Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing.

ABSTRACT: Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure that captures nonlinear and lagged dependencies between time series to infer a minimal directed network model. Greedy algorithms have been proposed to efficiently deal with high-dimensional datasets while avoiding redundant inferences and capturing synergistic effects. However, multiple statistical comparisons may inflate the false positive rate and are computationally demanding, which limited the size of previous validation studies. The algorithm we present-as implemented in the IDTxl open-source software-addresses these challenges by employing hierarchical statistical tests to control the family-wise err

SUBMITTER: Novelli L 

PROVIDER: S-EPMC6663300 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature