仅应满足有序－随机     

An Analytical Flow Injection System to Measure Glutamate in Microdialysis Samples Based on an Enzymatic Reaction and Electrochemical Detection

Glutamate (Glu) is the main excitatory neurotransmitter in the brain for which several methods have been developed to measure this compound in extracellular brain fluids. Most of these techniques are based on coupling microdialysis to HPLC and they have a resolution time of about 10 min. Here, we present a different approach to measure Glu with a resolution of about 1 min per microdialysis sample, enabling a better relationship to be established between EEG activity and biochemical changes. This new setup was used to determine the time delay between the tip of the microdialysis probe and the site of sample collection, and was accurate to within seconds. Indeed, the measurement of Glu concentrations was linear. Administration of 4-aminopyridine was used to provoke seizure convulsions and under these conditions, biochemical changes and EEG activity were evaluated. These experimental data support the key role of Glu in the initiation of a seizure convulsion. Special issue article in honor of Dr. Ricardo Tapia.     

Condition indices for conservation: new uses for evolving tools

Biologists have developed a wide range of morphological, biochemicaland physiological metrics to assess the health and, in particular,the energetic status of individual animals. These metrics originatedto quantify aspects of human health, but have also proven usefulto address questions in life history, ecology and resource managementof game and commercial animals. We review the application ofcondition indices (CI) for conservation studies and focus onmeasures that quantify fat reserves, known to be critical forenergetically challenging activities such as migration, reproductionand survival during periods of scarcity. Standard methods scorefat content, or rely on a ratio of body mass rationalized bysome measure of size, usually a linear dimension such as winglength or total body length. Higher numerical values of theseindices are interpreted to mean an animal has greater energyreserves. Such CIs can provide predictive information abouthabitat quality and reproductive output, which in turn can helpmanagers with conservation assessments and policies. We reviewthe issues about the methods and metrics of measurement anddescribe the linkage of CIs to measures of body shape. Debatesin the literature about the best statistical methods to usein computing and comparing CIs remain unresolved. Next, we commenton the diversity of methods used to measure body compositionand the diversity of physiological models that compute bodycomposition and CIs. The underlying physiological regulatorysystems that govern the allocation of energy and nutrients amongcompartments and processes within the body are poorly understood,especially for field situations, and await basic data from additionallaboratory studies and advanced measurement systems includingtelemetry. For now, standard physiological CIs can provide supportingevidence and mechanistic linkages for population studies thathave traditionally been the focus of conservation biology. Physiologistscan provide guidance for the field application of conditionsindices with validation studies and development of new instruments.     

Multi-criteria decision analysis to select metrics for design and monitoring of sustainable ecosystem restorations

The selection of metrics for ecosystem restoration programs is critical for improving the quality and utility of design and monitoring programs, informing adaptive management actions, and characterizing project success. The metrics selection process, that in practice is left to the subjective judgment of stakeholders, is often complex and should simultaneously take into account monitoring data, environmental models, socio-economic considerations, and stakeholder interests. With limited funding, it is often very difficult to balance the importance of multiple metrics, often competing, intended to measure different environmental, social, and economic aspects of the system. To help restoration planners and practitioners develop the most useful and informative design and monitoring programs, we propose the use of multi-criteria decision analysis (MCDA) methods, broadly defined, to select optimal ecosystem restoration metric sets. In this paper, we apply and compare two MCDA methods, multi-attribute utility theory (MAUT), and probabilistic multi-criteria acceptability analysis (ProMAA), for a hypothetical river restoration case study involving multiple stakeholders with competing interests. Overall, the MCDA results in a systematic, quantitative, and transparent evaluation and comparison of potential metrics that provides planners and practitioners with a clear basis for selecting the optimal set of metrics to evaluate restoration alternatives and to inform restoration design and monitoring. In our case study, the two MCDA methods provide comparable results in terms of selected metrics. However, because ProMAA can consider probability distributions for weights and utility values of metrics for each criterion, it is most likely the best option for projects with highly uncertain data and significant stakeholder involvement. Despite the increase in complexity in the metrics selection process, MCDA improves upon the current, commonly-used ad-hoc decision practice based on consultations with stakeholders by applying and presenting quantitative aggregation of data and judgment, thereby increasing the effectiveness of environmental design and monitoring and the transparency of decision making in restoration projects.     

Measuring temporal variability in residential magnetic field exposures

Considerable interest has developed during the past ten years regarding the hypothesis that living organisms may respond to temporal variability in ELF magnetic fields to which they are exposed. Consequently, methods to measure various aspects of temporal variability are of interest. In this paper, five measures of temporal variability were examined: Arithmetic means (D(mean)) and rms values (D(rms)) of the first differences (i.e., absolute value of the difference between consecutive measurements) of magnetic field recordings; "standardized" forms of D(rms), denoted RCMS, obtained by dividing D(rms) by the standard deviations of the magnetic field data; and mean (F(mean)) and rms (F(rms)) values of fractional first differences. Theoretical investigations showed that D(mean) and D(rms) are virtually unaffected by long-term systematic trends (changes) in exposure. These measures thus provide rather specific measures of short-term temporal variability. This was also true to a lesser extent for F(mean) and F(rms). In contrast, the RCMS metric was affected by both short-term and long-term exposure variabilities. The metrics were also investigated using a data set consisting of twice-repeated two-calendar-day recordings of bedroom magnetic fields and personal exposures of 203 women residing in the western portion of Washington State. The predominant source of short-term temporal variability in magnetic field exposures arose from the movement of subjects through spatially varying magnetic fields. Spearman correlations between TWA bedroom magnetic fields or TWA personal exposures and five measures of temporal variability were relatively low. Weak to moderate levels of correlation were observed between temporal variability measured during two different sessions separated in time by 3 or 6 months. We conclude that first difference and fractional difference metrics provide specific and fairly independent measures of short-term temporal variability. The RCMS metric does not provide an easily interpreted measure of short-term or long-term temporal variability. This last result raises uncertainties about the interpretation of published studies that use the RCMS metric.     

Joint Modelling of Repeated Measures and Survival Time Data

In many clinical trials both repeated measures data and event history data are simultaneously observed from the same subject. These two types of responses are usually correlated, because they are from the same subject. In this article, we propose a joint model for the combined analysis of repeated measures data and event history data in the framework of hierarchical generalized linear models. The correlation between repeated measures and event time is modelled by introducing a shared random effect. The model parameters are estimated using the hierarchical‐likelihood approach. The proposed model is illustrated using a real data set for the renal transplant patients.     

Surrogate data analysis of sleep electroencephalograms reveals evidence for nonlinearity

 We tested the hypothesis of whether sleep electroencephalographic (EEG) signals of different time windows (164 s, 82 s, 41 s and 20.5 s) are in accordance with linear stochastic models. For this purpose we analyzed the all-night sleep electroencephalogram of a healthy subject and corresponding Gaussian-rescaled phase randomized surrogates with a battery of five nonlinear measures. The following nonlinear measures were implemented: largest Lyapunov exponent L1, correlation dimension D2, and the Green-Savit measures δ2, δ4 and δ6. The hypothesis of linear stochastic data was rejected with high statistical significance. L1 and D2 yielded the most pronounced effects, while the Green-Savit measures were only partially successful in differentiating EEG epochs from the phase randomized surrogates. For L1 and D2 the efficiency of distinguishing EEG signals from linear stochastic data decreased with shortening of the time window. Altogether, our results indicate that EEG signals exhibit nonlinear elements and cannot completely be described by linear stochastic models. Received: 21 December 1995/Accepted in revised form: 19 March 1996