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      Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data


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          Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques. Here we propose a nonparametric significance method to test the nonzero values of multivariate autoregressive model to infer interactions in recurrent networks. We use random permutations or circular shifts of the original time series to generate the null-hypothesis distributions. The underlying network model is the same as used in multivariate Granger causality, but our test relies on the autoregressive coefficients instead of error residuals. By means of numerical simulation over multiple network configurations, we show that this method achieves a good control of false positives (type 1 error) and detects existing pairwise connections more accurately than using the standard parametric test for the ratio of error residuals. In practice, our method aims to detect temporal interactions in real neuronal networks with nodes possibly exhibiting redundant activity. As a proof of concept, we apply our method to multiunit activity (MUA) recorded from Utah electrode arrays in a monkey and examine detected interactions between 25 channels. We show that during stimulus presentation our method detects a large number of interactions that cannot be solely explained by the increase in the MUA level.

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          Dynamic causal modelling.

          In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
            • Record: found
            • Abstract: found
            • Article: not found

            Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance.

            We consider the question of evaluating causal relations among neurobiological signals. In particular, we study the relation between the directed transfer function (DTF) and the well-accepted Granger causality, and show that DTF can be interpreted within the framework of Granger causality. In addition, we propose a method to assess the significance of causality measures. Finally, we demonstrate the applications of these measures to simulated data and actual neurobiological recordings.
              • Record: found
              • Abstract: found
              • Article: not found

              Controlling the familywise error rate in functional neuroimaging: a comparative review.

              Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg's methods, are now applicable to dependent data. Continuous random field methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We find that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom. We also show the limitations of trying to find an equivalent number of independent tests for an image of correlated test statistics.

                Author and article information

                Netw Neurosci
                Netw Neurosci
                Network Neuroscience (Cambridge, Mass.)
                MIT Press (One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu )
                01 December 2017
                Winter 2018
                : 1
                : 4
                : 357-380
                [1 ]Computational Neuroscience Group, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
                [2 ]Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar Medical Research Institute, Barcelona, Spain
                [3 ]Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
                [4 ]Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Handling Editor: Pedro Valdes-Sosa

                Author information
                © 2017 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 03 January 2017
                : 01 June 2017
                Page count
                Figures: 9, Equations: 12, References: 55, Pages: 24
                Funded by: H2020 Marie Skłodowska-Curie Actions, FundRef http://dx.doi.org/10.13039/100010665;
                Award ID: H2020-MSCA-656547
                Funded by: H2020 European Research Council, FundRef http://dx.doi.org/10.13039/100010663;
                Award ID: DYSTRUCTURE (Grant 295129)
                Funded by: FP7 Ideas: European Research Council, FundRef http://dx.doi.org/10.13039/100011199;
                Award ID: FP7-FET-ICT-604102
                Funded by: H2020 European Research Council, FundRef http://dx.doi.org/10.13039/100010663;
                Award ID: H2020-720270 HBP SGA1
                Custom metadata
                Gilson, M., Tauste Campo, A., Chen, X., Thiele, A., & Deco, G. (2017). Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data. Network Neuroscience, 1(4), 357–380. https://doi.org/10.1162/netn_a_00019

                network connectivity detection,nonparametric significance method,multivariate autoregressive process,granger causality,multiunit activity


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