Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL _max ), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion’s sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL _max , a measure of phase (φ _max ) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α=0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL _max synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy.     

Quadratic Binary Programming and Dynamical System Approach to Determine the Predictability of Epileptic Seizures

Epilepsy is one of the most common disorders of the nervous system. The progressive entrainment between an epileptogenic focus and normal brain areas results to transitions of the brain from chaotic to less chaotic spatiotemporal states, the epileptic seizures. The entrainment between two brain sites can be quantified by the T-index from the measures of chaos (e.g., Lyapunov exponents) of the electrical activity (EEG) of the brain. By applying the optimization theory, in particular quadratic zero-one programming, we were able to select the most entrained brain sites 10 minutes before seizures and subsequently follow their entrainment over 2 hours before seizures. In five patients with 3–24 seizures, we found that over 90% of the seizures are predictable by the optimal selection of electrode sites. This procedure, which is applied to epilepsy research for the first time, shows the possibility of prediction of epileptic seizures well in advance (19.8 to 42.9 minutes) of their occurrence.     

Novel quadratic programming approach for time series clustering with biomedical application

Fundamental problems in data mining mainly involve discrete decisions based on numerical analyses of data (e.g., class assignment, feature selection, data categorization, identifying outlier samples). These decision-making problems in data mining are combinatorial in nature and can naturally be formulated as discrete optimization problems. One of the most widely studied problems in data mining is clustering. In this paper, we propose a new optimization model for hierarchical clustering based on quadratic programming and later show that this model is compact and scalable. Application of this clustering technique in epilepsy, the second most common brain disorder, is a case point in this study. In our empirical study, we will apply the proposed clustering technique to treatment problems in epilepsy through the brain dynamics analysis of electroencephalogram (EEG) recordings. This study is a proof of concept of our hypothesis that epileptic brains tend to be more synchronized (clustered) during the period before a seizure than a normal period. The results of this study suggest that data mining research might be able to revolutionize current diagnosis and treatment of epilepsy as well as give a greater understanding of brain functions (and other complex systems) from a system perspective. This work was partially supported by the NSF grant CCF 0546574 and Rutgers Research Council grant-202018.     

知识作业难度计量的脑电实验研究

在解析难度与知识作业难度操作定义的基础上,通过脑电实验证实:脑电功率谱与反应时两项实验效应指标可客现反映知识作业难度的变化趋势,反应时数据揭示主观赋值数量关系服从stevens定律,并可作为修订主观判定量表的精确效标.同时,本文实验结果支持认知科学的实验研究方法与测试手段在经济与管理问题的研究中具有广泛应用前景的观点.     

Differentiating transformational and non-transformational leaders on the basis of neurological imaging

The purpose of this study was to evaluate the viability of using neurological imaging to classify transformational leaders, versus non-transformational leaders, as identified through existing psychometric methods. Specifically, power spectral analysis measures based on electroencephalograms (EEG) were used to develop and validate a discriminant function that can classify individuals according to their transformational leadership behavior. Resting, eyes closed EEG was recorded from 19 scalp locations for 200 civilian and military leaders. We also assessed follower or peer perceptions of transformational leadership through the use of the Multifactor Leadership Questionnaire (MLQ). Our discriminant analysis, which involved a two-step, neural variable reduction and selection process, was 92.5% accurate in its classification of leaders. Patterns in the spectral measures of the brain of leaders, including activity and network dynamic metrics, are discussed as potential correlates of transformational leadership behavior. The current work provides a better understanding of the latent and dynamic neurological mechanisms that may underpin the transformational leadership qualities of individuals.     

When Financial Intermediaries are Corporate Owners: An Agency Model of Institutional Ownership

Increasingly, the equity investments of individual investors are being channeled through financial institutions. This article posits that the role of institutional owners as financial intermediaries, and the resulting complexity that institutions bring to ownership, distinguish institutional ownership from individual ownership. I develop a model of institutional ownership, referred to as the nexus agency model (NAM), which reflects this complexity. The model provides a framework for identifying the potential additional agency costs to beneficial owners that are associated with owning via financial institutions. The degree to which owning via institutions benefits individual owners depends on the adequacy of the legal and regulatory environment and governance mechanisms in protecting individual owners' interests. The applicability of the nexus model to different institutional owner types is then demonstrated in a discussion of U.S. public and private pension plans and mutual funds, leading to the generation of a NAM-based research agenda for each type and across the types. The article ends with discussion of the model's applicability to non-U.S. institutional environments.     

Controlling epileptic seizures in a neural mass model

In an effort to understand basic functional mechanisms that can produce epileptic seizures, we introduce some key features in a model of coupled neural populations that enable the generation of seizure-like events and similar dynamics with the ones observed during the route of the epileptic brain towards real seizures. In this model, modified from David and Friston’s neural mass model, an internal feedback mechanism is incorporated to maintain synchronous behavior within normal levels despite elevated coupling. Normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to hypersynchronization and the appearance of seizure-like high amplitude oscillations. Feedback decoupling is introduced as a robust seizure control strategy. An external feedback decoupling controller is introduced to maintain normal synchronous behavior. The results from the analysis in this model have an interesting physical interpretation and specific implications for the treatment of epileptic seizures. The proposed model and control scheme are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology.     

Complexity Absorption and Performance: A Structural Analysis of Acute-Care Hospitals

This study assesses the degree to which a complexity absorption response enhances performance in an industry increasingly described as highly complex: acute-care hospitals. Business-level strategy, organizational structure, and analytical comprehensiveness are viewed as mechanisms through which executives can increase internal complexity. A case is made that hospitals are well served to pursue a hybrid (or combination) strategy, employ an organic structure, and engage in a high degree of information acquisition and use. In keeping with a complexity absorption view, results indicate that these elements generally exhibit stronger associations with performance as dynamism increases.     

不确定环境下基于Bertrand竞争的企业创新模式比较研究

将技术不确定性与市场不确定性所组成的不确定性概念引入创新过程;在Bertrand竞争市场中,运用战略实物期权、动态博弈等工具对企业创新过程建立了一个两阶段动态博弈模型,得出了创新企业的价值函数,考察了不确定性参数对投资门槛值与破产临界值的影响;最后在技术投入溢出与互补度、创新产品互补度以及合作诚信度等四个参数的综合交互作用下,分析了独立研发、竞争RJV、合作RJV三种典型模式在均衡研发产出、公司利润、行业利润及社会福利方面的差异,得出了模式动态选择的适用条件。已有文献多在静态环境下考虑技术投入溢出参数在企业创新模式选择中的作用,本文研究旨在弥补此不足,同时为企业在不确定环境下进行适宜的创新组织选择提供决策参考。     

企业生命周期的系统动力学建模与仿真

从系统动力学角度研究企业生命周期变化中不同因素的影响.分析企业成长过程和主要影响之后,建立能够反映企业生命周期变化的因果关系图和动力学模型.用C#语言实现了系统动力学模型,验证了模型的有效性,讨论了模型的应用.仿真结果表明本文方法能够有效模拟企业生命周期的演化过程,并且能够为管理者进行企业组织管理与活力控制提供决策支持.     

Liability of foreignness as a barrier to knowledge spillovers: Lost in translation?

Multinational corporations (MNC) search increasingly for lead market knowledge and technological expertise around the globe. We investigate whether their subsidiaries gain access to these valuable sources of host country knowledge to the same degree as domestic rivals. We develop a theoretical framework for “why” and “how” a lack of embeddedness and legitimacy (liability of foreignness) may translate into additional obstacles for foreign subsidiaries. We test these hypotheses empirically using a broad dataset of more than 1100 firms in Germany. We find that MNCs can compete on an equal footing with host country competitors when it comes to generating impulses for innovations from universities. They are significantly challenged by liabilities of foreignness, though, when host country customers are involved. The disadvantages are especially pronounced when the host country industry is at the technological forefront. We suggest that the disadvantages arising from liability of foreignness in the host country are especially relevant when promising lead customers have to be identified and their tacit and often unarticulated impulses have to be transferred, understood and prioritized. Management recommendations are developed on the basis of these results.     

Partner selection in agile supply chains: a fuzzy intelligent approach

Partner selection is a fundamental issue in supply chain management as it contributes significantly to overall supply chain performance. However, such decision-making is problematic due to the need to consider both tangible and intangible factors, which cause vagueness, ambiguity and complexity. This paper proposes a new fuzzy intelligent approach for partner selection in agile supply chains by using fuzzy set theory in combination with radial basis function artificial neural network. Using these two approaches in combination enables the model to classify potential partners in the qualification phase of partner selection efficiently and effectively using very large amounts of both qualitative and quantitative data. The paper includes a worked empirical application of the model with data from 84 representative companies within the Chinese electrical components and equipment industry, to demonstrate its suitability for helping organisational decision-makers in partner selection.     

Optimal Stopping With Multiple Priors

We develop a theory of optimal stopping under Knightian uncertainty. A suitable martingale theory for multiple priors is derived that extends the classical dynamic programming or Snell envelope approach to multiple priors. We relate the multiple prior theory to the classical setup via a minimax theorem. In a multiple prior version of the classical model of independent and identically distributed random variables, we discuss several examples from microeconomics, operation research, and finance. For monotone payoffs, the worst‐case prior can be identified quite easily with the help of stochastic dominance arguments. For more complex payoff structures like barrier options, model ambiguity leads to stochastic changes in the worst‐case beliefs.     

A fuzzy AHP application in government-sponsored R&D project selection

Due to the funding scale and complexity of technology, the selection of government sponsored technology development projects can be viewed as a multiple-attribute decision that is normally made by a review committee with experts from academia, industry, and the government. In this paper, we present a fuzzy analytic hierarchy process method and utilize crisp judgment matrix to evaluate subjective expert judgments made by the technical committee of the Industrial Technology Development Program in Taiwan. Our results indicate that the scientific and technological merit is the most important evaluation criterion considered in overall technical committees. We demonstrate how the relative importance of the evaluation criteria changes under various risk environments via simulation.     

The Relationship Between Cognitive Maps,Industry Complexity and Strategies Implemented: The Case of the Carpi Textile-Clothing Industrial System

This paper discusses the relationship between the subjective organisation and the perceived competitive structure of an industry based on an empirical study of the Carpi textile-clothing district, which partially replicates a previous study of the Scottish knitwear industry conducted by other authors (Porac et al., 1995). The initial hypothesis to be tested is that an industry may be broken down into a number of subsets of firms within which competition is perceived as being fiercer as compared with firms in different subsets. These subsets, or groups, correspond to the groups or types of firms into which decision-makers -- managers and entrepreneurial business owners alike -- perceive the industry as being divided.The results of the study are then compared to the empirical evidence from the research conducted by other authors. This comparison provides some interesting elements for investigating the relationships between industry complexity, cognitive maps and strategies implemented by the firms belonging to it. After suggesting a definition of complexity of an industry as the number of strategies implemented in it by competitors, we discuss the hypothesis that key-actors in the firms develop a mental picture of their environment that tends to become less comprehensive and accurate as the degree of complexity of the environment itself increases.     

Testing the Interaction Effects of Task Complexity in Computer Training Using the Social Cognitive Model

Using a Modified Social Cognitive Theory framework, this study examines the behavior modeling and lecture‐based training approaches to computer training. It extends the existing Social Cognitive Model for computer training by adding the task complexity construct to training method, prior performance, computer self‐efficacy, outcome expectations, and performance. A sample of 249 students from a large state university served as participants in a laboratory experiment that was conducted to determine the task complexity*training method and task complexity* self‐efficacy interaction effects on performance. Structural equation modeling with interaction effects was used to analyze the data. The results show that behavior modeling outperforms lecture‐based training in a measure of final performance when task complexity is high. Further, it is found that computer self‐efficacy has a greater positive effect on performance when task complexity is high than when task complexity is low. Prior performance is also found to be an important variable in the model.     

基于文本先验信息的贷款信用风险评估模型

本文利用现有信用风险研究所蕴含的信息，构建了一个新的贷款信用风险评估模型—PIPL。该模型先通过文本挖掘技术对现有研究进行文本信息搜集，得到关于信用风险指标的先验词频以体现指标的重要性，再通过惩罚变量选择法将先验词频量化为先验因变量，最后基于先验因变量和原始数据构建模型，并通过弹性网筛选风险指标。模拟分析显示PIPL模型能自动识别先验信息的质量，当先验信息质量高时，它赋予先验信息较高的权重，从而改进了指标选择和分类效果，当先验信息质量较低时，它能自动降低先验因变量在模型中的权重，表现出稳健的分类效果。实证分析从知网挖掘123篇文献获取文本信息，并以P2P网贷数据为例，发现PIPL模型通过先验信息提高了分类的准确性，并表现出了良好的稳健性。     

组织结构的立体多核网络模型

对立体多核网络模型进行了新的阐释,将原来的概念模型向操作层次推进了一步,该模 型是将组织作为一个由相互作用的元素构成的、动态的复杂系统来考虑的,它高度概括了组织 结构的包括关系多样性、要素多样性、动态特性和层次交错性等方面在内的复杂性和网络中节 点的异质性和差异性,是为研究复杂组织而进行仿真设计的前期建模工作. 从要素的多样性和 关系的多样性出发,初步勾画出立体多核网络的图论描述框架,对以后的仿真设计和计算机编 程提出了粗略的设想. 最后用一个学术群体的网络研究案例对立体多核网络模型在组织行为 宏观处理方面的优点进行了示例性的说明.     

How Does Government Size and Structure Respond Empirically to Changes in its Organisational Environment?

Does government size and structure adapt to changes in government’s organisational environment (particularly to uncertainty and complexity) as predicted by organisational theory? We find – using a range of statistical analyses – support for each of the major theories of organisation adaptation (the contingency-based view, resource-based view, and the rational choice view). We find that both government size and structure change – holding other factors constant – for changes in the uncertainty and complexity of governments’ organisational environments. We find seven clusters of governments which adapt their organisational sizes differently in response to changes in the uncertainty and complexity of their organisational environments – and four clusters of governments with differing preferences for the way they adapt governmental structures. We also use the available data to divide governments according to the extent to which they adapt their organisational size and structure reactively (after changes occur in their organisational environment), contemporaneously or strategically (before these changes in their organisational environment occur).     

Simulating alternative organizational structures of production systems

Changes in international competition lead to changes of the requirements on production enterprises. The introduction of new production technologies does not seem to be exclusively an adequate reaction to the increasing problems. Therefore tools of computer-integrated production (CIM) and new organizational concepts have to be conceived. Because these organizational and technical means are linked on the one hand with severe changes in the production system and, on the other hand, with rather higher economic risks in their realization, the effects have to be estimated in advance in the planning stage. Consideration of dynamical system behaviour plays an important part because the main goal is the improvement of order processing. This paper presents a simulation program, which can be used during the design phase of the organizational structure of production systems as a powerful tool for predicting the effects of new computer-aided tools and structures.