Measure Of Interdependence Research Articles

Epileptogenic zone localization and seizure control in coupled neural mass models.

Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals. In this study, EZ localization is changed into a driver identification problem, and a nonlinear interdependence measure, the weighted rank interdependence, is proposed and used as a driver indicator because it can detect coupling information, especially directionality, from EEG signals. A proportional integral derivative (PID) controller is then explored, using simulations, to establish its suitability for seizure control. The seizure control we propose rests on identifying the EZ using nonlinear interdependence measures of directed functional connectivity. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters can adjust the sensitivity and completeness of the weighted rank interdependence for different applications, and their effect is discussed in the context of neural mass models. Simulation results demonstrate that use of the weighted rank interdependence for EZ identification can be applied to different EZ types, and the approach achieves an overall identification rate of 98.84 % for several EZ types. Simulations also indicate that PID control can effectively regulate synchronization between neural masses.

Credit Risk Interconnectedness: What Does the Market Really Know?

We analyze the relation between market-based credit risk interconnectedness among banks during the crisis and the associated balance sheet linkages via funding and securities holdings. For identification, we use a proprietary dataset that has the funding positions of banks at the bank-to-bank level for 2006-13 in conjunction with investments of banks at the security level and the credit register from Germany. We find asymmetries both cross-sectionally and over time: when banks face difficulties to raise funding, the interbank lending affects market-based bank interconnectedness. Moreover, banks with investments in securities related to troubled classes have a higher credit risk interconnectedness. Overall, our results suggest that market-based measures of interdependence can serve well as risk monitoring tools in the absence of disaggregated high-frequency bank fundamental data.

Does the Thalamo-Cortical Synchrony Play a Role in Seizure Termination?

The mechanisms underlying seizure termination are still unclear despite their therapeutic importance. We studied thalamo-cortical connectivity and synchrony in human mesial temporal lobe seizures in order to analyze their role in seizure termination. Twenty-two seizures from 10 patients with drug-resistant mesial temporal lobe epilepsy undergoing pre-surgical evaluation were analyzed using intracerebral recordings [stereoelectroencephalography (SEEG)]. We performed a measure of SEEG signal interdependencies (non-linear correlation), to estimate the functional connectivity between thalamus and cortical regions. Then, we derived synchronization indices, namely global, thalamic, mesio-temporal, and thalamo-mesio temporal index at the onset and the end of seizures. In addition, an estimation of thalamic “outputs and inputs” connectivity was proposed. Thalamus was consistently involved in the last phase of all analyzed seizures and thalamic synchronization index was significantly more elevated at the end of seizure than at the onset. The global synchronization index at the end of seizure negatively correlated with seizure duration (p = 0.045) and in the same way the thalamic synchronization index showed an inverse tendency with seizure duration. Six seizures out of twenty-two displayed a particular thalamo-cortical spike-and-wave pattern at the end. They were associated to higher values of all synchronization indices and outputs from thalamus (p = 0.0079). SWP seizures displayed a higher and sustained increase of cortical and thalamo-cortical synchronization with a stronger participation of thalamic outputs. We suggest that thalamo-cortical oscillations might contribute to seizure termination via modulation of cortical synchronization. In the subgroup of SWP seizures, thalamus may exert a control on temporal lobe structures by inducing a stable hypersynchronization that ultimately leads to seizure termination.

Entropy measure for the quantification of upper quantile interdependence in multivariate distributions

We study the applicability of a measure of interdependence among the components of a random vector along the main diagonal of the vector’s copula, i.e. along the line u 1 = ⋯ = u J , for ( u 1 , … , u J ) ∈ [ 0 , 1 ] J . Our measure is related to the Shannon entropy of a discrete random variable, and it is a measure of local divergence between the data empirical copula and the independent copula. Hence we call it a “local dependence entropy index”, which can be interpreted as an effective number of independent variables. It is invariant with respect to marginal non-decreasing transformations and can be used in arbitrary dimensions. We show the applicability of our entropy index by an example with real data of 4 stock prices of the DAX index. In case the random vector possesses an extreme value copula, the index is shown to have as limit the extremal coefficient, which can be interpreted as the effective number of asymptotically independent components in the vector.

Networks: On the relation of bi- and multivariate measures

A reliable inference of networks from observations of the nodes’ dynamics is a major challenge in physics. Interdependence measures such as a the correlation coefficient or more advanced methods based on, e.g., analytic phases of signals are employed. For several of these interdependence measures, multivariate counterparts exist that promise to enable distinguishing direct and indirect connections. Here, we demonstrate analytically how bivariate measures relate to the respective multivariate ones; this knowledge will in turn be used to demonstrate the implications of thresholded bivariate measures for network inference. Particularly, we show, that random networks are falsely identified as small-world networks if observations thereof are treated by bivariate methods. We will employ the correlation coefficient as an example for such an interdependence measure. The results can be readily transferred to all interdependence measures partializing for information of thirds in their multivariate counterparts.

Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors

This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t Markov switching (MS) model and the multiple-conditional value-at-risk (CoVaR) (conditional expected shortfall (CoES)) risk measures introduced in Bernardi et al. (2013), accounting for both the stylized facts of financial data and the contemporaneous multiple joint distress events. The Shapley value methodology is then applied to compose the puzzle of individual risk attributions, providing a synthetic measure of tail interdependence. Our empirical investigation finds that banks appear to contribute more to the tail risk evolution of all of the remaining sectors, followed by the financial services and the insurance sectors, showing that the insurance sector significantly contributes as well to the overall risk. We also find that the role of each sector in contributing to other sectors’ distress evolves over time according to the current predominant financial condition, implying different interdependence strength.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks.

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R, and the bivariate recurrence network measure, the average cross-clustering coefficient C(cross), can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L, and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient C(cross) is independent of the outcome of the randomness test based on the average clustering coefficient C. Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Interdependence and Social Identity in Youth Sport Teams

The degree to which team members believe that they rely on one another to perform successfully and achieve collective outcomes may relate to perceptions about the extent that they integrate the group within their own identity. This study examined the relationship between interdependence and social identity among 422 high school team sport athletes. Youth completed measures of task and outcome interdependence, as well as social identity. Multilevel analyses revealed that higher perceptions of outcome interdependence at an individual and team level predicted greater social identity. Results highlight the role of outcome interdependence on athletes’ perceptions of social identity in sport teams.

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

This review provides a summary of methods originated in (non-equilibrium) statistical mechanics and information theory, which have recently found successful applications to quantitatively studying complexity in various components of the complex system Earth. Specifically, we discuss two classes of methods: (i) entropies of different kinds (e.g., on the one hand classical Shannon and R´enyi entropies, as well as non-extensive Tsallis entropy based on symbolic dynamics techniques and, on the other hand, approximate entropy, sample entropy and fuzzy entropy); and (ii) measures of statistical interdependence and causality (e.g., mutual information and generalizations thereof, transfer entropy, momentary information transfer). We review a number of applications and case studies utilizing the above-mentioned methodological approaches for studying contemporary problems in some exemplary fields of the Earth sciences, highlighting the potentials of different techniques.

Simultaneous Electroencephalographic and Functional Magnetic Resonance Imaging Indicate Impaired Cortical Top–Down Processing in Association with Anesthetic-induced Unconsciousness

In imaging functional connectivity (FC) analyses of the resting brain, alterations of FC during unconsciousness have been reported. These results are in accordance with recent electroencephalographic studies observing impaired top-down processing during anesthesia. In this study, simultaneous records of functional magnetic resonance imaging (fMRI) and electroencephalogram were performed to investigate the causality of neural mechanisms during propofol-induced loss of consciousness by correlating FC in fMRI and directional connectivity (DC) in electroencephalogram. Resting-state 63-channel electroencephalogram and blood oxygen level-dependent 3-Tesla fMRI of 15 healthy subjects were simultaneously registered during consciousness and propofol-induced loss of consciousness. To indicate DC, electroencephalographic symbolic transfer entropy was applied as a nonlinear measure of mutual interdependencies between underlying physiological processes. The relationship between FC of resting-state networks of the brain (z values) and DC was analyzed by a partial correlation. Independent component analyses of resting-state fMRI showed decreased FC in frontoparietal default networks during unconsciousness, whereas FC in primary sensory networks increased. DC indicated a decline in frontal-parietal (area under the receiver characteristic curve, 0.92; 95% CI, 0.68-1.00) and frontooccipital (0.82; 0.53-1.00) feedback DC (P<0.05 corrected). The changes of FC in the anterior default network correlated with the changes of DC in frontal-parietal (rpartial=+0.62; P=0.030) and frontal-occipital (+0.63; 0.048) electroencephalographic electrodes (P<0.05 corrected). The simultaneous propofol-induced suppression of frontal feedback connectivity in the electroencephalogram and of frontoparietal FC in the fMRI indicates a fundamental role of top-down processing for consciousness.

Oxytocin effects on neural correlates of self-referential processing

Oxytocin (OT) influences how humans process information about others. Whether OT affects the processing of information about oneself remains unknown. Using a double-blind, placebo-controlled within-subject design, we recorded event-related potentials (ERPs) from adults during trait judgments about oneself and a celebrity and during judgments on word valence, after intranasal OT or placebo administration. We found that OT vs. placebo treatment reduced the differential amplitudes of a fronto-central positivity at 220–280ms (P2) during self- vs. valence-judgments. OT vs. placebo treatment tended to reduce the differential amplitude of a late positive potential at 520–1000ms (LPP) during self-judgments but to increase the differential LPP amplitude during other-judgments. OT effects on the differential P2 and LPP amplitudes to self- vs. celebrity-judgments were positively correlated with a measure of interdependence of self-construals. Thus OT modulates the neural correlates of self-referential processing and this effect varies as a function of interdependence.

Measuring Global Economic Interdependence: A Hierarchical Network Approach

AbstractThis paper investigates the business cycle co‐movement across countries and regions since 1950 as a measure for quantifying the economic interdependence in the ongoing globalisation process. Our methodological approach is based on analysis of a correlation matrix and the networks it contains. Such an approach summarises the interaction and interdependence of all elements, and it represents a more accurate measure of the global interdependence involved in an economic system. Our results show (1) the dynamics of interdependence has been driven more by synchronisation in regional growth patterns than by the synchronisation of the world economy, and (2) world crisis periods dramatically increase the global co‐movement in the world economy.

How to Measure Socioemotional Ties in Workgroups? Validation of Workgroup Socioaffective Interdependence Scale

The main purpose of this study is the validation of the Workgroup Socioaffective Interdependence Scale – WSAIS developed by the authors in a preliminary study. The authors aim is to confirm the multidimensional structure of the measure and to analyse its psychometric properties in a sample of 488 employees from 92 teams. To test construct validity a confirmatory factor analysis was conducted, and social network density of socioemotional ties were calculated to assess convergent and discriminant validity. The findings confirm the underlying factor structure of the scale and provide support for the adequacy of the instrument as a team multidimensional measure of socioaffective interdependence.

Network analysis of team communication in a busy emergency department

BackgroundThe Emergency Department (ED) is consistently described as a high-risk environment for patients and clinicians that demands colleagues quickly work together as a cohesive group. Communication between nurses, physicians, and other ED clinicians is complex and difficult to track. A clear understanding of communications in the ED is lacking, which has a potentially negative impact on the design and effectiveness of interventions to improve communications. We sought to use Social Network Analysis (SNA) to characterize communication between clinicians in the ED.MethodsOver three-months, we surveyed to solicit the communication relationships between clinicians at one urban academic ED across all shifts. We abstracted survey responses into matrices, calculated three standard SNA measures (network density, network centralization, and in-degree centrality), and presented findings stratified by night/day shift and over time.ResultsWe received surveys from 82% of eligible participants and identified wide variation in the magnitude of communication cohesion (density) and concentration of communication between clinicians (centralization) by day/night shift and over time. We also identified variation in in-degree centrality (a measure of power/influence) by day/night shift and over time.ConclusionsWe show that SNA measurement techniques provide a comprehensive view of ED communication patterns. Our use of SNA revealed that frequency of communication as a measure of interdependencies between ED clinicians varies by day/night shift and over time.

Prise en charge des patients atteints de maladies chroniques. Concepts, évaluations et enseignements internationaux

The growing number of people with chronic diseases is a major challenge for health care systems in terms of morbidity, mortality, quality of care and financial impact. A range of organizational concepts for structured chronic disease care have been developed to address this challenge. The purpose of this literature review is to identify the key concepts, results and success factors of recent initiatives in this area. Four main concepts related to the broader notion of integrated care were identified: case management, disease management, the chronic care model and population management. The available evidence suggests that the expected results in terms of health gains and cost reductions have only been partially achieved, but that the quality of care has improved. The study identified several critical success factors for initiatives aimed at improving chronic care: the integration of patient education, the implementation of a patient identification system, provider feedback, primary care performance and financial incentives. Improved care for patients with chronic illness requires a range of interdependent measures. However, it is important to note that initiatives in this area are not usually associated with shortterm savings, but represent an investment for the future.

Institutions and Political Instability in Kenya: Measurement and Tests of Institutional Persistence and Interdependence

Institutions and Political Instability in Kenya: Measurement and Tests of Institutional Persistence and Interdependence

Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients

To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed prior to and independent from our study as part of the epilepsy diagnostics. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control. This allowed us to define two distinct sets of signals: One set of signals recorded from brain areas where the first ictal EEG signal changes were detected as judged by expert visual inspection ("focal signals") and one set of signals recorded from brain areas that were not involved at seizure onset ("nonfocal signals"). We find more rejections for both the randomness and the nonlinear-independence test for focal versus nonfocal signals. In contrast more rejections of the stationarity test are found for nonfocal signals. Furthermore, while for nonfocal signals the rejection of the stationarity test increases the rejection probability of the randomness and nonlinear-independence test substantially, we find a much weaker influence for the focal signals. In consequence, the contrast between the focal and nonfocal signals obtained from the randomness and nonlinear-independence test is further enhanced when we exclude signals for which the stationarity test is rejected. To study the dependence between the randomness and nonlinear-independence test we include only focal signals for which the stationarity test is not rejected. We show that the rejection of these two tests correlates across signals. The rejection of either test is, however, neither necessary nor sufficient for the rejection of the other test. Thus, our results suggest that EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenic brain areas. We provide the data, source code, and detailed results in the public domain.

Framework to study dynamic dependencies in networks of interacting processes

The analysis of dynamic dependencies in complex systems such as the brain helps to understand how emerging properties arise from interactions. Here we propose an information-theoretic framework to analyze the dynamic dependencies in multivariate time-evolving systems. This framework constitutes a fully multivariate extension and unification of previous approaches based on bivariate or conditional mutual information and Granger causality or transfer entropy. We define multi-information measures that allow us to study the global statistical structure of the system as a whole, the total dependence between subsystems, and the temporal statistical structure of each subsystem. We develop a stationary and a nonstationary formulation of the framework. We then examine different decompositions of these multi-information measures. The transfer entropy naturally appears as a term in some of these decompositions. This allows us to examine its properties not as an isolated measure of interdependence but in the context of the complete framework. More generally we use causal graphs to study the specificity and sensitivity of all the measures appearing in these decompositions to different sources of statistical dependence arising from the causal connections between the subsystems. We illustrate that there is no straightforward relation between the strength of specific connections and specific terms in the decompositions. Furthermore, causal and noncausal statistical dependencies are not separable. In particular, the transfer entropy can be nonmonotonic in dependence on the connectivity strength between subsystems and is also sensitive to internal changes of the subsystems, so it should not be interpreted as a measure of connectivity strength. Altogether, in comparison to an analysis based on single isolated measures of interdependence, this framework is more powerful to analyze emergent properties in multivariate systems and to characterize functionally relevant changes in the dynamics.

The myth of decoupling

The decoupling hypothesis is the idea that business cycles in emerging market economies have become more independent from business cycles in advanced economies in recent years. Decoupling essentially amounts to a structural break in the degree of business cycle interdependence between the two groups of economies, and it can be tested as such. We develop an innovative measure of business cycle interdependence based on the Euclidean distance, available at the annual frequency, which allows for a proper test for a structural break in a graphical setup. We also make use of a standard econometric test. Both approaches point to the same conclusion: there has been no decoupling in recent years. If anything, the degree of business cycle interdependence has become stronger.

Autonomic mediation of the interdependence between variability signals of heart rate and blood pressure in the lizard Gallotia galloti

Autonomic nervous system (ANS) involvement in the mediation of the synchronization between beat-to-beat RR interval variability (RRIV) and systolic blood pressure variability (SBPV) signals of the lizard Gallotia galloti (Oudart, 1839) was investigated through linear and nonlinear time series analysis methods in a pharmacological blockade context. The ANS blockers used were atropine, prazosin, and propranolol. The interdependence between the signals was quantified by means of the magnitude-squared coherence (MSC), which measures amplitude and phase linear synchronization; the phase lag index (PLI), which evaluates the phase synchronization; and the index L, which quantifies the generalized linear and nonlinear synchronization. Atropine decreased the PLI in the low-frequency (LF: 0.01–0.05 Hz) range; prazosin decreased the MSC in the medium-frequency (MF: 0.06–0.15 Hz) range; and propranolol did not alter any of the interdependence measures. It is suggested that (i) the cholinoceptor activity mediates the phase cardiovascular synchronization in the LF range; (ii) the α1-adrenoceptor activity mediates the amplitude and phase linear cardiovascular synchronization in the MF range; and (iii) the β-adrenoceptor activity plays no role in mediating any dynamics of cardiovascular synchronization in the studied frequency range. When comparing these results with those in mammals, a lesser overall autonomic involvement in the mediation of the studied cardiovascular interdependences is seen in reptiles.