Science.gov

Sample records for finland granger causality

  1. Granger causality revisited.

    PubMed

    Friston, Karl J; Bastos, André M; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

    2014-11-01

    This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality - providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes - as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling.

  2. Paradoxical Behavior of Granger Causality

    NASA Astrophysics Data System (ADS)

    Witt, Annette; Battaglia, Demian; Gail, Alexander

    2013-03-01

    Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen

  3. Redundant variables and Granger causality

    NASA Astrophysics Data System (ADS)

    Angelini, L.; de Tommaso, M.; Marinazzo, D.; Nitti, L.; Pellicoro, M.; Stramaglia, S.

    2010-03-01

    We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.

  4. Nonlinear connectivity by Granger causality.

    PubMed

    Marinazzo, Daniele; Liao, Wei; Chen, Huafu; Stramaglia, Sebastiano

    2011-09-15

    The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. The state of the art to understand the communication between brain systems are dynamic causal modeling (DCM) and Granger causality. While DCM models nonlinear couplings, Granger causality, which constitutes a major tool to reveal effective connectivity, and is widely used to analyze EEG/MEG data as well as fMRI signals, is usually applied in its linear version. In order to capture nonlinear interactions between even short and noisy time series, a few approaches have been proposed. We review them and focus on a recently proposed flexible approach has been recently proposed, consisting in the kernel version of Granger causality. We show the application of the proposed approach on EEG signals and fMRI data.

  5. Effect of measurement noise on Granger causality

    NASA Astrophysics Data System (ADS)

    Nalatore, Hariharan; N, Sasikumar; Rangarajan, Govindan

    2014-12-01

    Most of the signals recorded in experiments are inevitably contaminated by measurement noise. Hence, it is important to understand the effect of such noise on estimating causal relations between such signals. A primary tool for estimating causality is Granger causality. Granger causality can be computed by modeling the signal using a bivariate autoregressive (AR) process. In this paper, we greatly extend the previous analysis of the effect of noise by considering a bivariate AR process of general order p . From this analysis, we analytically obtain the dependence of Granger causality on various noise-dependent system parameters. In particular, we show that measurement noise can lead to spurious Granger causality and can suppress true Granger causality. These results are verified numerically. Finally, we show how true causality can be recovered numerically using the Kalman expectation maximization algorithm.

  6. Effect of measurement noise on Granger causality.

    PubMed

    Nalatore, Hariharan; Sasikumar, N; Rangarajan, Govindan

    2014-12-01

    Most of the signals recorded in experiments are inevitably contaminated by measurement noise. Hence, it is important to understand the effect of such noise on estimating causal relations between such signals. A primary tool for estimating causality is Granger causality. Granger causality can be computed by modeling the signal using a bivariate autoregressive (AR) process. In this paper, we greatly extend the previous analysis of the effect of noise by considering a bivariate AR process of general order p. From this analysis, we analytically obtain the dependence of Granger causality on various noise-dependent system parameters. In particular, we show that measurement noise can lead to spurious Granger causality and can suppress true Granger causality. These results are verified numerically. Finally, we show how true causality can be recovered numerically using the Kalman expectation maximization algorithm.

  7. Conditional Granger causality and partitioned Granger causality: differences and similarities.

    PubMed

    Malekpour, Sheida; Sethares, William A

    2015-12-01

    Neural information modeling and analysis often requires a measurement of the mutual influence among many signals. A common technique is the conditional Granger causality (cGC) which measures the influence of one time series on another time series in the presence of a third. Geweke has translated this condition into the frequency domain and has explored the mathematical relationships between the time and frequency domain expressions. Chen has observed that in practice, the expressions may return (meaningless) negative numbers, and has proposed an alternative which is based on a partitioned matrix scheme, which we call partitioned Granger causality (pGC). There has been some confusion in the literature about the relationship between cGC and pGC; some authors treat them as essentially identical measures, while others have noted that some properties (such as the relationship between the time and frequency domain expressions) do not hold for the pGC. This paper presents a series of matrix equalities that simplify the calculation of the pGC. In this simplified expression, the essential differences and similarities between the cGC and the pGC become clear; in essence, the pGC is dependent on only a subset of the parameters in the model estimation, and the noise residuals (which are uncorrelated in the cGC) need not be uncorrelated in the pGC. The mathematical results are illustrated with a simulation, and the measures are applied to an EEG dataset.

  8. Granger causality for state-space models

    NASA Astrophysics Data System (ADS)

    Barnett, Lionel; Seth, Anil K.

    2015-04-01

    Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

  9. Multivariate Granger causality and generalized variance

    NASA Astrophysics Data System (ADS)

    Barrett, Adam B.; Barnett, Lionel; Seth, Anil K.

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or “ensembles” of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke’s seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define “partial” Granger causality in the multivariate context and we also motivate reformulations of “causal density” and “Granger autonomy.” Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  10. Granger causality for state-space models.

    PubMed

    Barnett, Lionel; Seth, Anil K

    2015-04-01

    Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations-commonplace in application domains as diverse as climate science, econometrics, and the neurosciences-induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

  11. Multivariate Granger causality and generalized variance.

    PubMed

    Barrett, Adam B; Barnett, Lionel; Seth, Anil K

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  12. Granger-causality maps of diffusion processes

    NASA Astrophysics Data System (ADS)

    Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A.

    2016-02-01

    Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.

  13. Granger-causality maps of diffusion processes.

    PubMed

    Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A

    2016-02-01

    Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.

  14. Synergy, redundancy and unnormalized Granger causality.

    PubMed

    Stramaglia, S; Angelini, L; Cortes, J M; Marinazzo, D

    2015-08-01

    We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definition differs from previous definitions of synergy.

  15. Granger causality in wall-bounded turbulence

    NASA Astrophysics Data System (ADS)

    Tissot, Gilles; Lozano-Durán, Adrian; Cordier, Laurent; Jiménez, Javier; Noack, Bernd R.

    2014-04-01

    Granger causality is based on the idea that if a variable helps to predict another one, then they are probably involved in a causality relationship. This technique is based on the identification of a predictive model for causality detection. The aim of this paper is to use Granger causality to study the dynamics and the energy redistribution between scales and components in wall-bounded turbulent flows. In order to apply it on flows, Granger causality is generalized for snapshot-based observations of large size using linear-model identification methods coming from model reduction. Optimized DMD, a variant of the Dynamic Mode Decomposition, is considered for building a linear model based on snapshots. This method is used to link physical events and extract physical mechanisms associated to the bursting process in the logarithmic layer of a turbulent channel flow.

  16. Canonical Granger causality between regions of interest.

    PubMed

    Ashrafulla, Syed; Haldar, Justin P; Joshi, Anand A; Leahy, Richard M

    2013-12-01

    Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Stiefel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.

  17. A copula approach to assessing Granger causality.

    PubMed

    Hu, Meng; Liang, Hualou

    2014-10-15

    In neuroscience, as in many other fields of science and engineering, it is crucial to assess the causal interactions among multivariate time series. Granger causality has been increasingly used to identify causal influence between time series based on multivariate autoregressive models. Such an approach is based on linear regression framework with implicit Gaussian assumption of model noise residuals having constant variance. As a consequence, this measure cannot detect the cause-effect relationship in high-order moments and nonlinear causality. Here, we propose an effective model-free, copula-based Granger causality measure that can be used to reveal nonlinear and high-order moment causality. We first formulate Granger causality as the log-likelihood ratio in terms of conditional distribution, and then derive an efficient estimation procedure using conditional copula. We use resampling techniques to build a baseline null-hypothesis distribution from which statistical significance can be derived. We perform a series of simulations to investigate the performance of our copula-based Granger causality, and compare its performance against other state-of-the-art techniques. Our method is finally applied to neural field potential time series recorded from visual cortex of a monkey while performing a visual illusion task.

  18. Analyzing multiple nonlinear time series with extended Granger causality

    NASA Astrophysics Data System (ADS)

    Chen, Yonghong; Rangarajan, Govindan; Feng, Jianfeng; Ding, Mingzhou

    2004-04-01

    Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations. In this work we consider nonlinear extensions of Granger's idea and refer to the result as extended Granger causality. A simple approach implementing the extended Granger causality is presented and applied to multiple chaotic time series and other types of nonlinear signals. In addition, for situations with three or more time series we propose a conditional extended Granger causality measure that enables us to determine whether the causal relation between two signals is direct or mediated by another process.

  19. BOLD Granger causality reflects vascular anatomy.

    PubMed

    Webb, J Taylor; Ferguson, Michael A; Nielsen, Jared A; Anderson, Jeffrey S

    2013-01-01

    A number of studies have tried to exploit subtle phase differences in BOLD time series to resolve the order of sequential activation of brain regions, or more generally the ability of signal in one region to predict subsequent signal in another region. More recently, such lag-based measures have been applied to investigate directed functional connectivity, although this application has been controversial. We attempted to use large publicly available datasets (FCON 1000, ADHD 200, Human Connectome Project) to determine whether consistent spatial patterns of Granger Causality are observed in typical fMRI data. For BOLD datasets from 1,240 typically developing subjects ages 7-40, we measured Granger causality between time series for every pair of 7,266 spherical ROIs covering the gray matter and 264 seed ROIs at hubs of the brain's functional network architecture. Granger causality estimates were strongly reproducible for connections in a test and replication sample (n=620 subjects for each group), as well as in data from a single subject scanned repeatedly, both during resting and passive video viewing. The same effect was even stronger in high temporal resolution fMRI data from the Human Connectome Project, and was observed independently in data collected during performance of 7 task paradigms. The spatial distribution of Granger causality reflected vascular anatomy with a progression from Granger causality sources, in Circle of Willis arterial inflow distributions, to sinks, near large venous vascular structures such as dural venous sinuses and at the periphery of the brain. Attempts to resolve BOLD phase differences with Granger causality should consider the possibility of reproducible vascular confounds, a problem that is independent of the known regional variability of the hemodynamic response.

  20. A nonlinear generalization of spectral Granger causality.

    PubMed

    He, Fei; Wei, Hua-Liang; Billings, Stephen A; Sarrigiannis, Ptolemaios G

    2014-06-01

    Spectral measures of linear Granger causality have been widely applied to study the causal connectivity between time series data in neuroscience, biology, and economics. Traditional Granger causality measures are based on linear autoregressive with exogenous (ARX) inputs models of time series data, which cannot truly reveal nonlinear effects in the data especially in the frequency domain. In this study, it is shown that the classical Geweke's spectral causality measure can be explicitly linked with the output spectra of corresponding restricted and unrestricted time-domain models. The latter representation is then generalized to nonlinear bivariate signals and for the first time nonlinear causality analysis in the frequency domain. This is achieved by using the nonlinear ARX (NARX) modeling of signals, and decomposition of the recently defined output frequency response function which is related to the NARX model.

  1. On the spectral formulation of Granger causality.

    PubMed

    Chicharro, D

    2011-12-01

    Spectral measures of causality are used to explore the role of different rhythms in the causal connectivity between brain regions. We study several spectral measures related to Granger causality, comprising the bivariate and conditional Geweke measures, the directed transfer function, and the partial directed coherence. We derive the formulation of dependence and causality in the spectral domain from the more general formulation in the information-theory framework. We argue that the transfer entropy, the most general measure derived from the concept of Granger causality, lacks a spectral representation in terms of only the processes associated with the recorded signals. For all the spectral measures we show how they are related to mutual information rates when explicitly considering the parametric autoregressive representation of the processes. In this way we express the conditional Geweke spectral measure in terms of a multiple coherence involving innovation variables inherent to the autoregressive representation. We also link partial directed coherence with Sims' criterion of causality. Given our results, we discuss the causal interpretation of the spectral measures related to Granger causality and stress the necessity to explicitly consider their specific formulation based on modeling the signals as linear Gaussian stationary autoregressive processes.

  2. More discussions for granger causality and new causality measures.

    PubMed

    Hu, Sanqing; Cao, Yu; Zhang, Jianhai; Kong, Wanzeng; Yang, Kun; Zhang, Yanbin; Li, Xun

    2012-02-01

    Granger causality (GC) has been widely applied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011), we proposed new causalities in time and frequency domains and particularly focused on new causality in frequency domain by pointing out the shortcomings/limitations of GC or Granger-alike causality metrics and the advantages of new causality. In this paper we continue our previous discussions and focus on new causality and GC or Granger-alike causality metrics in time domain. Although one strong motivation was introduced in our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011) we here present additional motivation for the proposed new causality metric and restate the previous motivation for completeness. We point out one property of conditional GC in time domain and the shortcomings/limitations of conditional GC which cannot reveal the real strength of the directional causality among three time series. We also show the shortcomings/limitations of directed causality (DC) or normalize DC for multivariate time series and demonstrate it cannot reveal real causality at all. By calculating GC and new causality values for an example we demonstrate the influence of one of the time series on the other is linearly increased as the coupling strength is linearly increased. This fact further supports reasonability of new causality metric. We point out that larger instantaneous correlation does not necessarily mean larger true causality (e.g., GC and new causality), or vice versa. Finally we conduct analysis of statistical test for significance and asymptotic distribution property of new causality metric by illustrative examples.

  3. Measuring autonomy and emergence via Granger causality.

    PubMed

    Seth, Anil K

    2010-01-01

    Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness.

  4. Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy

    ERIC Educational Resources Information Center

    von Eye, Alexander; Wiedermann, Wolfgang

    2015-01-01

    Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…

  5. Reliability of the Granger causality inference

    NASA Astrophysics Data System (ADS)

    Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

    2014-04-01

    How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.

  6. Spatio-temporal Granger causality: a new framework.

    PubMed

    Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

    2013-10-01

    That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data.

  7. Relating Granger causality to long-term causal effects.

    PubMed

    Smirnov, Dmitry A; Mokhov, Igor I

    2015-10-01

    In estimation of causal couplings between observed processes, it is important to characterize coupling roles at various time scales. The widely used Granger causality reflects short-term effects: it shows how strongly perturbations of a current state of one process affect near future states of another process, and it quantifies that via prediction improvement (PI) in autoregressive models. However, it is often more important to evaluate the effects of coupling on long-term statistics, e.g., to find out how strongly the presence of coupling changes the variance of a driven process as compared to an uncoupled case. No general relationships between Granger causality and such long-term effects are known. Here, we pose the problem of relating these two types of coupling characteristics, and we solve it for a class of stochastic systems. Namely, for overdamped linear oscillators, we rigorously derive that the above long-term effect is proportional to the short-term effects, with the proportionality coefficient depending on the prediction interval and relaxation times. We reveal that this coefficient is typically considerably greater than unity so that small normalized PI values may well correspond to quite large long-term effects of coupling. The applicability of the derived relationship to wider classes of systems, its limitations, and its value for further research are discussed. To give a real-world example, we analyze couplings between large-scale climatic processes related to sea surface temperature variations in equatorial Pacific and North Atlantic regions.

  8. Relating Granger causality to long-term causal effects

    NASA Astrophysics Data System (ADS)

    Smirnov, Dmitry A.; Mokhov, Igor I.

    2015-10-01

    In estimation of causal couplings between observed processes, it is important to characterize coupling roles at various time scales. The widely used Granger causality reflects short-term effects: it shows how strongly perturbations of a current state of one process affect near future states of another process, and it quantifies that via prediction improvement (PI) in autoregressive models. However, it is often more important to evaluate the effects of coupling on long-term statistics, e.g., to find out how strongly the presence of coupling changes the variance of a driven process as compared to an uncoupled case. No general relationships between Granger causality and such long-term effects are known. Here, we pose the problem of relating these two types of coupling characteristics, and we solve it for a class of stochastic systems. Namely, for overdamped linear oscillators, we rigorously derive that the above long-term effect is proportional to the short-term effects, with the proportionality coefficient depending on the prediction interval and relaxation times. We reveal that this coefficient is typically considerably greater than unity so that small normalized PI values may well correspond to quite large long-term effects of coupling. The applicability of the derived relationship to wider classes of systems, its limitations, and its value for further research are discussed. To give a real-world example, we analyze couplings between large-scale climatic processes related to sea surface temperature variations in equatorial Pacific and North Atlantic regions.

  9. Analysing connectivity with Granger causality and dynamic causal modelling.

    PubMed

    Friston, Karl; Moran, Rosalyn; Seth, Anil K

    2013-04-01

    This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.

  10. Granger causality and information flow in multivariate processes.

    PubMed

    Blinowska, Katarzyna J; Kuś, Rafał; Kamiński, Maciej

    2004-11-01

    The multivariate versus bivariate measures of Granger causality were considered. Granger causality in the application to multivariate physiological time series has the meaning of the information flow between channels. It was shown by means of simulations and by the example of experimental electroencephalogram signals that bivariate estimates of directionality in case of mutually interdependent channels give erroneous results, therefore multivariate measures such as directed transfer function should be used for determination of the information flow.

  11. Assessing Thalamocortical Functional Connectivity with Granger Causality

    PubMed Central

    Israel, David; Thakor, Nitish V.; Jia, Xiaofeng

    2014-01-01

    Assessment of network connectivity across multiple brain regions is critical to understanding the mechanisms underlying various neurological disorders. Conventional methods for assessing dynamic interactions include cross-correlation and coherence analysis. However, these methods do not reveal the direction of information flow, which is important for studying the highly directional neurological system. Granger causality (GC) analysis can characterize the directional influences between two systems. We tested GC analysis for its capability to capture directional interactions within both simulated and in-vivo neural networks. The simulated networks consisted of Hindmarsh-Rose neurons; GC analysis was used to estimate the causal influences between two model networks. Our analysis successfully detected asymmetrical interactions between these networks (p<10−10, t-test). Next, we characterized the relationship between the “electrical synaptic strength” in the model networks and interactions estimated by GC analysis. We demonstrated the novel application of GC to monitor interactions between thalamic and cortical neurons following ischemia induced brain injury in a rat model of cardiac arrest (CA). We observed that during the post-CA acute period the GC interactions from the thalamus to the cortex were consistently higher than those from the cortex to the thalamus (1.983±0.278 times higher, p=0.021). In addition, the dynamics of GC interactions between the thalamus and the cortex were frequency dependent. Our study demonstrated the feasibility of GC to monitor the dynamics of thalamocortical interactions after a global nervous system injury such as CA-induced ischemia, and offers preferred alternative applications in characterizing other inter-regional interactions in an injured brain. PMID:23864221

  12. Assessing thalamocortical functional connectivity with Granger causality.

    PubMed

    Chen, Cheng; Maybhate, Anil; Israel, David; Thakor, Nitish V; Jia, Xiaofeng

    2013-09-01

    Assessment of network connectivity across multiple brain regions is critical to understanding the mechanisms underlying various neurological disorders. Conventional methods for assessing dynamic interactions include cross-correlation and coherence analysis. However, these methods do not reveal the direction of information flow, which is important for studying the highly directional neurological system. Granger causality (GC) analysis can characterize the directional influences between two systems. We tested GC analysis for its capability to capture directional interactions within both simulated and in vivo neural networks. The simulated networks consisted of Hindmarsh-Rose neurons; GC analysis was used to estimate the causal influences between two model networks. Our analysis successfully detected asymmetrical interactions between these networks ( , t -test). Next, we characterized the relationship between the "electrical synaptic strength" in the model networks and interactions estimated by GC analysis. We demonstrated the novel application of GC to monitor interactions between thalamic and cortical neurons following ischemia induced brain injury in a rat model of cardiac arrest (CA). We observed that during the post-CA acute period the GC interactions from the thalamus to the cortex were consistently higher than those from the cortex to the thalamus ( 1.983±0.278 times higher, p = 0.021). In addition, the dynamics of GC interactions between the thalamus and the cortex were frequency dependent. Our study demonstrated the feasibility of GC to monitor the dynamics of thalamocortical interactions after a global nervous system injury such as CA-induced ischemia, and offers preferred alternative applications in characterizing other inter-regional interactions in an injured brain.

  13. Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

    NASA Astrophysics Data System (ADS)

    Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

    2009-12-01

    Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

  14. Kernel canonical-correlation Granger causality for multiple time series.

    PubMed

    Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

    2011-04-01

    Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

  15. Analyzing multiple spike trains with nonparametric Granger causality.

    PubMed

    Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou

    2009-08-01

    Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

  16. Statistical analysis of single-trial Granger causality spectra.

    PubMed

    Brovelli, Andrea

    2012-01-01

    Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

  17. New Insights into Signed Path Coefficient Granger Causality Analysis

    PubMed Central

    Zhang, Jian; Li, Chong; Jiang, Tianzi

    2016-01-01

    Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of “signed path coefficient Granger causality,” a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an “excitatory” or “inhibitory” influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation. PMID:27833547

  18. New Insights into Signed Path Coefficient Granger Causality Analysis.

    PubMed

    Zhang, Jian; Li, Chong; Jiang, Tianzi

    2016-01-01

    Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.

  19. Analyzing information flow in brain networks with nonparametric Granger causality.

    PubMed

    Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou

    2008-06-01

    Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

  20. Analyzing Information Flow in Brain Networks with Nonparametric Granger Causality

    PubMed Central

    Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou

    2009-01-01

    Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task. PMID:18394927

  1. Partial Granger causality--eliminating exogenous inputs and latent variables.

    PubMed

    Guo, Shuixia; Seth, Anil K; Kendrick, Keith M; Zhou, Cong; Feng, Jianfeng

    2008-07-15

    Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (latent) variables. To address this problem, we introduce a novel variant of a widely used statistical measure of causality--Granger causality--that is inspired by the definition of partial correlation. Our 'partial Granger causality' measure is extensively tested with toy models, both linear and nonlinear, and is applied to experimental data: in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of sheep. Our results demonstrate that partial Granger causality can reveal the underlying interactions among elements in a network in the presence of exogenous inputs and latent variables in many cases where the existing conditional Granger causality fails.

  2. On the statistical performance of Granger-causal connectivity estimators.

    PubMed

    Sameshima, Koichi; Takahashi, Daniel Y; Baccalá, Luiz A

    2015-06-01

    In this article, we extend the statistical detection performance evaluation of linear connectivity from Sameshima et al. (in: Slezak et al. (eds.) Lecture Notes in Computer Science, 2014) via brand new Monte Carlo simulations of three widely used toy models under different data record lengths for a classic time domain multivariate Granger causality test, information partial directed coherence, information directed transfer function, and include conditional multivariate Granger causality whose behaviour was found to be anomalous.

  3. Mitigating the effects of measurement noise on Granger causality

    SciTech Connect

    Nalatore, Hariharan; Ding Mingzhou; Rangarajan, Govindan

    2007-03-15

    Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the expectation-maximization algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.

  4. Analyzing brain networks with PCA and conditional Granger causality.

    PubMed

    Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

    2009-07-01

    Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series.

  5. Wiener-Granger causality: a well established methodology.

    PubMed

    Bressler, Steven L; Seth, Anil K

    2011-09-15

    For decades, the main ways to study the effect of one part of the nervous system upon another have been either to stimulate or lesion the first part and investigate the outcome in the second. This article describes a fundamentally different approach to identifying causal connectivity in neuroscience: a focus on the predictability of ongoing activity in one part from that in another. This approach was made possible by a new method that comes from the pioneering work of Wiener (1956) and Granger (1969). The Wiener-Granger method, unlike stimulation and ablation, does not require direct intervention in the nervous system. Rather, it relies on the estimation of causal statistical influences between simultaneously recorded neural time series data, either in the absence of identifiable behavioral events or in the context of task performance. Causality in the Wiener-Granger sense is based on the statistical predictability of one time series that derives from knowledge of one or more others. This article defines Wiener-Granger Causality, discusses its merits and limitations in neuroscience, and outlines recent developments in its implementation.

  6. On directed information theory and Granger causality graphs.

    PubMed

    Amblard, Pierre-Olivier; Michel, Olivier J J

    2011-02-01

    Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

  7. Granger causality--statistical analysis under a configural perspective.

    PubMed

    von Eye, Alexander; Wiedermann, Wolfgang; Mun, Eun-Young

    2014-03-01

    The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger causation by adopting a configural perspective. This perspective allows researchers to assume that effects exist for specific categories only or for specific sectors of the data space, but not for other categories or sectors. Configural Frequency Analysis (CFA) is proposed as the method of analysis from a configural perspective. CFA base models are derived for the exploratory analysis of Granger causation. These models are specified so that they parallel the regression models used for variable-oriented analysis of hypotheses of Granger causation. An example from the development of aggression in adolescence is used. The example shows that only one pattern of change in aggressive impulses over time Granger-causes change in physical aggression against peers.

  8. Validity of Time Reversal forTesting Granger Causality

    NASA Astrophysics Data System (ADS)

    Winkler, Irene; Panknin, Danny; Bartz, Daniel; Muller, Klaus-Robert; Haufe, Stefan

    2016-06-01

    Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe et al. (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-reversed data. They show that this procedure, time-reversed Granger causality (TRGC), robustly rejects causal interpretations on mixtures of independent signals. While promising results have been achieved in simulations, it was so far unknown whether time reversal leads to valid measures of information flow in the presence of true interaction. Here we prove that, for linear finite-order autoregressive processes with unidirectional information flow, the application of time reversal for testing Granger causality indeed leads to correct estimates of information flow and its directionality. Using simulations, we further show that TRGC is able to infer correct directionality with similar statistical power as the net Granger causality between two variables, while being much more robust to the presence of measurement noise.

  9. Increasing fMRI sampling rate improves Granger causality estimates.

    PubMed

    Lin, Fa-Hsuan; Ahveninen, Jyrki; Raij, Tommi; Witzel, Thomas; Chu, Ying-Hua; Jääskeläinen, Iiro P; Tsai, Kevin Wen-Kai; Kuo, Wen-Jui; Belliveau, John W

    2014-01-01

    Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

  10. Inferring connectivity in networked dynamical systems: Challenges using Granger causality

    NASA Astrophysics Data System (ADS)

    Lusch, Bethany; Maia, Pedro D.; Kutz, J. Nathan

    2016-09-01

    Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

  11. Inferring connectivity in networked dynamical systems: Challenges using Granger causality.

    PubMed

    Lusch, Bethany; Maia, Pedro D; Kutz, J Nathan

    2016-09-01

    Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

  12. Nonlinear parametric model for Granger causality of time series

    NASA Astrophysics Data System (ADS)

    Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-06-01

    The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

  13. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

    PubMed

    Chen, Yonghong; Bressler, Steven L; Ding, Mingzhou

    2006-01-30

    It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

  14. On the Inference of Functional Circadian Networks Using Granger Causality.

    PubMed

    Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.

  15. A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

    PubMed

    Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

    2016-01-01

    Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

  16. Multivariate Granger causality analysis of fMRI data.

    PubMed

    Deshpande, Gopikrishna; LaConte, Stephan; James, George Andrew; Peltier, Scott; Hu, Xiaoping

    2009-04-01

    This article describes the combination of multivariate Granger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.

  17. Granger Causality and National Procurement Spending: Applications to the CC130 Hercules Fleet Performance

    DTIC Science & Technology

    2011-09-01

    Assistant Deputy Minister (Materiel) DRDC CORA TM 2011-154 September 2011 Granger Causality and National Procurement Spending Applications to the CC130...Hercules Fleet Performance David W. Maybury Materiel Group Operational Research Dr aft Co py Dr aft Co py Granger Causality and National Procurement...Canada), telle que représentée par le ministre de la Défense nationale, 2011 Dr aft Co py Abstract Using Granger causality tests, we look for relationships

  18. [The Granger causality models and their applications in brain effective connectivity networks].

    PubMed

    Zhao, Tiezhu; Zheng, Gang; Pan, Zhiying; Li, Qiang; Wang, Li; Lu, Guangming

    2013-12-01

    Granger causality model is an analysis method that requires no priori knowledge and emphasizes time sequence. Such model applied to brain effective connectivity network can reflect the directional connectivity among brain regions or neurons. This paper reviews the principle of Granger causality model, basic test steps and improved models, analyzes and discusses applications and existing problems of Granger causality model in brain effective connectivity network.

  19. The effect of filtering on Granger causality based multivariate causality measures.

    PubMed

    Florin, Esther; Gross, Joachim; Pfeifer, Johannes; Fink, Gereon R; Timmermann, Lars

    2010-04-01

    In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals. In frequency domain analyses (power or coherence) of neural data, it is common to preprocess the time series by filtering or decimating. However, in other fields, it has been shown theoretically that filtering in combination with Granger causality may lead to spurious or missed causalities. We investigated whether this result translates to multivariate causality methods derived from Granger causality with (a) a simulation study and (b) an application to magnetoencephalographic data. To this end, we performed extensive simulations of the effect of applying different filtering techniques and evaluated the performance of five different multivariate causality measures in combination with two numerical significance measures (random permutation and leave one out method). The analysis included three of the most widely used filters (high-pass, low-pass, notch filter), four different filter types (Butterworth, Chebyshev I and II, elliptic filter), variation of filter order, decimating and interpolation. The simulation results suggest that preprocessing without a strong prior about the artifact to be removed disturbs the information content and time ordering of the data and leads to spurious and missed causalities. Only if apparent artifacts like a current or movement artifact are present, filtering out the respective disturbance seems advisable. While oversampling poses no problem, decimation by a factor greater than the minimum time shift between the time series may lead to wrong inferences. In general, the multivariate causality measures are very sensitive to data preprocessing.

  20. Multivariate Granger Causality Analysis of Obesity Related Variables.

    PubMed

    Mukhopadhyay, Nitai D; Wheeler, David; Sabo, Roy; Sun, Shumei S

    Obesity is a complex health outcome that is a combination of multiple health indicators. Here we attempt to explore the dependence network among multiple aspects of obesity. Two longitudinal cohort studies across multiple decades have been used. The concept of causality is defined similar to Granger causality among multiple time series, however, modified to accommodate multivariate time series as the nodes of the network. Our analysis reveals relatively central position of physical measurements and blood chemistry measures in the overall network across both genders. Also there are some patterns specific to only male or female population. The geometry of the causality network is expected to help in our strategy to control the increasing trend of obesity rate.

  1. Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

    PubMed Central

    Kong, Wanzeng; Lin, Weicheng; Babiloni, Fabio; Hu, Sanqing; Borghini, Gianluca

    2015-01-01

    Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies. PMID:26251909

  2. Investigating Driver Fatigue versus Alertness Using the Granger Causality Network.

    PubMed

    Kong, Wanzeng; Lin, Weicheng; Babiloni, Fabio; Hu, Sanqing; Borghini, Gianluca

    2015-08-05

    Driving fatigue has been identified as one of the main factors affecting drivers' safety. The aim of this study was to analyze drivers' different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers' fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain's ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers' fatigue levels, and as reference work for future studies.

  3. Video Sensor-Based Complex Scene Analysis with Granger Causality

    PubMed Central

    Fan, Yawen; Yang, Hua; Zheng, Shibao; Su, Hang; Wu, Shuang

    2013-01-01

    In this report, we propose a novel framework to explore the activity interactions and temporal dependencies between activities in complex video surveillance scenes. Under our framework, a low-level codebook is generated by an adaptive quantization with respect to the activeness criterion. The Hierarchical Dirichlet Processes (HDP) model is then applied to automatically cluster low-level features into atomic activities. Afterwards, the dynamic behaviors of the activities are represented as a multivariate point-process. The pair-wise relationships between activities are explicitly captured by the non-parametric Granger causality analysis, from which the activity interactions and temporal dependencies are discovered. Then, each video clip is labeled by one of the activity interactions. The results of the real-world traffic datasets show that the proposed method can achieve a high quality classification performance. Compared with traditional K-means clustering, a maximum improvement of 19.19% is achieved by using the proposed causal grouping method. PMID:24152928

  4. From Granger causality to long-term causality: Application to climatic data

    NASA Astrophysics Data System (ADS)

    Smirnov, Dmitry A.; Mokhov, Igor I.

    2009-07-01

    Quantitative characterization of interaction between processes from time series is often required in different fields of natural science including geophysics and biophysics. Typically, one estimates “short-term” influences, e.g., the widely used Granger causality is defined via one-step-ahead predictions. Such an approach does not reveal how strongly the “long-term” behavior of one process under study is affected by the others. To overcome this problem, we introduce the concept of long-term causality, which extends the concept of Granger causality. The long-term causality is estimated from data via empirical modeling and analysis of model dynamics under different conditions. Apart from mathematical examples, we apply both approaches to find out how strongly the global surface temperature (GST) is affected by variations in carbon dioxide atmospheric content, solar activity, and volcanic activity during the last 150 years. Influences of all the three factors on GST are detected with the Granger causality. However, the long-term causality shows that the rise in GST during the last decades can be explained only if the anthropogenic factor (CO2) is taken into account in a model.

  5. How to detect the Granger-causal flow direction in the presence of additive noise?

    PubMed

    Vinck, Martin; Huurdeman, Lisanne; Bosman, Conrado A; Fries, Pascal; Battaglia, Francesco P; Pennartz, Cyriel M A; Tiesinga, Paul H

    2015-03-01

    Granger-causality metrics have become increasingly popular tools to identify directed interactions between brain areas. However, it is known that additive noise can strongly affect Granger-causality metrics, which can lead to spurious conclusions about neuronal interactions. To solve this problem, previous studies have proposed the detection of Granger-causal directionality, i.e. the dominant Granger-causal flow, using either the slope of the coherency (Phase Slope Index; PSI), or by comparing Granger-causality values between original and time-reversed signals (reversed Granger testing). We show that for ensembles of vector autoregressive (VAR) models encompassing bidirectionally coupled sources, these alternative methods do not correctly measure Granger-causal directionality for a substantial fraction of VAR models, even in the absence of noise. We then demonstrate that uncorrelated noise has fundamentally different effects on directed connectivity metrics than linearly mixed noise, where the latter may result as a consequence of electric volume conduction. Uncorrelated noise only weakly affects the detection of Granger-causal directionality, whereas linearly mixed noise causes a large fraction of false positives for standard Granger-causality metrics and PSI, but not for reversed Granger testing. We further show that we can reliably identify cases where linearly mixed noise causes a large fraction of false positives by examining the magnitude of the instantaneous influence coefficient in a structural VAR model. By rejecting cases with strong instantaneous influence, we obtain an improved detection of Granger-causal flow between neuronal sources in the presence of additive noise. These techniques are applicable to real data, which we demonstrate using actual area V1 and area V4 LFP data, recorded from the awake monkey performing a visual attention task.

  6. Statistical threshold for nonlinear Granger Causality in motor intention analysis.

    PubMed

    Liu, MengTing; Kuo, Ching-Chang; Chiu, Alan W L

    2011-01-01

    Directed influence between multiple channel signal measurements is important for the understanding of large dynamic systems. This research investigates a method to analyze large, complex multi-variable systems using directional flow measure to extract relevant information related to the functional connectivity between different units in the system. The directional flow measure was completed through nonlinear Granger Causality (GC) which is based on the nonlinear predictive models using radial basis functions (RBF). In order to extract relevant information from the causality map, we propose a threshold method that can be set up through a spatial statistical process where only the top 20% of causality pathways is shown. We applied this approach to a brain computer interface (BCI) application to decode the different intended arm reaching movement (left, right and forward) using 128 surface electroencephalography (EEG) electrodes. We also evaluated the importance of selecting the appropriate radius in the region of interest and found that the directions of causal influence of active brain regions were unique with respect to the intended direction.

  7. Time-varying linear and nonlinear parametric model for Granger causality analysis.

    PubMed

    Li, Yang; Wei, Hua-Liang; Billings, Steve A; Liao, Xiao-Feng

    2012-04-01

    Statistical measures such as coherence, mutual information, or correlation are usually applied to evaluate the interactions between two or more signals. However, these methods cannot distinguish directions of flow between two signals. The capability to detect causalities is highly desirable for understanding the cooperative nature of complex systems. The main objective of this work is to present a linear and nonlinear time-varying parametric modeling and identification approach that can be used to detect Granger causality, which may change with time and may not be detected by traditional methods. A numerical example, in which the exact causal influences relationships, is presented to illustrate the performance of the method for time-varying Granger causality detection. The approach is applied to EEG signals to track and detect hidden potential causalities. One advantage of the proposed model, compared with traditional Granger causality, is that the results are easier to interpret and yield additional insights into the transient directed dynamical Granger causality interactions.

  8. Granger causality stock market networks: Temporal proximity and preferential attachment

    NASA Astrophysics Data System (ADS)

    Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard

    2015-06-01

    The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.

  9. Causality and persistence in ecological systems: a nonparametric spectral granger causality approach.

    PubMed

    Detto, Matteo; Molini, Annalisa; Katul, Gabriel; Stoy, Paul; Palmroth, Sari; Baldocchi, Dennis

    2012-04-01

    Abstract Directionality in coupling, defined as the linkage relating causes to their effects at a later time, can be used to explain the core dynamics of ecological systems by untangling direct and feedback relationships between the different components of the systems. Inferring causality from measured ecological variables sampled through time remains a formidable challenge further made difficult by the action of periodic drivers overlapping the natural dynamics of the system. Periodicity in the drivers can often mask the self-sustained oscillations originating from the autonomous dynamics. While linear and direct causal relationships are commonly addressed in the time domain, using the well-established machinery of Granger causality (G-causality), the presence of periodic forcing requires frequency-based statistics (e.g., the Fourier transform), able to distinguish coupling induced by oscillations in external drivers from genuine endogenous interactions. Recent nonparametric spectral extensions of G-causality to the frequency domain pave the way for the scale-by-scale decomposition of causality, which can improve our ability to link oscillatory behaviors of ecological networks to causal mechanisms. The performance of both spectral G-causality and its conditional extension for multivariate systems is explored in quantifying causal interactions within ecological networks. Through two case studies involving synthetic and actual time series, it is demonstrated that conditional G-causality outperforms standard G-causality in identifying causal links and their concomitant timescales.

  10. The opportune time to invest in residential properties - Engle-Granger cointegration test and Granger causality test approach

    NASA Astrophysics Data System (ADS)

    Chee-Yin, Yip; Hock-Eam, Lim

    2014-12-01

    This paper examines using housing supply as proxy to house prices, the causal relationship on house prices among 8 states in Malaysia by applying the Engle-Granger cointegration test and Granger causality test approach. The target states are Perak, Selangor, Penang, Federal Territory of Kuala Lumpur (WPKL or Kuala Lumpur), Kedah, Negeri Sembilan, Sabah and Sarawak. The primary aim of this study is to estimate how long (in months) house prices in Perak lag behind that of Selangor, Penang and WPKL. We classify the 8 states into two categories - developed and developing states. We use Engle-Granger cointegration test and Granger causality test to examine the long run and short run equilibrium relationship among the two categories.. It is found that the causal relationship is bidirectional in Perak and Sabah, Perak and Selangor while it is unidirectional for Perak and Sarawak, Perak and Penang, Perak and WPKL. The speed of deviation adjustment is about 273%, suggesting that the pricing dynamic of Perak has a 32- month or 2 3/4- year lag behind that of WPKL, Selangor and Penang. Such information will be useful to investors, house buyers and speculators.

  11. [Brain Efficient Connectivity Analysis of Attention Based on the Granger Causality Method].

    PubMed

    Yuan, Qin; Jiang, Tao

    2016-02-01

    The study of brain information flow is of great significance to understand brain function in the field of neuroscience. The Granger causality is widely used functional connectivity analysis using multivariate autoregressive model based on the predicted mechanism. High resolution electroencephalogram (EEG) signals of ten healthy subjects were collected with a visual selective attention task. Firstly, independent component analysis was used to extract three spatially independent components of the occipital, parietal, and frontal cortices. Secondly, the Granger causal connectivity was computed between these three regions based on the Granger causality method and then independent sample t-test and bootstrap were used to test the significance of connections. The results showed that Granger causal connectivity existed from frontal to occipital and from parietal to occipital in attentional condition, while causal connectivity from frontal to occipital disappeared in unattentional condition.

  12. Neural Connectivity in Epilepsy as Measured by Granger Causality.

    PubMed

    Coben, Robert; Mohammad-Rezazadeh, Iman

    2015-01-01

    Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended.

  13. Identifying Granger causal relationships between neural power dynamics and variables of interest.

    PubMed

    Winkler, Irene; Haufe, Stefan; Porbadnigk, Anne K; Müller, Klaus-Robert; Dähne, Sven

    2015-05-01

    Power modulations of oscillations in electro- and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to identify causal relationships by the statistical concept of Granger causality, and we investigate which methods are bests suited to reveal Granger causal links between the power of brain oscillations and experimental variables. As an alternative to testing such causal links on the sensor level, we propose to linearly combine the information contained in each sensor in order to create virtual channels, corresponding to estimates of underlying brain oscillations, the Granger-causal relations of which may be assessed. Such linear combinations of sensor can be given by source separation methods such as, for example, Independent Component Analysis (ICA) or by the recently developed Source Power Correlation (SPoC) method. Here we compare Granger causal analysis on power dynamics obtained from i) sensor directly, ii) spatial filtering methods that do not optimize for Granger causality (ICA and SPoC), and iii) a method that directly optimizes spatial filters to extract sources the power dynamics of which maximally Granger causes a given target variable. We refer to this method as Granger Causal Power Analysis (GrangerCPA). Using both simulated and real EEG recordings, we find that computing Granger causality on channel-wise spectral power suffers from a poor signal-to-noise ratio due to volume conduction, while all three multivariate approaches alleviate this issue. In real EEG recordings from subjects performing self-paced foot movements, all three multivariate methods identify neural oscillations with motor-related patterns at a similar performance level. In an auditory perception

  14. A study of industrial electricity consumption based on partial Granger causality network

    NASA Astrophysics Data System (ADS)

    Yao, Can-Zhong; Lin, Qing-Wen; Lin, Ji-Nan

    2016-11-01

    The paper studies the industrial energy transferring paths among the industries of China by distinguishing direct causality from the indirect. With complementary graphs, we propose that industrial causal relationship can be heterogeneous, and provide insights for refining robust industrial causality framework. First, by analyzing the in-weight and out-weight of the industries in Granger causality networks we find that public utilities have significant causality with other industries, and the industries with higher degree value tend to have stronger causality with others. Further, we eliminate the exogenous links by partial Granger causality model and find both Granger and partial Granger networks have consistent hub industries while some outliers emerge in partial Granger causality networks. Besides, compared with GX, GZ, HN and YN, the correlation between the volume of electricity consumption and the weight of each industry is more significant in the networks of GD and NF. By studying the characteristics of complementary graphs, we show that the industrial energy transferring paths in GD are more multidimensional, and the corresponding interdependent relationship among industries is more robust. Finally, using bootstrap method we verify the reliability of each industrial relationship network. Results exhibit that GD, GX and NF have more reliable causal relationship networks than other provinces, revealing their industrial structure to be more stable.

  15. Shortcomings/Limitations of Blockwise Granger Causality and Advances of Blockwise New Causality.

    PubMed

    Hu, Sanqing; Jia, Xinxin; Zhang, Jianhai; Kong, Wanzeng; Cao, Yu

    2016-12-01

    Multivariate blockwise Granger causality (BGC) is used to reflect causal interactions among blocks of multivariate time series. In particular, spectral BGC and conditional spectral BGC are used to disclose blockwise causal flow among different brain areas in various frequencies. In this paper, we demonstrate that: 1) BGC in time domain may not necessarily disclose true causality and 2) due to the use of the transfer function or its inverse matrix and partial information of the multivariate linear regression model, both of spectral BGC and conditional spectral BGC have shortcomings and/or limitations, which may inevitably lead to misinterpretation. We then, in time and frequency domains, develop two new multivariate blockwise causality methods for the linear regression model called blockwise new causality (BNC) and spectral BNC, respectively. By several examples, we confirm that BNC measures are more reasonable and sensitive to reflect true causality or trend of true causality than BGC or conditional BGC. Finally, for electroencephalograph data from an epilepsy patient, we analyze event-related potential causality and demonstrate that both of the BGC and BNC methods show significant causality flow in frequency domain, but the spectral BNC method yields satisfactory and convincing results, which are consistent with an event-related time-frequency power spectrum activity. The spectral BGC method is shown to generate misleading results. Thus, we deeply believe that our new blockwise causality definitions as well as our previous NC definitions may have wide applications to reflect true causality among two blocks of time series or two univariate time series in economics, neuroscience, and engineering.

  16. Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

    PubMed

    Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A; Zhang, Wenbo; He, Bin

    2016-10-11

    Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation timecourses and apply Granger analysis on the extracted series to study brain networks under realistic conditions.

  17. Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

    PubMed

    Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt

    2016-01-01

    Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

  18. Exploring Granger causality between global average observed time series of carbon dioxide and temperature

    SciTech Connect

    Kodra, Evan A; Chatterjee, Snigdhansu; Ganguly, Auroop R

    2010-01-01

    Detection and attribution methodologies have been developed over the years to delineate anthropogenic from natural drivers of climate change and impacts. A majority of prior attribution studies, which have used climate model simulations and observations or reanalysis datasets, have found evidence for humaninduced climate change. This papers tests the hypothesis that Granger causality can be extracted from the bivariate series of globally averaged land surface temperature (GT) observations and observed CO2 in the atmosphere using a reverse cumulative Granger causality test. This proposed extension of the classic Granger causality test is better suited to handle the multisource nature of the data and provides further statistical rigor. The results from this modified test show evidence for Granger causality from a proxy of total radiative forcing (RC), which in this case is a transformation of atmospheric CO2, to GT. Prior literature failed to extract these results via the standard Granger causality test. A forecasting test shows that a holdout set of GT can be better predicted with the addition of lagged RC as a predictor, lending further credibility to the Granger test results. However, since second-order-differenced RC is neither normally distributed nor variance stationary, caution should be exercised in the interpretation of our results.

  19. Does partial Granger causality really eliminate the influence of exogenous inputs and latent variables?

    PubMed

    Roelstraete, Bjorn; Rosseel, Yves

    2012-04-30

    Partial Granger causality was introduced by Guo et al. (2008) who showed that it could better eliminate the influence of latent variables and exogenous inputs than conditional G-causality. In the recent literature we can find some reviews and applications of this type of Granger causality (e.g. Smith et al., 2011; Bressler and Seth, 2010; Barrett et al., 2010). These articles apparently do not take into account a serious flaw in the original work on partial G-causality, being the negative F values that were reported and even proven to be plausible. In our opinion, this undermines the credibility of the obtained results and thus the validity of the approach. Our study is aimed to further validate partial G-causality and to find an answer why negative partial Granger causality estimates were reported. Time series were simulated from the same toy model as used in the original paper and partial and conditional causal measures were compared in the presence of confounding variables. Inference was done parametrically and using non-parametric block bootstrapping. We counter the proof that partial Granger F values can be negative, but the main conclusion of the original article remains. In the presence of unknown latent and exogenous influences, it appears that partial G-causality will better eliminate their influence than conditional G-causality, at least when non-parametric inference is used.

  20. Assessing Granger non-causality using nonparametric measure of conditional independence.

    PubMed

    Seth, Sohan; Príncipe, José C

    2012-01-01

    In recent years, Granger causality has become a popular method in a variety of research areas including engineering, neuroscience, and economics. However, despite its simplicity and wide applicability, the linear Granger causality is an insufficient tool for analyzing exotic stochastic processes such as processes involving non-linear dynamics or processes involving causality in higher order statistics. In order to analyze such processes more reliably, a different approach toward Granger causality has become increasingly popular. This new approach employs conditional independence as a tool to discover Granger non-causality without any assumption on the underlying stochastic process. This paper discusses the concept of discovering Granger non-causality using measures of conditional independence, and proposes a novel measure of conditional independence. In brief, the proposed approach estimates the conditional distribution function through a kernel based least square regression approach. This paper also explores the strengths and weaknesses of the proposed method compared to other available methods, and provides a detailed comparison of these methods using a variety of synthetic data sets.

  1. Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix.

    PubMed

    Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou

    2013-08-28

    Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.

  2. Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery.

    PubMed

    Hu, Sanqing; Wang, Hui; Zhang, Jianhai; Kong, Wanzeng; Cao, Yu; Kozma, Robert

    2016-07-01

    In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.

  3. Dynamic Granger-Geweke causality modeling with application to interictal spike propagation.

    PubMed

    Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W; Stufflebeam, Steven M; Hämäläinen, Matti S

    2009-06-01

    A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using structural equation modeling (SEM) and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested that the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain.

  4. Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

    PubMed Central

    Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W.; Stufflebeam, Steven M.; Hamalainen, Matti S.

    2010-01-01

    A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. PMID:19378280

  5. Inference of biological networks using Bi-directional Random Forest Granger causality.

    PubMed

    Furqan, Mohammad Shaheryar; Siyal, Mohammad Yakoob

    2016-01-01

    The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.

  6. Can Granger causality delineate natural versus anthropogenic drivers of climate change from global-average multivariate time series?

    NASA Astrophysics Data System (ADS)

    Kodra, E. A.; Chatterjee, S.; Ganguly, A. R.

    2009-12-01

    The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) notes with a high degree of certainty that global warming can be attributed to anthropogenic emissions. Detection and attribution studies, which attempt to delineate human influences on regional- and decadal-scale climate change or its impacts, use a variety of techniques, including Granger causality. Recently, Granger causality was used as a tool for detection and attribution in climate based on a spatio-temporal data mining approach. However, the degree to which Granger causality may be able to delineate natural versus anthropogenic drivers of change in these situations needs to be thoroughly investigated. As a first step, we use multivariate global-average time series of observations to test the performance of Granger causality. We apply the popular Granger F-tests to Radiative Forcing (RF), which is a transformation of carbon dioxide (CO2), and Global land surface Temperature anomalies (GT). Our preliminary results with observations appear to suggest that RF Granger-causes GT, which seem to become more apparent with more data. However, carefully designed simulations indicate that these results are not reliable and may, in fact, be misleading. On the other hand, the same observation- and simulation-driven methodologies, when applied to the El Niño Southern Oscillation (ENSO) index, clearly show reliable Granger-causality from ENSO to GT. We develop and test several hypotheses to explain why the Granger causality tests between RF and GT are not reliable. We conclude that the form of Granger causality used in this study, and in past studies reported in the literature, is sensitive to data availability, random variability, and especially whether the variables arise from a deterministic or stochastic process. Simulations indicate that Granger causality in this form performs poorly, even in simple linear effect cases, when applied to one deterministic and one stochastic time

  7. On Granger causality and the effect of interventions in time series.

    PubMed

    Eichler, Michael; Didelez, Vanessa

    2010-01-01

    We combine two approaches to causal reasoning. Granger causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially causal relations. On the other hand, the notion of causality as effect of interventions is predominant in fields like medical statistics or computer science. In this paper, we consider the effect of external, possibly multiple and sequential, interventions in a system of multivariate time series, the Granger causal structure of which is taken to be known. We address the following questions: under what assumptions about the system and the interventions does Granger causality inform us about the effectiveness of interventions, and when does the possibly smaller system of observable times series allow us to estimate this effect? For the latter we derive criteria that can be checked graphically and are in the same spirit as Pearl's back-door and front-door criteria (Pearl 1995).

  8. Identification of directed influence: Granger causality, Kullback-Leibler divergence, and complexity.

    PubMed

    Seghouane, Abd-Krim; Amari, Shun-Ichi

    2012-07-01

    Detecting and characterizing causal interdependencies and couplings between different activated brain areas from functional neuroimage time series measurements of their activity constitutes a significant step toward understanding the process of brain functions. In this letter, we make the simple point that all current statistics used to make inferences about directed influences in functional neuroimage time series are variants of the same underlying quantity. This includes directed transfer entropy, transinformation, Kullback-Leibler formulations, conditional mutual information, and Granger causality. Crucially, in the case of autoregressive modeling, the underlying quantity is the likelihood ratio that compares models with and without directed influences from the past when modeling the influence of one time series on another. This framework is also used to derive the relation between these measures of directed influence and the complexity or the order of directed influence. These results provide a framework for unifying the Kullback-Leibler divergence, Granger causality, and the complexity of directed influence.

  9. A study of causality structure and dynamics in industrial electricity consumption based on Granger network

    NASA Astrophysics Data System (ADS)

    Yao, Can-Zhong; Lin, Ji-Nan; Lin, Qing-Wen; Zheng, Xu-Zhou; Liu, Xiao-Feng

    2016-11-01

    Based on industrial electricity consumption, we model industrial networks by Granger causality method and MST (minimum spanning tree), and then further stick onto an industrial coupling mechanism from energy-consumption perspective. First, we construct Granger causality networks of five provinces in South China of GD, GX, GZ, HN and YN based on their industrial electricity consumption data, and we demonstrate from a network-topology perspective: the distribution of weight of links of all industrial electricity-consumption Granger causality networks approximately follows power-law distribution, revealing a phenomenon that few industries may bring a tremendous influence on the rest. Moreover, correlation analysis between weight and degree of a node shows that in most Granger causality networks, both span and strength of influence of a given industry will significantly increase. Further, we analyze the relationship between the thresholds of Granger causality significance and density of corresponding networks. Results show GD and HN could be classified into a group with relatively greater global differentiation in industries and unbalanced industrial development, however, GX, GZ and YN are grouped as second cluster with relatively balanced industrial development. Furthermore, using Chu-Liu-EdmondsMST algorithm, we extract graphs of MSTs or maximal cliques from industrial electricity-consumption Granger causality networks, and research on energy transmission structure, feedback loop, and bootstrap reliability. By analyzing MSTs, we find that only GD, GX and YN can be extracted with MST graphs, and capture the probable transmission routes of key nodes. Besides we illustrate all three MST graphs are involved with feedback loops structures with various characteristics: GX has complete feed-forward section, feed-back section and feedback loop section; YN has only feed-forward section and feedback loop section; GD has multiple feedback loops section. Finally, we conduct

  10. Attribution of precipitation changes on ground-air temperature offset: Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Cermak, Vladimir; Bodri, Louise

    2016-06-01

    This work examines the causal relationship between the value of the ground-air temperature offset and the precipitation changes for monitored 5-min data series together with their hourly and daily averages obtained at the Sporilov Geophysical Observatory (Prague). Shallow subsurface soil temperatures were monitored under four different land cover types (bare soil, sand, short-cut grass and asphalt). The ground surface temperature (GST) and surface air temperature (SAT) offset, ΔT(GST-SAT), is defined as the difference between the temperature measured at the depth of 2 cm below the surface and the air temperature measured at 5 cm above the surface. The results of the Granger causality test did not reveal any evidence of Granger causality for precipitation to ground-air temperature offsets on the daily scale of aggregation except for the asphalt pavement. On the contrary, a strong evidence of Granger causality for precipitation to the ground-air temperature offsets was found on the hourly scale of aggregation for all land cover types except for the sand surface cover. All results are sensitive to the lag choice of the autoregressive model. On the whole, obtained results contain valuable information on the delay time of ΔT(GST-SAT) caused by the rainfall events and confirmed the importance of using autoregressive models to understand the ground-air temperature relationship.

  11. Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

    PubMed

    Liu, Siwei; Molenaar, Peter

    2016-01-01

    This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

  12. A Granger causality measure for point process models of ensemble neural spiking activity.

    PubMed

    Kim, Sanggyun; Putrino, David; Ghosh, Soumya; Brown, Emery N

    2011-03-01

    The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.

  13. Granger causality estimate of information flow in temperature fields is consistent with wind direction

    NASA Astrophysics Data System (ADS)

    Jajcay, Nikola; Hlinka, Jaroslav; Hartman, David; Paluš, Milan

    2014-05-01

    Granger causality analysis is designed to quantify whether one time series is useful in forecasting another. We apply the time domain Granger causality analysis based on autoregressive processes to gridded daily surface air temperature data. For each grid-point pair, the direction and strength of the causal influence were computed with the one-day lag, effectively assessing the direction of the information flow in the temperature field. In order to remove the influence of different distances of the grid-points in the original angularly regular grid of the NCEP/NCAR reanalysis, the data were transformed into an equidistant geodesic grid of 642 grid points. The strongest causalities have been found in the Northern Hemisphere's extratropics, where the temperature information is flowing eastward, in agreement with the prevailing westerlies. In contrast, only weak causalities have been observed in the tropics, which may be arising from higher spatio-temporal homogeneity. In the second step, we quantitatively compared this estimate of information flow with the actual wind directions from NCEP/NCAR reanalysis data transformed onto the equidistant geodesic grid of 642 points. This was done for the surface layer and for the 850, 700, 500, 300 and 100hPa layers. The direction of the information flow matches the flow of the air masses, particularly well in the Northern Hemisphere's extratropics, i.e. for the strongest causalities. This agreement holds throughout the troposphere, slightly increasing with the height up to 500hPa level, then remains the same until bottom stratosphere. The agreement between the information flow in the air temperature field and the flow of air masses suggests the Granger causality as a suitable tools for constructing directed climate networks.

  14. The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

    NASA Technical Reports Server (NTRS)

    Nemani, Ramakrishna R.

    2016-01-01

    Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.

  15. Detecting changes in coupling with Granger causality method from time series with fast transient processes

    NASA Astrophysics Data System (ADS)

    Sysoev, Ilya V.; Sysoeva, Marina V.

    2015-08-01

    The ability of the Granger causality method to detect directed coupling between subsystems of a complex system in a moving time window is investigated on etalon oscillators. In particular, the time series consisting of alternate stationary regimes characterised by the different amplitude and shape of oscillations with fast transient processes between these regimes are considered, with similar transitions being possible due to changes either in the coupling or in the individual properties of subsystems. Two popular approaches to surrogate times series generation are used to check the significance of the method results. Two model structures: the standard linear and the special non-linear adapted to data are implemented. The Granger causality method using the model structure adapted to data is shown to be significantly advantageous in detecting coupling directionality and the instant time of the regime change than the standard linear method, while in some cases the sensitivity and the specificity of the adapted approach are insufficient.

  16. Estimating Granger causality from fourier and wavelet transforms of time series data.

    PubMed

    Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou

    2008-01-11

    Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. Here, we extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences. We illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.

  17. Cardiovascular control and time domain Granger causality: insights from selective autonomic blockade.

    PubMed

    Porta, Alberto; Castiglioni, Paolo; Di Rienzo, Marco; Bassani, Tito; Bari, Vlasta; Faes, Luca; Nollo, Giandomenico; Cividjan, Andrei; Quintin, Luc

    2013-08-28

    We studied causal relations among heart period (HP), systolic arterial pressure (SAP) and respiration (R) according to the definition of Granger causality in the time domain. Autonomic pharmacological challenges were used to alter the complexity of cardiovascular control. Atropine (AT), propranolol and clonidine (CL) were administered to block muscarinic receptors, β-adrenergic receptors and centrally sympathetic outflow, respectively. We found that: (i) at baseline, HP and SAP interacted in a closed loop with a dominant causal direction from HP to SAP; (ii) pharmacological blockades did not alter the bidirectional closed-loop interactions between HP and SAP, but AT reduced the dominance of the causal direction from HP to SAP; (iii) at baseline, bidirectional interactions between HP and R were frequently found; (iv) the closed-loop relation between HP and R was unmodified by the administration of drugs; (v) at baseline, unidirectional interactions from R to SAP were often found; and (vi) while AT induced frequently an uncoupling between R and SAP, CL favoured bidirectional interactions. These results prove that time domain measures of Granger causality can contribute to the description of cardiovascular control by suggesting the temporal direction of the interactions and by separating different causality schemes (e.g. closed loop versus unidirectional relations).

  18. Granger causal time-dependent source connectivity in the somatosensory network

    NASA Astrophysics Data System (ADS)

    Gao, Lin; Sommerlade, Linda; Coffman, Brian; Zhang, Tongsheng; Stephen, Julia M.; Li, Dichen; Wang, Jue; Grebogi, Celso; Schelter, Bjoern

    2015-05-01

    Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess.

  19. Granger causal time-dependent source connectivity in the somatosensory network

    PubMed Central

    Gao, Lin; Sommerlade, Linda; Coffman, Brian; Zhang, Tongsheng; Stephen, Julia M.; Li, Dichen; Wang, Jue; Grebogi, Celso; Schelter, Bjoern

    2015-01-01

    Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess. PMID:25997414

  20. Determination of ECoG information flow activity based on Granger causality and Hilbert transformation.

    PubMed

    Demirer, R Murat; Özerdem, Mehmet Siraç; Bayrak, Coskun; Mendi, Engin

    2013-12-01

    Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm×8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8×8 (0.016-300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub

  1. Granger causality analysis with nonuniform sampling and its application to pulse-coupled nonlinear dynamics.

    PubMed

    Zhang, Yaoyu; Xiao, Yanyang; Zhou, Douglas; Cai, David

    2016-04-01

    The Granger causality (GC) analysis is an effective approach to infer causal relations for time series. However, for data obtained by uniform sampling (i.e., with an equal sampling time interval), it is known that GC can yield unreliable causal inference due to aliasing if the sampling rate is not sufficiently high. To solve this unreliability issue, we consider the nonuniform sampling scheme as it can mitigate against aliasing. By developing an unbiased estimation of power spectral density of nonuniformly sampled time series, we establish a framework of spectrum-based nonparametric GC analysis. Applying this framework to a general class of pulse-coupled nonlinear networks and utilizing some particular spectral structure possessed by these nonlinear network data, we demonstrate that, for such nonlinear networks with nonuniformly sampled data, reliable GC inference can be achieved at a low nonuniform mean sampling rate at which the traditional uniform sampling GC may lead to spurious causal inference.

  2. Granger causality analysis with nonuniform sampling and its application to pulse-coupled nonlinear dynamics

    NASA Astrophysics Data System (ADS)

    Zhang, Yaoyu; Xiao, Yanyang; Zhou, Douglas; Cai, David

    2016-04-01

    The Granger causality (GC) analysis is an effective approach to infer causal relations for time series. However, for data obtained by uniform sampling (i.e., with an equal sampling time interval), it is known that GC can yield unreliable causal inference due to aliasing if the sampling rate is not sufficiently high. To solve this unreliability issue, we consider the nonuniform sampling scheme as it can mitigate against aliasing. By developing an unbiased estimation of power spectral density of nonuniformly sampled time series, we establish a framework of spectrum-based nonparametric GC analysis. Applying this framework to a general class of pulse-coupled nonlinear networks and utilizing some particular spectral structure possessed by these nonlinear network data, we demonstrate that, for such nonlinear networks with nonuniformly sampled data, reliable GC inference can be achieved at a low nonuniform mean sampling rate at which the traditional uniform sampling GC may lead to spurious causal inference.

  3. Granger Causality in Multivariate Time Series Using a Time-Ordered Restricted Vector Autoregressive Model

    NASA Astrophysics Data System (ADS)

    Siggiridou, Elsa; Kugiumtzis, Dimitris

    2016-04-01

    Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively short time series, CGCI may fail because it is based on vector autoregressive models (VAR) involving a large number of coefficients to be estimated. In this work, the VAR is restricted by a scheme that modifies the recently developed method of backward-in-time selection (BTS) of the lagged variables and the CGCI is combined with BTS. Further, the proposed approach is compared favorably to other restricted VAR representations, such as the top-down strategy, the bottom-up strategy, and the least absolute shrinkage and selection operator (LASSO), in terms of sensitivity and specificity of CGCI. This is shown by using simulations of linear and nonlinear, low and high-dimensional systems and different time series lengths. For nonlinear systems, CGCI from the restricted VAR representations are compared with analogous nonlinear causality indices. Further, CGCI in conjunction with BTS and other restricted VAR representations is applied to multi-channel scalp electroencephalogram (EEG) recordings of epileptic patients containing epileptiform discharges. CGCI on the restricted VAR, and BTS in particular, could track the changes in brain connectivity before, during and after epileptiform discharges, which was not possible using the full VAR representation.

  4. Neural network-based classification of anesthesia/awareness using Granger causality features.

    PubMed

    Nicolaou, Nicoletta; Georgiou, Julius

    2014-04-01

    This article investigates the signal processing part of a future system for monitoring awareness during surgery. The system uses features from the patients' electrical brain activity (EEG) to discriminate between "anesthesia" and "awareness." We investigate the use of a neural network classifier and Granger causality (GC) features for this purpose. GC captures anesthetic-induced changes in the causal relationships between pairs of signals from different brain areas. The differences in the pairwise causality estimated from the EEG activity are used as features for subsequent classification between "awake" and "anesthetized" states. EEG data from 31 subjects obtained during surgery and maintenance of anesthesia with propofol, sevoflurane, or desflurane, are classified using a neural network with one layer of hidden units. An average accuracy of 96% is obtained.

  5. Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series.

    PubMed

    Porta, Alberto; Bassani, Tito; Bari, Vlasta; Pinna, Gian D; Maestri, Roberto; Guzzetti, Stefano

    2012-03-01

    This study was designed to demonstrate the need of accounting for respiration (R) when causality between heart period (HP) and systolic arterial pressure (SAP) is under scrutiny. Simulations generated according to a bivariate autoregressive closed-loop model were utilized to assess how causality changes as a function of the model parameters. An exogenous (X) signal was added to the bivariate autoregressive closed-loop model to evaluate the bias on causality induced when the X source was disregarded. Causality was assessed in the time domain according to a predictability improvement approach (i.e., Granger causality). HP and SAP variability series were recorded with R in 19 healthy subjects during spontaneous and controlled breathing at 10, 15, and 20 breaths/min. Simulations proved the importance of accounting for X signals. During spontaneous breathing, assessing causality without taking into consideration R leads to a significantly larger percentage of closed-loop interactions and a smaller fraction of unidirectional causality from HP to SAP. This finding was confirmed during paced breathing and it was independent of the breathing rate. These results suggest that the role of baroreflex cannot be correctly assessed without accounting for R.

  6. The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses.

    PubMed

    Schippers, Marleen B; Renken, Remco; Keysers, Christian

    2011-07-01

    Granger causality analyses aim to reveal the direction of influence between brain areas by analyzing temporal precedence: if a signal change in area A consistently precedes a signal change in area B, then A Granger-causes B. fMRI-based Granger causality inferences are mediated by the hemodynamic response function which can vary across brain regions. This variability might induce a bias in Granger causality analyses. Here we use simulations to investigate the effect of hemodynamic response variability on Granger causality analyses at the level of a group of twenty participants. We used a set of hemodynamic responses measured by Handwerker et al. (Neuroimage, 2004) and simulated 200 experiments in which time series with known directions of influence are convolved with these hemodynamic responses and submitted to Granger causality analysis. Results show that the average chance to find a significant Granger causality effect when no actual influence is present in the data stays well below the p-level imposed on the second level statistics. Most importantly, when the analyses reveal a significant directed influence, this direction was accurate in the vast majority of the cases. The sensitivity of the analyses however depended on the neuronal delay between the source and target regions and their relative hemodynamic delay. Influences flowing from regions to one with the same or a slower hemodynamic response function were detected in over 80% of the cases when the neuronal delay was at least 100 ms. Influences flowing to a region with a faster hemodynamic delay were detected in over 80% of the cases when delays are above 1s.

  7. Experimental Validation of Dynamic Granger Causality for Inferring Stimulus-evoked Sub-100ms Timing Differences from fMRI.

    PubMed

    Wang, Yunzhi; Katwal, Santosh; Rogers, Baxter; Gore, John; Deshpande, Gopikrishna

    2016-07-20

    Decoding the sequential flow of events in the human brain non-invasively is critical for gaining a mechanistic understanding of brain function. In this study, we propose a method based on dynamic Granger causality analysis to measure timing differences in brain responses from fMRI. We experimentally validate this method by detecting sub-100ms timing differences in fMRI responses obtained from bilateral visual cortex using fast sampling, ultra-high field and an eventrelated visual hemifield paradigm with known timing difference between the hemifields. Classical Granger causality was previously shown to be able to detect sub-100 ms timing differences in the visual cortex. Since classical Granger causality does not differentiate between spontaneous and stimulus-evoked responses, dynamic Granger causality has been proposed as an alternative, thereby necessitating its experimental validation. In addition to detecting timing differences as low as 28 ms during dynamic Granger causality, the significance of the inference from our method increased with increasing delay both in simulations and experimental data. Therefore, it provides a methodology for understanding mental chronometry from fMRI in a data-driven way.

  8. Prior knowledge driven Granger causality analysis on gene regulatory network discovery

    SciTech Connect

    Yao, Shun; Yoo, Shinjae; Yu, Dantong

    2015-08-28

    Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.

  9. Prior knowledge driven Granger causality analysis on gene regulatory network discovery

    DOE PAGES

    Yao, Shun; Yoo, Shinjae; Yu, Dantong

    2015-08-28

    Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, themore » propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.« less

  10. Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics.

    PubMed

    Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

    2014-01-01

    Granger causality (GC) is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length τ, i.e., the GC value is a function of τ. Using the GC analysis for the topology extraction of the simplest integrate-and-fire neuronal network of two neurons, we discuss behaviors of the GC value as a function of τ, which exhibits (i) oscillations, often vanishing at certain finite sampling interval lengths, (ii) the GC vanishes linearly as one uses finer and finer sampling. We show that these sampling effects can occur in both linear and non-linear dynamics: the GC value may vanish in the presence of true causal influence or become non-zero in the absence of causal influence. Without properly taking this issue into account, GC analysis may produce unreliable conclusions about causal influence when applied to empirical data. These sampling artifacts on the GC value greatly complicate the reliability of causal inference using the GC analysis, in general, and the validity of topology reconstruction for networks, in particular. We use idealized linear models to illustrate possible mechanisms underlying these phenomena and to gain insight into the general spectral structures that give rise to these sampling effects. Finally, we present an approach to circumvent these sampling artifacts to obtain reliable GC values.

  11. Elastic-Net Copula Granger Causality for Inference of Biological Networks

    PubMed Central

    Siyal, Mohammad Yakoob

    2016-01-01

    Aim In bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively. However, with recent advances in data procurement technology, such as DNA microarray analysis and fMRI that can simultaneously process a large amount of data, it yields high-dimensional data sets. These high dimensional dataset analyses possess challenges for the analyst. Background Traditional methods of Granger causality inference use ordinary least-squares methods for structure estimation, which confront dimensionality issues when applied to high-dimensional data. Apart from dimensionality issues, most existing methods were designed to capture only the linear inferences from time series data. Method and Conclusion In this paper, we address the issues involved in assessing Granger causality for both linear and nonlinear high-dimensional data by proposing an elegant form of the existing LASSO-based method that we call “Elastic-Net Copula Granger causality”. This method provides a more stable way to infer biological networks which has been verified using rigorous experimentation. We have compared the proposed method with the existing method and demonstrated that this new strategy outperforms the existing method on all measures: precision, false detection rate, recall, and F1 score. We have also applied both methods to real HeLa cell data and StarPlus fMRI datasets and presented a comparison of the effectiveness of both methods. PMID:27792750

  12. The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

    NASA Astrophysics Data System (ADS)

    Green, J.; Lee, J. E.; Gentine, P.; Berry, J. A.; Konings, A. G.

    2015-12-01

    hotosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe. References:Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Ro¨denbeck, M. Altaf Arain, D. Baldocchi, G. B. Bonan, A. Bondeau, A. Cescatti, G. Lasslop, A. Lindroth, M. Lomas, S. Luyssaert, H. Margolis, K. W. Oleson, O. Roupsard, E. Veenendaal, N. Viovy, C. Williams, I. Woodward, and D. Papale, 2010: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. doi: 10.1126/science.1184984. Running, S.W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A Continuous Satellite Derived Measure of Global Terrestrial Primary Production. BioScience 54(6), 547-560.

  13. Effective connectivity of facial expression network by using Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Li, Xiaoting

    2013-10-01

    Functional magnetic resonance imaging (fMRI) is an advanced non-invasive data acquisition technique to investigate the neural activity in human brain. In addition to localize the functional brain regions that is activated by specific cognitive task, fMRI can also be utilized to measure the task-related functional interactions among the active regions of interest (ROI) in the brain. Among the variety of analysis tools proposed for modeling the connectivity of brain regions, Granger causality analysis (GCA) measure the directions of information interactions by looking for the lagged effect among the brain regions. In this study, we use fMRI and Granger Causality analysis to investigate the effective connectivity of brain network induced by viewing several kinds of expressional faces. We focus on four kinds of facial expression stimuli: fearful, angry, happy and neutral faces. Five face selective regions of interest are localized and the effective connectivity within these regions is measured for the expressional faces. Our result based on 8 subjects showed that there is significant effective connectivity from STS to amygdala, from amygdala to OFA, aFFA and pFFA, from STS to aFFA and from pFFA to aFFA. This result suggested that there is an information flow from the STS to the amygdala when perusing expressional faces. This emotional expressional information flow that is conveyed by STS and amygdala, flow back to the face selective regions in occipital-temporal lobes, which constructed a emotional face processing network.

  14. Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality.

    PubMed

    Montalto, Alessandro; Stramaglia, Sebastiano; Faes, Luca; Tessitore, Giovanni; Prevete, Roberto; Marinazzo, Daniele

    2015-11-01

    A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments performed will show that the method presented in this work can detect the correct dynamical information flows occurring in a system of time series. Additionally we adopt a non-uniform embedding framework according to which only the past states that actually help the prediction are entered into the model, improving the prediction and avoiding the risk of overfitting. This method also leads to a further improvement with respect to traditional Granger causality approaches when redundant variables (i.e. variables sharing the same information about the future of the system) are involved. Neural networks are also able to recognize dynamics in data sets completely different from the ones used during the training phase.

  15. Measures of Coupling between Neural Populations Based on Granger Causality Principle.

    PubMed

    Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

    2016-01-01

    This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

  16. Resting-state brain networks revealed by granger causal connectivity in frogs.

    PubMed

    Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

    2016-10-15

    Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli.

  17. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality.

    PubMed

    Gao, Qing; Duan, Xujun; Chen, Huafu

    2011-01-15

    The effective connectivity networks among overlapped core regions recruited by motor execution (ME) and motor imagery (MI) were explored by means of conditional Granger causality and graph-theoretic method, based on functional magnetic resonance imaging (fMRI) data. Our results demonstrated more circuits of effective connectivity among the selected seed regions during right-hand performance than during left-hand performance, implying the influences of brain asymmetry of right-handedness on effective connectivity networks. The increased causal connections were found during ME than during MI, suggesting that the ME network may have some additional connections compared to MI networks to execute the overt physical movement. Furthermore, the In-Out degrees of information flow suggested left dorsal premotor cortex (PMd), inferior parietal lobule (IPL) and superior parietal lobule (SPL) as causal sources in ME/MI tasks, highlighting the dominant function of left PMd, IPL and SPL. These findings depicted the causal connectivity of motor related core regions in fronto-parietal circuit and might indicate the conversion of causal networks between ME and MI.

  18. Measures of Coupling between Neural Populations Based on Granger Causality Principle

    PubMed Central

    Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J.

    2016-01-01

    This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a “weak node.” Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures. PMID:27833546

  19. Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis.

    PubMed

    Deshpande, Gopikrishna; Hu, Xiaoping

    2012-01-01

    Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence-synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis-hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of

  20. Evaluating the effective connectivity of resting state networks using conditional Granger causality.

    PubMed

    Liao, Wei; Mantini, Dante; Zhang, Zhiqiang; Pan, Zhengyong; Ding, Jurong; Gong, Qiyong; Yang, Yihong; Chen, Huafu

    2010-01-01

    The human brain has been documented to be spatially organized in a finite set of specific coherent patterns, namely resting state networks (RSNs). The interactions among RSNs, being potentially dynamic and directional, may not be adequately captured by simple correlation or anticorrelation. In order to evaluate the possible effective connectivity within those RSNs, we applied a conditional Granger causality analysis (CGCA) to the RSNs retrieved by independent component analysis (ICA) from resting state functional magnetic resonance imaging (fMRI) data. Our analysis provided evidence for specific causal influences among the detected RSNs: default-mode, dorsal attention, core, central-executive, self-referential, somatosensory, visual, and auditory networks. In particular, we identified that self-referential and default-mode networks (DMNs) play distinct and crucial roles in the human brain functional architecture. Specifically, the former RSN exerted the strongest causal influence over the other RSNs, revealing a top-down modulation of self-referential mental activity (SRN) over sensory and cognitive processing. In quite contrast, the latter RSN was profoundly affected by the other RSNs, which may underlie an integration of information from primary function and higher level cognition networks, consistent with previous task-related studies. Overall, our results revealed the causal influences among these RSNs at different processing levels, and supplied information for a deeper understanding of the brain network dynamics.

  1. What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data

    NASA Astrophysics Data System (ADS)

    Kang, Jian; Larsson, Rolf

    2014-05-01

    We use statistical methods to analyze whether there exists long-term causality between temperature and carbon dioxide concentration. The analysis is based on a the Vostok Ice Core data from 400,000 to 6,000 years ago, extended by the EPICA Dome C data which go back to 800,000 years ago. At first, to make the data equidistant, we reconstruct it by linear interpolation. Then, using an approximation of a piecewise exponential function, we adjust for a deterministic trend. Finally, we employ the Granger causality test. We are able to strongly reject the null hypothesis that carbon dioxide concentration does not Granger cause temperature as well as the reverse hypothesis that temperature does not Granger cause carbon dioxide concentration.

  2. Random forest Granger causality for detection of effective brain connectivity using high-dimensional data.

    PubMed

    Furqan, Mohammad Shaheryar; Siyal, Mohammad Yakoob

    2016-03-01

    Studies have shown that the brain functions are not localized to isolated areas and connections but rather depend on the intricate network of connections and regions inside the brain. These networks are commonly analyzed using Granger causality (GC) that utilizes the ordinary least squares (OLS) method for its standard implementation. In the past, several approaches have shown to solve the limitations of OLS by using diverse regularization systems. However, there are still some shortcomings in terms of accuracy, precision, and false discovery rate (FDR). In this paper, we are proposing a new strategy to use Random Forest as a regularization technique for computing GC that will improve these shortcomings. We have demonstrated the effectiveness of our proposed methodology by comparing the results with existing Least absolute shrinkage and selection operator (LASSO), and Elastic-Net regularized implementations of GC using simulated dataset. Later, we have used our proposed approach to map the network involved during deductive reasoning using real StarPlus dataset.

  3. Using phase shift Granger causality to measure directed connectivity in EEG recordings.

    PubMed

    Marshall, William J; Lackner, Christine L; Marriott, Paul; Santesso, Diane L; Segalowitz, Sidney J

    2014-12-01

    Cortical activity is maintained by neural networks working in tandem. Electroencephalographic (EEG) signals across two sites are said to be coherent with one another when they show consistent phase relations. However, periods of desynchrony beginning with a shift in phase relations are a necessary aspect of information processing. Traditional measures of EEG coherence lack the temporal resolution required to divide the relationship between two signals into periods of synchrony and desynchrony and are unable to specify the direction of information transmission (i.e., which site is leading and which is lagging), a goal referred to as directed connectivity. In this article, the authors introduce a novel method of measuring directed connectivity by applying the framework of Granger causality to phase shift events which are estimated with high temporal resolution. A simulation study is used to verify that the proposed method is able to identify connectivity patterns in situations similar to EEG recordings, such as high levels of noise and linear source mixing. Their method is able to correctly identify both the existence and direction of information transfer, and that the existence of spatiotemporal noise serves to reduce the spread of shift identification due to volume conduction. To demonstrate the method on real data, it is applied to EEG recordings from 18 adolescents during a resting period and auditory and visual vigilance tasks. Their new measure, Phase Shift Granger Causality (PSGC), is able to clearly distinguish between the resting task and the active tasks. The latter have higher rates of connectivity overall and specifically more long-range connections. As expected, the resting task appears to activate more localized neural circuitry, whereas the active tasks appear to increase communication across several neural regions involved in vigilance tasks. The vigilance tasks also showed significantly higher clustering coefficients than the resting task, a property

  4. Large-scale Granger causality analysis on resting-state functional MRI

    NASA Astrophysics Data System (ADS)

    D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

    2016-03-01

    We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

  5. Predicting depression based on dynamic regional connectivity: a windowed Granger causality analysis of MEG recordings.

    PubMed

    Lu, Qing; Bi, Kun; Liu, Chu; Luo, Guoping; Tang, Hao; Yao, Zhijian

    2013-10-16

    Abnormal inter-regional causalities can be mapped for the objective diagnosis of various diseases. These inter-regional connectivities are usually calculated over an entire scan and used to characterize the stationary strength of the connections. However, the connectivity within networks may undergo substantial changes during a scan. In this study, we developed an objective depression recognition approach using the dynamic regional interactions that occur in response to sad facial stimuli. The whole time-period magnetoencephalography (MEG) signals from the visual cortex, amygdala, anterior cingulate cortex (ACC) and inferior frontal gyrus (IFG) were separated into sequential time intervals. The Granger causality mapping method was used to identify the pairwise interaction pattern within each time interval. Feature selection was then undertaken within a minimum redundancy-maximum relevance (mRMR) framework. Typical classifiers were utilized to predict those patients who had depression. The overall performances of these classifiers were similar, and the highest classification accuracy rate was 87.5%. The best discriminative performance was obtained when the number of features was within a robust range. The discriminative network pattern obtained through support vector machine (SVM) analyses displayed abnormal causal connectivities that involved the amygdala during the early and late stages. These early and late connections in the amygdala appear to reveal a negative bias to coarse expression information processing and abnormal negative modulation in patients with depression, which may critically affect depression discrimination.

  6. Granger causality-based synaptic weights estimation for analyzing neuronal networks.

    PubMed

    Shao, Pei-Chiang; Huang, Jian-Jia; Shann, Wei-Chang; Yen, Chen-Tung; Tsai, Meng-Li; Yen, Chien-Chang

    2015-06-01

    Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions between neurons. (2) How is the estimated causality related to the underlying synaptic weights? Based on the GC, we propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic weights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects between neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results showed, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR.

  7. Hemodynamic and electrophysiological spontaneous low-frequency oscillations in the cortex: directional influences revealed by Granger causality.

    PubMed

    Huang, Liangming; Liu, Yadong; Li, Ming; Hu, Dewen

    2014-01-15

    We used a combined electrophysiological/hemodynamic system to examine low-frequency oscillations (LFOs) in spontaneous neuronal activities (spike trains and local field potentials) and hemodynamic signals (cerebral blood flow) recorded from the anesthetized rat somatosensory and visual cortices. The laser Doppler flowmetry (LDF) probe was tilted slightly to approach the area in which a microelectrode array (MEA) was implanted for simultaneous recordings. Spike trains (STs) were converted into continuous-time rate functions (CRFs) using the ST instantaneous firing rates. LFOs were detected for all three of the components using the multi-taper method (MTM). The frequencies of these LFOs ranged from 0.052 to 0.167 Hz (mean±SD, 0.10±0.026 Hz) for cerebral blood flow (CBF), from 0.027 to 0.26 Hz (mean±SD, 0.12±0.041 Hz) for the CRFs of the STs and from 0.04 to 0.19 Hz (mean±SD, 0.11±0.035 Hz) for local field potentials (LFPs). We evaluated the Granger causal relationships of spontaneous LFOs among CBF, LFPs and CRFs using Granger causality (GC) analysis. Significant Granger causal relationships were observed from LFPs to CBF, from STs to CBF and from LFPs to STs at approximately 0.1 Hz. The present results indicate that spontaneous LFOs exist not only in hemodynamic components but also in neuronal activities of the rat cortex. To the best of our knowledge, the present study is the first to identify Granger causal influences among CBF, LFPs and STs and show that spontaneous LFOs carry important Granger causal influences from neural activities to hemodynamic signals.

  8. Bridging the Gap: Dynamic Causal Modeling and Granger Causality Analysis of Resting State Functional Magnetic Resonance Imaging.

    PubMed

    Bajaj, Sahil; Adhikari, Bhim M; Friston, Karl J; Dhamala, Mukesh

    2016-09-16

    Granger causality (GC) and dynamic causal modeling (DCM) are the two key approaches used to determine the directed interactions among brain areas. Recent discussions have provided a constructive account of the merits and demerits. GC, on one side, considers dependencies among measured responses, whereas DCM, on the other, models how neuronal activity in one brain area causes dynamics in another. In this study, our objective was to establish construct validity between GC and DCM in the context of resting state functional magnetic resonance imaging (fMRI). We first established the face validity of both approaches using simulated fMRI time series, with endogenous fluctuations in two nodes. Crucially, we tested both unidirectional and bidirectional connections between the two nodes to ensure that both approaches give veridical and consistent results, in terms of model comparison. We then applied both techniques to empirical data and examined their consistency in terms of the (quantitative) in-degree of key nodes of the default mode. Our simulation results suggested a (qualitative) consistency between GC and DCM. Furthermore, by applying nonparametric GC and stochastic DCM to resting-state fMRI data, we confirmed that both GC and DCM infer similar (quantitative) directionality between the posterior cingulate cortex (PCC), the medial prefrontal cortex, the left middle temporal cortex, and the left angular gyrus. These findings suggest that GC and DCM can be used to estimate directed functional and effective connectivity from fMRI measurements in a consistent manner.

  9. Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis.

    PubMed

    Zhou, Guifei; Liu, Jiangang; Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2016-01-01

    Numerous developmental studies have suggested that other-race effect (ORE) in face recognition emerges as early as in infancy and develops steadily throughout childhood. However, there is very limited research on the neural mechanisms underlying this developmental ORE. The present study used Granger causality analysis (GCA) to examine the development of children's cortical networks in processing own- and other-race faces. Children were between 3 and 13 years. An old-new paradigm was used to assess their own- and other-race face recognition with ETG-4000 (Hitachi Medical Co., Japan) acquiring functional near infrared spectroscopy (fNIRS) data. After preprocessing, for each participant and under each face condition, we obtained the causal map by calculating the weights of causal relations between the time courses of [oxy-Hb] of each pair of channels using GCA. To investigate further the differential causal connectivity for own-race faces and other-race faces at the group level, a repeated measure analysis of variance (ANOVA) was performed on the GCA weights for each pair of channels with the face race task (own-race face vs. other-race face) as the within-subject variable and the age as a between-subject factor (continuous variable). We found an age-related increase in functional connectivity, paralleling a similar age-related improvement in behavioral face processing ability. More importantly, we found that the significant differences in neural functional connectivity between the recognition of own-race faces and that of other-race faces were modulated by age. Thus, like the behavioral ORE, the neural ORE emerges early and undergoes a protracted developmental course.

  10. Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis

    PubMed Central

    Zhou, Guifei; Liu, Jiangang; Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2016-01-01

    Numerous developmental studies have suggested that other-race effect (ORE) in face recognition emerges as early as in infancy and develops steadily throughout childhood. However, there is very limited research on the neural mechanisms underlying this developmental ORE. The present study used Granger causality analysis (GCA) to examine the development of children's cortical networks in processing own- and other-race faces. Children were between 3 and 13 years. An old-new paradigm was used to assess their own- and other-race face recognition with ETG-4000 (Hitachi Medical Co., Japan) acquiring functional near infrared spectroscopy (fNIRS) data. After preprocessing, for each participant and under each face condition, we obtained the causal map by calculating the weights of causal relations between the time courses of [oxy-Hb] of each pair of channels using GCA. To investigate further the differential causal connectivity for own-race faces and other-race faces at the group level, a repeated measure analysis of variance (ANOVA) was performed on the GCA weights for each pair of channels with the face race task (own-race face vs. other-race face) as the within-subject variable and the age as a between-subject factor (continuous variable). We found an age-related increase in functional connectivity, paralleling a similar age-related improvement in behavioral face processing ability. More importantly, we found that the significant differences in neural functional connectivity between the recognition of own-race faces and that of other-race faces were modulated by age. Thus, like the behavioral ORE, the neural ORE emerges early and undergoes a protracted developmental course. PMID:27713696

  11. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique.

    PubMed

    Faes, Luca; Nollo, Giandomenico; Porta, Alberto

    2011-05-01

    We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected according to the Granger notion of predictability improvement, and is quantified in terms of information transfer. We apply the approach to simulations of deterministic and stochastic systems, showing its superiority over standard uniform embedding. Effects of quantization, data length, and noise contamination are investigated. As practical applications, we consider the assessment of cardiovascular regulatory mechanisms from the analysis of heart period, arterial pressure, and respiration time series, and the investigation of the information flow across brain areas from multichannel scalp electroencephalographic recordings.

  12. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique

    NASA Astrophysics Data System (ADS)

    Faes, Luca; Nollo, Giandomenico; Porta, Alberto

    2011-05-01

    We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected according to the Granger notion of predictability improvement, and is quantified in terms of information transfer. We apply the approach to simulations of deterministic and stochastic systems, showing its superiority over standard uniform embedding. Effects of quantization, data length, and noise contamination are investigated. As practical applications, we consider the assessment of cardiovascular regulatory mechanisms from the analysis of heart period, arterial pressure, and respiration time series, and the investigation of the information flow across brain areas from multichannel scalp electroencephalographic recordings.

  13. Effective connectivity of neural pathways underlying disgust by multivariate Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Yan, Hao; Wang, Yonghui; Tian, Jie; Liu, Yijun

    2011-03-01

    The disgust system arises phylogenetically in response to dangers to the internal milieu from pathogens and their toxic products. Functional imaging studies have demonstrated that a much wider range of neural structures was involved in triggering disgust reactions. However, less is known regarding how and what neural pathways these neural structures interact. To address this issue, we adopted an effective connectivity based analysis, namely the multivariate Granger causality approach, to explore the causal interactions within these brain networks. Results presented that disgust can induce a wide range of brain activities, such as the insula, the anterior cingulate cortex, the parahippocampus lobe, the dorsal lateral prefrontal cortex, the superior occipital gyrus, and the supplementary motor cortex. These brain areas constitute as a whole, with much denser connectivity following disgust stimuli, in comparison with that of the neutral condition. Moreover, the anterior insula, showing multiple casual interactions with limbic and subcortical areas, was implicated as a central hub in organizing multiple information processing in the disgust system.

  14. Granger causality reveals a dominant role of memory circuit in chronic opioid dependence.

    PubMed

    Zhang, Yi; Li, Qiang; Wen, Xiaotong; Cai, Weiwei; Li, Guanya; Tian, Jie; Zhang, Yi Edi; Liu, Jixin; Yuan, Kai; Zhao, Jizheng; Wang, Wei; Zhou, Zhenyu; Ding, Mingzhou; Gold, Mark S; Liu, Yijun; Wang, Gene-Jack

    2016-03-14

    Resting-state magnetic resonance imaging has uncovered abnormal functional connectivity in heroin-dependent individuals (HDIs). However, it remains unclear how brain regions implicated in addictions are related in baseline state without conditioned cues in heroin dependent individuals during opioid maintenance treatment (HDIs-OMT). Previous connectivity analysis assessed the strength of correlated activity between brain regions but lacked the ability to infer directional neural interactions. In the current study, we employed Granger causality analysis to investigate directional causal influences among the brain circuits in HDIs-OMT and non-opioid users. The results revealed a weaker effective connectivity between the caudate nucleus implicated in mediating the reward circuit and other brain regions and also a weaker connectivity between the anterior cingulate cortex and medial prefrontal cortex implicated in mediating inhibitory control. Conversely, HDIs-OMT exhibited stronger effective connectivity between the hippocampus and amygdala implicated in mediating learning-memory, and the anterior cingulate cortex involved in mediating inhibitory control while the putamen mediated learned habits, suggesting that the hippocampus and amygdala may propel the memory circuit to override the control circuit and drive the learned habit in HDIs-OMT. Alterations in learning-memory and inhibitory control may contribute jointly and form a basis for relapse risk even after a period of heroin abstinence. Sustained neural effect of opioid dependence on methadone maintenance including hyperactivation in the memory circuit and impairment in the control circuit support the role of the memory circuitry in relapse and may help redefine targets for treatment.

  15. Correntropy-based partial directed coherence for testing multivariate Granger causality in nonlinear processes

    NASA Astrophysics Data System (ADS)

    Kannan, Rohit; Tangirala, Arun K.

    2014-06-01

    Identification of directional influences in multivariate systems is of prime importance in several applications of engineering and sciences such as plant topology reconstruction, fault detection and diagnosis, and neurosciences. A spectrum of related directionality measures, ranging from linear measures such as partial directed coherence (PDC) to nonlinear measures such as transfer entropy, have emerged over the past two decades. The PDC-based technique is simple and effective, but being a linear directionality measure has limited applicability. On the other hand, transfer entropy, despite being a robust nonlinear measure, is computationally intensive and practically implementable only for bivariate processes. The objective of this work is to develop a nonlinear directionality measure, termed as KPDC, that possesses the simplicity of PDC but is still applicable to nonlinear processes. The technique is founded on a nonlinear measure called correntropy, a recently proposed generalized correlation measure. The proposed method is equivalent to constructing PDC in a kernel space where the PDC is estimated using a vector autoregressive model built on correntropy. A consistent estimator of the KPDC is developed and important theoretical results are established. A permutation scheme combined with the sequential Bonferroni procedure is proposed for testing hypothesis on absence of causality. It is demonstrated through several case studies that the proposed methodology effectively detects Granger causality in nonlinear processes.

  16. [Analysis of characteristics of alpha electroencephalogram during the interaction between emotion and cognition based on Granger causality].

    PubMed

    Wang, Ning; Wei, Ling; Li, Yingjie

    2012-12-01

    Studying the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain and nowadays, it has become a hot topic in cognitive neuroscience. Granger causality (GC), based on multivariate autoregressive (MVAR) model, and being able to be used to analyse causal characteristic of brain regions has been widely used in electroencephalography (EEG) in event-related paradigms research. In this study, we recorded the EEGs from 13 normal subjects (6 males and 7 females) during emotional face search task. We utilized Granger causality to establish a causal model of different brain areas under different rhythms at specific stages of cognition, and then convinced the brain dynamic network topological properties in the process of emotion and cognition. Therefore, we concluded that in the alpha band, (1) negative emotion face induced larger causal effects than positive ones; (2) 100-200ms emotional signal was the most prominent ones while 300-400ms and 700-800ms would take the second place; (3) The rear brain region modulated the front in the process of causal modulation; (4) The frontal and pillow area involved in the brain causal modulation as a key brain area; and (5) Negative partiality existed in the information processing, especially during 0-100ms after the negative expression stimulation.

  17. Investigating Soil Moisture Feedbacks on Precipitation With Tests of Granger Causality

    NASA Astrophysics Data System (ADS)

    Salvucci, G. D.; Saleem, J. A.; Kaufmann, R.

    2002-05-01

    Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture (S) feedback on precipitation (P) using data from Illinois. In this framework S is said to Granger cause P if F(Pt;At-dt)does not equal F(P;(A-S)t-dt) where F denotes the conditional distribution of P at time t, At-dt represents the set of all knowledge available at time t-dt, and (A-S)t-dt represents all knowledge available at t-dt except S. Critical for land-atmosphere interaction research is that At-dt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed

  18. Investigating soil moisture feedbacks on precipitation with tests of Granger causality

    NASA Astrophysics Data System (ADS)

    Salvucci, Guido D.; Saleem, Jennifer A.; Kaufmann, Robert

    Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture ( S) feedback on precipitation ( P) using data from Illinois. In this framework S is said to Granger cause P if F(P t|Ω t- Δt )≠F(P t|Ω t- Δt -S t- Δt ) where F denotes the conditional distribution of P, Ω t- Δt represents the set of all knowledge available at time t-Δ t, and Ω t- Δt -S t- Δt represents all knowledge except S. Critical for land-atmosphere interaction research is that Ω t- Δt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed

  19. Componential Granger causality, and its application to identifying the source and mechanisms of the top-down biased activation that controls attention to affective vs sensory processing.

    PubMed

    Ge, Tian; Feng, Jianfeng; Grabenhorst, Fabian; Rolls, Edmund T

    2012-01-16

    We describe a new measure of Granger causality, componential Granger causality, and show how it can be applied to the identification of the directionality of influences between brain areas with functional neuroimaging data. Componential Granger causality measures the effect of y on x, but allows interaction effects between y and x to be measured. In addition, the terms in componential Granger causality sum to 1, allowing causal effects to be directly compared between systems. We show using componential Granger causality analysis applied to an fMRI investigation that there is a top-down attentional effect from the anterior dorsolateral prefrontal cortex to the orbitofrontal cortex when attention is paid to the pleasantness of a taste, and that this effect depends on the activity in the orbitofrontal cortex as shown by the interaction term. Correspondingly there is a top-down attentional effect from the posterior dorsolateral prefrontal cortex to the insular primary taste cortex when attention is paid to the intensity of a taste, and this effect depends on the activity of the insular primary taste cortex as shown by the interaction term. Componential Granger causality thus not only can reveal the directionality of effects between areas (and these can be bidirectional), but also allows the mechanisms to be understood in terms of whether the causal influence of one system on another depends on the state of the system being causally influenced. Componential Granger causality measures the full effects of second order statistics by including variance and covariance effects between each time series, thus allowing interaction effects to be measured, and also provides a systematic framework within which to measure the effects of cross, self, and noise contributions to causality. The findings reveal some of the mechanisms involved in a biased activation theory of selective attention.

  20. Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

    NASA Astrophysics Data System (ADS)

    Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.

    2007-11-01

    In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

  1. Lexical influences on speech perception: A Granger causality analysis of MEG and EEG source estimates

    PubMed Central

    Gow, David W.; Segawa, Jennifer A.; Ahlfors, Seppo P.; Lin, Fa-Hsuan

    2008-01-01

    Behavioural and functional imaging studies have demonstrated that lexical knowledge influences the categorization of perceptually ambiguous speech sounds. However, methodological and inferential constraints have so far been unable to resolve the question of whether this interaction takes the form of direct top-down influences on perceptual processing, or feedforward convergence during a decision process. We examined top-down lexical influences on the categorization of segments in a /s/−/∫/ continuum presented in different lexical contexts to produce a robust Ganong effect. Using integrated MEG/EEG and MRI data we found that, within a network identified by 40Hz gamma phase locking, activation in the supramarginal gyrus associated with wordform representation influences phonetic processing in the posterior superior temporal gyrus during a period of time associated with lexical processing. This result provides direct evidence that lexical processes influence lower level phonetic perception, and demonstrates the potential value of combining Granger causality analyses and high spatiotemporal resolution multimodal imaging data to explore the functional architecture of cognition. PMID:18703146

  2. Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients

    PubMed Central

    Wang, Li; Zhang, Jingna; Zhang, Ye; Yan, Rubing; Liu, Hongliang; Qiu, Mingguo

    2016-01-01

    Aims. Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients. Methods. Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery. Results. Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere. Conclusions. The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function. PMID:27200373

  3. Decomposing the transfer entropy to quantify lag-specific Granger causality in cardiovascular variability.

    PubMed

    Faes, Luca; Nollo, Giandomenico

    2013-01-01

    We present a modification of the well known transfer entropy (TE) which makes it able to detect, besides the direction and strength of the information transfer between coupled processes, its exact timing. The approach follows a decomposition strategy which identifies--according to a lag-specific formulation of the concept of Granger causality--the set of time delays carrying significant information, and then assigns to each of these delays an amount of information transfer such that the total contribution yields the overall TE. We propose also a procedure for the practical estimation from time series data of the relevant delays and lag-specific TE in both bivariate and multivariate settings. The proposed approach is tested in simulations and in real cardiovascular time series, showing the feasibility of lag-specific TE estimation, the ability to reflect expected mechanisms of cardiovascular regulation, and the necessity of using the multivariate TE to properly assess time-lagged information transfer in the presence of multiple interacting systems.

  4. Recovering directed networks in neuroimaging datasets using partially conditioned Granger causality.

    PubMed

    Wu, Guo-Rong; Liao, Wei; Stramaglia, Sebastiano; Chen, Huafu; Marinazzo, Daniele

    2013-01-01

    Recovering directed pathways of information transfer between brain areas is an important issue in neuroscience and helps to shed light on the brain function in several physiological and cognitive states. Granger causality (GC) analysis is a valuable tool to detect directed dynamical connectivity, and it is being increasingly used. Unfortunately, this approach encounters some limitations in particularly when applied to neuroimaging datasets, often consisting in short and noisy data and for which redundancy plays an important role. In this article, we address one of these limitations, namely, the computational and conceptual problems arising when conditional GC, necessary to disambiguate direct and mediated influences, is used on short and noisy datasets of many variables, as it is typically the case in some electroencephalography (EEG) protocols and in functional magnetic resonance imaging (fMRI). We show that considering GC in the framework of information theory we can limit the conditioning to a limited number of variables chosen as the most informative, obtaining more stable and reliable results both in EEG and fMRI data.

  5. Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients.

    PubMed

    Wang, Li; Zhang, Jingna; Zhang, Ye; Yan, Rubing; Liu, Hongliang; Qiu, Mingguo

    2016-01-01

    Aims. Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients. Methods. Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery. Results. Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere. Conclusions. The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function.

  6. Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence.

    PubMed

    Bellucci, Gabriele; Chernyak, Sergey; Hoffman, Morris; Deshpande, Gopikrishna; Dal Monte, Olga; Knutson, Kristine M; Grafman, Jordan; Krueger, Frank

    2017-04-01

    Third-party punishment (TPP) for norm violations is an essential deterrent in large-scale human societies, and builds on two essential cognitive functions: evaluating legal responsibility and determining appropriate punishment. Despite converging evidence that TPP is mediated by a specific set of brain regions, little is known about their effective connectivity (direction and strength of connections). Applying parametric event-related functional MRI in conjunction with multivariate Granger causality analysis, we asked healthy participants to estimate how much punishment a hypothetical perpetrator deserves for intentionally committing criminal offenses varying in levels of harm. Our results confirmed that TPP legal decisions are based on two domain-general networks: the mentalizing network for evaluating legal responsibility and the central-executive network for determining appropriate punishment. Further, temporal pole (TP) and dorsomedial prefrontal cortex (PFC) emerged as hubs of the mentalizing network, uniquely generating converging output connections to ventromedial PFC, temporo-parietal junction, and posterior cingulate. In particular, dorsomedial PFC received inputs only from TP and both its activation and its connectivity to dorsolateral PFC correlated with degree of punishment. This supports the hypothesis that dorsomedial PFC acts as the driver of the TPP activation pattern, leading to the decision on the appropriate punishment. In conclusion, these results advance our understanding of the organizational elements of the TPP brain networks and provide better insights into the mental states of judges and jurors tasked with blaming and punishing legal wrongs.

  7. Upsampling to 400-ms resolution for assessing effective connectivity in functional magnetic resonance imaging data with Granger causality.

    PubMed

    McFarlin, Daniel R; Kerr, Deborah L; Nitschke, Jack B

    2013-01-01

    Granger causality analysis of functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent signal data allows one to infer the direction and magnitude of influence that brain regions exert on one another. We employed a method for upsampling the time resolution of fMRI data that does not require additional interpolation beyond the interpolation that is regularly used for slice-timing correction. The mathematics for this new method are provided, and simulations demonstrate its viability. Using fMRI, 17 snake phobics and 19 healthy controls viewed snake, disgust, and neutral fish video clips preceded by anticipatory cues. Multivariate Granger causality models at the native 2-sec resolution and at the upsampled 400-ms resolution assessed directional associations of fMRI data among 13 anatomical regions of interest identified in prior research on anxiety and emotion. Superior sensitivity was observed for the 400-ms model, both for connectivity within each group and for group differences in connectivity. Context-dependent analyses for the 400-ms multivariate Granger causality model revealed the specific trial types showing group differences in connectivity. This is the first demonstration of effective connectivity of fMRI data using a method for achieving 400-ms resolution without sacrificing accuracy available at 2-sec resolution.

  8. Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations.

    PubMed

    Trongnetrpunya, Amy; Nandi, Bijurika; Kang, Daesung; Kocsis, Bernat; Schroeder, Charles E; Ding, Mingzhou

    2015-01-01

    Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method

  9. Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations

    PubMed Central

    Trongnetrpunya, Amy; Nandi, Bijurika; Kang, Daesung; Kocsis, Bernat; Schroeder, Charles E.; Ding, Mingzhou

    2016-01-01

    Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method

  10. Synergy and redundancy in the Granger causal analysis of dynamical networks

    NASA Astrophysics Data System (ADS)

    Stramaglia, Sebastiano; Cortes, Jesus M.; Marinazzo, Daniele

    2014-10-01

    We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy.

  11. Recurrent activity in higher order, modality non-specific brain regions: a Granger causality analysis of autobiographic memory retrieval.

    PubMed

    Lou, Hans C; Joensson, Morten; Biermann-Ruben, Katja; Schnitzler, Alfons; Østergaard, Leif; Kjaer, Troels W; Gross, Joachim

    2011-01-01

    It has been proposed that the workings of the brain are mainly intrinsically generated recurrent neuronal activity, with sensory inputs as modifiers of such activity in both sensory and higher order modality non-specific regions. This is supported by the demonstration of recurrent neuronal activity in the visual system as a response to visual stimulation. In contrast recurrent activity has never been demonstrated before in higher order modality non-specific regions. Using magneto-encephalography and Granger causality analysis, we tested in a paralimbic network the hypothesis that stimulation may enhance causal recurrent interaction between higher-order, modality non-specific regions. The network includes anterior cingulate/medial prefrontal and posterior cingulate/medial parietal cortices together with pulvinar thalami, a network known to be effective in autobiographic memory retrieval and self-awareness. Autobiographic memory retrieval of previous personal judgments of visually presented words was used as stimuli. It is demonstrated that the prestimulus condition is characterized by causal, recurrent oscillations which are maximal in the lower gamma range. When retrieving previous judgments of visually presented adjectives, this activity is dramatically increased during the stimulus task as ascertained by Granger causality analysis. Our results confirm the hypothesis that stimulation may enhance causal interaction between higher order, modality non-specific brain regions, exemplified in a network of autobiographical memory retrieval.

  12. Effective connectivity of the multiplication network: a functional MRI and multivariate Granger Causality Mapping study.

    PubMed

    Krueger, Frank; Landgraf, Steffen; van der Meer, Elke; Deshpande, Gopikrishna; Hu, Xiaoping

    2011-09-01

    Developmental neuropsychology and functional neuroimaging evidence indicates that simple and complex mental calculation is subserved by a fronto-parietal network. However, the effective connectivity (connection direction and strength) among regions within the fronto-parietal network is still unexplored. Combining event-related fMRI and multivariate Granger Causality Mapping (GCM), we administered a multiplication verification task to healthy participants asking them to solve single and double-digit multiplications. The goals of our study were first, to identify the effective connectivity of the multiplication network, and second, to compare the effective connectivity patterns between a low and a high arithmetical competence (AC) group. The manipulation of multiplication difficulty revealed a fronto-parietal network encompassing bilateral intraparietal sulcus (IPS), left pre-supplementary motor area (PreSMA), left precentral gyrus (PreCG), and right dorsolateral prefrontal cortex (DLPFC). The network was driven by an intraparietal IPS-IPS circuit hosting a representation of numerical quantity intertwined with a fronto-parietal DLPFC-IPS circuit engaged in temporary storage and updating of arithmetic operations. Both circuits received additional inputs from the PreCG and PreSMA playing more of a supportive role in mental calculation. The high AC group compared to the low AC group displayed a greater activation in the right IPS and based its calculation more on a feedback driven intraparietal IPS-IPS circuit, whereas the low competence group more on a feedback driven fronto-parietal DLPFC-IPS circuit. This study provides first evidence that multivariate GCM is a sensitive approach to investigate effective connectivity of mental processes involved in mental calculation and to compare group level performances for different populations.

  13. Hemispheric lateralization in top-down attention during spatial relation processing: a Granger causal model approach.

    PubMed

    Falasca, N W; D'Ascenzo, S; Di Domenico, A; Onofrj, M; Tommasi, L; Laeng, B; Franciotti, R

    2015-04-01

    Magnetoencephalography was recorded during a matching-to-sample plus cueing paradigm, in which participants judged the occurrence of changes in either categorical (CAT) or coordinate (COO) spatial relations. Previously, parietal and frontal lobes were identified as key areas in processing spatial relations and it was shown that each hemisphere was differently involved and modulated by the scope of the attention window (e.g. a large and small cue). In this study, Granger analysis highlighted the patterns of causality among involved brain areas--the direction of information transfer ran from the frontal to the visual cortex in the right hemisphere, whereas it ran in the opposite direction in the left side. Thus, the right frontal area seems to exert top-down influence, supporting the idea that, in this task, top-down signals are selectively related to the right side. Additionally, for CAT change preceded by a small cue, the right frontal gyrus was not involved in the information transfer, indicating a selective specialization of the left hemisphere for this condition. The present findings strengthen the conclusion of the presence of a remarkable hemispheric specialization for spatial relation processing and illustrate the complex interactions between the lateralized parts of the neural network. Moreover, they illustrate how focusing attention over large or small regions of the visual field engages these lateralized networks differently, particularly in the frontal regions of each hemisphere, consistent with the theory that spatial relation judgements require a fronto-parietal network in the left hemisphere for categorical relations and on the right hemisphere for coordinate spatial processing.

  14. A conditional Granger causality model approach for group analysis in functional MRI

    PubMed Central

    Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

    2011-01-01

    Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve

  15. A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging.

    PubMed

    Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

    2011-04-01

    Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy

  16. Reduced medial prefrontal-subcortical connectivity in dysphoria: Granger causality analyses of rapid functional magnetic resonance imaging.

    PubMed

    Sabatinelli, Dean; McTeague, Lisa M; Dhamala, Mukesh; Frank, David W; Wanger, Timothy J; Adhikari, Bhim M

    2015-02-01

    A cortico-limbic network consisting of the amygdala, medial prefrontal cortex (mPFC), and ventral striatum (vSTR) has been associated with altered function in emotional disorders. Here we used rapidly sampled functional magnetic resonance imaging and Granger causality analyses to assess the directional connectivity between these brain structures in a sample of healthy and age-matched participants endorsing moderate to severe depressive symptomatology as they viewed a series of natural scene stimuli varying systematically in pleasantness and arousal. Specifically during pleasant scene perception, dysphoric participants showed reduced activity in mPFC and vSTR, relative to healthy participants. In contrast, amygdala activity was enhanced to pleasant as well as unpleasant arousing scenes in both participant groups. Granger causality estimates of influence between mPFC and vSTR were significantly reduced in dysphoric relative to control participants during all picture contents. These findings provide direct evidence that during visual perception of evocative emotional stimuli, reduced reward-related activity in dysphoria is associated with dysfunctional causal connectivity between mPFC, amygdala, and vSTR.

  17. Memory-guided drawing training increases Granger causal influences from the perirhinal cortex to V1 in the blind.

    PubMed

    Cacciamani, Laura; Likova, Lora T

    2017-03-24

    The perirhinal cortex (PRC) is a medial temporal lobe structure that has been implicated in not only visual memory in the sighted, but also tactile memory in the blind (Cacciamani & Likova, 2016). It has been proposed that, in the blind, the PRC may contribute to modulation of tactile memory responses that emerge in low-level "visual" area V1 as a result of training-induced cortical reorganization (Likova, 2012; 2015). While some studies in the sighted have indicated that the PRC is indeed structurally and functionally connected to the visual cortex (Clavagnier, Falchier, & Kennedy, 2004; Peterson, Cacciamani, Barense, & Scalf, 2012), the PRC's direct modulation of V1 is unknown-particularly in those who lack the visual input that typically stimulates this region. In the present study, we tested Likova's PRC modulation hypothesis; specifically, we used fMRI to assess the PRC's Granger causal influence on V1 activation in the blind during a tactile memory task. To do so, we trained congenital and acquired blind participants on a unique memory-guided drawing technique previously shown to result in V1 reorganization towards tactile memory representations (Likova, 2012). The tasks (20s each) included: tactile exploration of raised line drawings of faces and objects, tactile memory retrieval via drawing, and a scribble motor/memory control. FMRI before and after a week of the Cognitive-Kinesthetic training on these tasks revealed a significant increase in PRC-to-V1 Granger causality from pre- to post-training during the memory drawing task, but not during the motor/memory control. This increase in causal connectivity indicates that the training strengthened the top-down modulation of visual cortex from the PRC. This is the first study to demonstrate enhanced directed functional connectivity from the PRC to the visual cortex in the blind, implicating the PRC as a potential source of the reorganization towards tactile representations that occurs in V1 in the blind brain

  18. Evaluation of net causal influences in the circuit responding to premotor control during the movement-readiness state using conditional Granger causality.

    PubMed

    Wang, Yuqing; Chen, Huafu; Gao, Qing; Yang, Yihong; Gong, Qiyong; Gao, Fabao

    2015-01-21

    As an initialization procedure for brain responding to subsequent movement execution (ME), the movement-readiness (MR) state is important for understanding the formation processes from daily movement training to long-term memory of movement pattern. As such, based on functional magnetic resonance imaging (fMRI), the net causal influences among regions contributing to premotor control during the MR state were explored by means of conditional Granger causality (CGC) and graph-theory methods in the present study. Our results found that net causal circuits responding to unimanual MR were identified during right-hand or left-hand MR, involving in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), upper precuneus (UPCU), caudate nucleus (CN), cingulate motor area (CMA), supplementary motor area (SMA) and primary sensorimotor area (S1M1). Moreover, the contralateral CN, SMA and S1M1 revealed greater net causal influences during unimanual MR, which highlighted the contralateral dominant modulations during unimanual MR. Furthermore, according as the graph-theory analysis, the higher In+Out degrees of upper precuneus (UPCU) during right-hand MR or higher In+Out degrees of cingulate motor area (CMA) and posterior cingulate cortex (PCC) during left-hand MR implied the brain asymmetry of causal connectivity in the circuit responding to right-hand or left-hand MR. This article is part of a Special Issue entitled SI: Brain and Memory.

  19. Investigation of effective connectivity in the motor cortex of fMRI data using Granger causality model

    NASA Astrophysics Data System (ADS)

    Wu, Xingchun; Tang, Ni; Yin, Kai; Wu, Xia; Wen, Xiaotong; Yao, Li; Zhao, Xiaojie

    2007-03-01

    Effective connectivity of brain regions based on brain data (e.g. EEG, fMRI, etc.) is a focused research at present. Many researchers tried to investigate it using different methods. Granger causality model (GCM) is presently used to investigate effective connectivity of brain regions more and more. It can explore causal relationship between time series, meaning that if a time-series y causes x, then knowledge of y should help predict future values of x. In present work, time invariant GCM was applied to fMRI data considering slow changing of blood oxygenation level dependent (BOLD). The time invariant GCM often requires determining model order, estimating model parameters and significance test. In particular, we extended significance test method to make results more reasonable. The fMRI data were acquired from finger movement experiment of two right-handed subjects. We obtained the activation maps of two subjects using SPM'2 software firstly. Then we chose left SMA and left SMC as regions of interest (ROIs) with different radiuses, and calculated causality from left SMA to left SMC using the mean time courses of the two ROIs. The results from both subjects showed that left SMA influenced on left SMC. Hence GCM was suggested to be an effective approach in investigation of effective connectivity based on fMRI data.

  20. Multivariate Granger Causality Analysis of Acupuncture Effects in Mild Cognitive Impairment Patients: An fMRI Study

    PubMed Central

    Bai, Lijun; Xu, Maosheng; Wang, Fang; Yin, Liang; Peng, Xuming; Chen, Xinghua; Shi, Xuemin

    2013-01-01

    Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments. PMID:24023568

  1. Sentential influences on acoustic-phonetic processing: A Granger causality analysis of multimodal imaging data

    PubMed Central

    Gow, David W.; Olson, Bruna B.

    2015-01-01

    Sentential context influences the way that listeners identify phonetically ambiguous or perceptual degraded speech sounds. Unfortunately, inherent inferential limitations on the interpretation of behavioral or BOLD imaging results make it unclear whether context influences perceptual processing directly, or acts at a post-perceptual decision stage. In this paper, we use Kalman-filter enabled Granger causation analysis of MR-constrained MEG/EEG data to distinguish between these possibilities. Using a retrospective probe verification task, we found that sentential context strongly affected the interpretation of words with ambiguous initial voicing (e.g. DUSK-TUSK). This behavioral context effect coincided with increased influence by brain regions associated with lexical representation on regions associated with acoustic-phonetic processing. These results support an interactive view of sentence context effects on speech perception. PMID:27595118

  2. Measuring frequency domain granger causality for multiple blocks of interacting time series.

    PubMed

    Faes, Luca; Nollo, Giandomenico

    2013-04-01

    In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing measures to the analysis of multiple blocks of time series. Specifically, the block DC (bDC) and block PDC (bPDC) extend DC and PDC to vector-valued processes, while their logarithmic counterparts, denoted as multivariate total feedback [Formula: see text] and direct feedback [Formula: see text], represent into a full multivariate framework the Geweke's measures. Theoretical analysis of the proposed measures shows that they: (i) possess desirable properties of causality measures; (ii) are able to reflect either direct causality (bPDC, [Formula: see text] or total (direct + indirect) causality (bDC, [Formula: see text] between time series blocks; (iii) reduce to the DC and PDC measures for scalar-valued processes, and to the Geweke's measures for pairs of processes; (iv) are able to capture internal dependencies between the scalar constituents of the analyzed vector processes. Numerical analysis showed that the proposed measures can be efficiently estimated from short time series, allow to represent in an objective, compact way the information derived from the causal analysis of several pairs of time series, and may detect frequency domain causality more accurately than existing measures. The proposed measures find their natural application in the evaluation of directional

  3. The role of the premotor cortex and the primary motor cortex in action verb comprehension: evidence from Granger causality analysis.

    PubMed

    Yang, Jie; Shu, Hua

    2012-08-01

    Although numerous studies find the premotor cortex and the primary motor cortex are involved in action language comprehension, so far the nature of these motor effects is still in controversy. Some researchers suggest that the motor effects reflect that the premotor cortex and the primary motor cortex make functional contributions to the semantic access of action verbs, while other authors argue that the motor effects are caused by comprehension. In the current study, we used Granger causality analysis to investigate the roles of the premotor cortex and the primary motor cortex in processing of manual-action verbs. Regions of interest were selected in the primary motor cortex (M1) and the premotor cortex based on a hand motion task, and in the left posterior middle temporal gyrus (lexical semantic area) based on the reading task effect. We found that (1) the left posterior middle temporal gyrus had a causal influence on the left M1; and (2) the left posterior middle temporal gyrus and the left premotor cortex had bidirectional causal relations. These results suggest that the premotor cortex and the primary motor cortex play different roles in manual verb comprehension. The premotor cortex may be involved in motor simulation that contributes to action language processing, while the primary motor cortex may be engaged in a processing stage influenced by the meaning access of manual-action verbs. Further investigation combining effective connectivity analysis and technique with high temporal resolution is necessary for better clarification of the roles of the premotor cortex and the primary motor cortex in action language comprehension.

  4. Higher Education, Real Income and Real Investment in China: Evidence from Granger Causality Tests

    ERIC Educational Resources Information Center

    Narayan, Paresh Kumar; Smyth, Russell

    2006-01-01

    This paper employs cointegration and error-correction modelling to test the causal relationship between real income, real investment and tertiary education using data for the People's Republic of China over the period 1952-1999. To proxy tertiary education we use higher education enrolments and higher education graduates in alternative empirical…

  5. Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory.

    PubMed

    Protopapa, Foteini; Siettos, Constantinos I; Evdokimidis, Ioannis; Smyrnis, Nikolaos

    2014-01-01

    We employed spectral Granger causality analysis on a full set of 56 electroencephalographic recordings acquired during the execution of either a 2D movement pointing or a perceptual (yes/no) change detection task with memory and non-memory conditions. On the basis of network characteristics across frequency bands, we provide evidence for the full dissociation of the corresponding cognitive processes. Movement-memory trial types exhibited higher degree nodes during the first 2 s of the delay period, mainly at central, left frontal and right-parietal areas. Change detection-memory trial types resulted in a three-peak temporal pattern of the total degree with higher degree nodes emerging mainly at central, right frontal, and occipital areas. Functional connectivity networks resulting from non-memory trial types were characterized by more sparse structures for both tasks. The movement-memory trial types encompassed an apparent coarse flow from frontal to parietal areas while the opposite flow from occipital, parietal to central and frontal areas was evident for the change detection-memory trial types. The differences among tasks and conditions were more profound in α (8-12 Hz) and β (12-30 Hz) and less in γ (30-45 Hz) band. Our results favor the hypothesis which considers spatial working memory as a by-product of specific mental processes that engages common brain areas under different network organizations.

  6. Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

    NASA Astrophysics Data System (ADS)

    Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

    2011-03-01

    Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

  7. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data.

    PubMed

    Fallani, Fabrizio De Vico; Corazzol, Martina; Sternberg, Jenna R; Wyart, Claire; Chavez, Mario

    2015-05-01

    The recent development of genetically encoded calcium indicators enables monitoring in vivo the activity of neuronal populations. Most analysis of these calcium transients relies on linear regression analysis based on the sensory stimulus applied or the behavior observed. To estimate the basic properties of the functional neural circuitry, we propose a network approach to calcium imaging recorded at single cell resolution. Differently from previous analysis based on cross-correlation, we used Granger-causality estimates to infer information propagation between the activities of different neurons. The resulting functional network was then modeled as a directed graph and characterized in terms of connectivity and node centralities. We applied our approach to calcium transients recorded at low frequency (4 Hz) in ventral neurons of the zebrafish spinal cord at the embryonic stage when spontaneous coiling of the tail occurs. Our analysis on population calcium imaging data revealed a strong ipsilateral connectivity and a characteristic hierarchical organization of the network hubs that supported established propagation of activity from rostral to caudal spinal cord. Our method could be used for detecting functional defects in neuronal circuitry during development and pathological conditions.

  8. Observational evidence for impacts of vegetation change on local surface climate over northern China using the Granger causality test

    NASA Astrophysics Data System (ADS)

    Jiang, Bo; Liang, Shunlin; Yuan, Wenping

    2015-01-01

    three-north region in China (northeastern, northwestern, and northern China) is one of the most environmentally vulnerable regions in the country. To improve the local natural environment, the Chinese government launched the Three-North Shelter Forest Program, one of the largest afforestation/reforestation programs in the world. This program has led to significant changes in vegetation. Although many studies have evaluated the impacts of vegetation changes on local climate in this region, their results are highly inconsistent. In this study, evidence for local monthly climate impacts of vegetation change was investigated using remotely sensed data and ground meteorological measurements during the growing season (May to September) from 1982 to 2011 using the bivariate Granger causality test. The results showed that the local near-surface climate is sensitive mostly to vegetation changes characterized by the normalized difference vegetation index (NDVI) in arid and semiarid regions and that vegetation plays a more important role in influencing hydroclimate in the arid/semiarid zones than in other zones, which has great implications for water resources in this dry region. Moreover, NDVI changes in northeastern China have a significantly negative influence on air tembut no other climatic variables, whereas the test results in northern China is not as objective as the other zones due to the rapid urbanization. All these results suggest that the local climate is very sensitive to the variations in vegetation in arid and semiarid regions, so extra caution should be taken when planting trees in this area.

  9. Theta-rhythmic drive between medial septum and hippocampus in slow-wave sleep and microarousal: a Granger causality analysis.

    PubMed

    Kang, D; Ding, M; Topchiy, I; Shifflett, L; Kocsis, B

    2015-11-01

    Medial septum (MS) plays a critical role in controlling the electrical activity of the hippocampus (HIPP). In particular, theta-rhythmic burst firing of MS neurons is thought to drive lasting HIPP theta oscillations in rats during waking motor activity and REM sleep. Less is known about MS-HIPP interactions in nontheta states such as non-REM sleep, in which HIPP theta oscillations are absent but theta-rhythmic burst firing in subsets of MS neurons is preserved. The present study used Granger causality (GC) to examine the interaction patterns between MS and HIPP in slow-wave sleep (SWS, a nontheta state) and during its short interruptions called microarousals (a transient theta state). We found that during SWS, while GC revealed a unidirectional MS→HIPP influence over a wide frequency band (2-12 Hz, maximum: ∼8 Hz), there was no theta peak in the hippocampal power spectra, indicating a lack of theta activity in HIPP. In contrast, during microarousals, theta peaks were seen in both MS and HIPP power spectra and were accompanied by bidirectional GC with MS→HIPP and HIPP→MS theta drives being of equal magnitude. Thus GC in a nontheta state (SWS) vs. a theta state (microarousal) primarily differed in the level of HIPP→MS. The present findings suggest a modification of our understanding of the role of MS as the theta generator in two regards. First, a MS→HIPP theta drive does not necessarily induce theta field oscillations in the hippocampus, as found in SWS. Second, HIPP theta oscillations entail bidirectional theta-rhythmic interactions between MS and HIPP.

  10. Olfactory Network Differences in Master Sommeliers: Connectivity Analysis Using Granger Causality and Graph Theoretical Approach.

    PubMed

    Sreenivasan, Karthik; Zhuang, Xiaowei; Banks, Sarah J; Mishra, Virendra; Yang, Zhengshi; Deshpande, Gopikrishna; Cordes, Dietmar

    2017-03-01

    Previous studies investigating the differences in olfactory processing and judgments between trained sommeliers and controls have shown increased activations in brain regions involving higher level cognitive processes in sommeliers. However, there is little information about the influence of expertise on causal connectivity and topological properties of the connectivity networks between these regions. Therefore, the current study focuses on addressing these questions in a functional magnetic resonance imaging (fMRI) study of olfactory perception in Master Sommeliers. fMRI data were acquired from Master Sommeliers and control participants during different olfactory and nonolfactory tasks. Mean time series were extracted from 90 different regions of interest (ROIs; based on Automated Anatomical Labeling atlas). The underlying neuronal variables were extracted using blind hemodynamic deconvolution and then input into a dynamic multivariate autoregressive model to obtain connectivity between every pair of ROIs as a function of time. These connectivity values were then statistically compared to obtain paths that were significantly different between the two groups. The obtained connectivity matrices were further studied using graph theoretical methods. In sommeliers, significantly greater connectivity was observed in connections involving the precuneus, caudate, putamen, and several frontal and temporal regions. The controls showed increased connectivity from the left hippocampus to the frontal regions. Furthermore, the sommeliers exhibited significantly higher small-world topology than the controls. These findings are significant, given that learning about neuroplasticity in adulthood in these regions may then have added clinical importance in diseases such as Alzheimer's and Parkinson's where early neurodegeneration is isolated to regions important in smell.

  11. Exploring the effective connectivity of resting state networks in mild cognitive impairment: an fMRI study combining ICA and multivariate Granger causality analysis.

    PubMed

    Liu, Zhenyu; Bai, Lijun; Dai, Ruwei; Zhong, Chongguang; Wang, Hu; You, Youbo; Wei, Wenjuan; Tian, Jie

    2012-01-01

    Mild cognitive impairment (MCI) was recognized as the prodromal stage of Alzheimer's disease (AD). Recent neuroimaging studies have shown that the cognitive and memory decline in AD and MCI patients is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses of small-world attributes. However, the causal interactions among the spatially isolated but function-related resting state networks (RSNs) are still largely unexplored in MCI patients. In this study, we first identified eight RSNs by independent components analysis (ICA) from resting state functional MRI data of 16 MCI patients and 18 age-matched healthy subjects respectively. Then, we performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that MCI patients exhibited decreased causal interactions among the RSNs in both intensity and quantity compared with normal controls. Results from mGCA indicated that the causal interactions involving the default mode network (DMN) became weaker in MCI patients, while stronger causal connectivity emerged related to the memory network and executive control network. Our findings suggested that the DMN played a less important role in MCI patients. Increased causal connectivity of the memory network and executive control network may elucidate the dysfunctional and compensatory processes in the brain networks of MCI patients. These preliminary findings may be helpful for further understanding the pathological mechanisms of MCI and provide a new clue to explore the neurophysiological mechanisms of MCI.

  12. Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism.

    PubMed

    Doerfler, Hannes; Lyon, David; Nägele, Thomas; Sun, Xiaoliang; Fragner, Lena; Hadacek, Franz; Egelhofer, Volker; Weckwerth, Wolfram

    2013-06-01

    Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to mass spectrometry (LC-MS). Each platform has a specific performance detecting subsets of metabolites. GC-MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC-MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC-MS and LC-MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC-LC-MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.

  13. Lexical mediation of phonotactic frequency effects on spoken word recognition: A Granger causality analysis of MRI-constrained MEG/EEG data

    PubMed Central

    Gow, David W.; Olson, Bruna B.

    2015-01-01

    Phonotactic frequency effects play a crucial role in a number of debates over language processing and representation. It is unclear however, whether these effects reflect prelexical sensitivity to phonotactic frequency, or lexical “gang effects” in speech perception. In this paper, we use Granger causality analysis of MR-constrained MEG/EEG data to understand how phonotactic frequency influences neural processing dynamics during auditory lexical decision. Effective connectivity analysis showed weaker feedforward influence from brain regions involved in acoustic-phonetic processing (superior temporal gyrus) to lexical areas (supramarginal gyrus) for high phonotactic frequency words, but stronger top-down lexical influence for the same items. Low entropy nonwords (nonwords judged to closely resemble real words) showed a similar pattern of interactions between brain regions involved in lexical and acoustic-phonetic processing. These results contradict the predictions of a feedforward model of phonotactic frequency facilitation, but support the predictions of a lexically mediated account. PMID:25883413

  14. Lexical mediation of phonotactic frequency effects on spoken word recognition: A Granger causality analysis of MRI-constrained MEG/EEG data.

    PubMed

    Gow, David W; Olson, Bruna B

    2015-07-01

    Phonotactic frequency effects play a crucial role in a number of debates over language processing and representation. It is unclear however, whether these effects reflect prelexical sensitivity to phonotactic frequency, or lexical "gang effects" in speech perception. In this paper, we use Granger causality analysis of MR-constrained MEG/EEG data to understand how phonotactic frequency influences neural processing dynamics during auditory lexical decision. Effective connectivity analysis showed weaker feedforward influence from brain regions involved in acoustic-phonetic processing (superior temporal gyrus) to lexical areas (supramarginal gyrus) for high phonotactic frequency words, but stronger top-down lexical influence for the same items. Low entropy nonwords (nonwords judged to closely resemble real words) showed a similar pattern of interactions between brain regions involved in lexical and acoustic-phonetic processing. These results contradict the predictions of a feedforward model of phonotactic frequency facilitation, but support the predictions of a lexically mediated account.

  15. Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson's disease: New Insight of Resting State Analysis Using Granger Causality and Graphical Approach.

    PubMed

    Ghasemi, Mahdieh; Mahloojifar, Ali

    2013-04-01

    Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that regions with high degree values could be detected as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in PD.

  16. Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson's disease: New Insight of Resting State Analysis Using Granger Causality and Graphical Approach

    PubMed Central

    Ghasemi, Mahdieh; Mahloojifar, Ali

    2013-01-01

    Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that regions with high degree values could be detected as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in PD. PMID:24098860

  17. Altered amygdala and hippocampus effective connectivity in mild cognitive impairment patients with depression: a resting-state functional MR imaging study with granger causality analysis.

    PubMed

    Zheng, Li Juan; Yang, Gui Fen; Zhang, Xin Yuan; Wang, Yun Fei; Liu, Ya; Zheng, Gang; Lu, Guang Ming; Zhang, Long Jiang; Han, Ying

    2017-02-15

    Neuroimaging studies have demonstrated that the major depression disorder would increase the risk of dementia in the older with amnestic cognitive impairment. We used granger causality analysis algorithm to explore the amygdala- and hippocampus-based directional connectivity patterns in 12 patients with major depression disorder and amnestic cognitive impairment (mean age: 69.5 ± 10.3 years), 13 amnestic cognitive impairment patients (mean age: 72.7 ± 8.5 years) and 14 healthy controls (mean age: 64.7 ± 7.0 years). Compared with amnestic cognitive impairment patients and control groups respectively, the patients with both major depression disorder and amnestic cognitive impairment displayed increased effective connectivity from the right amygdala to the right lingual and calcarine gyrus, as well as to the bilateral supplementary motor areas. Meanwhile, the patients with both major depression disorder and amnestic cognitive impairment had enhanced effective connectivity from the left superior parietal gyrus, superior and middle occipital gyrus to the left hippocampus, the z values of which was also correlated with the scores of mini-mental state examination and auditory verbal learning test-immediate recall. Our findings indicated that the directional effective connectivity of right amygdala - occipital-parietal lobe - left hippocampus might be the pathway by which major depression disorder inhibited the brain activity in patients with amnestic cognitive impairment.

  18. Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

    PubMed Central

    Hu, Yanzhu; Ai, Xinbo

    2016-01-01

    Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring association network from multivariate time series. The method synthesizes surrogate data, partial symbolic transfer entropy (PSTE) and Granger causality. A proper threshold selection is crucial for common correlation identification methods and it is not easy for users. The proposed method can not only identify the strong correlation without selecting a threshold but also has the ability of correlation quantification, direction identification and temporal relation identification. The method can be divided into three layers, i.e. data layer, model layer and network layer. In the model layer, the method identifies all the possible pair-wise correlation. In the network layer, we introduce a filter algorithm to remove the indirect weak correlation and retain strong correlation. Finally, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pair-wise variables, and then get the weighted directed association network. Two numerical simulated data from linear system and nonlinear system are illustrated to show the steps and performance of the proposed approach. The ability of the proposed method is approved by an application finally. PMID:27832153

  19. Altered Effective Connectivity of the Primary Motor Cortex in Stroke: A Resting-State fMRI Study with Granger Causality Analysis

    PubMed Central

    Fan, Mingxia; Yin, Dazhi; Sun, Limin; Jia, Jie; Tang, Chaozheng; Zheng, Xiaohui; Jiang, Yuwei; Wu, Jie; Gong, Jiayu

    2016-01-01

    The primary motor cortex (M1) is often abnormally recruited in stroke patients with motor disabilities. However, little is known about the alterations in the causal connectivity of M1 following stroke. The purpose of the present study was to investigate whether the effective connectivity of the ipsilesional M1 is disturbed in stroke patients who show different outcomes in hand motor function. 23 patients with left-hemisphere subcortical stroke were selected and divided into two subgroups: partially paralyzed hands (PPH) and completely paralyzed hands (CPH). Further, 24 matched healthy controls (HCs) were recruited. A voxel-wise Granger causality analysis (GCA) on the resting-state fMRI data between the ipsilesional M1 and the whole brain was performed to explore differences between the three groups. Our results showed that the influence from the frontoparietal cortices to ipsilesional M1 was diminished in both stroke subgroups and the influence from ipsilesional M1 to the sensorimotor cortices decreased greater in the CPH group than in the PPH group. Moreover, compared with the PPH group, the decreased influence from ipsilesional M1 to the contralesional cerebellum and from the contralesional superior parietal lobe to ipsilesional M1 were observed in the CPH group, and their GCA values were positively correlated with the FMA scores; Conversely, the increased influence from ipsilesional M1 to the ipsilesional middle frontal gyrus and middle temporal gyrus were observed, whose GCA values were negatively correlated with the FMA scores. This study suggests that the abnormalities of casual flow in the ipsilesional M1 are related to the severity of stroke-hand dysfunction, providing valuable information to understand the deficits in resting-state effective connectivity of motor execution and the frontoparietal motor control network during brain plasticity following stroke. PMID:27846290

  20. Control over the strength of connections between modules: a double dissociation between stimulus format and task revealed by Granger causality mapping in fMRI.

    PubMed

    Anderson, Britt; Soliman, Sherif; O'Malley, Shannon; Danckert, James; Besner, Derek

    2015-01-01

    Drawing on theoretical and computational work with the localist dual route reading model and results from behavioral studies, Besner et al. (2011) proposed that the ability to perform tasks that require overriding stimulus-specific defaults (e.g., semantics when naming Arabic numerals, and phonology when evaluating the parity of number words) necessitate the ability to modulate the strength of connections between cognitive modules for lexical representation, semantics, and phonology on a task- and stimulus-specific basis. We used functional magnetic resonance imaging to evaluate this account by assessing changes in functional connectivity while participants performed tasks that did and did not require such stimulus-task default overrides. The occipital region showing the greatest modulation of BOLD signal strength for the two stimulus types was used as the seed region for Granger causality mapping (GCM). Our GCM analysis revealed a region of rostromedial frontal cortex with a crossover interaction. When participants performed tasks that required overriding stimulus type defaults (i.e., parity judgments of number words and naming Arabic numerals) functional connectivity between the occipital region and rostromedial frontal cortex was present. Statistically significant functional connectivity was absent when the tasks were the default for the stimulus type (i.e., parity judgments of Arabic numerals and reading number words). This frontal region (BA 10) has previously been shown to be involved in goal-directed behavior and maintenance of a specific task set. We conclude that overriding stimulus-task defaults requires a modulation of connection strengths between cognitive modules and that the override mechanism predicted from cognitive theory is instantiated by frontal modulation of neural activity of brain regions specialized for sensory processing.

  1. Globally conditioned Granger causality in brain-brain and brain-heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study.

    PubMed

    Duggento, Andrea; Bianciardi, Marta; Passamonti, Luca; Wald, Lawrence L; Guerrisi, Maria; Barbieri, Riccardo; Toschi, Nicola

    2016-05-13

    The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain-heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain-brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-brain and brain-heart interactions reflecting

  2. Wiener-Granger causality for effective connectivity in the hidden states: Indication from probabilistic causality. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

    NASA Astrophysics Data System (ADS)

    Tang, Wei

    2015-12-01

    Statistics and probability theory have advanced our understanding of random processes widely observed in the physical world. There is a remarkable trend in studying the brain by looking into the stochastic information processing in large-scale brain networks [1,2]. As the review by Mannino and Bressler [3] points out, the probabilistic notion of causality, with its rooted philosophical foundations, represents a revolutionary view on how different parts of the brain interact and integrate to generate function. Specifically, Probabilistic Causality (PC) asserts that a cause should increase the probability of occurrence of its effect, and PC between two brain regions entails that the probability for the activity in one region to occur increases when conditioned on the activity of the other. This definition claims inherent randomness in the causal relationship.

  3. Information Theoretic Causal Coordination

    DTIC Science & Technology

    2013-09-12

    his 1969 paper, Clive Granger , British economist and Nobel laureate, proposed a statistical def- inition of causality between stochastic processes. It...showed that the directed infor- mation, an information theoretic quantity, quantifies Granger causality . We also explored a more pessimistic setup...Final Technical Report Project Title: Information Theoretic Causal Coordination AFOSR Award Number: AF FA9550-10-1-0345 Reporting Period: July 15

  4. Causality

    NASA Astrophysics Data System (ADS)

    Pearl, Judea

    2000-03-01

    Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

  5. Granger causality from changes in level of atmospheric CO2 to global surface temperature and the El Niño-Southern Oscillation, and a candidate mechanism in global photosynthesis

    NASA Astrophysics Data System (ADS)

    Leggett, L. M. W.; Ball, D. A.

    2015-10-01

    A significant difference, now of some 16 years' duration, has been shown to exist between the observed global surface temperature trend and that expected from the majority of climate simulations. For its own sake, and to enable better climate prediction for policy use, the reasons behind this mismatch need to be better understood. While an increasing number of possible causes have been proposed, the candidate causes have not yet converged. With this background, this paper reinvestigates the relationship between change in the level of CO2 and two of the major climate variables, atmospheric temperature and the El Niño-Southern Oscillation (ENSO). Using time-series analysis in the form of dynamic regression modelling with autocorrelation correction, it is shown that first-difference CO2 leads temperature and that there is a highly statistically significant correlation between first-difference CO2 and temperature. Further, a correlation is found for second-difference CO2 with the Southern Oscillation Index, the atmospheric-pressure component of ENSO. This paper also shows that both these correlations display Granger causality. It is shown that the first-difference CO2 and temperature model shows no trend mismatch in recent years. These results may contribute to the prediction of future trends for global temperature and ENSO. Interannual variability in the growth rate of atmospheric CO2 is standardly attributed to variability in the carbon sink capacity of the terrestrial biosphere. The terrestrial biosphere carbon sink is created by the difference between photosynthesis and respiration (net primary productivity): a major way of measuring global terrestrial photosynthesis is by means of satellite measurements of vegetation reflectance, such as the Normalized Difference Vegetation Index (NDVI). In a preliminary analysis, this study finds a close correlation between an increasing NDVI and the increasing climate model/temperature mismatch (as quantified by the difference

  6. Membership Finland

    ScienceCinema

    None

    2016-07-12

    Le DG C.Rubbia et la vice présidente du conseil du Cern souhaite la bienvenue à l'adhésion de la Finlande, comme 15me membre du Cern depuis le 1. janvier 1991 en présence du secrétaire generale et de l'ambassadeur

  7. Membership Finland

    SciTech Connect

    2008-04-10

    Le DG C.Rubbia et la vice présidente du conseil du Cern souhaite la bienvenue à l'adhésion de la Finlande, comme 15me membre du Cern depuis le 1. janvier 1991 en présence du secrétaire generale et de l'ambassadeur

  8. Parallel versus Serial Processing Dependencies in the Perisylvian Speech Network: A Granger Analysis of Intracranial EEG Data

    ERIC Educational Resources Information Center

    Gow, David W., Jr.; Keller, Corey J.; Eskandar, Emad; Meng, Nate; Cash, Sydney S.

    2009-01-01

    In this work, we apply Granger causality analysis to high spatiotemporal resolution intracranial EEG (iEEG) data to examine how different components of the left perisylvian language network interact during spoken language perception. The specific focus is on the characterization of serial versus parallel processing dependencies in the dominant…

  9. Causality between Literacy and Labor Productivity.

    ERIC Educational Resources Information Center

    Khan, Shahrukh R.; And Others

    1991-01-01

    Utilizes the Granger causality test to estimate case and effect relationships between literacy and labor productivity in Pakistani manufacturing and agricultural sectors. Despite data set limitations, literacy appears to "cause" labor productivity to increase in the manufacturing sector. (27 references) (MLH)

  10. A Causality Analysis of the Link between Higher Education and Economic Development.

    ERIC Educational Resources Information Center

    De Meulemeester, Jean-Luc; Rochat, Denis

    1995-01-01

    Summarizes a study exploring the relationship between higher education and economic development, using cointegration and Granger-causality tests. Results show a significant causality from higher education efforts in Sweden, United Kingdom, Japan, and France. However, a similar causality link has not been found for Italy or Australia. (68…

  11. Distinguishing causal interactions in neural populations.

    PubMed

    Seth, Anil K; Edelman, Gerald M

    2007-04-01

    We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.

  12. Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

    PubMed Central

    Hu, Sanqing; Dai, Guojun; Worrell, Gregory A.; Dai, Qionghai; Liang, Hualou

    2012-01-01

    Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time–frequency power spectrum

  13. Causality analysis of neural connectivity: critical examination of existing methods and advances of new methods.

    PubMed

    Hu, Sanqing; Dai, Guojun; Worrell, Gregory A; Dai, Qionghai; Liang, Hualou

    2011-06-01

    Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time-frequency power spectrum

  14. Coping with dating errors in causality estimation

    NASA Astrophysics Data System (ADS)

    Smirnov, D. A.; Marwan, N.; Breitenbach, S. F. M.; Lechleitner, F.; Kurths, J.

    2017-01-01

    We consider the problem of estimating causal influences between observed processes from time series possibly corrupted by errors in the time variable (dating errors) which are typical in palaeoclimatology, planetary science and astrophysics. “Causality ratio” based on the Wiener-Granger causality is proposed and studied for a paradigmatic class of model systems to reveal conditions under which it correctly indicates directionality of unidirectional coupling. It is argued that in the case of a priori known directionality, the causality ratio allows a characterization of dating errors and observational noise. Finally, we apply the developed approach to palaeoclimatic data and quantify the influence of solar activity on tropical Atlantic climate dynamics over the last two millennia. A stronger solar influence in the first millennium A.D. is inferred. The results also suggest a dating error of about 20 years in the solar proxy time series over the same period.

  15. Effective connectivity: Influence, causality and biophysical modeling

    PubMed Central

    Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl

    2011-01-01

    This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655

  16. Adult Education in Finland

    ERIC Educational Resources Information Center

    Szekely, Radu

    2006-01-01

    Ever since the first ideas of national independence appeared in Finland, adult education has played an essential role in shaping the destiny of the Finns. With a history of almost 130 years, during which it has continuously increased in quality and quantity, the Finnish adult education system has ensured that Finland stays among the most…

  17. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression

    PubMed Central

    Nicolaou, Nicoletta; Constandinou, Timothy G.

    2016-01-01

    Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, CNPMR, Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply CNPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). CNPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. CNPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. PMID:27378901

  18. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression.

    PubMed

    Nicolaou, Nicoletta; Constandinou, Timothy G

    2016-01-01

    Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C NPMR , Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C NPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C NPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications.

  19. Causal and causally separable processes

    NASA Astrophysics Data System (ADS)

    Oreshkov, Ognyan; Giarmatzi, Christina

    2016-09-01

    The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

  20. Improving causal inferences in risk analysis.

    PubMed

    Cox, Louis Anthony Tony

    2013-10-01

    Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi-experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change-point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure-specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure-induced health effects, helping to overcome pervasive false-positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.

  1. Finland's Cleanup Campaign

    ERIC Educational Resources Information Center

    Environmental Science and Technology, 1975

    1975-01-01

    Finland has received a $20 million loan from the World Bank to attack its pollution problems, mainly water. Improved quality of life, as well as resource conservation are both motives and goals of that country's environmental programs. (BT)

  2. Algorithms of causal inference for the analysis of effective connectivity among brain regions

    PubMed Central

    Chicharro, Daniel; Panzeri, Stefano

    2014-01-01

    In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl’s causality, algorithms of inductive causation (IC and IC*) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity. PMID:25071541

  3. Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey

    ERIC Educational Resources Information Center

    Erdem, Ekrem; Tugcu, Can Tansel

    2012-01-01

    This article analyses the short and the long-term relations between higher education and unemployment in Turkey for the period 1960-2007. It chooses the recently developed ARDL cointegration and Granger causality of Dolado and Lutkepohl (1996) methods. While the proxy of unemployment is total unemployment rate, higher education graduates were…

  4. Education and Economic Growth in Pakistan: A Cointegration and Causality Analysis

    ERIC Educational Resources Information Center

    Afzal, Muhammad; Rehman, Hafeez Ur; Farooq, Muhammad Shahid; Sarwar, Kafeel

    2011-01-01

    This study explored the cointegration and causality between education and economic growth in Pakistan by using time series data on real gross domestic product (RGDP), labour force, physical capital and education from 1970-1971 to 2008-2009 were used. Autoregressive Distributed Lag (ARDL) Model of Cointegration and the Augmented Granger Causality…

  5. Women in physics in Finland

    NASA Astrophysics Data System (ADS)

    Banzuzi, Kukka

    2013-03-01

    The representation of women in physics and related fields of study in Finland, career advancement of female physicists in Finland, and the actions carried out in recent years to improve the situation are summarized.

  6. Causal measures of structure and plasticity in simulated and living neural networks.

    PubMed

    Cadotte, Alex J; DeMarse, Thomas B; He, Ping; Ding, Mingzhou

    2008-10-07

    A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify "causal" relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal

  7. Investigation of cointegration and causality between energy consumption and employment with implications for the environment

    SciTech Connect

    Cheng, B.S.; Andrews, D.R.; Birkett, B.S.

    1998-08-01

    Applying Hsiao`s version of the Granger causality method, this article examines the multivariate causality between energy consumption and employment with the environmental implications for the US. Unit root and cointegration tests are performed. While no causality from energy consumption to employment is found, a reverse causality from employment to energy consumption is detected. This study implies that a reduction in energy consumption may reduce air pollution and preserve clean air, yet it may not cause major changes in the level of employment for those economies that are service oriented.

  8. Causal and Structural Connectivity of Pulse-Coupled Nonlinear Networks

    NASA Astrophysics Data System (ADS)

    Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

    2013-08-01

    We study the reconstruction of structural connectivity for a general class of pulse-coupled nonlinear networks and show that the reconstruction can be successfully achieved through linear Granger causality (GC) analysis. Using spike-triggered correlation of whitened signals, we obtain a quadratic relationship between GC and the network couplings, thus establishing a direct link between the causal connectivity and the structural connectivity within these networks. Our work may provide insight into the applicability of GC in the study of the function of general nonlinear networks.

  9. Discrimination of coupling structures using causality networks from multivariate time series

    NASA Astrophysics Data System (ADS)

    Koutlis, Christos; Kugiumtzis, Dimitris

    2016-09-01

    Measures of Granger causality on multivariate time series have been used to form the so-called causality networks. A causality network represents the interdependence structure of the underlying dynamical system or coupled dynamical systems, and its properties are quantified by network indices. In this work, it is investigated whether network indices on networks generated by an appropriate Granger causality measure can discriminate different coupling structures. The information based Granger causality measure of partial mutual information from mixed embedding (PMIME) is used to form causality networks, and a large number of network indices are ranked according to their ability to discriminate the different coupling structures. The evaluation of the network indices is done with a simulation study based on two dynamical systems, the coupled Mackey-Glass delay differential equations and the neural mass model, both of 25 variables, and three prototypes of coupling structures, i.e., random, small-world, and scale-free. It is concluded that the setting of PMIME combined with a network index attains high level of discrimination of the coupling structures solely on the basis of the observed multivariate time series. This approach is demonstrated to identify epileptic seizures emerging during electroencephalogram recordings.

  10. Relativistic causality

    NASA Astrophysics Data System (ADS)

    Valente, Giovanni; Owen Weatherall, James

    2014-11-01

    Relativity theory is often taken to include, or to imply, a prohibition on superluminal propagation of causal processes. Yet, what exactly the prohibition on superluminal propagation amounts to and how one should deal with its possible violation have remained open philosophical problems, both in the context of the metaphysics of causation and the foundations of physics. In particular, recent work in philosophy of physics has focused on the causal structure of spacetime in relativity theory and on how this causal structure manifests itself in our most fundamental theories of matter. These topics were the subject of a workshop on "Relativistic Causality in Quantum Field Theory and General Relativity" that we organized (along with John Earman) at the Center for Philosophy of Science in Pittsburgh on April 5-7, 2013. The present Special Issue comprises contributions by speakers in that workshop as well as several other experts exploring different aspects of relativistic causality. We are grateful to the journal for hosting this Special Issue, to the journal's managing editor, Femke Kuiling, for her help and support in putting the issue together, and to the authors and the referees for their excellent work.

  11. Epidemiological causality.

    PubMed

    Morabia, Alfredo

    2005-01-01

    Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population.

  12. Sami Education in Finland

    ERIC Educational Resources Information Center

    Keskitalo, Pigga; Maatta, Kaarina; Uusiautti, Satu

    2012-01-01

    The purpose of this article is, first, to describe Sami children's education and its status in the Finnish education system and, secondly, to contemplate its development in Finland. The core of the article is intertwined with issues concerning the status, language, and culture of indigenous peoples. According to the article, the western school…

  13. Exploratory Causal Analysis in Bivariate Time Series Data

    NASA Astrophysics Data System (ADS)

    McCracken, James M.

    Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data

  14. Causality links among renewable energy consumption, CO2 emissions, and economic growth in Africa: evidence from a panel ARDL-PMG approach.

    PubMed

    Attiaoui, Imed; Toumi, Hassen; Ammouri, Bilel; Gargouri, Ilhem

    2017-04-05

    This research examines the causality (For the remainder of the paper, the notion of causality refers to Granger causality.) links among renewable energy consumption (REC), CO2 emissions (CE), non-renewable energy consumption (NREC), and economic growth (GDP) using an autoregressive distributed lag model based on the pooled mean group estimation (ARDL-PMG) and applying Granger causality tests for a panel consisting of 22 African countries for the period between 1990 and 2011. There is unidirectional and irreversible short-run causality from CE to GDP. The causal direction between CE and REC is unobservable over the short-term. Moreover, we find unidirectional, short-run causality from REC to GDP. When testing per pair of variables, there are short-run bidirectional causalities among REC, CE, and GDP. However, if we add CE to the variables REC and NREC, the causality to GDP is observable, and causality from the pair REC and NREC to economic growth is neutral. Likewise, if we add NREC to the variables GDP and REC, there is causality. There are bidirectional long-run causalities among REC, CE, and GDP, which supports the feedback assumption. Causality from GDP to REC is not strong for the panel. If we test per pair of variables, the strong causality from GDP and CE to REC is neutral. The long-run PMG estimates show that NREC and gross domestic product increase CE, whereas REC decreases CE.

  15. Modeling positive Granger causality and negative phase lag between cortical areas.

    PubMed

    Matias, Fernanda S; Gollo, Leonardo L; Carelli, Pedro V; Bressler, Steven L; Copelli, Mauro; Mirasso, Claudio R

    2014-10-01

    Different measures of directional influence have been employed to infer effective connectivity in the brain. When the connectivity between two regions is such that one of them (the sender) strongly influences the other (the receiver), a positive phase lag is often expected. The assumption is that the time difference implicit in the relative phase reflects the transmission time of neuronal activity. However, Brovelli et al. (2004) observed that, in monkeys engaged in processing a cognitive task, a dominant directional influence from one area of sensorimotor cortex to another may be accompanied by either a negative or a positive time delay. Here we present a model of two brain regions, coupled with a well-defined directional influence, that displays similar features to those observed in the experimental data. This model is inspired by the theoretical framework of Anticipated Synchronization developed in the field of dynamical systems. Anticipated Synchronization is a form of synchronization that occurs when a unidirectional influence is transmitted from a sender to a receiver, but the receiver leads the sender in time. This counterintuitive synchronization regime can be a stable solution of two dynamical systems coupled in a master-slave (sender-receiver) configuration when the slave receives a negative delayed self-feedback. Despite efforts to understand the dynamics of Anticipated Synchronization, experimental evidence for it in the brain has been lacking. By reproducing experimental delay times and coherence spectra, our results provide a theoretical basis for the underlying mechanisms of the observed dynamics, and suggest that the primate cortex could operate in a regime of Anticipated Synchronization as part of normal neurocognitive function.

  16. Causality and Cointegration Analysis between Macroeconomic Variables and the Bovespa

    PubMed Central

    da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

    2014-01-01

    The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelth month, followed by the country risk, with less than 5%. PMID:24587019

  17. Causality and cointegration analysis between macroeconomic variables and the Bovespa.

    PubMed

    da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

    2014-01-01

    The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

  18. Effect of Causal Stories in Solving Mathematical Story Problems

    ERIC Educational Resources Information Center

    Smith, Glenn Gordon; Gerretson, Helen; Olkun, Sinan; Joutsenlahti, Jorma

    2010-01-01

    This study investigated whether infusing "causal" story elements into mathematical word problems improves student performance. In one experiment in the USA and a second in USA, Finland and Turkey, undergraduate elementary education majors worked word problems in three formats: 1) standard (minimal verbiage), 2) potential causation…

  19. Acidification in Finland

    SciTech Connect

    Kauppi, P.; Kenttamies, K.; Anttila, P. )

    1990-01-01

    This book reports on the acidic deposition issue in Finland, representing the main research report of the Finnish Acidification Research Program (HAPRO), designed to support research in this field between 1985 and 1989 with a total budget of about 50 million FIM (about 11 million USD). This overview focuses on a wide selection of acidic deposition topics from an analytical perspective, detailing the background, materials, methods and results of different individual studies. Reports include emission inventories, deposition studies, forest vegetation studies, forest soil investigations, lake chemistry surveys, etc.

  20. Teachers as Leaders in Finland

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2013-01-01

    During the last decade, thousands of visitors have flocked to Finland--now a leader in education rankings--to uncover this small Nordic country's secret to its education success. In this article, Finnish educator and scholar Pasi Sahlberg explains how Finland has managed such a feat. A rigorous graduate degree and at least five years of full-time…

  1. CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks

    PubMed Central

    Emad, Amin; Milenkovic, Olgica

    2014-01-01

    We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime. PMID:24622336

  2. New Levels of Language Processing Complexity and Organization Revealed by Granger Causation

    PubMed Central

    Gow, David W.; Caplan, David N.

    2012-01-01

    Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that even “early” processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of “language-specific” localized processes. PMID:23293611

  3. Granger Lake, Embankment-Outlet Works-Spillway. Volume 2.

    DTIC Science & Technology

    1983-08-01

    conditiLOns (during the construction phase), Problems encountered and nethlods/solutiOris to resolve foundation problems during actual construction of Granger...only 44 Slab Drainage System Section D-D II Spillway sec- tions only 45 Logs of Borings, Drawing Index Index to borings 6DC-60 through 8A-599 (Plat 46...TYPICAL SECTION F ~(EMBANKMENT OVER" BUILD ) NOT To SCALE 5TA 0 +00 TO STA 12 +10 t !red ’co- 5 ee 5he f fL e-- E 3 .mrper-vL1iounc * El 51,3

  4. Echinococcus granulosus in Finland.

    PubMed

    Hirvelä-Koski, Varpu; Haukisalmi, Voitto; Kilpelä, Seija-Sisko; Nylund, Minna; Koski, Perttu

    2003-02-13

    Echinococcus granulosus is shown to occur in eastern Finland in a sylvatic cycle involving wolves (Canis lupus) as the definitive host and reindeer (Rangifer tarandus tarandus) and probably also elk (Alces alces) and the wild forest reindeer (Rangifer tarandus fennicus) as intermediate hosts. Even though the prevalence of E. granulosus in reindeer has increased in recent years, it is still very low (<0.013%). The results suggest, however, that the reindeer is a good sentinel animal to show the contamination of soil with Echinococcus eggs. This is the first report of wolves acting as a definitive host for E. granulosus in northern Europe. The parasite seems to be quite common in the Finnish wolf population, with a prevalence of approximately 30% in both intestinal and faecal samples. The present results and previous ones indicate that E. granulosus infection has not spread to dogs in the reindeer herding area.

  5. Causal reasoning with forces

    PubMed Central

    Wolff, Phillip; Barbey, Aron K.

    2015-01-01

    Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611

  6. International report Finland

    SciTech Connect

    Not Available

    1982-04-01

    The Valentin Shashin, the world's first dynamically positioned ice-class drillship for Arctic conditions, has been completed by Rauma-Repola Oy's Mantyluoto Works in Pori, Finland and delivered to V/O Sudoimport, the Soviet Union. This drillship is the first of three such vessels ordered by the Soviet Union in 1979 for oil exploration in Russia's Arctic waters. All three drillships will be capable of operating in water depths to 300 m and of drilling to 20,000 ft in winds of 23 m/sec, in significant wave heights to 4.7 m and currents to 1 m/sec. Since the vessels are to be used in hostile Arctic conditions, the design incorporates a great deal of sophisticated equipment for operating in heavy seas and avoiding hazards, such as icebergs, that may exist in the drilling area. Included is a quick disconnect system that will allow the ship to detach from the drilling mode in approximately three minutes.

  7. Stochastic causality, criticality, and non-locality in brain networks. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

    NASA Astrophysics Data System (ADS)

    Kozma, Robert; Hu, Sanqing

    2015-12-01

    For millennia, causality served as a powerful guiding principle to our understanding of natural processes, including the functioning of our body, mind, and brain. The target paper presents an impressive vista of the field of causality in brain networks, starting from philosophical issues, expanding on neuroscience effects, and addressing broad engineering and societal aspects as well. The authors conclude that the concept of stochastic causality is more suited to characterize the experimentally observed complex dynamical processes in large-scale brain networks, rather than the more traditional view of deterministic causality. We strongly support this conclusion and provide two additional examples that may enhance and complement this review: (i) a generalization of the Wiener-Granger Causality (WGC) to fit better the complexity of brain networks; (ii) employment of criticality as a key concept highly relevant to interpreting causality and non-locality in large-scale brain networks.

  8. Information flow and causality as rigorous notions ab initio.

    PubMed

    Liang, X San

    2016-11-01

    Information flow or information transfer the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its logical association with causality is firmly rooted in the dynamical system that lies beneath. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which transfer entropy analysis and Granger causality test fail to verify in many situations, turns out to be a proven theorem here. Established in this study are the information flows among the components of time-discrete mappings and time-continuous dynamical systems, both deterministic and stochastic. They have been obtained explicitly in closed form, and put to applications with the benchmark systems such as the Kaplan-Yorke map, Rössler system, baker transformation, Hénon map, and stochastic potential flow. Besides unraveling the causal relations as expected from the respective systems, some of the applications show that the information flow structure underlying a complex trajectory pattern could be tractable. For linear systems, the resulting remarkably concise formula asserts analytically that causation implies correlation, while correlation does not imply causation, providing a mathematical basis for the long-standing philosophical debate over causation versus correlation.

  9. Information flow and causality as rigorous notions ab initio

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2016-11-01

    Information flow or information transfer the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its logical association with causality is firmly rooted in the dynamical system that lies beneath. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which transfer entropy analysis and Granger causality test fail to verify in many situations, turns out to be a proven theorem here. Established in this study are the information flows among the components of time-discrete mappings and time-continuous dynamical systems, both deterministic and stochastic. They have been obtained explicitly in closed form, and put to applications with the benchmark systems such as the Kaplan-Yorke map, Rössler system, baker transformation, Hénon map, and stochastic potential flow. Besides unraveling the causal relations as expected from the respective systems, some of the applications show that the information flow structure underlying a complex trajectory pattern could be tractable. For linear systems, the resulting remarkably concise formula asserts analytically that causation implies correlation, while correlation does not imply causation, providing a mathematical basis for the long-standing philosophical debate over causation versus correlation.

  10. Testing for causality in reconstructed state spaces by an optimized mixed prediction method

    NASA Astrophysics Data System (ADS)

    Krakovská, Anna; Hanzely, Filip

    2016-11-01

    In this study, a method of causality detection was designed to reveal coupling between dynamical systems represented by time series. The method is based on the predictions in reconstructed state spaces. The results of the proposed method were compared with outcomes of two other methods, the Granger VAR test of causality and the convergent cross-mapping. We used two types of test data. The first test example is a unidirectional connection of chaotic systems of Rössler and Lorenz type. The second one, the fishery model, is an example of two correlated observables without a causal relationship. The results showed that the proposed method of optimized mixed prediction was able to reveal the presence and the direction of coupling and distinguish causality from mere correlation as well.

  11. Detecting nonlinear causal interactions between dynamical systems by non-uniform embedding of multiple time series.

    PubMed

    Faes, Luca; Nollo, Giandomenico; Erla, Silvia; Papadelis, Christos; Braun, Christoph; Porta, Alberto

    2010-01-01

    This study introduces a new approach for the detection of nonlinear Granger causality between dynamical systems. The approach is based on embedding the multivariate (MV) time series measured from the systems X and Y by means of a sequential, non-uniform procedure, and on using the corrected conditional entropy (CCE) as unpredictability measure. The causal coupling from X to Y is quantified as the relative decrease of CCE measured after allowing the series of X to enter the embedding procedure for the description of Y. The ability of the approach to quantify nonlinear causality is assessed on MV time series measured from simulated dynamical systems with unidirectional coupling (the Rössler-Lorenz deterministic system) and bidirectional coupling (two coupled stochastic systems). The method is then applied to real magnetoencephalographic data measured during a visuo-tactile cognitive experiment, showing values of causal coupling consistent with the hypothesis of a cross-processing of different sensory modalities.

  12. Canadian Art Partnership Program in Finland

    ERIC Educational Resources Information Center

    Ketovuori, Mikko

    2011-01-01

    This article is about a multidisciplinary R&D project in which a Canadian Learning Through The Arts (LTTA) program was imported to Finland in 2003-2004. Cultural differences in arts education in Finland and Canada are discussed. While Finland has a national school curriculum with all the arts included. Canada relies more on partnerships to…

  13. Assessing dynamic spectral causality by lagged adaptive directed transfer function and instantaneous effect factor.

    PubMed

    Xu, Haojie; Lu, Yunfeng; Zhu, Shanan; He, Bin

    2014-07-01

    It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The nonzero covariance of the model's residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the "causal ordering" is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In this study, we first investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in time

  14. Assessing Dynamic Spectral Causality by Lagged Adaptive Directed Transfer Function and Instantaneous Effect Factor

    PubMed Central

    Xu, Haojie; Lu, Yunfeng; Zhu, Shanan

    2014-01-01

    It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The non-zero covariance of the model’s residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In the present study, we firstly investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in

  15. Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data.

    PubMed

    Zhao, Yifan; Billings, Steve A; Wei, Hualiang; Sarrigiannis, Ptolemaios G

    2012-11-01

    This paper introduces an error reduction ratio-causality (ERR-causality) test that can be used to detect and track causal relationships between two signals. In comparison to the traditional Granger method, one significant advantage of the new ERR-causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Numerical examples are provided to illustrate the effectiveness of the new method together with the determination of the causality between electroencephalograph signals from different cortical sites for patients during an epileptic seizure.

  16. Causality analysis in business performance measurement system using system dynamics methodology

    NASA Astrophysics Data System (ADS)

    Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

    2014-07-01

    One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

  17. Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

    PubMed

    Wang, Zhe; Alahmadi, Ahmed; Zhu, David C; Li, Tongtong

    2016-05-01

    This paper aims to conduct fMRI-based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well-known Granger causality (GC) analysis, which relies on the linear prediction technique, the DI theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the GC graphs. In this paper, first, we introduce the core concepts in the DI framework. Second, we present how to conduct causality analysis using DI measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite-time series. The two major steps involved here are optimal bin size selection for data digitization and probability estimation. Finally, we demonstrate the applicability of DI-based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the GC analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI-based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship.

  18. When two become one: the limits of causality analysis of brain dynamics.

    PubMed

    Chicharro, Daniel; Ledberg, Anders

    2012-01-01

    Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.

  19. Causal Imprinting in Causal Structure Learning

    PubMed Central

    Taylor, Eric G.; Ahn, Woo-kyoung

    2012-01-01

    Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures “causal imprinting.” Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. PMID:22859019

  20. Multisource causal data mining

    NASA Astrophysics Data System (ADS)

    Woodley, Robert; Gosnell, Michael; Shallenberger, Kevin

    2012-06-01

    Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything available that may have relevance to an individual, location, associations, and more. This data is then processed by Data- Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the type and relevance of the information fields to look at. The same data is extracted from the database, but the Data Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the data, respectively. The newly formed information is then published to the agent communication backbone of Causal- View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our initial findings.

  1. Causality and Composite Structure

    SciTech Connect

    Joglekar, Satish D.

    2007-10-03

    In this talk, we discuss the question of whether a composite structure of elementary particles, with a length scale 1/{lambda}, can leave observable effects of non-locality and causality violation at higher energies (but {<=}{lambda}); employing a model-independent approach based on Bogoliubov-Shirkov formulation of causality. We formulate a condition which must be fulfilled for the derived theory to be causal, if the fundamental theory is so; and analyze it to exhibit possibilities which fulfil and which violate the condition. We comment on how causality violating amplitudes can arise.

  2. Altered Hippocampo-Cerebello-Cortical Circuit in Schizophrenia by a Spatiotemporal Consistency and Causal Connectivity Analysis

    PubMed Central

    Chen, Xi; Jiang, Yuchao; Chen, Lin; He, Hui; Dong, Li; Hou, Changyue; Duan, Mingjun; Yang, Mi; Yao, Dezhong; Luo, Cheng

    2017-01-01

    In the current study, FOur-dimensional Consistency of local neural Activities (FOCA) analysis was used to investigate the local consistency by integrating the temporal and spatial information of the local region. In the current study, resting-state fMRI data of 69 schizophrenia patients and 70 healthy controls were collected. FOCA was utilized to investigate the local consistency. Moreover, Granger causal analysis was used to investigate causal functional connectivity among these areas, which exhibited significantly different local consistency between groups. Compared with the healthy controls, the schizophrenia patients exhibited increased local consistency in hippocampus, basal ganglia and cerebellum regions, and decreased local consistency in sensoriperceptual cortex. In addition, altered causal functional connectivity was observed in hippocampo–cerebello-cortical (occipital) circuit. These findings suggested that this circuit might play a role in the motor dysfunction in schizophrenia, and should be paid more attention in future. PMID:28194095

  3. STUDY INSTRUMENTS: SECOND WAVE BRAZIL AND FINLAND,

    DTIC Science & Technology

    POLITICAL SCIENCE, PUBLIC OPINION), (*PUBLIC OPINION, POLITICAL SCIENCE), FOREIGN POLICY, ATTITUDES(PSYCHOLOGY), SAMPLING, STATISTICAL ANALYSIS, SOCIOLOGY, TEST METHODS, PROBABILITY, COMMUNISM, BRAZIL , FINLAND

  4. 75 FR 30431 - Carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-06-01

    ... COMMISSION Carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden AGENCY: United States... on carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden. SUMMARY: The Commission... carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden would be likely to lead to continuation...

  5. Population and growth causality in developing countries.

    PubMed

    Kapuria-foreman, V

    1995-07-01

    This study empirically tests the null hypotheses of no causality between population growth and economic growth and of no causality between economic growth and population growth in 15 developing countries. The model follows the Cheng Hsiao form with lag lengths to minimize Akaike's Final Prediction Error (FPE). Equations are run separately for each country. Lag lengths and Granger causality test were chosen according to three steps. 1) Each of the variables was regressed on its own lagged values with a maximum lag of five years. A lag length was chosen that minimized FPE, which was calculated for each regression. 2) Bivariate regressions were run with a fixed lag length for population growth and mixed lag lengths for the other variable, until the lag length which minimized FPE was determined. 3) The last step involved checking the lag length of population growth by keeping the lag fixed for economic growth. The economic growth measure was gross domestic product per capita. Findings indicate that in seven countries the null hypothesis of no causality between population growth and economic growth, either positive or negative, cannot be rejected (Ghana, Sri Lanka, Bolivia, Philippines, Syria, Thailand, and Argentina). In Nepal, India, China, Guatemala, Peru, Turkey, Chile, and Mexico lagged values of population growth improve predictions of economic growth. Higher economic growth has no significant effect on population growth rates in Nepal, Bolivia, Philippines, Guatemala, Peru, Thailand, Argentina, and Mexico. Interaction between economic growth and population growth was found in India, China, Turkey, and Chile. The direction of causation tests indicate that population growth has a significant positive impact on income growth in China, Guatemala, Turkey, Chile, and Mexico. India shows a negative impact of population growth on income. A significant negative impact of economic growth on population growth is evident only in Sri Lanka. There is weak evidence of a

  6. Causality in Classical Electrodynamics

    ERIC Educational Resources Information Center

    Savage, Craig

    2012-01-01

    Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…

  7. Causal Analysis After Haavelmo

    PubMed Central

    Heckman, James; Pinto, Rodrigo

    2014-01-01

    Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123

  8. Causal Learning Across Domains

    ERIC Educational Resources Information Center

    Schulz, Laura E.; Gopnik, Alison

    2004-01-01

    Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of…

  9. Bat rabies surveillance in Finland

    PubMed Central

    2013-01-01

    Background In 1985, a bat researcher in Finland died of rabies encephalitis caused by European bat lyssavirus type 2 (EBLV-2), but an epidemiological study in 1986 did not reveal EBLV-infected bats. In 2009, an EBLV-2-positive Daubenton’s bat was detected. The EBLV-2 isolate from the human case in 1985 and the isolate from the bat in 2009 were genetically closely related. In order to assess the prevalence of EBLVs in Finnish bat populations and to gain a better understanding of the public health risk that EBLV-infected bats pose, a targeted active surveillance project was initiated. Results Altogether, 1156 bats of seven species were examined for lyssaviruses in Finland during a 28–year period (1985–2012), 898 in active surveillance and 258 in passive surveillance, with only one positive finding of EBLV-2 in a Daubenton’s bat in 2009. In 2010–2011, saliva samples from 774 bats of seven species were analyzed for EBLV viral RNA, and sera from 423 bats were analyzed for the presence of bat lyssavirus antibodies. Antibodies were detected in Daubenton’s bats in samples collected from two locations in 2010 and from one location in 2011. All seropositive locations are in close proximity to the place where the EBLV-2 positive Daubenton’s bat was found in 2009. In active surveillance, no EBLV viral RNA was detected. Conclusions These data suggest that EBLV-2 may circulate in Finland, even though the seroprevalence is low. Our results indicate that passive surveillance of dead or sick bats is a relevant means examine the occurrence of lyssavirus infection, but the number of bats submitted for laboratory analysis should be higher in order to obtain reliable information on the lyssavirus situation in the country. PMID:24011337

  10. Agency, time, and causality

    PubMed Central

    Widlok, Thomas

    2014-01-01

    Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition. PMID:25414683

  11. Agency, time, and causality.

    PubMed

    Widlok, Thomas

    2014-01-01

    Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition.

  12. Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data

    PubMed Central

    Levine, Matthew E; Albers, David J; Hripcsak, George

    2016-01-01

    Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models’ explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data. PMID:28269874

  13. An analysis of global climate-vegetation interactions over arid and semi-arid regions via causal statistics

    NASA Astrophysics Data System (ADS)

    Eibedingil, Iyasu; Casagrande, Erik; Molini, Annalisa

    2014-05-01

    Precipitation and Temperature are two of the major drivers of ecosystem dynamics. Their control is the result of complex dynamical interactions, often non-linear, and is exerted over a wide range of space and temporal scales. Rainfall intermittency, as an example, is known to be among the main drivers of plants production, with a consequent influence on carbon and nitrogen cycles. However, a clear understanding of the complete pathway of such a forcing remains still unclear. Traditional time series analysis bases the study of these inter-connections on linear correlation statistics. However, the possible presence of causal dynamical connections, as well as non-linear couplings and non-stationarity can affect the performance of these tools. Additionally, dynamical drivers can act simultaneously over different space and time scales. Given this premise, this talk explores different approaches to the estimation of global causal relationships between two main climatic variables (temperature and precipitation) and vegetation over arid and transitional (semi-arid) regions. By using monthly globally gridded precipitation and temperature data (University of Delaware, NOAA/ESRL/PSD) and remotely sensed normalized difference vegetation index (NDVI, Global Inventory Modeling and Mapping Studies-GIMMS) as a proxy of vegetation dynamics we explore possible direct and conditional causal relationships between climate and vegetation. Pairwise Granger causality (GC) test is applied in order to assess bi-directional causal influences between each couple of variables due to their direct interaction and instantaneous causality deriving from exogenous variables. In addition to this, conditional Granger causality tests were performed on the three variables system. Conditional GC has the ability to resolve whether the interaction between two variables is direct or is mediated by a third variable, and whether the causal influence is simply due to differential time delays in their

  14. Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

    ERIC Educational Resources Information Center

    Johnson, Samuel G. B.; Ahn, Woo-kyoung

    2015-01-01

    Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…

  15. Causality and headache triggers

    PubMed Central

    Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.

    2013-01-01

    Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872

  16. Causal networks in EIA

    SciTech Connect

    Perdicoulis, Anastassios . E-mail: tasso@utad.pt; Glasson, John . E-mail: jglasson@brookes.ac.uk

    2006-08-15

    Causal networks have been used in Environmental Impact Assessment (EIA) since its early days, but they appear to have a minimal use in modern practice. This article reviews the typology of causal networks in EIA as well as in other academic and professional fields, verifies their contribution to EIA against the principles and requirements of the process, and discusses alternative scenarios for their future in EIA.

  17. The Secret to Finland's Success: Educating Teachers. Research Brief

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2010-01-01

    In the last decade, Finland has emerged as the leading OECD country in educational achievement. In examining the sources of Finland's dramatic rise to the top, research shows one key element that has impacted Finland's success above all others: excellent teachers. This policy brief details the key elements of Finland's successful system, examining…

  18. University Mergers in Finland: Mediating Global Competition

    ERIC Educational Resources Information Center

    Välimaa, Jussi; Aittola, Helena; Ursin, Jani

    2014-01-01

    University mergers have become a common strategy for increasing global competitiveness. In this chapter, the authors analyze the implementation of mergers in Finnish universities from the perspective of social justice as conceived within Finland and other Nordic countries.

  19. Causality discovery technology

    NASA Astrophysics Data System (ADS)

    Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

    2012-11-01

    Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

  20. Causal conditionals and counterfactuals

    PubMed Central

    Frosch, Caren A.; Byrne, Ruth M.J.

    2012-01-01

    Causal counterfactuals e.g., ‘if the ignition key had been turned then the car would have started’ and causal conditionals e.g., ‘if the ignition key was turned then the car started’ are understood by thinking about multiple possibilities of different sorts, as shown in six experiments using converging evidence from three different types of measures. Experiments 1a and 1b showed that conditionals that comprise enabling causes, e.g., ‘if the ignition key was turned then the car started’ primed people to read quickly conjunctions referring to the possibility of the enabler occurring without the outcome, e.g., ‘the ignition key was turned and the car did not start’. Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have). Experiments 3a and 3b showed that people made different inferences from counterfactuals presented with enabling conditions compared to none. The implications of the results for alternative theories of conditionals are discussed. PMID:22858874

  1. Estimating equation–based causality analysis with application to microarray time series data

    PubMed Central

    Hu, Jianhua; Hu, Feifang

    2009-01-01

    Microarray time-course data can be used to explore interactions among genes and infer gene network. The crucial step in constructing gene network is to develop an appropriate causality test. In this regard, the expression profile of each gene can be treated as a time series. A typical existing method establishes the Granger causality based on Wald type of test, which relies on the homoscedastic normality assumption of the data distribution. However, this assumption can be seriously violated in real microarray experiments and thus may lead to inconsistent test results and false scientific conclusions. To overcome the drawback, we propose an estimating equation–based method which is robust to both heteroscedasticity and nonnormality of the gene expression data. In fact, it only requires the residuals to be uncorrelated. We will use simulation studies and a real-data example to demonstrate the applicability of the proposed method. PMID:19329818

  2. Systemic risk and causality dynamics of the world international shipping market

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Podobnik, Boris; Kenett, Dror Y.; Eugene Stanley, H.

    2014-12-01

    Various studies have reported that many economic systems have been exhibiting an increase in the correlation between different market sectors, a factor that exacerbates the level of systemic risk. We measure this systemic risk of three major world shipping markets, (i) the new ship market, (ii) the second-hand ship market, and (iii) the freight market, as well as the shipping stock market. Based on correlation networks during three time periods, that prior to the financial crisis, during the crisis, and after the crisis, minimal spanning trees (MSTs) and hierarchical trees (HTs) both exhibit complex dynamics, i.e., different market sectors tend to be more closely linked during financial crisis. Brownian distance correlation and Granger causality test both can be used to explore the directional interconnectedness of market sectors, while Brownian distance correlation captures more dependent relationships, which are not observed in the Granger causality test. These two measures can also identify and quantify market regression periods, implying that they contain predictive power for the current crisis.

  3. Causal networks or causal islands? The representation of mechanisms and the transitivity of causal judgment

    PubMed Central

    Johnson, Samuel G. B.; Ahn, Woo-kyoung

    2014-01-01

    Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we used causal transitivity—the inference given A causes B and B causes C that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links (Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4–5). PMID:25556901

  4. Causal relationship between CO₂ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia.

    PubMed

    Farhani, Sahbi; Ozturk, Ilhan

    2015-10-01

    The aim of this paper is to examine the causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia over the period of 1971-2012. The long-run relationship is investigated by the auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). The results of the analysis reveal a positive sign for the coefficient of financial development, suggesting that the financial development in Tunisia has taken place at the expense of environmental pollution. The Tunisian case also shows a positive monotonic relationship between real GDP and CO2 emissions. This means that the results do not support the validity of environmental Kuznets curve (EKC) hypothesis. In addition, the paper explores causal relationship between the variables by using Granger causality models and it concludes that financial development plays a vital role in the Tunisian economy.

  5. Causal Modes in the Low-frequency variability of Mediterranean and Middle-Eastern climates

    NASA Astrophysics Data System (ADS)

    Casagrande, Erik; Zampieri, Matteo; Artale, Vincenzo; Gualdi, Silvio; Molini, Annalisa

    2015-04-01

    In the last three decades, the Mediterranean and the Middle East experienced a phase of warming larger than the one that could be expected from global warming, and largely ascribable to natural (e.g. internal) climate variability. To better understand this process we explore here the presence of causal relationships among the diverse modes of variability of the climate system, focusing in particular on inter-annual and decadal scales of variability, influencing the climate of Mediterranean and Middle-Eastern regions. Causality measures used in this study include time and frequency-domain Granger causality (GC) and the phase slope index (Ψ), a directional coupling statistic developed by Nolte et. al. in 2007. GC metrics are applied to signals before and after the filtering of high frequency (inter-annual) components, while Ψ is designed to discern between low-frequency causal flow and higher frequency components. To assure the necessary sample size, the analysis is based on the preindustrial runs of the Fifth Coupled Model Intercomparison Project (CMIP5), which are free from external perturbation and last some hundred years. We selected the runs based on ENSO stationarity - to ensure that the simulations reached the equilibrium - and the consistent representation of the Atlantic Multidecadal Oscillation (AMO), which is considered one of the main drivers for the low-frequency (decadal) climate variability of the Mediterranean and the Middle East in summer. Finally, we discuss the potential of causality metrics for the predictability of future decadal variability in these regions.

  6. Environmental Setting of the Granger Drain and DR2 Basins, Washington, 2003-04

    USGS Publications Warehouse

    Payne, Karen L.; Johnson, Henry M.; Black, Robert W.

    2007-01-01

    The Granger Drain and DR2 basins are located in the Yakima River basin in south central Washington. These agricultural basins are one of five areas in the United States selected for study as part of the National Water-Quality Assessment Program Agricultural Chemicals: Source, Transport, and Fate Study. The Program is designed to describe water-quality conditions and trends based on representative surface- and ground-water resources across the Nation. The objective of the Agricultural Chemicals topical study is to investigate the sources, transport, and fate of selected agricultural chemicals in a variety of agriculturally diverse environmental settings. The Granger Drain and DR2 basins were selected for the Agricultural Chemicals topical study because they represent the irrigated agricultural setting that characterizes eastern Washington. These basins are located in one of the most productive agricultural areas in the United States. This report describes the environmental setting of the Granger Drain and DR2 basins in the context of how agricultural practices, including agricultural chemical applications and irrigation methods, interface with natural settings and hydrologic processes.

  7. Occupational eye injuries in Finland.

    PubMed

    Saari, K M; Parvi, V

    1984-01-01

    In Finland 11.9% of all industrial accidents in 1973 were eye injuries including superficial eye injuries (79.2%), ultraviolet burns of the cornea (3.9%), eye burns (3.6%), blunt ocular trauma (2,5%), wounds (2.4%), and post-traumatic infections (5.8%). Eye injuries constituted 34.3% of all industrial accidents which needed only ambulatory treatment and 17.5% of all industrial accidents causing an absence for 1-2 days. In 1981 2.1% of all compensated industrial accidents (incapacity for work 3 days or more) were eye injuries. Most compensated eye injuries occurred in manufacturing and in construction work (80.4%) and 8.5% occurred in agriculture. The annual incidence rates of compensated accidents to the eyes (accidents X 1 000/number of employees) were highest in several branches of metal industry (4.96-6.88), excavating and foundation work (6.88), and in logging (5.64). Compensated eye injuries were caused by machines (32.8%), hand tools (25.6%), other equipment and constructions (4.8%), work environment (23.6%), chemical substances (10.8%), and other accidents (2.3%).

  8. The development of causal reasoning.

    PubMed

    Kuhn, Deanna

    2012-05-01

    How do inference rules for causal learning themselves change developmentally? A model of the development of causal reasoning must address this question, as well as specify the inference rules. Here, the evidence for developmental changes in processes of causal reasoning is reviewed, with the distinction made between diagnostic causal inference and causal prediction. Also addressed is the paradox of a causal reasoning literature that highlights the competencies of young children and the proneness to error among adults. WIREs Cogn Sci 2012, 3:327-335. doi: 10.1002/wcs.1160 For further resources related to this article, please visit the WIREs website.

  9. Productivity Analysis of Public and Private Airports: A Causal Investigation

    NASA Technical Reports Server (NTRS)

    Vasigh, Bijan; Gorjidooz, Javad

    2007-01-01

    Around the world, airports are being viewed as enterprises, rather than public services, which are expected to be managed efficiently and provide passengers with courteous customer services. Governments are, increasingly, turning to the private sectors for their efficiency in managing the operation, financing, and development, as well as providing security for airports. Operational and financial performance evaluation has become increasingly important to airport operators due to recent trends in airport privatization. Assessing performance allows the airport operators to plan for human resources and capital investment as efficiently as possible. Productivity measurements may be used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an airport in transportation industry. The primary purpose of this paper is to investigate the operational and financial efficiencies of 22 major airports in the United States and Europe. These airports are divided into three groups based on private ownership (7 British Airport Authority airports), public ownership (8 major United States airports), and a mix of private and public ownership (7 major European Union airports. The detail ownership structures of these airports are presented in Appendix A. Total factor productivity (TFP) model was utilized to measure airport performance in terms of financial and operational efficiencies and to develop a benchmarking tool to identify the areas of strength and weakness. A regression model was then employed to measure the relationship between TFP and ownership structure. Finally a Granger causality test was performed to determine whether ownership structure is a Granger cause of TFP. The results of the analysis presented in this paper demonstrate that there is not a significant relationship between airport TFP and ownership structure. Airport productivity and efficiency is

  10. Causality: Physics and Philosophy

    ERIC Educational Resources Information Center

    Chatterjee, Atanu

    2013-01-01

    Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…

  11. Evaluating Causal Models.

    ERIC Educational Resources Information Center

    Watt, James H., Jr.

    Pointing out that linear causal models can organize the interrelationships of a large number of variables, this paper contends that such models are particularly useful to mass communication research, which must by necessity deal with complex systems of variables. The paper first outlines briefly the philosophical requirements for establishing a…

  12. Causal Responsibility and Counterfactuals

    ERIC Educational Resources Information Center

    Lagnado, David A.; Gerstenberg, Tobias; Zultan, Ro'i

    2013-01-01

    How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in…

  13. Causal Attributions in Young Children.

    ERIC Educational Resources Information Center

    Friedberg, Robert D.; Dalenberg, Constance J.

    1990-01-01

    Investigated the causal explanations children use to account for common experiences. In the study, 60 preschoolers watched videotaped puppet shows designed to elicit causal attributions. Most children predominantly used internal, unstable, and specific attributions. (CB)

  14. Causal Discovery of Dynamic Systems

    ERIC Educational Resources Information Center

    Voortman, Mark

    2010-01-01

    Recently, several philosophical and computational approaches to causality have used an interventionist framework to clarify the concept of causality [Spirtes et al., 2000, Pearl, 2000, Woodward, 2005]. The characteristic feature of the interventionist approach is that causal models are potentially useful in predicting the effects of manipulations.…

  15. Chromium in the environment of Finland.

    PubMed

    Mukherjee, A B

    1998-06-30

    This paper focuses upon the use, import and release of chromium to the environment of Finland. In addition, the behavior of trivalent and hexavalent chromium in soils has been briefly reviewed. In Finland, consumption of chromium compounds occurs in the following pattern: stainless steel > leather tanning > metal plating > chemicals. The emission of chromium has decreased from 114 t in 1979 to 28 t in 1995. The highest release of chromium continues to be from ferrochromium and stainless steel plants. From these facilities, 85% of slag and dusts are used by a secondary facility to recover valuable metal. In the industrial areas, the oxidizing behavior of chromium (III) is still unknown. In this study, the leachability of chromium (VI) to ground water and its effects on terrestrial and aquatic species in Finland are discussed.

  16. Dynamic causal modelling revisited.

    PubMed

    Friston, K J; Preller, Katrin H; Mathys, Chris; Cagnan, Hayriye; Heinzle, Jakob; Razi, Adeel; Zeidman, Peter

    2017-02-17

    This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells - or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.

  17. Finland Becomes Eleventh ESO Member State

    NASA Astrophysics Data System (ADS)

    2004-07-01

    Finland has become the eleventh member state of the European Southern Observatory (ESO) [1]. The formal accession procedure was carried through as planned and has now been completed. Following the signing of the corresponding Agreement earlier this year (ESO PR 02/04), acceptance by the Finnish Parliament and ratification by the Finnish President of the Agreement as well as the ESO Convention and the associated protocols in June [2] and the deposit of the instruments of accession today, Finland has now officially joined ESO. ESO warmly welcomes the new member country and its scientific community that is renowned for their expertise in many frontline areas. The related opportunities will contribute to strenghtening of pioneering research with the powerful facilities at ESO's observatories, to the benefit of Astronomy and Astrophysics as well as European science in general. ESO also looks forward to collaboration with the Finnish high-tech industry. For Finland, the membership in ESO is motivated by scientific and technological objectives as well as by the objective of improving the public understanding of science. The Finnish Government is committed to increasing the public research funding in order to improve the quality, impact and internationalisation of research. Membership in ESO offers unique facilities for astronomical research which would not otherwise be available for Finnish astronomers. Finland is also very interested in taking part in technological development projects in fields like ICT, optics and instrumentation. For young scientists and engineers, ESO is a challenging, international working and learning environment. Finland has already taken part in the educational programmes of ESO, and as a member this activity will be broadened and intensified. In Finland there are also several science journalists and a large community of amateur astronomers who will be very happy to take part in ESO's outreach activities.

  18. Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests

    NASA Astrophysics Data System (ADS)

    Hassani, Hossein; Huang, Xu; Gupta, Rangan; Ghodsi, Mansi

    2016-10-01

    In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples, namely 1880:1-1936:2, ​1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2-2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) conclude that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures.

  19. Early asymmetric cardio-cerebral causality and outcome after severe traumatic brain injury.

    PubMed

    Gao, Lei; Smieleweski, Peter; Czosnyka, Marek; Ercole, Ari

    2017-03-23

    The brain and heart are two vital systems in health and disease, increasingly recognised as a complex, interdependent network with constant information flow in both directions. After severe traumatic brain injury (TBI), the causal, directed interactions between the brain, heart and autonomic nervous system have not been well established. Novel methods are needed to probe unmeasured, potentially prognostic information in complex biological networks that are not revealed via traditional means. In this study, we examined potential bi-directional causality between intracranial pressure (ICP), mean arterial pressure (MAP) and heart rate (HR) and its relationship to mortality in a 24-hour period early after TBI. We applied Granger causality (GC) analysis to cardio-cerebral monitoring data from 171 severe traumatic brain injury patients admitted to a single neurocritical care center over a ten-year period. There was significant bi-directional causality between ICP and MAP, MAP and HR, ICP and HR in the majority of patients (p < 0.01). MAP influenced both ICP and HR to a greater extent (higher GC, p < 0. 00001), but there was no dominant unidirectional causality between ICP and HR (p = 0.85). Those who died had significantly lower GC for ICP causing MAP and HR causing ICP (p = 0.006 and p = 0.004 respectively) and were predictors of mortality independent of age, sex and traditional intracranial variables (ICP, cerebral perfusion pressure, GCS and PRx). Examining the brain and heart with GC-based features for the first time in severe TBI patients has confirmed strong interdependence, and reveals a significant relationship between select causality pairs and mortality. These results support the notion that impaired causal information flow between the cerebrovascular, autonomic and cardiovascular systems are of central importance in severe TBI.

  20. Update on women in physics in Finland

    NASA Astrophysics Data System (ADS)

    Miikkulainen, Kukka; Vapaavuori, Jaana

    2015-12-01

    Despite Finland's role as a forerunner in many areas of gender equality, in the field of physics, the advancement of females to reach the full gender equality has been stagnated for the past decade, and no significant improvements since 2011 can be reported. However, a few interesting PhD theses have focused on gaining a better understanding of the phenomena, and a few seminars on the topic have been organized. However, good, systematically collected statistics on the numbers and salaries of female researches in Finland are still lacking.

  1. Sharable EHR systems in Finland.

    PubMed

    Harno, Kari; Ruotsalainen, Pekka

    2006-01-01

    In Finland, the shared record is a virtual electronic health record (EHR). It consists of health data generated, maintained and preserved by different health care service providers. Two different kinds of technologies for integrating regional EHR-systems are applied, but mainly by using a common middleware. Services provided by this middleware are EHR location services using a link repository and combining EHR-viewing services with security management services including consent management and identification services for health professionals. The Regional Health Information Organization (UUMA) approach is based on a stepwise implementation of integrated regional healthcare services to create a virtually borderless healthcare organization--a patient centered virtual workspace. In the virtual workspace multi-professional teams and patients collaborate and share information regardless of time and place. Presently the regional health information network (RHIN) is comprised of three integrated services between primary, secondary and tertiary care within the county of Uusimaa. The regional healthcare modules consist of an (1) eReferral network, (2) integrated EHR service between health care professionals and (3) PACS system. The eReferral between primary and secondary care not only speeds up the transfer, but also offers an option for communication in the form of eConsultation between general practitioners and hospital specialists. By sharing information and knowledge remote eConsultations create a new working environment for integrated delivery of eServices between the health care providers. Over 100,000 eReferral messages (40 %) were transferred between health care providers. Interactive eConsultations enable supervised care leading to the reduction of outpatient visits and more timely appointments. One third (10/31) of the municipal health centers are connected to the clinics in the Helsinki University Central Hospital by the eReferral system. The link directory

  2. Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

    NASA Astrophysics Data System (ADS)

    Xu, Chuan-Ming; Yan, Yan; Zhu, Xiao-Wu; Li, Xiao-Teng; Chen, Xiao-Song

    2013-11-01

    The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

  3. An investigation of cointegration and causality between energy consumption and economic growth

    SciTech Connect

    Cheng, B.S.

    1995-12-31

    This paper reexamines the causality between energy consumption and economic growth with both bivariate and multivariate models by applying the recently developed methods of cointegration and Hsiao`s version of the Granger causality to transformed U.S. data for the period 1947-1990. The Phillips-Perron (PP) tests reveal that the original series are not stationary and, therefore, a first differencing is performed to secure stationarity. The study finds no causal linkages between energy consumption and economic growth. Energy and gross national product (GNP) each live a life of its own. The results of this article are consistent with some of the past studies that find no relationship between energy and GNP but are contrary to some other studies that find GNP unidirectionally causes energy consumption. Both the bivariate and trivariate models produce the similar results. We also find that there is no causal relationship between energy consumption and industrial production. The United States is basically a service-oriented economy and changes in energy consumption can cause little or no changes in GNP. In other words, an implementation of energy conservation policy may not impair economic growth. 27 refs., 5 tabs.

  4. How Finland Serves Gifted and Talented Pupils

    ERIC Educational Resources Information Center

    Tirri, Kirsi; Kuusisto, Elina

    2013-01-01

    The purpose of this article is to provide an overview of the ways gifted and talented pupils are served in Finland. The trend toward individualism and freedom of choice as well as national policy affecting gifted education are discussed. Empirical research on Finnish teachers' attitudes toward gifted education with respect to the national…

  5. Vocational Education and Training Trends in Finland.

    ERIC Educational Resources Information Center

    Lasonen, Johanna L.

    Vocational education and training in Finland consists of prevocational education at the preprimary and comprehensive school levels and formal vocational training at the secondary and postsecondary levels. Prevocational education aims to foster children's harmonious personality development by integrating handicraft and practical subjects teaching…

  6. Mathematics Lessons from Finland and Sweden

    ERIC Educational Resources Information Center

    Seaberg, Rebecca L.

    2015-01-01

    In many ways, mathematics classrooms in Finland and Sweden are very similar to what would be considered traditional classrooms in the United States. Classes begin with checking homework and questions, followed by the teacher giving instruction in the new material, and end with students working on their new assignment. There are also interesting…

  7. The Professional Educator: Lessons from Finland

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2011-01-01

    Since Finland emerged in 2000 as the top-scoring Organisation for Economic Co-operation and Development (OECD) nation on the Programme for International Student Assessment (PISA), researchers have been pouring into the country to study the so-called "Finnish miracle." How did a country with an undistinguished education system in the…

  8. Equality and Cooperation: Finland's Path to Excellence

    ERIC Educational Resources Information Center

    Sarjala, Jukka

    2013-01-01

    For the past decade, Finland has been lauded for consistently being a top performer on international assessments of student achievement. Having spent 25 years in the Ministry of Education, and then another 8 as director general of the National Board of Education, the author was heartened by these accomplishment--but he is also concerned about how…

  9. Deep drilling for geothermal energy in Finland

    NASA Astrophysics Data System (ADS)

    Kukkonen, Ilmo

    2016-04-01

    There is a societal request to find renewable CO2-free energy resources. One of the biggest such resources is provided by geothermal energy. In addition to shallow ground heat already extensively used in Finland, deep geothermal energy provides an alternative so far not exploited. Temperatures are high at depth, but the challenge is, how to mine the heat? In this presentation, the geological and geophysical conditions for deep geothermal energy production in Finland are discussed as well as challenges for drilling and conditions at depth for geothermal energy production. Finland is located on ancient bedrock with much lower temperatures than geologically younger volcanically and tectonically active areas. In order to reach sufficiently high temperatures drilling to depths of several kilometres are needed. Further, mining of the heat with, e.g., the principle of Enhanced Geothermal System (EGS) requires high hydraulic conductivity for efficient circulation of fluid in natural or artificial fractures of the rock. There are many issues that must be solved and/or improved: Drilling technology, the EGS concept, rock stress and hydraulic fracturing, scale formation, induced seismicity and ground movements, possible microbial activity, etc. An industry-funded pilot project currently in progress in southern Finland is shortly introduced.

  10. Children's Early Numeracy in Finland and Iran

    ERIC Educational Resources Information Center

    Aunio, Pirjo; Korhonen, Johan; Bashash, Laaya; Khoshbakht, Fariba

    2014-01-01

    This research investigates similarities and differences in young children's early numeracy skills related to age, nationality and gender. The participants were five- to seven-year-old children from Finland and Iran. Early numeracy was investigated by using tasks measuring number-related relational skills (e.g. comparison, one-to-one…

  11. Quantum information causality.

    PubMed

    Pitalúa-García, Damián

    2013-05-24

    How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs.

  12. Fast causal multicast

    NASA Technical Reports Server (NTRS)

    Birman, Kenneth P.; Schiper, Andre; Stephenson, Pat

    1990-01-01

    A new protocol is presented that efficiently implements a reliable, causally ordered multicast primitive and is easily extended into a totally ordered one. Intended for use in the ISIS toolkit, it offers a way to bypass the most costly aspects of ISIS while benefiting from virtual synchrony. The facility scales with bounded overhead. Measured speedups of more than an order of magnitude were obtained when the protocol was implemented within ISIS. One conclusion is that systems such as ISIS can achieve performance competitive with the best existing multicast facilities - a finding contradicting the widespread concern that fault-tolerance may be unacceptably costly.

  13. Causal events enter awareness faster than non-causal events

    PubMed Central

    Wagemans, Johan; de-Wit, Lee

    2017-01-01

    Philosophers have long argued that causality cannot be directly observed but requires a conscious inference (Hume, 1967). Albert Michotte however developed numerous visual phenomena in which people seemed to perceive causality akin to primary visual properties like colour or motion (Michotte, 1946). Michotte claimed that the perception of causality did not require a conscious, deliberate inference but, working over 70 years ago, he did not have access to the experimental methods to test this claim. Here we employ Continuous Flash Suppression (CFS)—an interocular suppression technique to render stimuli invisible (Tsuchiya & Koch, 2005)—to test whether causal events enter awareness faster than non-causal events. We presented observers with ‘causal’ and ‘non-causal’ events, and found consistent evidence that participants become aware of causal events more rapidly than non-causal events. Our results suggest that, whilst causality must be inferred from sensory evidence, this inference might be computed at low levels of perceptual processing, and does not depend on a deliberative conscious evaluation of the stimulus. This work therefore supports Michotte’s contention that, like colour or motion, causality is an immediate property of our perception of the world. PMID:28149698

  14. Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

    NASA Astrophysics Data System (ADS)

    Lozano, A. C.

    2010-12-01

    Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring

  15. Causal inference based on counterfactuals

    PubMed Central

    Höfler, M

    2005-01-01

    Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept. PMID:16159397

  16. Experimental test of nonlocal causality

    PubMed Central

    Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G.; Fedrizzi, Alessandro

    2016-01-01

    Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell’s local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045

  17. Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease.

    PubMed

    McBride, Joseph C; Zhao, Xiaopeng; Munro, Nancy B; Jicha, Gregory A; Schmitt, Frederick A; Kryscio, Richard J; Smith, Charles D; Jiang, Yang

    2015-01-01

    Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of

  18. The relationships between Shanghai stock market and CNY/USD exchange rate: New evidence based on cross-correlation analysis, structural cointegration and nonlinear causality test

    NASA Astrophysics Data System (ADS)

    Liu, Li; Wan, Jieqiu

    2012-12-01

    This paper explores the co-movement of Shanghai stock market and China Yuan (CNY) exchange rates. First, we find that stock price and exchange rate are significantly cross-correlated. Second, employing a cointegration test allowing for a structural break, we find that the Shanghai Composite Index (SCI) is not cointegrated with the exchange rate of CNY/USD. The so-called “cointegration” found in previous studies is just caused by the shock of the recent financial crisis. Third, using linear and nonlinear Granger causality tests, we find no causality between stock prices and exchange rates during the period before the recent financial crisis. After the financial crisis, a unidirectional causality behavior running from exchange rates to stock index is present.

  19. Structural Equations and Causal Explanations: Some Challenges for Causal SEM

    ERIC Educational Resources Information Center

    Markus, Keith A.

    2010-01-01

    One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

  20. The Cradle of Causal Reasoning: Newborns' Preference for Physical Causality

    ERIC Educational Resources Information Center

    Mascalzoni, Elena; Regolin, Lucia; Vallortigara, Giorgio; Simion, Francesca

    2013-01-01

    Perception of mechanical (i.e. physical) causality, in terms of a cause-effect relationship between two motion events, appears to be a powerful mechanism in our daily experience. In spite of a growing interest in the earliest causal representations, the role of experience in the origin of this sensitivity is still a matter of dispute. Here, we…

  1. Towards the Gulf of Finland Year 2014

    NASA Astrophysics Data System (ADS)

    Myrberg, K.; Vesikko, L.

    2012-04-01

    The trilateral co-operation between Russia, Estonia and Finland was initiated in 1992 to protect the marine environment of the GoF . In 1996 the first Gulf of Finland Year was organized with the support of the responsible ministries in all three countries. At present the joint activity aims at supporting the implementation of the Ecosystem Approach to the Management of the Human Activities, more specifically HELCOM BSAP as a "sub-regional" activity. Currently, the second Gulf of Finland Year is planned to be held in 2014 and the preparations are under way. It is crucial that the best available scientific information is provided into the environmental decision making process to guarantee the sound decision-making. Thus, we need up-to-date information concerning the pressures on and state of the marine ecosystem of the GoF and how to focus optimally our resources for the protection and restoration of the sea in the future. The organizing of the second Gulf of Finland Year originates from the scientific community which finds, taking into account the specific and vulnerable nature of the sea, the GoF Year 2014 to give additional value for the protection and restoration of its environment in terms of the enhanced political presence, as well as through the communication and interaction with private sector, environmental decision-makers, politicians and ordinary citizens. The GoF Year 2014 brings scientists together to make extensive process-oriented studies leading to high-quality data sets, which will produce new and topical data and information in more detail than ever before by using e.g. modern observation techniques and ecosystem modeling as examples of advanced tool. The trilateral co-operation between Estonia, Finland and Russia ensures the new achievements in marine research will be translated to reach the environmental decision-making efficiently. Consequently, a Gulf of Finland declaration is given in 2014 where the necessary actions to improve state the marine

  2. A causality between fund performance and stock market

    NASA Astrophysics Data System (ADS)

    Kim, Ho-Yong; Kwon, Okyu; Oh, Gabjin

    2016-02-01

    We investigate whether the characteristic fund performance indicators (FPI), such as the fund return, the Net asset value (NAV) and the cash flow, are correlated with the asset price movement using information flows estimated by the Granger causality test. First, we find that the information flow of FPI is most sensitive to extreme events of the Korean stock market, which include negative events such as the sub-prime crisis and the impact of QE (quantitative easing) by the US subprime and Europe financial crisis as well as the positive events of the golden period of Korean Composite Stock Price Index (KOSPI), except for the fund cash flow. Second, both the fund return and the NAV exhibit significant correlations with the KOSPI, whereas the cash flow is not correlated with the stock market. This result suggests that the information resulting from the ability of the fund manager should influence stock market. Finally, during market crisis period, information flows between FPI and the Korean stock market are significantly positively correlated with the market volatility.

  3. Estimating Causal Effects of Local Air Pollution on Daily Deaths: Effect of Low Levels

    PubMed Central

    Schwartz, Joel; Bind, Marie-Abele; Koutrakis, Petros

    2016-01-01

    Background: Although many time-series studies have established associations of daily pollution variations with daily deaths, there are fewer at low concentrations, or focused on locally generated pollution, which is becoming more important as regulations reduce regional transport. Causal modeling approaches are also lacking. Objective: We used causal modeling to estimate the impact of local air pollution on mortality at low concentrations. Methods: Using an instrumental variable approach, we developed an instrument for variations in local pollution concentrations that is unlikely to be correlated with other causes of death, and examined its association with daily deaths in the Boston, Massachusetts, area. We combined height of the planetary boundary layer and wind speed, which affect concentrations of local emissions, to develop the instrument for particulate matter ≤ 2.5 μm (PM2.5), black carbon (BC), or nitrogen dioxide (NO2) variations that were independent of year, month, and temperature. We also used Granger causality to assess whether omitted variable confounding existed. Results: We estimated that an interquartile range increase in the instrument for local PM2.5 was associated with a 0.90% increase in daily deaths (95% CI: 0.25, 1.56). A similar result was found for BC, and a weaker association with NO2. The Granger test found no evidence of omitted variable confounding for the instrument. A separate test confirmed the instrument was not associated with mortality independent of pollution. Furthermore, the association remained when all days with PM2.5 concentrations > 30 μg/m3 were excluded from the analysis (0.84% increase in daily deaths; 95% CI: 0.19, 1.50). Conclusions: We conclude that there is a causal association of local air pollution with daily deaths at concentrations below U.S. EPA standards. The estimated attributable risk in Boston exceeded 1,800 deaths during the study period, indicating that important public health benefits can follow from

  4. Expert Causal Reasoning and Explanation.

    ERIC Educational Resources Information Center

    Kuipers, Benjamin

    The relationship between cognitive psychologists and researchers in artificial intelligence carries substantial benefits for both. An ongoing investigation in causal reasoning in medical problem solving systems illustrates this interaction. This paper traces a dialectic of sorts in which three different types of causal resaoning for medical…

  5. Theory-Based Causal Induction

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Tenenbaum, Joshua B.

    2009-01-01

    Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

  6. Commentary on Causal Prescriptive Statements

    ERIC Educational Resources Information Center

    Graesser, Arthur C.; Hu, Xiangen

    2011-01-01

    Causal prescriptive statements are valued in the social sciences when there is the goal of helping people through interventions. The articles in this special issue cover different methods for testing causal prescriptive statements. This commentary identifies both virtues and liabilities of these different approaches. We argue that it is extremely…

  7. Causality in Solving Economic Problems

    ERIC Educational Resources Information Center

    Robinson, A. Emanuel; Sloman, Steven A.; Hagmayer, York; Hertzog, Christopher K.

    2010-01-01

    The role of causal beliefs in people's decisions when faced with economic problems was investigated. Two experiments are reported that vary the causal structure in prisoner's dilemma-like economic situations. We measured willingness to cooperate or defect and collected justifications and think-aloud protocols to examine the strategies that people…

  8. Causal Inference and Developmental Psychology

    ERIC Educational Resources Information Center

    Foster, E. Michael

    2010-01-01

    Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

  9. Causal Inference in Retrospective Studies.

    ERIC Educational Resources Information Center

    Holland, Paul W.; Rubin, Donald B.

    1988-01-01

    The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

  10. Differences in hemispherical thalamo-cortical causality analysis during resting-state fMRI.

    PubMed

    Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Wolff, Stephan; Deuschl, Guunther; Heute, Ulrich; Muthuraman, Muthuraman

    2014-01-01

    Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus. We used conditional Granger causality (CGC) and time-resolved partial directed coherence (tPDC) to investigate the functional connectivity. Results of CGC analysis revealed the asymmetry between connection strengths of the bilateral thalamus. Upon testing the functional connectivity of the default-mode network (DMN) at low-frequency fluctuations (LFF) and comparing coherence vectors using Spearman's rank correlation, we found that thalamus is a better source for the signals directed towards the contralateral regions of the brain, however, when thalamus acts as sink, it is a better sink for signals generated from ipsilateral regions of the brain.

  11. Area Handbook Series: Finland: A Country Study

    DTIC Science & Technology

    1988-12-01

    by the Roman Catholic Church and soon afterward began missionary activities in Finland. Most Finns were converted to the Roman Catholic Church by...drawn into a different religious and cultural orbit from Swedish-ruled, Roman Catholic Finns in the west. About 1240, Rome sanctioned two crusades in an...aristocracy, to break the political power of the Roman Catholic Church, which had stood in the way of his desire for a stronger centralized state. The

  12. Lung parasites of least weasels in Finland.

    PubMed

    Laakkonen, J; Sundell, J; Soveri, T

    1998-10-01

    Because of their constant exposure to normal rodent definitive hosts, least weasels (Mustela nivalis) were trapped in southern Finland in late fall 1994 and examined for lung parasites. Histological examination showed that 46% of the weasels (n = 46) were infected with adiaspores identified as Chrysosporium sp. Granulomas surrounding the adiaspores consisted of mostly unorganized layers of mononuclear cells. The adiaspores from least weasels were much smaller than those reported from their prey animals. Infection with Pneumocystis carinii also was found in two weasels.

  13. On causality of extreme events

    PubMed Central

    2016-01-01

    Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available. PMID:27330866

  14. Leucocytozoonosis and trypanosomiasis in redstarts in Finland.

    PubMed

    Rintamäki, P T; Huhta, E; Jokimäki, J; Squires-Parsons, D

    1999-07-01

    Leucocytozoon spp. and Trypanosoma spp. blood parasites in the redstart (Phoenicurus phoenicurus) were studied during spring migration 1994 in southern Finland (53 individuals) and the breeding season 1992-1994 in northern Finland (69). Parasite prevalence was higher during the breeding season (48%) than during the migration period (13%), with no age or sex differences in the breeding site birds. In both periods, redstarts were infected by the same blood parasites Leucocytozoon shaartusicum (46% prevalence at the breeding site and 71% during the migration period) and Trypanosoma avium, complex (58% and 43%, respectively). One individual at the breeding site had contracted L. dubreuili and one at the stop-over site had T. everetti. Our results may support the assumption that tissue-hidden parasites relapse during the breeding season when birds may have diminished immune response related to egg production and brood rearing. Another explanation could be that the high abundance of ornithophilic vectors enhance parasite transmission during breeding season in northern Finland.

  15. The causal nexus between carbon dioxide emissions and agricultural ecosystem-an econometric approach.

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2017-01-01

    Achieving a long-term food security and preventing hunger include a better nutrition through sustainable systems of production, distribution, and consumption. Nonetheless, the quest for an alternative to increasing global food supply to meet the growing demand has led to the use of poor agricultural practices that promote climate change. Given the contribution of the agricultural ecosystem towards greenhouse gas (GHG) emissions, this study investigated the causal nexus between carbon dioxide emissions and agricultural ecosystem by employing a data spanning from 1961 to 2012. Evidence from long-run elasticity shows that a 1 % increase in the area of rice paddy harvested will increase carbon dioxide emissions by 1.49 %, a 1 % increase in biomass-burned crop residues will increase carbon dioxide emissions by 1.00 %, a 1 % increase in cereal production will increase carbon dioxide emissions by 1.38 %, and a 1 % increase in agricultural machinery will decrease carbon dioxide emissions by 0.09 % in the long run. There was a bidirectional causality between carbon dioxide emissions, cereal production, and biomass-burned crop residues. The Granger causality shows that the agricultural ecosystem in Ghana is sensitive to climate change vulnerability.

  16. History, causality, and sexology.

    PubMed

    Money, John

    2003-08-01

    In 1896, Krafft-Ebing published Psychopathia Sexualis. Popularly defined as hereditary weakness or taintedness in the family pedigree, degeneracy was called upon as a causal explanation for perversions of the sexual instinct. Although Krafft-Ebing accepted Karl Ulrichs proposal that homosexuality could be innate and probably located in the brain, he paid little attention to neuropathological sexology. Alfred Binet challenged Krafft-Ebing's orthodoxy by explaining fetishism in terms of associative learning, to which Krafft-Ebing's response was that only those with a hereditary taint would be vulnerable. Thus did the venerable nature-nurture antithesis maintain its rhetoric, even to the present day. Krafft-Ebing died too soon to meet the Freudian challenge of endopsychic determinism, and too soon also to encounter the idea of a developmental multivariate outcome of what I have termed the lovemap. Like other brain maps, for example the languagemap, the lovemap requires an intact human brain in which to develop. The personalized content of the lovemap has access to the brain by way of the special senses.

  17. Causal Rasch models

    PubMed Central

    Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.

    2013-01-01

    Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726

  18. Causal inference and developmental psychology.

    PubMed

    Foster, E Michael

    2010-11-01

    Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference-tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of "ignorability." The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development.

  19. 75 FR 57815 - Purified Carboxymethylcellulose From Finland, Mexico, Netherlands, and Sweden

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-09-22

    ... COMMISSION Purified Carboxymethylcellulose From Finland, Mexico, Netherlands, and Sweden AGENCY: United... antidumping duty orders on purified carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden... antidumping duty orders on purified carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden...

  20. Causal evolution of wave packets

    NASA Astrophysics Data System (ADS)

    Eckstein, Michał; Miller, Tomasz

    2017-03-01

    Drawing from the optimal transport theory adapted to the relativistic setting we formulate the principle of a causal flow of probability and apply it in the wave-packet formalism. We demonstrate that whereas the Dirac Hamiltonian impels a causal evolution of probabilities, even in the presence of interactions, the relativistic-Schrödinger model is acausal. We quantify the causality breakdown in the latter model and argue that, in contrast to the popular viewpoint, it is not related to the localization properties of the states.

  1. On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: the case of Tunisia.

    PubMed

    Ben Jebli, Mehdi

    2016-08-01

    This study employs the autoregressive distributed lag (ARDL) approach and Granger causality test to investigate the short- and long-run relationships between health indicator, real GDP, combustible renewables and waste consumption, rail transport, and carbon dioxide (CO2) emissions for the case of Tunisia, spanning the period of 1990-2011. The empirical findings suggest that the Fisher statistic of the Wald test confirm the existence of a long-run relationship between the variables. Moreover, the long-run estimated elasticities of the ARDL model provide that output and combustible renewables and waste consumption have a positive and statistically significant impact on health situation, while CO2 emissions and rail transport both contribute to the decrease of health indicator. Granger causality results affirm that, in the short-run, there is a unidirectional causality running from real GDP to health, a unidirectional causality from health to combustible renewables and waste consumption, and a unidirectional causality from all variables to CO2 emissions. In the long-run, all the computed error correction terms are significant and confirm the existence of long-run association among the variables. Our recommendations for the Tunisian policymakers are as follows: (i) exploiting wastes and renewable fuels can be a good strategy to eliminate pollution caused by emissions and subsequently improve health quality, (ii) the use of renewable energy as a main source for national rail transport is an effective strategy for public health, (iii) renewable energy investment projects are beneficial plans for the country as this contributes to the growth of its own economy and reduce energy dependence, and (iii) more renewable energy consumption leads not only to decrease pollution but also to stimulate health situation because of the increase of doctors and nurses numbers.

  2. Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities.

    PubMed

    Cox, Tony; Popken, Douglas; Ricci, Paolo F

    2012-01-01

    Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures.

  3. [Causal analysis approaches in epidemiology].

    PubMed

    Dumas, O; Siroux, V; Le Moual, N; Varraso, R

    2014-02-01

    Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the

  4. Relationship between daylength and suicide in Finland

    PubMed Central

    2011-01-01

    Background Many previous studies have documented seasonal variation in suicides globally. We re-assessed the seasonal variation of suicides in Finland and tried to relate it to the seasonal variation in daylength and ambient temperature and in the discrepancy between local time and solar time. Methods The daily data of all suicides from 1969 to 2003 in Finland (N = 43,393) were available. The calendar year was divided into twelve periods according to the length of daylight and the routinely changing time difference between sun time and official time. The daily mean of suicide mortality was calculated for each of these periods and the 95% confidence intervals of the daily means were used to evaluate the statistical significance of the means. In addition, daily changes in sunshine hours and mean temperature were compared to the daily means of suicide mortality in two locations during these afore mentioned periods. Results A significant peak of the daily mean value of suicide mortality occurred in Finland between May 15th and July 25th, a period that lies symmetrically around the solstice. Concerning the suicide mortality among men in the northern location (Oulu), the peak was postponed as compared with the southern location (Helsinki). The daily variation in temperature or in sunshine did not have significant association with suicide mortality in these two locations. Conclusions The period with the longest length of the day associated with the increased suicide mortality. Furthermore, since the peak of suicide mortality seems to manifest later during the year in the north, some other physical or biological signals, besides the variation in daylight, may be involved. In order to have novel means for suicide prevention, the assessment of susceptibility to the circadian misalignment might help. PMID:21943377

  5. Radon Policy in Finland, Achievements and Challenges

    SciTech Connect

    Arvela, Hannu; Maekelaeinen, Ilona; Reisbacka, Heikki

    2008-08-07

    Finland is a country of high indoor radon concentrations. Since 1980 the authority regulations, guidance, radon mapping and research work supporting decision making have been developed continuously. Clear regulations directed to citizens and authorities form the basis for radon policy. Active mapping work and measurement ordered by private home owners has resulted in 100.000 houses measured. National indoor radon data base forms a good basis for decision making, communication and research. The number of new houses provided with radon preventive constructions has increased remarkably. New radon campaigns has increased measurement and mitigation activity. Furher increasing of public awareness is the key challenge.

  6. Silica, silicosis and cancer in Finland.

    PubMed

    Partanen, T; Jaakkola, J; Tossavainen, A

    1995-01-01

    Approximately 100 000 Finnish workers are currently employed in jobs and tasks that may involve exposure to airborne silica dust. The major industries involved are mining and quarrying; production of glass, ceramics, bricks and other building materials; metal industry, particularly iron and steel founding; and construction. Over 1500 cases of silicosis have occurred in Finland since 1935. Tuberculosis has been a frequent complication of silicosis. Results of studies from several countries strongly suggest that silica dust also causes lung cancer. The results of the relevant Finnish epidemiologic and industrial hygiene studies addressing cancer risk and exposure to quartz dust are summarized.

  7. An introduction to causal inference.

    PubMed

    Pearl, Judea

    2010-02-26

    This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.

  8. Causal Inference in Public Health

    PubMed Central

    Glass, Thomas A.; Goodman, Steven N.; Hernán, Miguel A.; Samet, Jonathan M.

    2014-01-01

    Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action’s consequences rather than the less precise notion of a risk factor’s causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world. PMID:23297653

  9. Contaminated water caused the first outbreak of giardiasis in Finland, 2007: a descriptive study.

    PubMed

    Rimhanen-Finne, Ruska; Hänninen, Marja-Liisa; Vuento, Risto; Laine, Janne; Jokiranta, T Sakari; Snellman, Marja; Pitkänen, Tarja; Miettinen, Ilkka; Kuusi, Markku

    2010-08-01

    The severe sewage contamination of a drinking water distribution network affected inhabitants in the town of Nokia, Finland in November 2007-February 2008. One of the pathogens found in patient and environmental samples was Giardia, which for the first time was detected as the causal agent of an outbreak in Finland. To describe the existence and the importance of Giardia infections related to this outbreak, we described characteristics of the giardiasis cases and calculated the incidence of giardiasis as well as the frequency of positive Giardia tests both before and during the outbreak. Persons reported to the Finnish Infectious Disease Registry (FIDR) with Giardia infections were interviewed. The number of persons tested for Giardia was obtained from the Centre for Laboratory Medicine at the Tampere University Hospital. The investigations provided strong evidence that Giardia infections in Nokia resulted from the contaminated water. The proportion of persons testing positive for Giardia and the incidence of giardiasis multiplied during the outbreak. To improve outbreak management, national guidelines on testing environmental samples for Giardia should be developed, and further resources should be allocated to both clinical and environmental laboratories that perform parasitological analyses.

  10. The Discourse on Multicultural Education in Finland: Education for Whom?

    ERIC Educational Resources Information Center

    Holm, Gunilla; Londen, Monica

    2010-01-01

    Finland is experiencing increased immigration and therefore increased cultural diversity in its schools. This paper examines the multicultural education discourse in Finland by analysing the national and municipal curricula for the comprehensive school, educational policy documents and teacher education curricula. The focus is on how multicultural…

  11. ADHD in Finland and Types of Scandinavian Cooperation.

    ERIC Educational Resources Information Center

    Tapper, Marie-Louise; Michelsson, Katarina

    This paper reviews the history and current status of services to children with attention deficit hyperactivity disorder in Finland. It notes the availability of free or almost free health services in Finland and the resulting very low infant mortality rate. The history of attention deficit hyperactivity disorders (ADHD), termed "minimal brain…

  12. Youth Suicide Trends in Finland, 1969-2008

    ERIC Educational Resources Information Center

    Lahti, Anniina; Rasanen, Pirkko; Riala, Kaisa; Keranen, Sirpa; Hakko, Helina

    2011-01-01

    Background: There are only a few recent studies on secular trends in child and adolescent suicides. We examine here trends in rates and methods of suicide among young people in Finland, where suicide rates at these ages are among the highest in the world. Methods: The data, obtained from Statistics Finland, consisted of all suicides (n = 901)…

  13. The Quality and Effectiveness of Confirmation Classes in Finland

    ERIC Educational Resources Information Center

    Niemela, Kati

    2006-01-01

    Every year some 90% of 15-year-olds in Finland attend confirmation classes in the Evangelical Lutheran Church of Finland, which is greater than the percentage of that age group belonging to the Church. What is behind the popularity of Finnish confirmation classes? This article scrutinizes the quality and effectiveness of confirmation classes.…

  14. Different Kinds of Causality in Event Cognition

    ERIC Educational Resources Information Center

    Radvansky, Gabriel A.; Tamplin, Andrea K.; Armendarez, Joseph; Thompson, Alexis N.

    2014-01-01

    Narrative memory is better for information that is more causally connected and occurs at event boundaries, such as a causal break. However, it is unclear whether there are common or distinct influences of causality. For the event boundaries that arise as a result of causal breaks, the events that follow may subsequently become more causally…

  15. Risk Assessment in Finland: Theory and Practice

    PubMed Central

    Pääkkönen, Rauno

    2010-01-01

    The Finnish risk assessment practice is based on the Occupational Safety and Health (OSH) Act aiming to improve working conditions in order maintain the employees' work ability, and to prevent occupational accidents and diseases. In practice there are hundreds of risk assessment methods in use. A simple method is used in small and medium sized enterprises and more complex risk evaluation methods in larger work places. Does the risk management function in the work places in Finland? According to our experience something more is needed. That is, understanding of common and company related benefits of risk management. The wider conclusion is that commitment for risk assessment in Finland is high enough. However, in those enterprises where OSH management was at an acceptable level or above it, there were also more varied and more successfully accomplished actions to remove or reduce the risks than in enterprises, where OSH management was in lower level. In risk assessment it is important to process active technical prevention and exact communication, increase work place attraction and increase job satisfaction and motivation. Investments in OSH are also good business. Low absenteeism due to illness or accidents increases directly the production results by improved quality and quantity of the product. In general Finnish studies have consistently shown that the return of an invested euro is three to seven-old. In national level, according to our calculations the savings could be even 20% of our gross national product. PMID:22953157

  16. History of cosmic ray research in Finland

    NASA Astrophysics Data System (ADS)

    Usoskin, I. G.; Valtonen, E.; Vainio, R.; Tanskanen, P. J.; Aurela, A. M.

    2009-11-01

    The history of cosmic ray research in Finland can be traced back to the end of 1950s, when first ground-based cosmic ray measurements started in Turku. The first cosmic ray station was founded in Oulu in 1964 performing measurements of cosmic rays by a muon telescope, which was later complemented by a neutron monitor. Since the 1990s, several research centers and universities, such as The Finnish Meteorological Institute, Helsinki University of Technology, University of Oulu, University of Turku and University of Helsinki have been involved in space science projects, such as SOHO, AMS, Cluster, Cassini, BepiColombo, etc. At the same time, ground-based cosmic ray measurements have reached a new level, including a fully automatic on-line database in Oulu and a new muon measuring underground site in Pyhäsalmi. Research groups in Helsinki, Oulu and Turku have also extensive experience in theoretical investigations of different aspects of cosmic ray physics. Cosmic ray research has a 50-year long history in Finland, covering a wide range from basic long-running ground-based observations to high-technology space-borne instrumentation and sophisticated theoretical studies. Several generations of researchers have been involved in the study ensuring transfer of experience and building the recognized Finnish research school of cosmic ray studies.

  17. Risk assessment in Finland: theory and practice.

    PubMed

    Anttonen, Hannu; Pääkkönen, Rauno

    2010-09-01

    The Finnish risk assessment practice is based on the Occupational Safety and Health (OSH) Act aiming to improve working conditions in order maintain the employees' work ability, and to prevent occupational accidents and diseases. In practice there are hundreds of risk assessment methods in use. A simple method is used in small and medium sized enterprises and more complex risk evaluation methods in larger work places. Does the risk management function in the work places in Finland? According to our experience something more is needed. That is, understanding of common and company related benefits of risk management. The wider conclusion is that commitment for risk assessment in Finland is high enough. However, in those enterprises where OSH management was at an acceptable level or above it, there were also more varied and more successfully accomplished actions to remove or reduce the risks than in enterprises, where OSH management was in lower level. In risk assessment it is important to process active technical prevention and exact communication, increase work place attraction and increase job satisfaction and motivation. Investments in OSH are also good business. Low absenteeism due to illness or accidents increases directly the production results by improved quality and quantity of the product. In general Finnish studies have consistently shown that the return of an invested euro is three to seven-old. In national level, according to our calculations the savings could be even 20% of our gross national product.

  18. On the Causality and K-Causality between Measures

    NASA Astrophysics Data System (ADS)

    Miller, Tomasz

    2017-03-01

    Drawing from our earlier works on the notion of causality for nonlocal phenomena, we propose and study the extension of the Sorkin--Woolgar relation $K^+$ onto the space of Borel probability measures on a given spacetime. We show that it retains its fundamental properties of transitivity and closedness. Furthermore, we list and prove several characterizations of this relation, including the `nonlocal' analogue of the characterization of $K^+$ in terms of time functions. This generalizes and casts new light on our earlier results concerning the causal precedence relation $J^+$ between measures.

  19. Causal reasoning with mental models

    PubMed Central

    Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

    2014-01-01

    This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

  20. Wormholes, baby universes, and causality

    SciTech Connect

    Visser, M. )

    1990-02-15

    In this paper wormholes defined on a Minkowski signature manifold are considered, both at the classical and quantum levels. It is argued that causality in quantum gravity may best be imposed by restricting the functional integral to include only causal Lorentzian spacetimes. Subject to this assumption, one can put very tight constraints on the quantum behavior of wormholes, their cousins the baby universes, and topology-changing processes in general. Even though topology-changing processes are tightly constrained, this still allows very interesting geometrical (rather than topological) effects. In particular, the laboratory construction of baby universes is {ital not} prohibited provided that the umbilical cord'' is never cut. Methods for relaxing these causality constraints are also discussed.

  1. Causal reasoning with mental models.

    PubMed

    Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N

    2014-01-01

    This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

  2. [Mental health in Chile and Finland: Challenges and lessons].

    PubMed

    Retamal C, Pedro; Markkula, Niina; Peña, Sebastián

    2016-07-01

    This article analyses and compares the epidemiology of mental disorders and relevant public policies in Chile and Finland. In Chile, a specific mental health law is still lacking. While both countries highlight the role of primary care, Finland places more emphasis on participation and recovery of service users. Comprehensive mental health policies from Finland, such as a successful suicide prevention program, are presented. Both countries have similar prevalence of mental disorders, high alcohol consumption and high suicide rates. In Chile, the percentage of total disease burden due to psychiatric disorders is 13% and in Finland 14%. However, the resources to address these issues are very different. Finland spends 4.5% of its health budget on mental health, while in Chile the percentage is 2.2%. This results in differences in human resources and service provision. Finland has five times more psychiatric outpatient visits, four times more psychiatrists, triple antidepressant use and twice more clinical guidelines for different psychiatric conditions. In conclusion, both countries have similar challenges but differing realities. This may help to identify gaps and potential solutions for public health challenges in Chile. Finland’s experience demonstrates the importance of political will and long-term vision in the construction of mental health policies.

  3. Information thermodynamics on causal networks.

    PubMed

    Ito, Sosuke; Sagawa, Takahiro

    2013-11-01

    We study nonequilibrium thermodynamics of complex information flows induced by interactions between multiple fluctuating systems. Characterizing nonequilibrium dynamics by causal networks (i.e., Bayesian networks), we obtain novel generalizations of the second law of thermodynamics and the fluctuation theorem, which include an informational quantity characterized by the topology of the causal network. Our result implies that the entropy production in a single system in the presence of multiple other systems is bounded by the information flow between these systems. We demonstrate our general result by a simple model of biochemical adaptation.

  4. The salience network causally influences default mode network activity during moral reasoning

    PubMed Central

    Wilson, Stephen M.; D’Esposito, Mark; Kayser, Andrew S.; Grossman, Scott N.; Poorzand, Pardis; Seeley, William W.; Miller, Bruce L.; Rankin, Katherine P.

    2013-01-01

    Large-scale brain networks are integral to the coordination of human behaviour, and their anatomy provides insights into the clinical presentation and progression of neurodegenerative illnesses such as Alzheimer’s disease, which targets the default mode network, and behavioural variant frontotemporal dementia, which targets a more anterior salience network. Although the default mode network is recruited when healthy subjects deliberate about ‘personal’ moral dilemmas, patients with Alzheimer’s disease give normal responses to these dilemmas whereas patients with behavioural variant frontotemporal dementia give abnormal responses to these dilemmas. We hypothesized that this apparent discrepancy between activation- and patient-based studies of moral reasoning might reflect a modulatory role for the salience network in regulating default mode network activation. Using functional magnetic resonance imaging to characterize network activity of patients with behavioural variant frontotemporal dementia and healthy control subjects, we present four converging lines of evidence supporting a causal influence from the salience network to the default mode network during moral reasoning. First, as previously reported, the default mode network is recruited when healthy subjects deliberate about ‘personal’ moral dilemmas, but patients with behavioural variant frontotemporal dementia producing atrophy in the salience network give abnormally utilitarian responses to these dilemmas. Second, patients with behavioural variant frontotemporal dementia have reduced recruitment of the default mode network compared with healthy control subjects when deliberating about these dilemmas. Third, a Granger causality analysis of functional neuroimaging data from healthy control subjects demonstrates directed functional connectivity from nodes of the salience network to nodes of the default mode network during moral reasoning. Fourth, this Granger causal influence is diminished in

  5. Investigating neural primacy in Major Depressive Disorder: multivariate Granger causality analysis of resting-state fMRI time-series data.

    PubMed

    Hamilton, J P; Chen, G; Thomason, M E; Schwartz, M E; Gotlib, I H

    2011-07-01

    Major Depressive Disorder (MDD) has been conceptualized as a neural network-level disease. Few studies of the neural bases of depression, however, have used analytical techniques that are capable of testing network-level hypotheses of neural dysfunction in this disorder. Moreover, of those that have, fewer still have attempted to determine the directionality of influence within functionally abnormal networks of structures. We used multivariate GC analysis, a technique that estimates the extent to which preceding neural activity in one or more seed regions predicts subsequent activity in target brain regions, to analyze blood-oxygen-level-dependent (BOLD) data collected during eyes-closed rest from depressed and never-depressed persons. We found that activation in the hippocampus predicted subsequent increases in ventral anterior cingulate cortex (vACC) activity in depression, and that activity in the medial prefrontal cortex and vACC were mutually reinforcing in MDD. Hippocampal and vACC activation in depressed participants predicted subsequent decreases in dorsal cortical activity. This study shows that, on a moment-by-moment basis, there is increased excitatory activity among limbic and paralimbic structures, as well as increased inhibition in the activity of dorsal cortical structures, by limbic structures in depression; these aberrant patterns of effective connectivity implicate disturbances in the mesostriatal dopamine system in depression. These findings advance the neural theory of depression by detailing specific patterns of limbic excitation in MDD, by making explicit the primary role of limbic inhibition of dorsal cortex in the cortico-limbic relation posited to underlie depression, and by presenting an integrated neurofunctional account of altered dopamine function in this disorder.

  6. Radon measurement and mitigation activity in Finland.

    PubMed

    Valmari, T; Arvela, H; Reisbacka, H; Holmgren, O

    2014-07-01

    Radon prevention, measurement and mitigation activities have been increasing in Finland during the 2000s. Nowadays, many municipal authorities, especially those located in high-radon areas, require radon prevention measures. This has activated radon measurements. Owners of new houses having radon piping installed under the floor slab are the most active group to measure and reduce the found high-radon values. Their radon awareness is apparently better than on the average, and the existing piping makes it easier and cheaper to reduce the radon levels. Local campaigns involving invitation flyers mailed to the residents have been a cost-effective means to activate measurements of older houses. So far 116,611 dwellings in low-rise residential buildings have been measured. At least 15% of the 16,860 dwellings found to exceed the reference level of 400 Bq m(-3) had their indoor radon level reduced below that.

  7. Chernobyl fallout and cancer incidence in Finland.

    PubMed

    Auvinen, Anssi; Seppä, Karri; Pasanen, Kari; Kurttio, Päivi; Patama, Toni; Pukkala, Eero; Heinävaara, Sirpa; Arvela, Hannu; Verkasalo, Pia; Hakulinen, Timo

    2014-05-01

    Twenty-five years have passed since the Chernobyl accident, but its health consequences remain to be well established. Finland was one of the most heavily affected countries by the radioactive fallout outside the former Soviet Union. We analyzed the relation of the estimated external radiation exposure from the fallout to cancer incidence in Finland in 1988-2007. The study cohort comprised all ∼ 3.8 million Finns who had lived in the same dwelling for 12 months following the accident (May 1986-April 1987). Radiation exposure was estimated using data from an extensive mobile dose rate survey. Cancer incidence data were obtained for the cohort divided into four exposure categories (the lowest with the first-year committed dose <0.1 mSv and the highest ≥ 0.5 mSv) allowing for a latency of 5 years for leukemia and thyroid cancer, and 10 years for other cancers. Of the eight predefined cancer sites regarded as radiation-related from earlier studies, only colon cancer among women showed an association with exposure from fallout [excess rate ratio per increment in exposure category 0.06, 95% confidence interval (CI) 0.02-0.11]. No such effect was observed for men, or other cancer sites. Our analysis of a large cohort over two decades did not reveal an increase in cancer incidence following the Chernobyl accident, with the possible exception of colon cancer among women. The largely null findings are consistent with extrapolation from previous studies suggesting that the effect is likely to remain too small to be empirically detectable and of little public health impact.

  8. Information sources in science and technology in Finland

    NASA Technical Reports Server (NTRS)

    Haarala, Arja-Riitta

    1994-01-01

    Finland poses some problems to be overcome in the field of scientific and technical information: a small user community which makes domestic systems costly; great distances within the country between users and suppliers of information; great distances to international data systems and large libraries abroad; and inadequate collections of scientific and technical information. The national bibliography Fennica includes all books and journals published in Finland. Data base services available in Finland include: reference data bases in science and technology; data banks for decision making such as statistical time series or legal proceedings; national bibliographies; and library catalogs.

  9. Hypothesizing and Refining Causal Models,

    DTIC Science & Technology

    1984-12-01

    the purposes of this research, it was critica ! to be able to represent a sequence of events, in which the learning program would look for causal... tlc sense because tliv imply random behavior. This is an oversimplified, but usc^ul telcological assumption about the nature of dependences in designed

  10. Causal Categories: Relativistically Interacting Processes

    NASA Astrophysics Data System (ADS)

    Coecke, Bob; Lal, Raymond

    2013-04-01

    A symmetric monoidal category naturally arises as the mathematical structure that organizes physical systems, processes, and composition thereof, both sequentially and in parallel. This structure admits a purely graphical calculus. This paper is concerned with the encoding of a fixed causal structure within a symmetric monoidal category: causal dependencies will correspond to topological connectedness in the graphical language. We show that correlations, either classical or quantum, force terminality of the tensor unit. We also show that well-definedness of the concept of a global state forces the monoidal product to be only partially defined, which in turn results in a relativistic covariance theorem. Except for these assumptions, at no stage do we assume anything more than purely compositional symmetric-monoidal categorical structure. We cast these two structural results in terms of a mathematical entity, which we call a causal category. We provide methods of constructing causal categories, and we study the consequences of these methods for the general framework of categorical quantum mechanics.

  11. Identity, causality, and pronoun ambiguity.

    PubMed

    Sagi, Eyal; Rips, Lance J

    2014-10-01

    This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs.

  12. Causal inference in obesity research.

    PubMed

    Franks, P W; Atabaki-Pasdar, N

    2017-03-01

    Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.

  13. Checklist of the Empidoidea of Finland (Insecta, Diptera)

    PubMed Central

    Kahanpää, Jere

    2014-01-01

    Abstract An updated checklist of the Atelestidae, Brachystomatidae, Dolichopodidae, Empididae and Hybotidae (Diptera) recorded from Finland is presented. The genera with uncertain placement within superfamily Empidoidea (= the Iteaphila group) are also included in this paper. PMID:25337016

  14. Benchmark Study of Industrial Needs for Additive Manufacturing in Finland

    NASA Astrophysics Data System (ADS)

    Lindqvist, Markku; Piili, Heidi; Salminen, Antti

    Additive manufacturing (AM) is a modern way to produce parts for industrial use. Even though the technical knowledge and research of AM processes are strong in Finland, there are only few industrial applications. Aim of this study is to collect practical knowledge of companies who are interested in industrial use of AM, especially in South-Eastern Finland. Goal of this study is also to investigate demands and requirements of applications for industrial use of AM in this area of Finland. It was concluded, that two of the reasons prohibiting wider industrial use of AM in Finland, are wrong expectations against this technology as well as lack of basic knowledge of possibilities of the technology. Especially, it was noticed that strong 3D-hype is even causing misunderstandings. Nevertheless, the high-level industrial know-how in the area, built around Finnish lumber industry is a strong foundation for the additive manufacturing technology.

  15. Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-07-01

    In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

  16. Causal interaction following the alteration of target region activation during motor imagery training using real-time fMRI

    PubMed Central

    Zhao, Xiaojie; Zhang, Hang; Song, Sutao; Ye, Qing; Guo, Jia; Yao, Li

    2013-01-01

    Motor imagery training is an effective approach for motor skill learning and motor function rehabilitation. As a novel method of motor imagery training, real-time fMRI (rtfMRI) enables individuals to acquire self-control of localized brain activation, achieving desired changes in behavior. The regulation of target region activation by rtfMRI often alters the activation of related brain regions. However, the interaction between the target region and these related regions is unclear. The Granger causality model (GCM) is a data-driven method that can explore the causal interaction between brain regions. In this study, we employed rtfMRI to train subjects to regulate the activation of the ipsilateral dorsal premotor area (dPMA) during motor imagery training, and we calculated the causal interaction of the dPMA with other motor-related regions based on the GCM. The results demonstrated that as the activity of the dPMA changed during rtfMRI training, the interaction of the target region with other related regions became significantly altered, and behavioral performance was improved after training. The altered interaction primarily exhibited as an increased unidirectional interaction from the dPMA to the other regions. These findings support the dominant role of the dPMA in motor skill learning via rtfMRI training and may indicate how activation of the target region interacts with the activation of other related regions. PMID:24379775

  17. Climate-Vegetation Interactions over Arid and Semi-Arid Regions: A Multi-Scale Causality Analysis

    NASA Astrophysics Data System (ADS)

    Molini, A.; Casagrande, E.

    2014-12-01

    This talk explores the mechanisms underlying global-scale feedbacks of vegetation on climate, with a special focus on arid and transitional (semi-arid) regions and cross-scale interactions. Whether precipitation and temperature are known to be two of the major drivers of ecosystem dynamics, the inference of the forcing of vegetation on climate from observed data still leads to extremely contradictory results. This is mainly due to the intrinsic complex and nonlinear nature of climate-vegetation interactions, which is exerted over a wide range of space, temporal and frequency scales. Beside, traditional statistical tools applied to these feedbacks rely on linear correlation measures that can hardly distinguish the different components of these interactions. We analyze monthly and sub-monthly globally gridded data of precipitation, temperature and NDVI (from both MODIS and AVHRR) by using an ensemble of different directional coupling statistics, and spectral metrics able to resolve cross-scale interactions. Based on the concept of Granger causality, we assess the bi-directional causal influences between precipitation, temperature and NDVI. In particular, we focus on spectral causality measures, in order to infer sub-processes acting across different time and frequency scales. Several examples from arid and semi-arid regions are introduced and examined. During the discussion of the result, we highlight the strength and weakness of the approach, also in the occurrence of nonlinear couplings.

  18. Towards an Algebra for Analyzing Causal Relations.

    ERIC Educational Resources Information Center

    Ellett, Frederick S., Jr.; Ericson, David P.

    Correlation-based approaches to causal analysis contain too much irrelevant information that masks and modulates the true nature of causal processes in the world. Both causal modeling and path analysis/structural equations give the wrong answers for certain conceptions of causation, given certain assumptions about the "error" variables.…

  19. Designing Effective Supports for Causal Reasoning

    ERIC Educational Resources Information Center

    Jonassen, David H.; Ionas, Ioan Gelu

    2008-01-01

    Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the "cement of the universe" [Hume (1739/2000). Causal reasoning is required for making predictions, drawing implications and…

  20. Representing Personal Determinants in Causal Structures.

    ERIC Educational Resources Information Center

    Bandura, Albert

    1984-01-01

    Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…

  1. Constraints on Children's Judgments of Magical Causality

    ERIC Educational Resources Information Center

    Woolley, Jacqueline D.; Browne, Cheryl A.; Boerger, Elizabeth A.

    2006-01-01

    In 3 studies we addressed the operation of constraints on children's causal judgments. Our primary focus was whether children's beliefs about magical causality, specifically wishing, are constrained by features that govern the attribution of ordinary causality. In Experiment 1, children witnessed situations in which a confederate's wish appeared…

  2. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-05

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

  3. Inference of directed climate networks: role of instability of causality estimation methods

    NASA Astrophysics Data System (ADS)

    Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

    2013-04-01

    Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

  4. Time-Varying Causal Inference From Phosphoproteomic Measurements in Macrophage Cells

    PubMed Central

    Masnadi-Shirazi, Maryam; Maurya, Mano Ram; Subramaniam, Shankar

    2015-01-01

    Cellular signaling circuitry in eukaryotes can be studied by analyzing the regulation of protein phosphorylation and its impact on downstream mechanisms leading to a pheno-type. A primary role of phosphorylation is to act as a switch to turn “on” or “off” a protein activity or a cellular pathway. Specifically, protein phosphorylation is a major leit motif for transducing molecular signals inside the cell. Errors in transferring cellular information can alter the normal function and may lead to diseases such as cancer; an accurate reconstruction of the “true” signaling network is essential for understanding the molecular machinery involved in normal and pathological function. In this study, we have developed a novel framework for time-dependent reconstruction of signaling networks involved in the activation of macrophage cells leading to an inflammatory response. Several signaling pathways have been identified in macrophage cells, but the time-varying causal relationship that can produce a dynamic directed graph of these molecules has not been explored in detail. Here, we use the notion of Granger causality, and apply a vector autoregressive model to phosphoprotein time-course data in RAW 264.7 macrophage cells. Through the reconstruction of the phosphoprotein network, we were able to estimate the directionality and the dynamics of information flow. Significant interactions were selected through statistical hypothesis testing (t-test) of the coefficients of a linear model and were used to reconstruct the phosphoprotein signaling network. Our approach results in a three-stage phosphoprotein network that represents the evolution of the causal interactions in the intracellular signaling pathways. PMID:24681921

  5. Two roads to noncommutative causality

    NASA Astrophysics Data System (ADS)

    Besnard, Fabien

    2015-08-01

    We review the physical motivations and the mathematical results obtained so far in the isocone-based approach to noncommutative causality. We also give a briefer account of the alternative framework of Franco and Eckstein which is based on Lorentzian spectral triples. We compare the two theories on the simple example of the product geometry of the Minkowski plane by the finite noncommutative space with algebra M2(C).

  6. An Introduction to Causal Inference

    DTIC Science & Technology

    2009-11-02

    or new measurements. These tasks are managed well by standard statistical analysis so long as experimental conditions remain the same. Causal analysis...combines features of the structural equation models (SEM) used in economics and social science (Goldberger, 1973; Duncan, 1975), the potential-outcome...analysis which, by definition, are un- correlated with the regressors. The formers are part of physical reality (e.g., genetic factors, socio- economic

  7. Obesity and infection: reciprocal causality.

    PubMed

    Hainer, V; Zamrazilová, H; Kunešová, M; Bendlová, B; Aldhoon-Hainerová, I

    2015-01-01

    Associations between different infectious agents and obesity have been reported in humans for over thirty years. In many cases, as in nosocomial infections, this relationship reflects the greater susceptibility of obese individuals to infection due to impaired immunity. In such cases, the infection is not related to obesity as a causal factor but represents a complication of obesity. In contrast, several infections have been suggested as potential causal factors in human obesity. However, evidence of a causal linkage to human obesity has only been provided for adenovirus 36 (Adv36). This virus activates lipogenic and proinflammatory pathways in adipose tissue, improves insulin sensitivity, lipid profile and hepatic steatosis. The E4orf1 gene of Adv36 exerts insulin senzitizing effects, but is devoid of its pro-inflammatory modalities. The development of a vaccine to prevent Adv36-induced obesity or the use of E4orf1 as a ligand for novel antidiabetic drugs could open new horizons in the prophylaxis and treatment of obesity and diabetes. More experimental and clinical studies are needed to elucidate the mutual relations between infection and obesity, identify additional infectious agents causing human obesity, as well as define the conditions that predispose obese individuals to specific infections.

  8. Modeling of causality with metamaterials

    NASA Astrophysics Data System (ADS)

    Smolyaninov, Igor I.

    2013-02-01

    Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space-time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space-time. While this model may be used to build interesting space-time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space-time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler-Feynman absorber theory of causality.

  9. THE CAUSAL ANALYSIS / DIAGNOSIS DECISION ...

    EPA Pesticide Factsheets

    CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based on the US EPA's Stressor Identification process which is a formal method for identifying causes of impairments in aquatic systems. CADDIS 2007 increases access to relevant information useful for causal analysis and provides methods and tools that practitioners can use to analyze their own data. The new Candidate Cause section provides overviews of commonly encountered causes of impairments to aquatic systems: metals, sediments, nutrients, flow alteration, temperature, ionic strength, and low dissolved oxygen. CADDIS includes new Conceptual Models that illustrate the relationships from sources to stressors to biological effects. An Interactive Conceptual Model for phosphorus links the diagram with supporting literature citations. The new Analyzing Data section helps practitioners analyze their data sets and interpret and use those results as evidence within the USEPA causal assessment process. Downloadable tools include a graphical user interface statistical package (CADStat), and programs for use with the freeware R statistical package, and a Microsoft Excel template. These tools can be used to quantify associations between causes and biological impairments using innovative methods such as species-sensitivity distributions, biological inferenc

  10. Phenology cameras observing boreal ecosystems of Finland

    NASA Astrophysics Data System (ADS)

    Peltoniemi, Mikko; Böttcher, Kristin; Aurela, Mika; Kolari, Pasi; Tanis, Cemal Melih; Linkosalmi, Maiju; Loehr, John; Metsämäki, Sari; Nadir Arslan, Ali

    2016-04-01

    Cameras have become useful tools for monitoring seasonality of ecosystems. Low-cost cameras facilitate validation of other measurements and allow extracting some key ecological features and moments from image time series. We installed a network of phenology cameras at selected ecosystem research sites in Finland. Cameras were installed above, on the level, or/and below the canopies. Current network hosts cameras taking time lapse images in coniferous and deciduous forests as well as at open wetlands offering thus possibilities to monitor various phenological and time-associated events and elements. In this poster, we present our camera network and give examples of image series use for research. We will show results about the stability of camera derived color signals, and based on that discuss about the applicability of cameras in monitoring time-dependent phenomena. We will also present results from comparisons between camera-derived color signal time series and daily satellite-derived time series (NVDI, NDWI, and fractional snow cover) from the Moderate Resolution Imaging Spectrometer (MODIS) at selected spruce and pine forests and in a wetland. We will discuss the applicability of cameras in supporting phenological observations derived from satellites, by considering the possibility of cameras to monitor both above and below canopy phenology and snow.

  11. [The plague in Finland in 1710].

    PubMed

    Engström, N G

    1994-01-01

    In the autumn of 1710 Helsinki was struck by the so-called oriental plague during four months. The infection was transferred by black rats which harboured fleas. The flea-bites caused boils. It was believed that the plague was air-borne, and the air was very humid that autumn. Big fires were lit in order to reduce the humidity, the purpose being to make it easier for the infected to breathe. Attempts were also made to dissect the boils. The carriers of the contamination came as refugees from Estland over the Gulf of Finland. The infection had spread from Turkey to Poland and Balticum after the defeat of the Finnish-Swedish army in the summer of 1709 at Poltava in Ucraine. Helsingfors (Helsinki) was struck extremely hard. About two-thirds of the inhabitants died of the pestilence. Some escaped by fleeing to the countryside. The plague spread through the country as far north as to Uleåborg (Oulu) and Cajana (Kajaani). Marketplaces became important centres of infection. With the advent of the frost in December the plague dwindled. At that time Helsinki was practically a dead town.

  12. CADDIS Volume 1. Stressor Identification: About Causal Assessment

    EPA Pesticide Factsheets

    An introduction to the history of our approach to causal assessment, A chronology of causal history and philosophy, An introduction to causal history and philosophy, References for the Causal Assessment Background section of Stressor Identification

  13. Experimental verification of an indefinite causal order

    PubMed Central

    Rubino, Giulia; Rozema, Lee A.; Feix, Adrien; Araújo, Mateus; Zeuner, Jonas M.; Procopio, Lorenzo M.; Brukner, Časlav; Walther, Philip

    2017-01-01

    Investigating the role of causal order in quantum mechanics has recently revealed that the causal relations of events may not be a priori well defined in quantum theory. Although this has triggered a growing interest on the theoretical side, creating processes without a causal order is an experimental task. We report the first decisive demonstration of a process with an indefinite causal order. To do this, we quantify how incompatible our setup is with a definite causal order by measuring a “causal witness.” This mathematical object incorporates a series of measurements that are designed to yield a certain outcome only if the process under examination is not consistent with any well-defined causal order. In our experiment, we perform a measurement in a superposition of causal orders—without destroying the coherence—to acquire information both inside and outside of a “causally nonordered process.” Using this information, we experimentally determine a causal witness, demonstrating by almost 7 SDs that the experimentally implemented process does not have a definite causal order. PMID:28378018

  14. Improving Causality Induction with Category Learning

    PubMed Central

    Wang, Zhihong; Shao, Zhiqing

    2014-01-01

    Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning. PMID:24883419

  15. Checklist of tapeworms (Platyhelminthes, Cestoda) of vertebrates in Finland

    PubMed Central

    Haukisalmi, Voitto

    2015-01-01

    Abstract A checklist of tapeworms (Cestoda) of vertebrates (fishes, birds and mammals) in Finland is presented, based on published observations, specimens deposited in the collections of the Finnish Museum of Natural History (Helsinki) and the Zoological Museum of the University of Turku, and additional specimens identified by the present author. The checklist includes 170 tapeworm species from 151 host species, comprising 447 parasite species/host species combinations. Thirty of the tapeworm species and 96 of the parasite/host species combinations have not been previously reported from Finland. The total number of tapeworm species in Finland (170 spp.) is significantly lower than the corresponding figure for the Iberian Peninsula (257 spp.), Slovakia (225 spp.) and Poland (279 spp.). The difference between Finland and the other three regions is particularly pronounced for anseriform, podicipediform, charadriiform and passeriform birds, reflecting inadequate and/or biased sampling of these birds in Finland. It is predicted that there are actually ca. 270 species of tapeworms in Finland, assuming that true number of bird tapeworms in Finland corresponds to that in other European countries with more comprehensive knowledge of the local tapeworm fauna. The other main pattern emerging from the present data is the seemingly unexplained absence in (northern) Fennoscandia of several mammalian tapeworms that otherwise have extensive distributions in the Holarctic region or in Eurasia, including the northern regions. Previously unknown type specimens, that is, the holotype of Bothrimonus nylandicus Schneider, 1902 (a junior synonym of Diplocotyle olrikii Krabbe, 1874) (MZH 127096) and the syntypes of Caryophyllaeides fennica (Schneider, 1902) (MZH 127097) were located in the collections of the Finnish Museum of Natural History. PMID:26668540

  16. Checklist of tapeworms (Platyhelminthes, Cestoda) of vertebrates in Finland.

    PubMed

    Haukisalmi, Voitto

    2015-01-01

    A checklist of tapeworms (Cestoda) of vertebrates (fishes, birds and mammals) in Finland is presented, based on published observations, specimens deposited in the collections of the Finnish Museum of Natural History (Helsinki) and the Zoological Museum of the University of Turku, and additional specimens identified by the present author. The checklist includes 170 tapeworm species from 151 host species, comprising 447 parasite species/host species combinations. Thirty of the tapeworm species and 96 of the parasite/host species combinations have not been previously reported from Finland. The total number of tapeworm species in Finland (170 spp.) is significantly lower than the corresponding figure for the Iberian Peninsula (257 spp.), Slovakia (225 spp.) and Poland (279 spp.). The difference between Finland and the other three regions is particularly pronounced for anseriform, podicipediform, charadriiform and passeriform birds, reflecting inadequate and/or biased sampling of these birds in Finland. It is predicted that there are actually ca. 270 species of tapeworms in Finland, assuming that true number of bird tapeworms in Finland corresponds to that in other European countries with more comprehensive knowledge of the local tapeworm fauna. The other main pattern emerging from the present data is the seemingly unexplained absence in (northern) Fennoscandia of several mammalian tapeworms that otherwise have extensive distributions in the Holarctic region or in Eurasia, including the northern regions. Previously unknown type specimens, that is, the holotype of Bothrimonus nylandicus Schneider, 1902 (a junior synonym of Diplocotyle olrikii Krabbe, 1874) (MZH 127096) and the syntypes of Caryophyllaeides fennica (Schneider, 1902) (MZH 127097) were located in the collections of the Finnish Museum of Natural History.

  17. Tobacco industry strategy to undermine tobacco control in Finland

    PubMed Central

    Hiilamo, H

    2003-01-01

    Objective: To identify and explain tobacco industry strategy in undermining tobacco control measures in Finland and results of these interferences in tobacco policy development during the 1980s and early 1990s. Methods: Tobacco industry documents, which have been publicly available on the internet as a result of litigation in the USA, were analysed. Documents were sought by Finland and by names of organisations and tobacco control activists. Documents were accessed and assessed between September 2000 and November 2002. Tactics of the tobacco industry activities were categorised as presented by Saloojee and Dagli. Results: The international tobacco companies utilised similar strategies in Finland as in other industrial markets to fight tobacco control and legislation, the health advocacy movement, and litigation. These activities slowed down the development and implementation of the Tobacco Act in Finland. However, despite the extensive pressure, the industry was not able to prevent the most progressive tobacco legislation in Europe from being passed and coming into force in Finland in 1977 and in 1995. Conclusion: Denying the health hazards caused by tobacco—despite indisputable scientific evidence—decreased the credibility of the tobacco industry. Strategy of denial was falsely chosen, as health advocacy groups were active both in society and the parliamentary system. The strong influence of the tobacco industry may have in fact increased the visibility of tobacco control in Finland as the litigation process was also drawing attention to negative health effects of tobacco. Therefore the tobacco industry did not manage to convince public opinion. However, the tobacco industry did obtain experience in Finland in how to object to tobacco control measures. PMID:14660780

  18. Norms and customs: causally important or causally impotent?

    PubMed

    Jones, Todd

    2010-01-01

    In this article, I argue that norms and customs, despite frequently being described as being causes of behavior in the social sciences and ordinary conversation, cannot really cause behavior. Terms like "norms" and the like seem to refer to philosophically disreputable disjunctive properties. More problematically, even if they do not, or even if there can be disjunctive properties after all, I argue that norms and customs still cannot cause behavior. The social sciences would be better off without referring to properties like norms and customs as if they could be causal.

  19. Aquifer Properties in Hepokangas, Northern Finland

    NASA Astrophysics Data System (ADS)

    Pihlaja, M. Sc.

    2012-04-01

    Hepokangas study area is located in northern Finland, app. 60 km north-east of the city of Oulu. It consists of an esker ridge which ranges in elevation from 95 to 105 m.a.s.l. Consequently, all Quaternary deposits in the area have been influenced by erosional and depositional processes during two Baltic Sea stages (Ancylus Lake and Littorina Sea). Therefore, raised beaches are found on the esker slopes and fine grained sediments on the lowlands. The studied aquifer, the Hepokangas esker is part of an discontinuous chain of eskers which, in total, is about 100 km long and is elongated from north-west to south-east. The direction indicates that the esker was deposited by the melt waters during the latest phase of Weichselian glaciation. The primary part of the esker is located in the western segment of the area and a delta-like expansion of an esker is in the eastern part of the study area . Level of the ground water table (GWT) was measured at 14 ground water pipes which were located in varying parts of the Hepokangas formation. Ground penetrating radar (GPR) surveys were conducted on the primary part of the esker in order to determine internal structures and estimate permeability of the formation. Ground water flow directions were interpreted based on these measurements. The GWT varies from 91.91 to 97.98 m.a.s.l. Since the Hepokangas formation is surrounded by mires the height of the GWT decreased towards them. There was a water pumping station on the primary part of the formation, but no clear effect to the GWT could be seen to be caused by that. From the GPR results, some locations of the coarse grain sediments with high permeability were found.

  20. Upgrading the Northern Finland Seismological Network

    NASA Astrophysics Data System (ADS)

    Narkilahti, Janne; Kozlovskaya, Elena; Silvennoinen, Hanna; Hurskainen, Riitta; Nevalainen, Jouni

    2016-04-01

    The Finnish National Seismic Network (FNSN) comprises national Helsinki University Seismological network (HE) ISUH and the Northern Finland Seismological Network (FN) hosted by the Sodankylä Geophysical Observatory (SGO) of the University of Oulu. The FN network currently consists of four real-time permanent stations equipped with Streckeisen STS-2 broad band seismometers that are recording continuous digital seismic data. At present, the network is a part of GEOFON Extended Virtual Network and of the ORFEUS Virtual European Broadband Seismograph Network. In the future, the network will be the part of EPOS-European Plate Observing System research infrastructure. As a part of EPOS project activities, the SGO started to upgrade their own network in 2014. The main target of the network upgrade is to increase the permanent station coverage in the European Arctic region, particularly behind the Polar Circle. Another target is to transform the network into a broadband seismic array capable to detect long-period seismic signals originating from seismic events in the Arctic. The first upgrade phase started in 2014, when two new stations were installed and now are working in the test regime. These stations are used as prototypes for testing seismic equipment and technical solutions for real-time data transmission and vault construction under cold climate conditions. The first prototype station is installed in a surface vault and equipped with Nanometrics Trillium 120P sensor, while the other one is installed in a borehole and equipped with Trillium Posthole seismometer. These prototype stations have provided to us valuable experience on the downhole and surface deployment of broadband seismic instruments. We also have been able to compare the capabilities and performance of high sensitivity broadband sensor deployed in borehole with that deployed in surface vault. The results of operation of prototype stations will be used in site selection and installation of four new

  1. The development of railway safety in Finland.

    PubMed

    Silla, Anne; Kallberg, Veli-Pekka

    2012-03-01

    This study reviews the development of railway safety in Finland from 1959 to 2008. The results show that the level of safety has greatly improved over the past five decades. The total number of railway fatalities did not show any obvious decreasing or increasing trend during the first decade, but since the early 1970s the annual number of fatalities has decreased from about 100 to 20. The estimated overall annual reduction per year from 1970 to 2008 was 5.4% (with a 95% confidence interval from -8.2% to -2.6%). The reduction in subcategories per million train-kilometres from 1959 to 2008 was 4.4% per year for passengers, 8.3% for employees, 5.0% for road users at level crossings and 3.6% for others (mainly trespassers). The safety improvement for passengers and staff was probably influenced by the introduction of central locking of doors in passenger cars and improved procedures to protect railway employees working on the tracks. The number of road users killed at level crossings has fallen due to the installation of barriers and the construction of overpasses and underpasses at crossings with dense traffic, removal of level crossings, and an improvement of conditions such as visibility at crossings. The number of trespasser fatalities has seen the least decline. Key plans for the future include further reduction of the number of level crossings on the state railway network from the current roughly 3500-2200 by 2025, and involving communities in safety work related to railway trespassers.

  2. Independence and dependence in human causal reasoning.

    PubMed

    Rehder, Bob

    2014-07-01

    Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult's causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned "associatively," that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects' assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.

  3. How prescriptive norms influence causal inferences.

    PubMed

    Samland, Jana; Waldmann, Michael R

    2016-11-01

    Recent experimental findings suggest that prescriptive norms influence causal inferences. The cognitive mechanism underlying this finding is still under debate. We compare three competing theories: The culpable control model of blame argues that reasoners tend to exaggerate the causal influence of norm-violating agents, which should lead to relatively higher causal strength estimates for these agents. By contrast, the counterfactual reasoning account of causal selection assumes that norms do not alter the representation of the causal model, but rather later causal selection stages. According to this view, reasoners tend to preferentially consider counterfactual states of abnormal rather than normal factors, which leads to the choice of the abnormal factor in a causal selection task. A third view, the accountability hypothesis, claims that the effects of prescriptive norms are generated by the ambiguity of the causal test question. Asking whether an agent is a cause can be understood as a request to assess her causal contribution but also her moral accountability. According to this theory norm effects on causal selection are mediated by accountability judgments that are not only sensitive to the abnormality of behavior but also to mitigating factors, such as intentionality and knowledge of norms. Five experiments are presented that favor the accountability account over the two alternative theories.

  4. Space and time in perceptual causality.

    PubMed

    Straube, Benjamin; Chatterjee, Anjan

    2010-01-01

    Inferring causality is a fundamental feature of human cognition that allows us to theorize about and predict future states of the world. Michotte suggested that humans automatically perceive causality based on certain perceptual features of events. However, individual differences in judgments of perceptual causality cast doubt on Michotte's view. To gain insights in the neural basis of individual difference in the perception of causality, our participants judged causal relationships in animations of a blue ball colliding with a red ball (a launching event) while fMRI-data were acquired. Spatial continuity and temporal contiguity were varied parametrically in these stimuli. We did not find consistent brain activation differences between trials judged as caused and those judged as non-caused, making it unlikely that humans have universal instantiation of perceptual causality in the brain. However, participants were slower to respond to and showed greater neural activity for violations of causality, suggesting that humans are biased to expect causal relationships when moving objects appear to interact. Our participants demonstrated considerable individual differences in their sensitivity to spatial and temporal characteristics in perceiving causality. These qualitative differences in sensitivity to time or space in perceiving causality were instantiated in individual differences in activation of the left basal ganglia or right parietal lobe, respectively. Thus, the perception that the movement of one object causes the movement of another is triggered by elemental spatial and temporal sensitivities, which themselves are instantiated in specific distinct neural networks.

  5. Measuring causal perception: connections to representational momentum?

    PubMed

    Choi, Hoon; Scholl, Brian J

    2006-01-01

    In a collision between two objects, we can perceive not only low-level properties, such as color and motion, but also the seemingly high-level property of causality. It has proven difficult, however, to measure causal perception in a quantitatively rigorous way which goes beyond perceptual reports. Here we focus on the possibility of measuring perceived causality using the phenomenon of representational momentum (RM). Recent studies suggest a relationship between causal perception and RM, based on the fact that RM appears to be attenuated for causally 'launched' objects. This is explained by appeal to the visual expectation that a 'launched' object is inert and thus should eventually cease its movement after a collision, without a source of self-propulsion. We first replicated these demonstrations, and then evaluated this alleged connection by exploring RM for different types of displays, including the contrast between causal launching and non-causal 'passing'. These experiments suggest that the RM-attenuation effect is not a pure measure of causal perception, but rather may reflect lower-level spatiotemporal correlates of only some causal displays. We conclude by discussing the strengths and pitfalls of various methods of measuring causal perception.

  6. 77 FR 14733 - Purified Carboxymethylcellulose From Finland and the Netherlands: Extension of Time Limit for...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-03-13

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF COMMERCE International Trade Administration Purified Carboxymethylcellulose From Finland and the Netherlands: Extension..., inter alia, purified carboxymethylcellulose from Finland and the Netherlands covering the period July...

  7. Checklist of the leaf-mining flies (Diptera, Agromyzidae) of Finland

    PubMed Central

    Kahanpää, Jere

    2014-01-01

    Abstract A checklist of the Agromyzidae (Diptera) recorded from Finland is presented. 279 (or 280) species are currently known from the country. Phytomyza linguae Lundqvist, 1947 is recorded as new to Finland. PMID:25337025

  8. Screening for late-onset Pompe disease in Finland.

    PubMed

    Palmio, Johanna; Auranen, Mari; Kiuru-Enari, Sari; Löfberg, Mervi; Bodamer, Olaf; Udd, Bjarne

    2014-11-01

    Pompe disease (glycogen storage disease type II) is caused by autosomal recessive mutations in GAA gene. The estimated frequency of late-onset Pompe disease is around 1:60,000. However, only two infantile and one late-onset Pompe patients have been reported in Finland with a population of 5 million. We screened for late-onset Pompe disease in a cohort of undetermined myopathy patients with proximal muscle weakness and/or elevated serum creatine kinase values. Acid α-glucosidase (GAA) activity in dried blood spots was measured and clinical data collected in 108 patients. Four patients had low normal GAA activity; all the others had activities well within the normal range. Re-analyses of these patients did not reveal new Pompe patients. Our findings suggest that Pompe disease is extremely rare in Finland. Finland is an example of an isolated population with enrichment of certain mutations for genetic disorders and low occurrence of some autosomal recessive diseases.

  9. Causal diagrams in systems epidemiology

    PubMed Central

    2012-01-01

    Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback. PMID:22429606

  10. Comparison theorems for causal diamonds

    NASA Astrophysics Data System (ADS)

    Berthiere, Clément; Gibbons, Gary; Solodukhin, Sergey N.

    2015-09-01

    We formulate certain inequalities for the geometric quantities characterizing causal diamonds in curved and Minkowski spacetimes. These inequalities involve the redshift factor which, as we show explicitly in the spherically symmetric case, is monotonic in the radial direction, and it takes its maximal value at the center. As a by-product of our discussion we rederive Bishop's inequality without assuming the positivity of the spatial Ricci tensor. We then generalize our considerations to arbitrary, static and not necessarily spherically symmetric, asymptotically flat spacetimes. In the case of spacetimes with a horizon our generalization involves the so-called domain of dependence. The respective volume, expressed in terms of the duration measured by a distant observer compared with the volume of the domain in Minkowski spacetime, exhibits behaviors which differ if d =4 or d >4 . This peculiarity of four dimensions is due to the logarithmic subleading term in the asymptotic expansion of the metric near infinity. In terms of the invariant duration measured by a comoving observer associated with the diamond we establish an inequality which is universal for all d . We suggest some possible applications of our results including comparison theorems for entanglement entropy, causal set theory, and fundamental limits on computation.

  11. Control of typhus fever in Finland during World War II.

    PubMed

    Laurent, Helene

    2009-12-01

    The article describes the measures taken against the threat of typhus epidemic in Finland during the Second World War. Comparisons between countries at war and their different typhus prevention methods are made. The main method of typhus prevention in Finland consisted of regular sauna bathing, which was culturally acceptable and very efficient when combined with heating of the clothing. The Finnish troops remained virtually louse-free by ecological and traditional methods, and thus the spread of typhus fever in the army could be prevented.

  12. Multiple Causality: Consequences for Medical Practice

    PubMed Central

    Nydegger, Corinne N.

    1983-01-01

    When a scientifically trained health professional is called upon to deal with patients holding differing causal views of illness, the resulting lack of communication is frustrating to both. This discussion traces some implications for medical practice of significant cultural differences in two aspects of causal paradigms of illness: (1) terms accepted and (2) dimension or level of causality typically sought. The second is the more pervasive and intractable problem, having distinctive consequences for the role of curer, symptomatology, diagnosis and treatment. PMID:6858133

  13. Causal inference in economics and marketing

    PubMed Central

    Varian, Hal R.

    2016-01-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

  14. Age and Meanings of Violence: Women's Experiences of Partner Violence in Finland

    ERIC Educational Resources Information Center

    Piispa, Minna

    2004-01-01

    The first survey carried out in Finland specifically to study men's violence against women showed that partner violence is quite common in Finland and it is directed especially toward young women. The statistical findings don't support the idea that violence has become more widespread in Finland. Life situation factors that are usually viewed as…

  15. Causal compensated perturbations in cosmology

    NASA Technical Reports Server (NTRS)

    Veeraraghavan, Shoba; Stebbins, Albert

    1990-01-01

    A theoretical framework is developed to calculate linear perturbations in the gravitational and matter fields which arise causally in response to the presence of stiff matter sources in a FRW cosmology. It is shown that, in order to satisfy energy and momentum conservation, the gravitational fields of the source must be compensated by perturbations in the matter and gravitational fields, and the role of such compensation in containing the initial inhomogeneities in their subsequent evolution is discussed. A complete formal solution is derived in terms of Green functions for the perturbations produced by an arbitrary source in a flat universe containing cold dark matter. Approximate Green function solutions are derived for the late-time density perturbations and late-time gravitational waves in a universe containing a radiation fluid. A cosmological energy-momentum pseudotensor is defined to clarify the nature of energy and momentum conservation in the expanding universe.

  16. Causal models and learning from data: integrating causal modeling and statistical estimation.

    PubMed

    Petersen, Maya L; van der Laan, Mark J

    2014-05-01

    The practice of epidemiology requires asking causal questions. Formal frameworks for causal inference developed over the past decades have the potential to improve the rigor of this process. However, the appropriate role for formal causal thinking in applied epidemiology remains a matter of debate. We argue that a formal causal framework can help in designing a statistical analysis that comes as close as possible to answering the motivating causal question, while making clear what assumptions are required to endow the resulting estimates with a causal interpretation. A systematic approach for the integration of causal modeling with statistical estimation is presented. We highlight some common points of confusion that occur when causal modeling techniques are applied in practice and provide a broad overview on the types of questions that a causal framework can help to address. Our aims are to argue for the utility of formal causal thinking, to clarify what causal models can and cannot do, and to provide an accessible introduction to the flexible and powerful tools provided by causal models.

  17. Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena

    ERIC Educational Resources Information Center

    Rottman, Benjamin M.; Gentner, Dedre; Goldwater, Micah B.

    2012-01-01

    We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative…

  18. A Theory of Causal Learning in Children: Causal Maps and Bayes Nets

    ERIC Educational Resources Information Center

    Gopnik, Alison; Glymour, Clark; Sobel, David M.; Schulz, Laura E.; Kushnir, Tamar; Danks, David

    2004-01-01

    The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously…

  19. The Power of Causal Beliefs and Conflicting Evidence on Causal Judgments and Decision Making

    ERIC Educational Resources Information Center

    Garcia-Retamero, Rocio; Muller, Stephanie M.; Catena, Andres; Maldonado, Antonio

    2009-01-01

    In two experiments, we investigated the relative impact of causal beliefs and empirical evidence on both decision making and causal judgments, and whether this relative impact could be altered by previous experience. 2. Selected groups of participants in both experiments received pre-training with either causal or neutral cues, or no pre-training…

  20. Influence of early life stress on intra- and extra-amygdaloid causal connectivity.

    PubMed

    Grant, Merida M; Wood, Kimberly; Sreenivasan, Karthik; Wheelock, Muriah; White, David; Thomas, Jasmyne; Knight, David C; Deshpande, Gopikrishna

    2015-06-01

    Animal models of early life stress (ELS) are characterized by augmented amygdala response to threat and altered amygdala-dependent behaviors. These models indicate the amygdala is a heterogeneous structure with well-differentiated subnuclei. The most well characterized of these being basolateral (BLA) and central nucleus (CeA). Parallel human imaging findings relative to ELS also reveal enhanced amygdala reactivity and disrupted connectivity but the influence of ELS on amygdala subregion connectivity and modulation of emotion is unclear. Here we employed cytoarchitectonic probability maps of amygdala subregions and Granger causality methods to evaluate task-based intra-amygdaloid and extra-amygdaloid connectivity with the network underlying implicit regulation of emotion in response to unconditioned auditory threat in healthy controls with ELS (N=20) and without a history of ELS (N=14). Groups were determined by response to the Childhood Trauma Questionnaire and threat response determined by unpleasantness ratings. Non-ELS demonstrated narrowly defined BLA-driven intra-amygdaloid paths and concise orbitofrontal cortex (OFC)-CeA-driven extra-amygdaloid connectivity. In contrast, ELS was associated with extensive and robust CeA-facilitated intra- and extra-amygdaloid paths. Non-ELS findings paralleled the known anatomical organization and functional relationships for both intra- and extra-amygdaloid connectivity, while ELS demonstrated atypical intra- and extra-amygdaloid CeA-dominant paths with compensatory modulation of emotion. Specifically, negative causal paths from OFC/BA32 to BLA predicted decreased threat response among non-ELS, while a unique within-amygdala path predicted modulation of threat among ELS. These findings are consistent with compensatory mechanisms of emotion regulation following ELS among resilient persons originating both within the amygdala complex as well as subsequent extra-amygdaloid communication.

  1. Molecular epidemiology of tuberculosis in Finland, 2008-2011.

    PubMed

    Smit, Pieter Willem; Haanperä, Marjo; Rantala, Pirre; Couvin, David; Lyytikäinen, Outi; Rastogi, Nalin; Ruutu, Petri; Soini, Hanna

    2013-01-01

    In industrialized countries the majority of tuberculosis (TB) cases are linked to immigration. In Finland, most cases are still Finnish born but the number of foreign born cases is steadily increasing. In this 4-year population based study, the TB situation in Finland was characterized by a genotypic analysis of Mycobacterium tuberculosis isolates. A total of 1048 M. tuberculosis isolates (representing 99.4% of all culture positive cases) were analyzed by spoligotyping and MIRU. Spoligotype lineages belonging to the Euro-American family were predominant among the Finnish isolates, particularly T (n=346, 33.0%) and Haarlem (n=237, 22.6%) strains. The lineage signature was unknown for 130 (12.4%) isolates. Out of the 17 multi-drug resistant TB strains, 10 (58.8%) belonged to the Beijing lineage. In total, 23 new SIT designations were given and 51 orphan strains were found, of which 58 patterns were unique to Finland. Phylogeographical TB mapping as compared to neighboring countries showed that the population structure in Finland most closely resembled that observed in Sweden. By combining spoligotyping and MIRU results, 98 clusters comprising 355 isolates (33.9%) were found. Only 10 clusters contained both Finnish and foreign born cases. In conclusion, a large proportion of the M. tuberculosis isolates were from Finnish born elderly patients. Moreover, many previously unidentified spoligotype profiles and isolates belonging to unknown lineages were encountered.

  2. Evaluating the Quality of the Child Care in Finland

    ERIC Educational Resources Information Center

    Hujala, Eeva; Fonsen, Elina; Elo, Janniina

    2012-01-01

    In this study we examine parents' and teachers' perceptions of the early childhood education and care (ECEC) quality in Finland. The study is based on the paradigm of inclusionary quality and the assessment is based on the quality evaluation model. The parents and teachers assess the quality to be good. The strength of the quality was the effect…

  3. Thematic Review on Adult Learning: Finland. Background Report.

    ERIC Educational Resources Information Center

    Organisation for Economic Cooperation and Development, Paris (France).

    In international comparisons, participation in adult learning in Finland is high. Work or career development is the main reason for participation. Persons starting with greater educational attainment participate in adult learning opportunities more. Roots of adult education and training (AET) lie in liberal education; those of occupational AET in…

  4. School Autonomy, Leadership and Student Achievement: Reflections from Finland

    ERIC Educational Resources Information Center

    Saarivirta, Toni; Kumpulainen, Kristiina

    2016-01-01

    Purpose: The purpose of this paper is to provide national information on school autonomy, leadership and student achievements in Finland. Design/methodology/approach: The paper is a literature review on Finnish studies focusing on school autonomy, leadership and student achievement. The studies have been reviewed on the basis of a content…

  5. Streptococcus dysgalactiae subsp. equisimilis Bacteremia, Finland, 1995–2004

    PubMed Central

    Vähäkuopus, Susanna; Vuopio-Varkila, Jaana; Vuento, Risto; Syrjänen, Jaana

    2010-01-01

    We conducted a retrospective population-based study of 140 episodes of Streptococcus dysgalactiae subsp. equisimilis bacteremia occurring in Finland during 1995–2004. Rare emm types were associated with more severe disease and increased mortality rates. Skin and soft tissue infections were more frequent clinical signs among cases caused by common emm types. PMID:20409380

  6. Streptococcus dysgalactiae subsp. equisimilis Bacteremia, Finland, 1995-2004.

    PubMed

    Rantala, Sari; Vahakuopus, Susanna; Vuopio-Varkila, Jaana; Vuento, Risto; Syrjanen, Jaana

    2010-05-01

    We conducted a retrospective population-based study of 140 episodes of Streptococcus dysgalactiae subsp. equisimilis bacteremia occurring in Finland during 1995-2004. Rare emm types were associated with more severe disease and increased mortality rates. Skin and soft tissue infections were more frequent clinical signs among cases caused by common emm types.

  7. Young Children's Number Sense in Finland, Hong Kong and Singapore

    ERIC Educational Resources Information Center

    Aunio, Pirjo; Ee, Jessie; Lim, Swee Eng Audrey; Hautamaki, Jarkko; Van Luit, Johannes E. H.

    2004-01-01

    This study examines young children's number sense in subjects from Finland (n =254), Hong Kong (n =246), and Singapore (n =130). Chinese, English and Finnish versions of the Early Numeracy Test (ENT; Van Luit et al., 1994) were used. Two highly correlated aspects of number sense were measured, one reflecting children's abilities to organize and…

  8. Teaching Popular Music in Finland: What's Up, What's Ahead?

    ERIC Educational Resources Information Center

    Vakeva, Lauri

    2006-01-01

    This article describes the history and current situation of popular music pedagogy in Finland. While popular music is widely accepted in the curriculum, there are differences in its application in the comprehensive schools and music institutions. Popular styles were first introduced into Finnish music education by secondary school music teachers;…

  9. Bear attack--A unique fatality in Finland.

    PubMed

    De Giorgio, Fabio; Rainio, Juha; Pascali, Vincenzo; Lalu, Kaisa

    2007-11-15

    Fatalities due to animal bites, the vast majority of which are associated with dogs and big cats, are relatively uncommon and rarely described in the literature. Especially rare are fatal bear attacks on humans. We herein present a forensic investigation of a fatal assault, involving numerous bites on a 42-year-old man in Finland by an European brown bear (Ursus arctos arctos).

  10. Surveying Supported Employment in Finland: A Follow-up

    ERIC Educational Resources Information Center

    Saloviita, Timo; Pirttimaa, Raija

    2007-01-01

    The longitudinal status of supported employment in Finland was examined via a 2003 nationwide survey sent to job coaches involved in supporting workers with intellectual and other disabilities. Sustained supported employment, defined as "paid work in integrated settings with ongoing supports that contained at least two on-site visits per…

  11. Marital Status as a Predictor of Perinatal Outcome in Finland.

    ERIC Educational Resources Information Center

    Manderbacka, Kristiina; And Others

    1992-01-01

    Examined association between mother's marital status and perinatal outcome among single births in Finland in 1987 (n=56,595 infants). Found that perinatal deaths, low birthweight, and preterm infants were more common among single mothers than among married mothers. Results for cohabiting mothers were more similar to those of married than to those…

  12. Needs for Rural Research in the Northern Finland Context

    ERIC Educational Resources Information Center

    Muilu, Toivo

    2010-01-01

    The aim of this paper is to discuss the needs and demands which rural research faces at the interface between research and development. The case study area is northern Finland, which constitutes the most remote and sparsely populated areas of the European Union. This paper is based on the tradition of rural research since the 1980s in connection…

  13. Problematizing Finland's Pursuit of Intercultural (Kindergarten) Teacher Education

    ERIC Educational Resources Information Center

    Layne, Heidi; Dervin, Fred

    2016-01-01

    The argument that teachers should become ethical intercultural teachers is increasingly recognized as legitimate. This article presents a case study in kindergarten teacher education in Finland, a country that has been at the center of global discussions about quality education. The authors question the agenda for studying and teaching in an…

  14. My Lifelong Learning Realm: An Autoethnography Experiential Learning in Finland

    ERIC Educational Resources Information Center

    Rajbhandari, Mani Man Singh

    2011-01-01

    My journey to write autoethnography report started with inclination to learn cultural and social phenomena in Finland. This was my realm of learning through experiential learning. The ontological philosophy was perceived through objectivistic and subjectivistic approaches. The lifelong experiential learning realm was a benchmark for me to perceive…

  15. Learning Practices of Femininity through Gendered Craft Education in Finland

    ERIC Educational Resources Information Center

    Kokko, Sirpa

    2009-01-01

    This paper discusses the processes and practices that link crafts and gender in the upbringing and education of girls. The paper is based on a study conducted among female primary school trainee teachers in Finland. The data are comprised of their experiences with crafts as schoolgirls. The methods of the study were memory work and writing of…

  16. Teacher Education in Italy, Germany, England, Sweden and Finland

    ERIC Educational Resources Information Center

    Ostinelli, Giorgio

    2009-01-01

    This article presents a brief analysis of teacher education in five European countries: Italy, Germany, England, Sweden and Finland. In the post-industrial world, the sense of teaching has profoundly changed, influenced by a rapidly evolving socio-economic context. The responses given by each country are different, but two tendencies emerge: on…

  17. Interaction of Language Policy and Assessment in Finland

    ERIC Educational Resources Information Center

    Tarnanen, Mirja; Huhta, Ari

    2008-01-01

    This paper reviews developments and future challenges in language policy, planning and assessment in Finland, where several important changes in legislation, curricula and assessment systems have recently taken place. Language proficiency requirement of immigrants and civil servants have been redefined, school curricula have been revised and new…

  18. Adults' Numeracy in Finland: What Do We Know about It?

    ERIC Educational Resources Information Center

    Hassi, Marja-Liisa; Hannula, Aino; Salo i Nevado, Laia

    2008-01-01

    Adult education has become a significant aspect of Finnish educational and developmental policy as well as of Finnish labor and social policies. Such factors as the need for occupational proficiency, employment, and economic growth have strongly influenced adult education in Finland. Besides the development of personality and support for the life…

  19. Thematic Review on Adult Learning: Finland. Country Note. Revised.

    ERIC Educational Resources Information Center

    Organisation for Economic Cooperation and Development, Paris (France).

    This country note analyzes main issues concerning adult learning and policy responses in Finland. Section 2 describes the political, economic, and social context in which adult learning fits. Sections 3-6 follow these four themes impinging on adult participation in learning: inadequate incentives and motivations; complex pathways between learning…

  20. Teaching Russian as a Heritage Language in Finland

    ERIC Educational Resources Information Center

    Protassova, Ekaterina

    2008-01-01

    Originating from many different sources, Russian as a heritage language in Finland displays a spectrum of developmental tendencies: both attrition and maintenance can be observed in various degrees. The Finnish educational system allows for the organization of bilingual pre-schools and schools when there are sufficient numbers of potential pupils.…

  1. Children as Consumers of Historical Culture in Finland

    ERIC Educational Resources Information Center

    Rantala, Jukka

    2011-01-01

    The article examines the reception of history by 7-10-year-old children in Finland and the role of historical culture in the formation of children's conceptions of the past. It scrutinizes how history is used to build individual and collective identities and bring significance to the past in children's everyday lives. Interviews with 174 pupils in…

  2. Career Burnout and Its Relationship to Couple Burnout in Finland.

    ERIC Educational Resources Information Center

    Laes, Timo; Laes, Tuula

    This study is part of cross cultural research on the relationship between career and couple burnout in six countries (England, Finland, Israel, Portugal, Spain, and the USA.) This pilot study presents first results of Finnish data. Female elementary school teachers (N=56) and male students (N=70) in academic professional education completed the…

  3. Cystic fibrosis in Finland: a molecular and genealogical study.

    PubMed

    Kere, J; Norio, R; Savilahti, E; Estivill, X; de la Chapelle, A

    1989-08-01

    The incidence of cystic fibrosis (CF) in Finland is one tenth that in other Caucasian populations. To study the genetics of CF in Finland, we used a combined molecular and genealogical approach. Out of the 20 Finnish families with a living CF patient, 19 were typed for eight closely linked restriction fragment length polymorphisms (RFLP) at the MET, D7S8, and D7S23 loci. The birthplaces of the parents and grandparents were traced using population registries. Allele and haplotype frequencies in Finland are similar to those of other European and North American populations, but are modified by sampling: two regional CF gene clusters, evidently the results of a founder effect, were identified. Generally, the gene was evenly distributed over the population, carrier frequency being estimated at approximately 1.3%. We conclude that CF in Finland is caused by the common Caucasian mutation(s), and that the low frequency of the gene can be explained by a negative sampling effect and genetic drift.

  4. Segregation, Integration, Inclusion--The Ideology and Reality in Finland

    ERIC Educational Resources Information Center

    Kivirauma, Joel; Klemela, Kirsi; Rinne, Risto

    2006-01-01

    In this paper, we try to examine the classical sociological points of special education, especially the organizational form of special education, social background of students and the minority status of students. The material of the study was collected mostly during 2003 from one large city in Finland. This city has more than a 100-year-long…

  5. Students' Attitudes towards Craft and Technology in Iceland and Finland

    ERIC Educational Resources Information Center

    Thorsteinsson, Gísli; Ólafsson, Brynjar; Autio, Ossi

    2012-01-01

    Craft education in both Finland and Iceland originated over 140 years ago and was influenced by the Scandinavian Sloyd pedagogy. Since then, the subject has moved away from craft and towards technology, with the aim being to increase students' technological abilities. In the beginning, the subject largely focused on the students copying artefacts,…

  6. Teaching Linear Equations: Case Studies from Finland, Flanders and Hungary

    ERIC Educational Resources Information Center

    Andrews, Paul; Sayers, Judy

    2012-01-01

    In this paper we compare how three teachers, one from each of Finland, Flanders and Hungary, introduce linear equations to grade 8 students. Five successive lessons were videotaped and analysed qualitatively to determine how teachers, each of whom was defined against local criteria as effective, addressed various literature-derived…

  7. Causal Indicators Can Help to Interpret Factors

    ERIC Educational Resources Information Center

    Bentler, Peter M.

    2016-01-01

    The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

  8. Essays on Causal Inference for Public Policy

    ERIC Educational Resources Information Center

    Zajonc, Tristan

    2012-01-01

    Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, the potential trade-offs between competing objectives, and, ultimately, the optimal policy. This dissertation studies causal…

  9. "Comments on Slavin": Synthesizing Causal Inferences

    ERIC Educational Resources Information Center

    Briggs, Derek C.

    2008-01-01

    When causal inferences are to be synthesized across multiple studies, efforts to establish the magnitude of a causal effect should be balanced by an effort to evaluate the generalizability of the effect. The evaluation of generalizability depends on two factors that are given little attention in current syntheses: construct validity and external…

  10. Preschoolers' Understanding of Temporal and Causal Relations.

    ERIC Educational Resources Information Center

    Sharp, Kay Colby

    1982-01-01

    Two experiments investigated psychological factors determining preschoolers' success or failure on a sequence-completion task involving temporal and causal ordering of events. Overall findings demonstrate that while preschoolers do understand temporal/causal relationships, their abilities are dependent on process variables demanded by the task…

  11. Updating during Reading Comprehension: Why Causality Matters

    ERIC Educational Resources Information Center

    Kendeou, Panayiota; Smith, Emily R.; O'Brien, Edward J.

    2013-01-01

    The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in…

  12. Causal Mediation Analysis: Warning! Assumptions Ahead

    ERIC Educational Resources Information Center

    Keele, Luke

    2015-01-01

    In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…

  13. Causal Moderation Analysis Using Propensity Score Methods

    ERIC Educational Resources Information Center

    Dong, Nianbo

    2012-01-01

    This paper is based on previous studies in applying propensity score methods to study multiple treatment variables to examine the causal moderator effect. The propensity score methods will be demonstrated in a case study to examine the causal moderator effect, where the moderators are categorical and continuous variables. Moderation analysis is an…

  14. Campbell's and Rubin's Perspectives on Causal Inference

    ERIC Educational Resources Information Center

    West, Stephen G.; Thoemmes, Felix

    2010-01-01

    Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

  15. Quasi-Experimental Designs for Causal Inference

    ERIC Educational Resources Information Center

    Kim, Yongnam; Steiner, Peter

    2016-01-01

    When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

  16. Causal Inferences during Text Comprehension and Production.

    ERIC Educational Resources Information Center

    Kemper, Susan

    As comprehension failure results whenever readers are unable to infer missing causal connections, recent comprehension research has focused both on assessing the inferential complexity of texts and on investigating students' developing ability to infer causal relationships. Studies have demonstrated that texts rely on four types of causal…

  17. Inference and Action in Early Causal Reasoning.

    ERIC Educational Resources Information Center

    Frye, Douglas; And Others

    1996-01-01

    Two marble-and-ramp experiments investigated whether a simple-to-embedded-rules account can explain changes in children's causal reasoning. Results indicated that the same difference between three- and four-year olds in the prediction experiment appeared in the action experiment, suggesting that the same rules may underlie causal action as well as…

  18. Causal Inferences in the Campbellian Validity System

    ERIC Educational Resources Information Center

    Lund, Thorleif

    2010-01-01

    The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…

  19. The handling of causality in SEA guidance

    SciTech Connect

    Perdicoulis, A. . E-mail: tasso@utad.pt; Hanusch, M. . E-mail: marie.hanusch@ufz.de; Kasperidus, H.D. . E-mail: hans.kasperidus@ufz.de; Weiland, U. . E-mail: ulrike.weiland@ufz.de

    2007-03-15

    Causality, or the relation of cause and effect, is a fundamental notion in science, planning, and management. It is also a fundamental notion in impact assessment, as it relates action proposals with environmental impacts. Building on recent research about causality in impact assessment, this article turns to Strategic Environmental Assessment (SEA) and examines eleven guidance documents to see whether and how they handle causality. The findings of this research, in line with results from similar research in EIA, indicate a weak handling of causality with regards to instruction, obligation, and theoretical support. The recommendations are to investigate the reasons for the observed trend, as there is no evidence in the guidance documents themselves, and to have the fundamental premise of causality in impact assessment safeguarded in future SEA guidance.

  20. Classical sequential growth dynamics for causal sets

    NASA Astrophysics Data System (ADS)

    Rideout, D. P.; Sorkin, R. D.

    2000-01-01

    Starting from certain causality conditions and a discrete form of general covariance, we derive a very general family of classically stochastic, sequential growth dynamics for causal sets. The resulting theories provide a relatively accessible ``halfway house'' to full quantum gravity that possibly contains the latter's classical limit (general relativity). Because they can be expressed in terms of state models for an assembly of Ising spins residing on the relations of the causal set, these theories also illustrate how nongravitational matter can arise dynamically from the causal set without having to be built in at the fundamental level. Additionally, our results bring into focus some interpretive issues of importance for a causal set dynamics and for quantum gravity more generally.

  1. Acid sulfate soils are an environmental hazard in Finland

    NASA Astrophysics Data System (ADS)

    Pihlaja, Jouni

    2016-04-01

    Acid sulfate soils (ASS) create significant threats to the environment on coastal regions of the Baltic Sea in Finland. The sediments were deposited during the ancient Litorina Sea phase of the Baltic Sea about 7500-4500 years ago. Finland has larger spatial extent of the ASS than any other European country. Mostly based on anthropogenic reasons (cultivation, trenching etc.) ASS deposits are currently being exposed to oxygen which leads to chemical reaction creating sulfuric acid. The acidic waters then dissolve metals form the soil. Acidic surface run off including the metals are then leached into the water bodies weakening the water quality and killing fish or vegetation. In constructed areas acidic waters may corrode building materials. Geological Survey of Finland (GTK) is mapping ASS deposits in Finland. The goal is to map a total of 5 million hectares of the potentially ASS affected region. It has been estimated that the problematic Litorina Sea deposits, which are situated 0-100 m above the recent Baltic Sea shoreline, cover 500 000 hectares area. There are several phases in mapping. The work begins at the office with gathering the existing data, interpreting airborne geophysical data and compiling a field working plan. In the field, quality of the soil is studied and in uncertain cases samples are taken to laboratory analyses. Also electrical conductivity and pH of soil and water are measured in the field. Laboratory methods include multielemental determinations with ICP-OES, analyses of grain size and humus content (LOI), and incubation. So far, approximately 60 % of the potential ASS affected regions in Finland are mapped. Over 15 000 sites have been studied in the field and 4000 laboratory analyses are done. The spatial database presented in the scale of 1: 250 000 can be viewed at the GTK's web pages (http://gtkdata.gtk.fi/hasu/index.html).

  2. Spread of entanglement and causality

    NASA Astrophysics Data System (ADS)

    Casini, Horacio; Liu, Hong; Mezei, Márk

    2016-07-01

    We investigate causality constraints on the time evolution of entanglement entropy after a global quench in relativistic theories. We first provide a general proof that the so-called tsunami velocity is bounded by the speed of light. We then generalize the free particle streaming model of [1] to general dimensions and to an arbitrary entanglement pattern of the initial state. In more than two spacetime dimensions the spread of entanglement in these models is highly sensitive to the initial entanglement pattern, but we are able to prove an upper bound on the normalized rate of growth of entanglement entropy, and hence the tsunami velocity. The bound is smaller than what one gets for quenches in holographic theories, which highlights the importance of interactions in the spread of entanglement in many-body systems. We propose an interacting model which we believe provides an upper bound on the spread of entanglement for interacting relativistic theories. In two spacetime dimensions with multiple intervals, this model and its variations are able to reproduce intricate results exhibited by holographic theories for a significant part of the parameter space. For higher dimensions, the model bounds the tsunami velocity at the speed of light. Finally, we construct a geometric model for entanglement propagation based on a tensor network construction for global quenches.

  3. Diagnosis and causal explanation in psychiatry.

    PubMed

    Maung, Hane Htut

    2016-12-01

    In clinical medicine, a diagnosis can offer an explanation of a patient's symptoms by specifying the pathology that is causing them. Diagnoses in psychiatry are also sometimes presented in clinical texts as if they pick out pathological processes that cause sets of symptoms. However, current evidence suggests the possibility that many diagnostic categories in psychiatry are highly causally heterogeneous. For example, major depressive disorder may not be associated with a single type of underlying pathological process, but with a range of different causal pathways, each involving complex interactions of various biological, psychological, and social factors. This paper explores the implications of causal heterogeneity for whether psychiatric diagnoses can be said to serve causal explanatory roles in clinical practice. I argue that while they may fall short of picking out a specific cause of the patient's symptoms, they can nonetheless supply different sorts of clinically relevant causal information. In particular, I suggest that some psychiatric diagnoses provide negative information that rules out certain causes, some provide approximate or disjunctive information about the range of possible causal processes, and some provide causal information about the relations between the symptoms themselves.

  4. Causal inference, probability theory, and graphical insights.

    PubMed

    Baker, Stuart G

    2013-11-10

    Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design.

  5. Quantum correlations with no causal order

    PubMed Central

    Oreshkov, Ognyan; Costa, Fabio; Brukner, Časlav

    2012-01-01

    The idea that events obey a definite causal order is deeply rooted in our understanding of the world and at the basis of the very notion of time. But where does causal order come from, and is it a necessary property of nature? Here, we address these questions from the standpoint of quantum mechanics in a new framework for multipartite correlations that does not assume a pre-defined global causal structure but only the validity of quantum mechanics locally. All known situations that respect causal order, including space-like and time-like separated experiments, are captured by this framework in a unified way. Surprisingly, we find correlations that cannot be understood in terms of definite causal order. These correlations violate a 'causal inequality' that is satisfied by all space-like and time-like correlations. We further show that in a classical limit causal order always arises, which suggests that space-time may emerge from a more fundamental structure in a quantum-to-classical transition. PMID:23033068

  6. Causal localizations in relativistic quantum mechanics

    SciTech Connect

    Castrigiano, Domenico P. L. Leiseifer, Andreas D.

    2015-07-15

    Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a mere consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac’s localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.

  7. Causal localizations in relativistic quantum mechanics

    NASA Astrophysics Data System (ADS)

    Castrigiano, Domenico P. L.; Leiseifer, Andreas D.

    2015-07-01

    Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a mere consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac's localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.

  8. Perception of Causality in Schizophrenia Spectrum Disorder

    PubMed Central

    Tschacher, Wolfgang; Kupper, Zeno

    2006-01-01

    Patients with schizophrenia spectrum disorders often maintain deviating views on cause-effect relationships, especially when positive and disorganization symptoms are manifest. Altered perceived causality is prominent in delusional ideation, in ideas of reference, and in the mentalizing ability (theory of mind [ToM]) of patients. Perceiving causal relationships may be understood either as higher order cognitive reasoning or as low-level information processing. In the present study, perception of causality was investigated as a low-level, preattentional capability similar to gestalt-like perceptual organization. Thirty-one patients (24 men and 7 women with mean age 27.7 years) and the same number of healthy control subjects matched to patients with respect to age and sex were tested. A visual paradigm was used in which 2 identical discs move, from opposite sides of a monitor, steadily toward and then past one another. Their coincidence generates an ambiguous, bistable percept (discs either “stream through” or “bounce off” one another). The bouncing perception, ie, perceived causality, is enhanced when auditory stimuli are presented at the time of coincidence. Psychopathology was measured using the Positive and Negative Syndrome Scale. It was found that positive symptoms were strongly associated with increased perceived causality and disorganization with attenuated perceived causality. Patients in general were not significantly different from controls, but symptom subgroups showed specifically altered perceived causality. Perceived causality as a basic preattentional process may contribute to higher order cognitive alterations and ToM deficiencies. It is suggested that cognitive remediation therapy should address both increased and reduced perception of causality. PMID:16896057

  9. Dual Causality and the Autonomy of Biology.

    PubMed

    Bock, Walter J

    2017-03-01

    Ernst Mayr's concept of dual causality in biology with the two forms of causes (proximate and ultimate) continues to provide an essential foundation for the philosophy of biology. They are equivalent to functional (=proximate) and evolutionary (=ultimate) causes with both required for full biological explanations. The natural sciences can be classified into nomological, historical nomological and historical dual causality, the last including only biology. Because evolutionary causality is unique to biology and must be included for all complete biological explanations, biology is autonomous from the physical sciences.

  10. On the origin of Hill's causal criteria.

    PubMed

    Morabia, A

    1991-09-01

    The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

  11. Short-term dynamics of causal information transfer in thalamocortical networks during natural inputs and microstimulation for somatosensory neuroprosthesis

    PubMed Central

    Semework, Mulugeta; DiStasio, Marcello

    2014-01-01

    Recording the activity of large populations of neurons requires new methods to analyze and use the large volumes of time series data thus created. Fast and clear methods for finding functional connectivity are an important step toward the goal of understanding neural processing. This problem presents itself readily in somatosensory neuroprosthesis (SSNP) research, which uses microstimulation (MiSt) to activate neural tissue to mimic natural stimuli, and has the capacity to potentiate, depotentiate, or even destroy functional connections. As the aim of SSNP engineering is artificially creating neural responses that resemble those observed during natural inputs, a central goal is describing the influence of MiSt on activity structure among groups of neurons, and how this structure may be altered to affect perception or behavior. In this paper, we demonstrate the concept of Granger causality, combined with maximum likelihood methods, applied to neural signals recorded before, during, and after natural and electrical stimulation. We show how these analyses can be used to evaluate the changing interactions in the thalamocortical somatosensory system in response to repeated perturbation. Using LFPs recorded from the ventral posterolateral thalamus (VPL) and somatosensory cortex (S1) in anesthetized rats, we estimated pair-wise functional interactions between functional microdomains. The preliminary results demonstrate input-dependent modulations in the direction and strength of information flow during and after application of MiSt. Cortico-cortical interactions during cortical MiSt and baseline conditions showed the largest causal influence differences, while there was no statistically significant difference between pre- and post-stimulation baseline causal activities. These functional connectivity changes agree with physiologically accepted communication patterns through the network, and their particular parameters have implications for both rehabilitation and brain

  12. Short-term dynamics of causal information transfer in thalamocortical networks during natural inputs and microstimulation for somatosensory neuroprosthesis.

    PubMed

    Semework, Mulugeta; DiStasio, Marcello

    2014-01-01

    Recording the activity of large populations of neurons requires new methods to analyze and use the large volumes of time series data thus created. Fast and clear methods for finding functional connectivity are an important step toward the goal of understanding neural processing. This problem presents itself readily in somatosensory neuroprosthesis (SSNP) research, which uses microstimulation (MiSt) to activate neural tissue to mimic natural stimuli, and has the capacity to potentiate, depotentiate, or even destroy functional connections. As the aim of SSNP engineering is artificially creating neural responses that resemble those observed during natural inputs, a central goal is describing the influence of MiSt on activity structure among groups of neurons, and how this structure may be altered to affect perception or behavior. In this paper, we demonstrate the concept of Granger causality, combined with maximum likelihood methods, applied to neural signals recorded before, during, and after natural and electrical stimulation. We show how these analyses can be used to evaluate the changing interactions in the thalamocortical somatosensory system in response to repeated perturbation. Using LFPs recorded from the ventral posterolateral thalamus (VPL) and somatosensory cortex (S1) in anesthetized rats, we estimated pair-wise functional interactions between functional microdomains. The preliminary results demonstrate input-dependent modulations in the direction and strength of information flow during and after application of MiSt. Cortico-cortical interactions during cortical MiSt and baseline conditions showed the largest causal influence differences, while there was no statistically significant difference between pre- and post-stimulation baseline causal activities. These functional connectivity changes agree with physiologically accepted communication patterns through the network, and their particular parameters have implications for both rehabilitation and brain

  13. Lifelong Learning in Finland: The Extent to Which Vocational Education and Training Policy Is Nurturing Lifelong Learning in Finland. CEDEFOP Panorama Series.

    ERIC Educational Resources Information Center

    Nyyssola, Kari; Hamalainen, Kimmo

    The extent to which vocational education and training policy is nurturing lifelong learning in Finland was examined. The analysis focused on the following issues: the political and structural framework of education in Finland; mechanisms supporting lifelong learning; and pedagogical solutions and learning environments facilitating lifelong…

  14. Knowledge and Life-Experiences: Finland Seen through Its Libraries and Information Services = Kunskap och Upplevelser: Finland som BDI-land.

    ERIC Educational Resources Information Center

    Laaksovirta, Tuula H.; Haavisto, Tuula

    This illustrated publication, printed in both English and Finnish, describes Finland's libraries and information services. Topics covered include: (1) library users; (2) the conceptual role of the library; (3) the growth and development of the library system through Finland's agrarian, industrial, and budding infotech social phases; (4) the…

  15. Acute Human Inkoo and Chatanga Virus Infections, Finland.

    PubMed

    Putkuri, Niina; Kantele, Anu; Levanov, Lev; Kivistö, Ilkka; Brummer-Korvenkontio, Markus; Vaheri, Antti; Vapalahti, Olli

    2016-05-01

    Inkoo virus (INKV) and Chatanga virus (CHATV), which are circulating in Finland, are mosquitoborne California serogroup orthobunyaviruses that have a high seroprevalence among humans. Worldwide, INKV infection has been poorly described, and CHATV infection has been unknown. Using serum samples collected in Finland from 7,961 patients suspected of having viral neurologic disease or Puumala virus infection during the summers of 2001-2013, we analyzed the samples to detect California serogroup infections. IgM seropositivity revealed 17 acute infections, and cross-neutralization tests confirmed presence of INKV or CHATV infections. All children (<16 years of age) with INKV infection were hospitalized; adults were outpatients with mild disease, except for 1 who was hospitalized with CHATV infection. Symptoms included fever, influenza-like illness, nausea or vomiting, disorientation, nuchal rigidity, headache, drowsiness, and seizures. Although many INKV and CHATV infections appear to be subclinical, these viruses can cause more severe disease, especially in children.

  16. Economic hardship and suicide mortality in Finland, 1875-2010.

    PubMed

    Korhonen, Marko; Puhakka, Mikko; Viren, Matti

    2016-03-01

    We investigate the determinants of suicide in Finland using annual data for consumption and suicides from 1860 to 2010. Instead of using some ad hoc measures of cyclical movements of the economy, we build our analysis on a more solid economic theory. A key feature is the habit persistence in preferences, which provides a way to measure individual well-being and predict suicide. We estimate time series of habit levels and develop an indicator (the hardship index) to describe the economic hardship of consumers. The higher the level of the index, the worse off consumers are. As a rational response to such a bad situation, some consumers might commit suicide. We employ the autoregressive distributed lags cointegration method and find that our index works well in explaining the long-term behavior of people committing suicide in Finland.

  17. Heat Wave-Associated Vibriosis, Sweden and Finland, 2014.

    PubMed

    Baker-Austin, Craig; Trinanes, Joaquin A; Salmenlinna, Saara; Löfdahl, Margareta; Siitonen, Anja; Taylor, Nick G H; Martinez-Urtaza, Jaime

    2016-07-01

    During summer 2014, a total of 89 Vibrio infections were reported in Sweden and Finland, substantially more yearly infections than previously have been reported in northern Europe. Infections were spread across most coastal counties of Sweden and Finland, but unusually, numerous infections were reported in subarctic regions; cases were reported as far north as 65°N, ≈100 miles (160 km) from the Arctic Circle. Most infections were caused by non-O1/O139 V. cholerae (70 cases, corresponding to 77% of the total, all strains were negative for the cholera toxin gene). An extreme heat wave in northern Scandinavia during summer 2014 led to unprecedented high sea surface temperatures, which appear to have been responsible for the emergence of Vibrio bacteria at these latitudes. The emergence of vibriosis in high-latitude regions requires improved diagnostic detection and clinical awareness of these emerging pathogens.

  18. Acute Human Inkoo and Chatanga Virus Infections, Finland

    PubMed Central

    Kantele, Anu; Levanov, Lev; Kivistö, Ilkka; Brummer-Korvenkontio, Markus; Vaheri, Antti; Vapalahti, Olli

    2016-01-01

    Inkoo virus (INKV) and Chatanga virus (CHATV), which are circulating in Finland, are mosquitoborne California serogroup orthobunyaviruses that have a high seroprevalence among humans. Worldwide, INKV infection has been poorly described, and CHATV infection has been unknown. Using serum samples collected in Finland from 7,961 patients suspected of having viral neurologic disease or Puumala virus infection during the summers of 2001–2013, we analyzed the samples to detect California serogroup infections. IgM seropositivity revealed 17 acute infections, and cross-neutralization tests confirmed presence of INKV or CHATV infections. All children (<16 years of age) with INKV infection were hospitalized; adults were outpatients with mild disease, except for 1 who was hospitalized with CHATV infection. Symptoms included fever, influenza-like illness, nausea or vomiting, disorientation, nuchal rigidity, headache, drowsiness, and seizures. Although many INKV and CHATV infections appear to be subclinical, these viruses can cause more severe disease, especially in children. PMID:27088268

  19. Occupational exposures of nuclear power plant workers in Finland.

    PubMed

    Alm-Lytz, K; Riihiluoma, V; Hyvönen, H

    2001-01-01

    In Finland, the Radiation and Nuclear Safety Authority (STUK) maintains a central dose register where all occupational doses of radiation workers are recorded. The computerised register enables easy control of personal doses, including annual, 5 year and lifetime doses. The type of radiation work is also recorded in the dose register. Finland was one of the first countries in the world to introduce dose limits based on the recommendations of ICRP 60. In this article, the radiation dose data of the Finnish nuclear power plant workers are analysed. The majority of the radiation doses are received during the maintenance outages. The trend of the 5 year doses and their distribution are presented. Doses received during different work assignments were averaged over the years 1996-1999 and they are also discussed in this article.

  20. Vitalistic causality in young children's naive biology.

    PubMed

    Inagaki, Kayoko; Hatano, Giyoo

    2004-08-01

    One of the key issues in conceptual development research concerns what kinds of causal devices young children use to understand the biological world. We review evidence that children predict and interpret biological phenomena, especially human bodily processes, on the basis of 'vitalistic causality'. That is, they assume that vital power or life force taken from food and water makes humans active, prevents them from being taken ill, and enables them to grow. These relationships are also extended readily to other animals and even to plants. Recent experimental results show that a majority of preschoolers tend to choose vitalistic explanations as most plausible. Vitalism, together with other forms of intermediate causality, constitute unique causal devices for naive biology as a core domain of thought.

  1. Causality, mediation and time: a dynamic viewpoint

    PubMed Central

    Aalen, Odd O; Røysland, Kjetil; Gran, Jon Michael; Ledergerber, Bruno

    2012-01-01

    Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented. PMID:23193356

  2. Risk and causality in newspaper reporting.

    PubMed

    Boholm, Max

    2009-11-01

    The study addresses the textual representation of risk and causality in news media reporting. The analytical framework combines two theoretical perspectives: media frame analysis and the philosophy of causality. Empirical data derive from selected newspaper articles on risks in the Göta älv river valley in southwest Sweden from 1994 to 2007. News media content was coded and analyzed with respect to causal explanations of risk issues. At the level of individual articles, this study finds that the media provide simple causal explanations of risks such as water pollution, landslides, and flooding. Furthermore, these explanations are constructed, or framed, in various ways, the same risk being attributed to different causes in different articles. However, the study demonstrates that a fairly complex picture of risks in the media emerges when extensive material is analyzed systematically.

  3. Nuclear energy: Its strengths, weaknesses and role in Finland

    NASA Astrophysics Data System (ADS)

    Ruuskanen, A.

    Nuclear energy has, in principle, various strengths as an energy form. In spite of the drawbacks of nuclear power, the benefits exceed its cost. That is why power companies in Finland have decided to apply for the construction, of the fifth nuclear power plant during Spring 1991. Electricity consumption is increasing, and new power producing capacity will be needed. Nuclear power is seen as the best alternative for producing baseload power in Finnish conditions.

  4. Anaplasma phagocytophilum in questing Ixodes ricinus ticks in southwestern Finland.

    PubMed

    Sormunen, Jani J; Penttinen, Ritva; Klemola, Tero; Vesterinen, Eero J; Hänninen, Jari

    2016-12-01

    Anaplasma phagocytophilum is the causative agent of an emerging tick-borne disease, human granulocytic anaplasmosis. While the bacterium has been reported from questing ticks in neighboring Sweden, Norway and Russia, the few surveys regarding questing ticks in Finland have thus far been negative. In the current study, the prevalence of A. phagocytophilum in Ixodes ricinus populations was evaluated in several study localities around southwestern Finland during 2013-2014. Some of these populations were previously screened and found negative for A. phagocytophilum in 2000. A total of 3158 I. ricinus collected by blanket dragging were screened for Anaplasma spp. using qPCR. Anaplasma were detected in 9.2% of adult ticks (n = 87) and 3.1% of nymphs (n = 979). All larval samples were negative for infection. All Anaplasma-positive samples were identified as A. phagocytophilum by sequencing. This is, to the best of our knowledge, the first report of the pathogen from questing ticks in Finland. Furthermore, the pathogen was detected from several localities found negative during the previous screening 13 years earlier.

  5. Increasing nuclear power at liberalised energy markets- case Finland

    NASA Astrophysics Data System (ADS)

    Syri, S.; Kurki-Suonio, T.; Satka, V.

    2012-10-01

    Several Finnish projections for future electricity demand and the need for peak load capacity indicate a demand growth of about 2 GW from the present to the year 2030. The retirement of existing fossil fuel plants and old nuclear power plants will cause increased net import needs during 2020's, even when assuming additional energy efficiency measures and the commissioning of two new nuclear power plants recently approved by the Finnish Parliament. By the year 2030, the need for additional new capacity will be about 6 GW. The increased dependence on import is in contradiction with the official Government targets. This situation is not unique to Finland, but rather is likely to be the case in many other European countries as well. Both the energy company Fortum and energy-intensive industry in Finland see nuclear energy as a viable future generation technology. We describe the « Mankala » concept which is successfully used to build new nuclear capacity at liberalised electricity market in Finland.

  6. Green energy products in the United Kingdom, Germany and Finland

    NASA Astrophysics Data System (ADS)

    Hast, Aira; McDermott, Liisa; Järvelä, Marja; Syri, Sanna

    2014-12-01

    In liberalized electricity markets, suppliers are offering several kinds of voluntary green electricity products marketed as environmentally friendly. This paper focuses on the development of these voluntary markets at household level in the UK, Germany and Finland. Since there are already existing renewable energy policies regulating and encouraging the use of renewable energy, it is important to consider whether voluntary products offer real additional benefits above these policies. Problems such as double counting or re-marketing hydropower produced in existing plants are identified. According to our study, the demand varies between countries: in Germany the number of green electricity customers has increased and is also higher than in the UK or Finland. Typically the average additional cost to consumer from buying green electricity product instead of standard electricity product is in the range of 0-5% in all studied countries, although the level of price premium depends on several factors like electricity consumption. Case study of Finland and literature show that the impacts of green energy are not solely environmental. Renewable energy can benefit local public policy.

  7. Causality violation, gravitational shockwaves and UV completion

    NASA Astrophysics Data System (ADS)

    Hollowood, Timothy J.; Shore, Graham M.

    2016-03-01

    The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of "time machines", i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.

  8. Introduction to causal diagrams for confounder selection.

    PubMed

    Williamson, Elizabeth J; Aitken, Zoe; Lawrie, Jock; Dharmage, Shyamali C; Burgess, John A; Forbes, Andrew B

    2014-04-01

    In respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis. Common statistical methods used to achieve this include multivariable regression models adjusting for selected confounding variables or stratification on those variables. Therefore, a key question is which measured variables need to be controlled for in order to remove confounding. An approach to confounder-selection based on the use of causal diagrams (often called directed acyclic graphs) is discussed. A causal diagram is a visual representation of the causal relationships believed to exist between the variables of interest, including the exposure, outcome and potential confounding variables. After creating a causal diagram for the research question, an intuitive and easy-to-use set of rules can be applied, based on a foundation of rigorous mathematics, to decide which measured variables must be controlled for in the statistical analysis in order to remove confounding, to the extent that is possible using the available data. This approach is illustrated by constructing a causal diagram for the research question: 'Does personal smoking affect the risk of subsequent asthma?'. Using data taken from the Tasmanian Longitudinal Health Study, the statistical analysis suggested by the causal diagram approach was performed.

  9. Kant on causal laws and powers.

    PubMed

    Henschen, Tobias

    2014-12-01

    The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets.

  10. The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

    PubMed Central

    Ward, Andrew C

    2009-01-01

    As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally – the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims. PMID:19534788

  11. Dynamics of positional warfare malaria: Finland and Korea compared

    PubMed Central

    Huldén, Lena; Huldén, Larry

    2008-01-01

    Background A sudden outbreak of vivax malaria among Finnish troops in SE-Finland and along the front line in Hanko peninsula in the southwest occurred in 1941 during World War II. The common explanation has been an invasion of infective Anopheles mosquitoes from the Russian troops crossing the front line between Finland and Soviet Union. A revised explanation is presented based on recent studies of Finnish malaria. Methods The exact start of the epidemic and the phenology of malaria cases among the Finnish soldiers were reanalyzed. The results were compared with the declining malaria in Finland. A comparison with a corresponding situation starting in the 1990's in Korea was performed. Results and discussion The malaria cases occurred in July in 1941 when it was by far too early for infective mosquitoes to be present. The first Anopheles mosquitoes hatched at about the same time as the first malaria cases were observed among the Finnish soldiers. It takes about 3 – 6 weeks for the completion of the sporogony in Finland. The new explanation is that soldiers in war conditions were suddenly exposed to uninfected mosquitoes and those who still were carriers of hypnozoites developed relapses triggered by these mosquitoes. It is estimated that about 0.5% of the Finnish population still were carriers of hypnozoites in the 1940's. A corresponding outbreak of vivax malaria in Korea in the 1990's is similarly interpreted as relapses from activated hypnozoites among Korean soldiers. The significance of the mosquito induced relapses is emphasized by two benefits for the Plasmodium. There is a synchronous increase of gametocytes when new mosquitoes emerge. It also enables meiotic recombination between different strains of the Plasmodium. Conclusion The malaria peak during the positional warfare in the 1940's was a short outbreak during the last phase of declining indigenous malaria in Finland. The activation of hypnozoites among a large number of soldiers and subsequent

  12. Impacts of peatland forestation on regional climate conditions in Finland

    NASA Astrophysics Data System (ADS)

    Gao, Yao; Markkanen, Tiina; Backman, Leif; Henttonen, Helena M.; Pietikäinen, Joni-Pekka; Laaksonen, Ari

    2014-05-01

    Climate response to anthropogenic land cover change happens more locally and occurs on a shorter time scale than the global warming due to increased GHGs. Over the second half of last Century, peatlands were vastly drained in Finland to stimulate forest growth for timber production. In this study, we investigate the biophysical effects of peatland forestation on near-surface climate conditions in Finland. For this, the regional climate model REMO, developed in Max Plank Institute (currently in Climate Service Center, Germany), provides an effective way. Two sets of 15-year climate simulations were done by REMO, using the historic (1920s; The 1st Finnish National Forest Inventory) and present-day (2000s; the 10th Finnish National Forest Inventory) land cover maps, respectively. The simulated surface air temperature and precipitation were then analyzed. In the most intensive peatland forestation area in Finland, the differences in monthly averaged daily mean surface air temperature show a warming effect around 0.2 to 0.3 K in February and March and reach to 0.5 K in April, whereas a slight cooling effect, less than 0.2 K, is found from May till October. Consequently, the selected snow clearance dates in model gridboxes over that area are advanced 0.5 to 4 days in the mean of 15 years. The monthly averaged precipitation only shows small differences, less than 10 mm/month, in a varied pattern in Finland from April to September. Furthermore, a more detailed analysis was conducted on the peatland forestation area with a 23% decrease in peatland and a 15% increase in forest types. 11 day running means of simulated temperature and energy balance terms, as well as snow depth were averaged over 15 years. Results show a positive feedback induced by peatland forestation between the surface air temperature and snow depth in snow melting period. This is because the warmer temperature caused by lower surface albedo due to more forest in snow cover period leads to a quicker and

  13. Civil Engineering Applications of Ground Penetrating Radar in Finland

    NASA Astrophysics Data System (ADS)

    Pellinen, Terhi; Huuskonen-Snicker, Eeva; Olkkonen, Martta-Kaisa; Eskelinen, Pekka

    2014-05-01

    Ground penetrating radar (GPR) has been used in Finland since 1980's for civil engineering applications. First applications in this field were road surveys and dam inspections. Common GPR applications in road surveys include the thickness evaluation of the pavement, subgrade soil evaluation and evaluation of the soil moisture and frost susceptibility. Since the 1990's, GPR has been used in combination with other non-destructive testing (NDT) methods in road surveys. Recently, more GPR applications have been adopted, such as evaluating bridges, tunnels, railways and concrete elements. Nowadays, compared with other countries GPR is relatively widely used in Finland for road surveys. Quite many companies, universities and research centers in Finland have their own GPR equipment and are involved in the teaching and research of the GPR method. However, further research and promotion of the GPR techniques are still needed since GPR could be used more routinely. GPR has been used to evaluate the air void content of asphalt pavements for years. Air void content is an important quality measure of pavement condition for both the new and old asphalt pavements. The first Finnish guideline was released in 1999 for the method. Air void content is obtained from the GPR data by measuring the dielectric value as continuous record. To obtain air void content data, few pavement cores must be taken for calibration. Accuracy of the method is however questioned because there are other factors that affect the dielectric value of the asphalt layer, in addition to the air void content. Therefore, a research project is currently carried out at Aalto University in Finland. The overall objective is to investigate if the existing GPR technique used in Finland is accurate enough to be used as QC/QA tool in assessing the compaction of asphalt pavements. The project is funded by the Finnish Transport Agency. Further research interests at Aalto University include developing new microwave asphalt

  14. The Mental Representation of Causal Conditional Reasoning: Mental Models or Causal Models

    ERIC Educational Resources Information Center

    Ali, Nilufa; Chater, Nick; Oaksford, Mike

    2011-01-01

    In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving "pairs" of conditionals--such as "if P[subscript 1] then Q" and "if P[subscript…

  15. Aging and retrospective revaluation of causal learning.

    PubMed

    Mutter, Sharon A; Atchley, Anthony R; Plumlee, Leslie M

    2012-01-01

    In a 2-stage causal learning task, young and older participants first learned which foods presented in compound were followed by an allergic reaction (e.g., STEAK-BEANS→ REACTION) and then the causal efficacy of 1 food from these compounds was revalued (e.g., BEANS→ NO REACTION). In Experiment 1, unrelated food pairs were used, and although there were no age differences in compound- or single-cue-outcome learning, older adults did not retrospectively revalue the causal efficacy of the absent target cues (e.g., STEAK). However, they had weaker within-compound associations for the unrelated foods, and this may have prevented them from retrieving the representations of these cues. In Experiment 2, older adults still showed no retrospective revaluation of absent cues even though compound food cues with pre-existing associations were used (e.g., STEAK-POTATO), and they received additional learning trials. Finally, in Experiment 3, older adults revalued the causal efficacy of the target cues when small, unobtrusive icons of these cues were present during single-cue revaluation. These findings suggest that age-related deficits in causal learning for absent cues are due to ineffective associative binding and reactivation processes.

  16. Inferring causal structure: a quantum advantage

    NASA Astrophysics Data System (ADS)

    Ried, Katja; Spekkens, Robert

    2014-03-01

    The problem of inferring causal relations from observed correlations is central to science, and extensive study has yielded both important conceptual insights and widely used practical applications. Yet some of the simplest questions are impossible to answer classically: for instance, if one observes correlations between two variables (such as taking a new medical treatment and the subject's recovery), does this show a direct causal influence, or is it due to some hidden common cause? We develop a framework for quantum causal inference, and show how quantum theory provides a unique advantage in this decision problem. The key insight is that certain quantum correlations can only arise from specific causal structures, whereas pairs of classical variables can exhibit any pattern of correlation regardless of whether they have a common cause or a direct-cause relation. For example, suppose one measures the same Pauli observable on two qubits. If they share a common cause, such as being prepared in an entangled state, then one never finds perfect (positive) correlations in every basis, whereas perfect anticorrelations are possible (if one prepares the singlet state). Conversely, if a channel connects the qubits, hence a direct causal influence, perfect anticorrelations are impossible.

  17. Causal impressions: predicting when, not just whether.

    PubMed

    Young, Michael E; Rogers, Ester T; Beckmann, Joshua S

    2005-03-01

    In 1739, David Hume established the so-called cues to causality--environmental cues that are important to the inference of causality. Although this descriptive account has been corroborated experimentally, it has not been established why these cues are useful, except that they may reflect statistical regularities in the environment. One of the cues to causality, covariation, helps predict whether an effect will occur, but not its time of occurrence. In the present study, evidence is provided that spatial and temporal contiguity improve an observer's ability to predict when an effect will occur, thus complementing the utility of covariation as a predictor of whether an effect will occur. While observing Michotte's (1946/1963) launching effect, participants showed greater accuracy and precision in their predictions of the onset of movement by the launched object when there was spatial and temporal contiguity. Furthermore, when auditory cues that bridged a delayed launch were included, causal ratings and predictability were similarly affected. These results suggest that the everyday inference of causality relies on our ability to predict whether and when an effect will occur.

  18. Normalizing the causality between time series

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2015-08-01

    Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market.

  19. Causal structure of general relativistic spacetimes

    SciTech Connect

    Howard, Ecaterina

    2010-06-15

    We present some of the recent results and open questions on the causality problem in General Relativity. The concept of singularity is intimately connected with future trapped surface and inner event horizon formation. We offer a brief overview of the Hawking-Penrose singularity theorems and discuss a few open problems concerning the future Cauchy development (domain of dependence), break-down criteria and energy conditions for the horizon stability. A key question is whether causality violating regions, generating a Cauchy horizon are allowed.We raise several questions concerning the invisibility and stability of closed trapped surfaces from future null infinity and derive the imprisonment conditions. We provide an up-to-date perspective of the causal boundaries and spacelike conformal boundary extensions for time oriented Lorentzian manifolds and more exotic settings.

  20. [Clinical research III. The causality studies].

    PubMed

    Talavera, Juan O; Wacher-Rodarte, Niels H; Rivas-Ruiz, Rodolfo

    2011-01-01

    The need to solve a clinical problem leads us to establish a starting point to address (risk, prognosis or treatment studies), all these cases seek to attribute causality. Clinical reasoning described in the book Clinical Epidemiology. The architecture of clinical research, offers a simple guide to understanding this phenomenon. And proposes three basic components: baseline, maneuver and outcome. In this model, different systematic errors (bias) are described, which may be favored by omitting characteristics of the three basic components. Thus, omissions in the baseline characteristics cause an improper assembly of the population and susceptibility bias, omissions in the application or evaluation of the maneuver provoke performance bias, and omissions in the assessment of out-come cause detection bias and transfer bias. Importantly, if this way of thinking facilitates understanding of the causal phenomenon, the appropriateness of the variables to be selected in the studies to which attribute or not causality, require additional arguments for evaluate clinical relevance.

  1. Estimating causal effects from epidemiological data

    PubMed Central

    Hernán, Miguel A; Robins, James M

    2006-01-01

    In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods—standardisation and inverse probability weighting—to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting. PMID:16790829

  2. Causal inheritence in plane wave quotients

    SciTech Connect

    Hubeny, Veronika E.; Rangamani, Mukund; Ross, Simon F.

    2003-11-24

    We investigate the appearance of closed timelike curves in quotients of plane waves along spacelike isometries. First we formulate a necessary and sufficient condition for a quotient of a general spacetime to preserve stable causality. We explicitly show that the plane waves are stably causal; in passing, we observe that some pp-waves are not even distinguishing. We then consider the classification of all quotients of the maximally supersymmetric ten-dimensional plane wave under a spacelike isometry, and show that the quotient will lead to closed timelike curves iff the isometry involves a translation along the u direction. The appearance of these closed timelike curves is thus connected to the special properties of the light cones in plane wave spacetimes. We show that all other quotients preserve stable causality.

  3. Causal binding of actions to their effects.

    PubMed

    Buehner, Marc J; Humphreys, Gruffydd R

    2009-10-01

    According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.

  4. Detection of Francisella tularensis in voles in Finland.

    PubMed

    Rossow, Heidi; Sissonen, Susanna; Koskela, Katja A; Kinnunen, Paula M; Hemmilä, Heidi; Niemimaa, Jukka; Huitu, Otso; Kuusi, Markku; Vapalahti, Olli; Henttonen, Heikki; Nikkari, Simo

    2014-03-01

    Francisella tularensis is a highly virulent intracellular bacterium causing the zoonotic disease tularemia. It recurrently causes human and animal outbreaks in northern Europe, including Finland. Although F. tularensis infects several mammal species, only rodents and lagomorphs seem to have importance in its ecology. Peak densities of rodent populations may trigger tularemia outbreaks in humans; however, it is still unclear to which extent rodents or other small mammals maintain F. tularensis in nature. The main objective of this study was to obtain information about the occurrence of F. tularensis in small mammals in Finland. We snap-trapped 547 wild small mammals representing 11 species at 14 locations around Finland during 6 years and screened them for the presence of F. tularensis DNA using PCR analysis. High copy number of F. tularensis-specific DNA was detected in tissue samples of five field voles (Microtus agrestis) originating from one location and 2 years. According to DNA sequences of the bacterial 23S ribosomal RNA gene amplified from F. tularensis-infected voles, the infecting agent belongs to the subspecies holarctica. To find out the optimal tissue for tularemia screening in voles, we compared the amounts of F. tularensis DNA in lungs, liver, spleen, and kidney of the infected animals. F. tularensis DNA was detectable in high levels in all four organs except for one animal, whose kidney was F. tularensis DNA-negative. Thus, at least liver, lung, and spleen seem suitable for F. tularensis screening in voles. Thus, liver, lung, and spleen all seem suitable for F. tularensis screening in voles. In conclusion, field voles can be heavily infected with F. tularensis subsp. holarctica and thus potentially serve as the source of infection in humans and other mammals.

  5. Detection of Francisella tularensis in Voles in Finland

    PubMed Central

    Sissonen, Susanna; Koskela, Katja A.; Kinnunen, Paula M.; Hemmilä, Heidi; Niemimaa, Jukka; Huitu, Otso; Kuusi, Markku; Vapalahti, Olli; Henttonen, Heikki; Nikkari, Simo

    2014-01-01

    Abstract Francisella tularensis is a highly virulent intracellular bacterium causing the zoonotic disease tularemia. It recurrently causes human and animal outbreaks in northern Europe, including Finland. Although F. tularensis infects several mammal species, only rodents and lagomorphs seem to have importance in its ecology. Peak densities of rodent populations may trigger tularemia outbreaks in humans; however, it is still unclear to which extent rodents or other small mammals maintain F. tularensis in nature. The main objective of this study was to obtain information about the occurrence of F. tularensis in small mammals in Finland. We snap-trapped 547 wild small mammals representing 11 species at 14 locations around Finland during 6 years and screened them for the presence of F. tularensis DNA using PCR analysis. High copy number of F. tularensis-specific DNA was detected in tissue samples of five field voles (Microtus agrestis) originating from one location and 2 years. According to DNA sequences of the bacterial 23S ribosomal RNA gene amplified from F. tularensis–infected voles, the infecting agent belongs to the subspecies holarctica. To find out the optimal tissue for tularemia screening in voles, we compared the amounts of F. tularensis DNA in lungs, liver, spleen, and kidney of the infected animals. F. tularensis DNA was detectable in high levels in all four organs except for one animal, whose kidney was F. tularensis DNA-negative. Thus, at least liver, lung, and spleen seem suitable for F. tularensis screening in voles. Thus, liver, lung, and spleen all seem suitable for F. tularensis screening in voles. In conclusion, field voles can be heavily infected with F. tularensis subsp. holarctica and thus potentially serve as the source of infection in humans and other mammals. PMID:24575824

  6. Do tachyons violate the causality principle?

    NASA Astrophysics Data System (ADS)

    Nibart, Gilles

    2000-05-01

    Very early, A. EINSTEIN has shown that particles with velocities greater than the velocity of light in vacuum may produce causal anomalies. Later, in quantum mechanics CPT transformations have allowed causal loops at a microscopic scale. So the possibility of faster-than-light particles has been analyzed again. The Meta-Relativity has extended the special theory of Relativity to particles beyond the light barrier (tachyons), by using the relativist formula with complex values. It has assigned to any tachyon an imaginary proper mass which does not easily offer a physical interpretation. In the framework of that theory, tachyons may appear to travel backwards in time and have negative energies, but they have to be interpreted as travelling forwards in time with positive energies (reinterpretation principle). The Meta-Relativity allows a tachyon reflection or re-emission to produce a causal loop, but some authors rejects the objection by postulating the tachyon emission cannot be systematically repeated. So causal loops can only occur at a microscopic scale. The theory of Relativity in the spacelike region has been developed by R. DUTHEIL using the tensor formalism of the general theory of Relativity. He defined tachyonic referential frames (TRF) with an other metric tensor and he showed it leads to an other LORENTZ group of transformations—the superluminal LORENTZ group. In this theory, tachyons always have a positive energy and a real proper mass, but their behavior must be described with tachyonic referential frames. R. DUTHEIL argued from the isomorphism of the both LORENTZ groups to prove the ZEEMAN'S theorem is respected by tachyons; so a sequence order is always preserved by any superluminal transformation. In the present communication, I show that time coordinates of tachyonic referential frames do not preserve causal order and do not make sense for natural observers. Nevertheless I show that the causal order is preserved within the superluminal proper time

  7. The impact of generic substitution on price competition in Finland.

    PubMed

    Aalto-Setälä, Ville

    2008-05-01

    Generic substitution by pharmacists was introduced in April 2003 in Finnish pharmaceutical markets. This article examines the impact of generic substitution on price development. This study examined all of the 2,100 substitutable drugs in Finland. The impact of generic substitution on price competition was significant. The average price of substitutable drugs decreased by more than 10%. However, the price development was uneven; some prices increased whereas others decreased by more than 50%. The most important factors that influenced the price development were the number of competitors, whether the drug was originator or generic and the width of the price band.

  8. Biomass burning smoke episodes in Finland from Eastern European wildfires

    NASA Astrophysics Data System (ADS)

    Leino, Katri E.; Riuttanen, Laura; Nieminen, Tuomo; Dal Maso, Miikka; Väänänen, Riikka; Pohja, Toivo; Keronen, Petri; Järvi, Leena; Aalto, Pasi P.; Virkkula, Aki; Kerminen, Veli-Matti; Petäjä, Tuukka; Kulmala, Markku

    2014-05-01

    Biomass burning emissions from Eastern Europe are occasionally observed in Finland. In spring of 2006 and the late summers of 2006 and 2010, smoke plumes were transported to large parts of Finland. By combining multiple methods we were able to study the horizontal and vertical properties of long-range transported smoke plume, as well as time evolution of particle number size distributions in an aged biomass burning smoke. In this study we used trace gas and aerosol particle number size distribution measurements at three SMEAR stations (Station for Measuring Forest Ecosystem - Atmosphere Relations; Ruuskanen et al., 2003; Hari & Kulmala, 2005; Järvi et al., 2009). Vertical distribution of the smoke was studied by a small aircraft, Cessna FR172F, instrumented with Ultrafine Condensation Particle Counter and CO2/H2O -gas analyser. The airborne measurements were compared with vertical profiles from a polarization-sensitive, two-wavelength lidar (CALIOP; the Cloud-Aerosol Lidar with Orthogonal Polarization; Winker et al., 2009) onboard the CALIPSO satellite (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). HYSPLIT 4 (Hybrid Single Particle Lagrangian Integrated Trajectory Model; Draxler, 1999) backward trajectories as well as MODIS (Moderate Resolution Imaging Spectroradiometer) Terra thermal anomalies data (MOD14A1) were used together with synoptic analyses to study the transport and the horizontal distribution of the smoke. In the spring 2006, there was a blocking high pressure system in Eastern Europe and smoke from the Eastern European fires was transported far to the north-west. The smoke episode in Finland lasted for two weeks. In summers of 2006 and 2010 the smoke came to Finland in a warm sector of a low-pressure system and the episodes lasted for less than two days. Smoke plumes had elevated concentrations of aerosol particles, black carbon and CO, and varying concentrations of CO2, SO2, O3 and NOx. The difference to the background air

  9. Changing notions of lone motherhood in twentieth-century Finland.

    PubMed

    May, Vanessa

    2011-01-01

    Through written life stories of lone mothers, this article examines changes in lone motherhood in twentieth-century Finland. While the older life-story writers' narratives are clearly influenced by an 'ethos of survival' and the regulation of female sexuality, the younger writers relate their experiences with the help of scripts on gender equality and the psychological importance of 'good' parenting. These narrative shifts point to important changes in cultural scripts on women's positions in families, on the labour market, and in society.

  10. Causalities of the Taiwan stock market

    NASA Astrophysics Data System (ADS)

    Ting, Julian Juhi-Lian

    2003-06-01

    Volatility, fitting with first-order Landau expansion, stationarity, and causality of the Taiwan stock market (TAIEX) are investigated based on daily records. Instead of consensuses that consider stock market index change as a random time series we propose the market change as a dual time series consists of the index and the corresponding volume. Therefore, causalities between these two time series are investigated. Our results suggest the volume time series is of second-order importance than the index time series. The index time series receives slightly stronger influence from the previous 67th trading day, while the volume time series is slightly stronger influenced by the previous 62nd trading day.

  11. Century-scale causal relationships between global dry/wet conditions and the state of the Pacific and Atlantic Oceans

    NASA Astrophysics Data System (ADS)

    Sun, Qiaohong; Miao, Chiyuan; AghaKouchak, Amir; Duan, Qingyun

    2016-06-01

    The Granger causality test is used to examine the effects of the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the North Atlantic Oscillation (NAO) on global dry/wet conditions. The results show robust relationships between dry/wet conditions and the ocean states, as assessed through a multi-index (standardized precipitation evapotranspiration index and standardized precipitation index) and multiscale (3 months and 12 months) evaluation. The influence of ENSO events is widespread, dominating about 38% of the global land surface (excluding Antarctica). Southern and western North America, northern South America, and eastern Russia are influenced by the PDO. The NAO influences not only dry/wet conditions in Europe but also dry/wet conditions in northern Africa. Similarly, climate variability in southern Europe and northern Africa may be due to the concurrence of the ENSO and the NAO. Knowledge of the spatial influence of ocean states on global dry/wet conditions is valuable for improving drought and flood forecasting.

  12. Century-scale causal relationships between global drought conditions and the state of the Pacific and Atlantic Oceans

    NASA Astrophysics Data System (ADS)

    Sun, Qiaohong; Miao, Chiyuan; Duan, Qingyun

    2016-04-01

    Drought is one of the costliest and least understood natural hazards. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) are atmosphere-ocean coupled modes of climate variability that occur in the Pacific and Atlantic Oceans. In this study, the Granger causality test is used to examine the effects of ENSO, PDO, and NAO on global drought conditions. The results show robust relationships between drought conditions and the ocean states, as assessed through a multi-index (SPEI and SPI) and multiscalar (3-month and 12-month) evaluation. The influence of ENSO events is widespread, dominating about 40% of the global land droughts. Southern and western North America, northern South America, and eastern Russia are more influenced by PDO. Results show that NAO influence on drought is not restricted to Europe and includes northern Africa. The role of NAO is most evident at 3-month time scale. Moreover, the results provide evidence that drought conditions can be affected by multiple factors. ENSO and PDO may reinforce each other to dominate climate variability over North America and northern South America. Climate variability in southern Europe and northern Africa may be forced by the concurrence of ENSO and NAO. The spatial patterns of the influence of ocean states on global droughts provide valuable information for improving drought forecasting.

  13. Causal connectivity alterations of cortical-subcortical circuit anchored on reduced hemodynamic response brain regions in first-episode drug-naïve major depressive disorder

    PubMed Central

    Gao, Qing; Zou, Ke; He, Zongling; Sun, Xueli; Chen, Huafu

    2016-01-01

    Some efforts were done to investigate the disruption of brain causal connectivity networks involved in major depressive disorder (MDD) using Granger causality (GC) analysis. However, the homogenous hemodynamic response function (HRF) assumption over the brain may disturb the inference of temporal precedence. Here we applied a blind deconvolution approach to examine the altered HRF shape in first-episode, drug-naïve MDD patients. The regions with abnormal HRF shape in patients were chosen as seeds to detect the GC alterations in MDD. The results demonstrated significantly decreased magnitude of spontaneous hemodynamic response of the orbital frontal cortex (OFC) and the caudate nucleus (CAU) in MDD comparing to healthy controls, suggesting MDD patients likely had alterations in neurovascular coupling and cerebrovascular physiology in these two regions. GC mapping showed increased/decreased GC in OFC-/CAU centered networks in MDD. The outgoing GC values from OFC to anterior cingulate cortex and occipital regions were positively correlated with Hamilton Depression Scale (HAMD) scores, while the incoming GC from insula, middle and superior temporal gyrus to CAU were negatively correlated with HAMD scores of MDD. The abnormalities of directional connections in the cortico-subcortico-cerebellar network may lead to unbalanced integrating the emotional-related information for MDD, and further exacerbating depressive symptoms. PMID:26911651

  14. A Recipe for Success: A Comparative View of Mathematics Teacher Education in Finland and Singapore

    ERIC Educational Resources Information Center

    Gísladóttir, Berglind; Jóhannsdóttir, Björg

    2010-01-01

    Finland and Singapore are both nations that have excelled in mathematics on international assessments, such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). Evidence of Finland's educational success emerged with the outcome of the first PISA study in 2000. Since…

  15. Assessment in Finland: A Scholarly Reflection on One Country's Use of Formative, Summative, and Evaluative Practices

    ERIC Educational Resources Information Center

    Hendrickson, Katie A.

    2012-01-01

    Finland's high test scores have prompted international comparisons of educational policy. This article explores the use of assessment in Finland, particularly the intended use of student assessment and evaluation of schools as described in the National Curriculum. This article explores Finnish educational policy through the lens of formative and…

  16. The Use of Address Pronouns among Finnish and Finland-Swedish Students

    ERIC Educational Resources Information Center

    Nyblom, Heidi

    2006-01-01

    This article examines the use and choice of address pronouns among Finnish and Finland-Swedish students in various situations. The study is based on a questionnaire on address usage distributed to university students in the city of Vaasa in Finland. The aim of the study is to investigate potential differences between the use of T and V in Finnish…

  17. A Model Lesson: Finland Shows Us What Equal Opportunity Looks Like

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2012-01-01

    International indicators show that Finland has one of the most educated citizenries in the world, provides educational opportunities in an egalitarian manner, and makes efficient use of resources. But at the beginning of the 1990s, education in Finland was nothing special in international terms. The performance of Finnish students on international…

  18. Comparative Study of Teaching Content in Teacher Education Programmes in Canada, Denmark, Finland and Singapore

    ERIC Educational Resources Information Center

    Rasmussen, Jens; Bayer, Martin

    2014-01-01

    This article presents the results of a comparative study of the content in selected teacher education programmes for primary and lower secondary teachers in Canada, Denmark, Finland and Singapore. First and foremost, the study is a comparison between teacher education programmes in, on the one hand, Canada, Finland and Singapore, all of which…

  19. Environmental Education in Finland--A Case Study of Environmental Education in Nature Schools

    ERIC Educational Resources Information Center

    Jeronen, Eila; Jeronen, Juha; Raustia, Hanna

    2009-01-01

    The article aims to introduce Environmental Education (EE) in Finland and to discuss how it has been taken into account in Finnish nature schools. Firstly, we present EE models used in Finland. Thereafter we describe a qualitative case study on EE in nature schools (NS). The aim of the study was to get information for the development of EE. The…

  20. Discourses about School-Based Mathematics Teacher Education in Finland and Sweden

    ERIC Educational Resources Information Center

    Ryve, Andreas; Hemmi, Kirsti; Borjesson, Mats

    2013-01-01

    In this cross-case study we focus on school-based teacher education in Sweden and Finland. Through the use of focus-group interviews with mathematics teacher educators in Finland and Sweden, the study shows that there are substantial differences in how school-based teacher education is introduced and portrayed in the discourse about teacher…