{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Benozzo D"],"funding":["Finnish Cultural Foundation","Fondazione Bruno Kessler","Dutch Research Council (NWO)"],"pagination":["e0177359"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5436686"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(5)"],"pubmed_abstract":["In many fields of science, there is the need of assessing the causal influences among time series. Especially in neuroscience, understanding the causal interactions between brain regions is of primary importance. A family of measures have been developed from the parametric implementation of the Granger criteria of causality based on the linear autoregressive modelling of the signals. We propose a new Bayesian method for linear model identification with a structured prior (GMEP) aiming to apply it as linear regression method in the context of the parametric Granger causal inference. GMEP assumes a Gaussian scale mixture distribution for the group sparsity prior and it enables flexible definition of the coefficient groups. Approximate posterior inference is achieved using Expectation Propaga"],"journal":["PloS one"],"pubmed_title":["Bayesian estimation of directed functional coupling from brain recordings."],"pmcid":["PMC5436686"],"funding_grant_id":["612.001.211","639.072.513"],"pubmed_authors":["Avesani P","Jylanki P","Olivetti E","van Gerven MAJ","Benozzo D"],"additional_accession":[]},"is_claimable":false,"name":"Bayesian estimation of directed functional coupling from brain recordings.","description":"In many fields of science, there is the need of assessing the causal influences among time series. Especially in neuroscience, understanding the causal interactions between brain regions is of primary importance. A family of measures have been developed from the parametric implementation of the Granger criteria of causality based on the linear autoregressive modelling of the signals. We propose a new Bayesian method for linear model identification with a structured prior (GMEP) aiming to apply it as linear regression method in the context of the parametric Granger causal inference. GMEP assumes a Gaussian scale mixture distribution for the group sparsity prior and it enables flexible definition of the coefficient groups. Approximate posterior inference is achieved using Expectation Propaga","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017","modification":"2026-05-04T14:56:54.488Z","creation":"2019-03-26T22:53:05Z"},"accession":"S-EPMC5436686","cross_references":{"pubmed":["28545066"],"doi":["10.1371/journal.pone.0177359"]}}