  Functional and effective networks inferred from time series are at the core
of network neuroscience. Interpreting their properties requires inferred
network models to reflect key underlying structural features; however, even a
few spurious links can distort network measures, challenging 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 estimators
for direct comparison to functional connectivity and Granger causality). We
find that multivariate transfer entropy captures key properties of all networks
for 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 overestimated
clustering, small-world, and rich-club coefficients, underestimated shortest
path lengths and hub centrality, and fattened degree distribution tails.
Caution should therefore be used when interpreting network properties of
functional connectomes obtained via correlation or pairwise statistical
dependence measures, rather than more holistic (yet data-hungry) multivariate
models.
