From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals.

ABSTRACT: Knowing brain connectivity is of great importance both in basic research and for clinical applications. We are proposing a method to infer directed connectivity from zero-lag covariances of neuronal activity recorded at multiple sites. This allows us to identify causal relations that are reflected in neuronal population activity. To derive our strategy, we assume a generic linear model of interacting continuous variables, the components of which represent the activity of local neuronal populations. The suggested method for inferring connectivity from recorded signals exploits the fact that the covariance matrix derived from the observed activity contains information about the existence, the direction and the sign of connections. Assuming a sparsely coupled network, we disambiguate the unde

SUBMITTER: Schiefer J 

PROVIDER: S-EPMC5886625 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature