ArticleNetwork neuroscience (Cambridge, Mass.)2019
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The research progress on effective connectivity in adolescent depression based on resting-state fMRI.Frontiers in neurology · 2025Pooled it
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- An integratediScience · 2026Article
- Mapping the Human Performance Envelope Through Multivariate Information Transfer.Brain sciences · 2026Article
- Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip.Neuroinformatics · 2026Article
- Towards an informational account of interpersonal coordination.Nature reviews. Neuroscience · 2026Review
- NIPS: Network Inference with Partial State measurements using forced-delay embedding.PNAS nexus · 2026Article
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.PLoS computational biology · 2025Article
- Article
- Minimum-Phase Property of the Hemodynamic Response Function, and Implications for Granger Causality in fMRI.Human brain mapping · 2025Article
- Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series.Human brain mapping · 2025Article
- A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning.Scientific data · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Entropy and Complexity Tools Across Scales in Neuroscience: A Review.Entropy (Basel, Switzerland) · 2025Review
- Identification of brain-like complex information architectures in embryonic tissue ofCommunicative & integrative biology · 2025Article
- Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons.Frontiers in computational neuroscience · 2025Article
- Cortex level connectivity between ACT-R modules during EEG-based n-back task.Cognitive neurodynamics · 2024Article
- Inferring Dealer Networks in the Foreign Exchange Market Using Conditional Transfer Entropy: Analysis of a Central Bank Announcement.Entropy (Basel, Switzerland) · 2024Article
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5 authors.
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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 parallelization. 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 10,000 time samples. Varying the statistical significance threshold showed a more favorable 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 magnetoencephalography experiments.
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