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

TL;DR

This study evaluates how well multivariate and bivariate transfer entropy and mutual information methods infer network properties from time series data, highlighting the advantages of multivariate approaches for accurate network characterization.

Contribution

It provides a comprehensive validation of multivariate versus bivariate algorithms in inferring network features from time series, emphasizing the importance of multivariate methods for accurate network analysis.

Findings

Multivariate transfer entropy captures key network properties with longer time series.

Bivariate methods have higher recall for short time series but lower specificity.

Bivariate methods tend to overestimate clustering and small-world coefficients.

Abstract

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.