Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing

Abstract

Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure that captures nonlinear and lagged dependencies between time series to infer a minimal directed network model. Greedy algorithms have been proposed to efficiently deal with high-dimensional datasets while avoiding redundant inferences and capturing synergistic effects. However, multiple statistical comparisons may inflate the false positive rate and are computationally demanding, which limited the size of previous validation studies. The algorithm we present---as implemented in the IDTxl open-source software---addresses these challenges by employing hierarchical statistical tests to control the family-wise error rate and to allow for efficient parallelisation. The method was validated on synthetic datasets involving random networks of increasing size (up to 100 nodes), for both linear and nonlinear dynamics. The performance increased with the length of the time series, reaching consistently high precision, recall, and specificity (>98% on average) for 10000 time samples. Varying the statistical significance threshold showed a more favourable precision-recall trade-off for longer time series. Both the network size and the sample size are one order of magnitude larger than previously demonstrated, showing feasibility for typical EEG and MEG experiments.

References in corpus (14)

• Measuring Information Transfer
• Estimating Mutual Information
• Surrogate time series
• Granger causality and transfer entropy are equivalent for Gaussian variables
• JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
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• Transfer Entropy as a Log-likelihood Ratio
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• Generalised Measures of Multivariate Information Content
• Deriving pairwise transfer entropy from network structure and motifs
• Broken detailed balance and entropy production in directed networks
• Estimating Conditional Transfer Entropy in Time Series using Mutual Information and Non-linear Prediction
• Quantifying the predictability of visual scanpaths using active information storage
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