Inferring network properties from time series using transfer entropy and mutual information: Validation of multivariate versus bivariate approaches.

ABSTRACT: Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting properties of these networks requires inferred network models to reflect key underlying structural features. However, even a few spurious links can severely distort network measures, posing a challenge for functional connectomes. We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/multivariate transfer entropy. The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular). Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estim

SUBMITTER: Novelli L 

PROVIDER: S-EPMC8233116 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature