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 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.

References in corpus (27)

• Emergence of scaling in random networks
• Fast unfolding of communities in large networks
• Finding and evaluating community structure in networks
• Efficient Behavior of Small-World Networks
• Measuring Information Transfer
• Granger causality and transfer entropy are equivalent for Gaussian variables
• Powerlaw: a Python package for analysis of heavy-tailed distributions
• Detecting rich-club ordering in complex networks
• Detecting causal associations in large nonlinear time series datasets
• Clustering in Complex Directed Networks
• The Rich-Club Phenomenon In The Internet Topology
• JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
• Power-law distributions in binned empirical data
• Model-free reconstruction of neuronal network connectivity from calcium imaging signals
• Causal Network Inference by Optimal Causation Entropy
• Non-uniform state space reconstruction and coupling detection
• Direct coupling information measure from non-uniform embedding
• Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
• IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
• Transfer Entropy reconstruction and labeling of neuronal connections from simulated calcium imaging
• Synergy and redundancy in the Granger causal analysis of dynamical networks
• From brain to earth and climate systems: Small-world interaction networks or not?
• How structure sculpts function: unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure
• Beware of the Small-World neuroscientist!
• Small-world topology of functional connectivity in randomly connected dynamical systems
• Generalization of the small-world effect on a model approaching the Erdős-Rényi random graph
• Deriving pairwise transfer entropy from network structure and motifs