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

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• dc.contributor.author Gilson, Matthieuca
• dc.contributor.author Tauste Campo, Adrià, 1982-ca
• dc.contributor.author Chen, X.ca
• dc.contributor.author Thiele, Alexanderca
• dc.contributor.author Deco, Gustavoca
• dc.date.accessioned 2018-04-24T09:51:12Z
• dc.date.available 2018-04-24T09:51:12Z
• dc.date.issued 2017
• dc.description.abstract 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.en
• dc.description.sponsorship MG acknowledges funding from the Marie Sklodowska-Curie Action (grant H2020-MSCA656547). MG and GD were supported by the Human Brain Project (grant FP7-FET-ICT-604102 and H2020-720270 HBP SGA1). GD and ATC were supported by the European Research Council Advanced Grant DYSTRUCTURE (Grant 295129). AT was supported by the UK Medical Research Council (grant MRC G0700976).
• dc.format.mimetype application/pdf
• dc.identifier.citation Gilson M, Tauste Campo A, Chen X, Thiele A, Deco G. Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data. Netw Neurosci. 2017;1(4): 357-80. DOI: 10.1162/NETN_a_00019
• dc.identifier.doi http://dx.doi.org/10.1162/NETN_a_00019
• dc.identifier.issn 2472-1751
• dc.identifier.uri http://hdl.handle.net/10230/34429
• dc.language.iso eng
• dc.publisher MIT Pressca
• dc.relation.ispartof Network Neuroscience. 2017;1(4): 357-80.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/656547
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/604102
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/720270
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129
• dc.rights © 2017 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Publisher version at http://mitpress.mit.edu
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Network connectivity detectionen
• dc.subject.keyword Nonparametric significance methoden
• dc.subject.keyword Multivariate autoregressive processen
• dc.subject.keyword Granger causalityen
• dc.subject.keyword Multiunit activityen
• dc.title Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity dataca
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion