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Functional and effective networks inferred from time series are at the coreof network neuroscience. Interpreting their properties requires inferrednetwork models to reflect key underlying structural features; however, even afew spurious links can distort network measures, challenging functionalconnectomes. We study the extent to which micro- and macroscopic properties ofunderlying networks can be inferred by algorithms based on mutual informationand bivariate/multivariate transfer entropy. The validation is performed on twomacaque connectomes and on synthetic networks with various topologies (regularlattice, small-world, random, scale-free, modular). Simulations are based on aneural mass model and on autoregressive dynamics (employing Gaussian estimatorsfor direct comparison to functional connectivity and Granger causality). Wefind that multivariate transfer entropy captures key properties of all networksfor longer time series. Bivariate methods can achieve higher recall(sensitivity) for shorter time series but are unable to control false positives(lower specificity) as available data increases. This leads to overestimatedclustering, small-world, and rich-club coefficients, underestimated shortestpath lengths and hub centrality, and fattened degree distribution tails.Caution should therefore be used when interpreting network properties offunctional connectomes obtained via correlation or pairwise statisticaldependence measures, rather than more holistic (yet data-hungry) multivariatemodels.

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