ArticleNetwork neuroscience (Cambridge, Mass.)2021
Inferring network properties from time series using transfer entropy and mutual information: Validation of multivariate versus bivariate approaches.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 7 citations in OpenAlex.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Dynamic Bayesian networks for neural information flow: evaluation of continuous and discrete scoring metrics.Journal of computational neuroscience · 2026Article
- NIPS: Network Inference with Partial State measurements using forced-delay embedding.PNAS nexus · 2026Article
- Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.PLoS computational biology · 2025Article
- Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series.Human brain mapping · 2025Article
- Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Article
- The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus.bioRxiv : the preprint server for biology · 2024Article
- A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems.Entropy (Basel, Switzerland) · 2024Article
- Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.Nature communications · 2024Article
- Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.Network neuroscience (Cambridge, Mass.) · 2024Article
- Transient destabilization of interhemispheric functional connectivity induced by spreading depolarization.Network neuroscience (Cambridge, Mass.) · 2024Article
- Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice.Nature communications · 2023Article
- Static and dynamic functional connectomes represent largely similar information.bioRxiv : the preprint server for biology · 2023Article
- Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Significant subgraph mining for neural network inference with multiple comparisons correction.Network neuroscience (Cambridge, Mass.) · 2023Article
- Arousal system stimulation and anesthetic state alter visuoparietal connectivity.Frontiers in systems neuroscience · 2023Article
- Static and dynamic fMRI-derived functional connectomes represent largely similar information.Network neuroscience (Cambridge, Mass.) · 2023Article
- Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition.Entropy (Basel, Switzerland) · 2022Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting properties of these networks requires inferred network models to reflect key underlying structural features. However, even a few spurious links can severely distort network measures, posing a challenge for 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 network structures 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.