ArticlePLoS computational biology2025
Null models for comparing information decomposition across complex systems.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Sampling bias corrections for discrete and Gaussian partial information decompositions.Patterns (New York, N.Y.) · 2026Article
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Synergistic and redundant information dynamics are modulated by Alzheimer's disease and cognitive impairment.bioRxiv : the preprint server for biology · 2026Article
- Synergy mediates long-range correlations in the visual cortex near criticality.Frontiers in computational neuroscience · 2026Article
- Synergy mediates Long-Range Correlations in the Visual Cortex Near Criticality.bioRxiv : the preprint server for biology · 2025Article
- The topology of synergy: Linking topological and information-theoretic approaches to higher-order interactions in complex systems.PLoS computational biology · 2025Article
- Information dynamics and the emergence of high-order individuality in ecosystems.Communications biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
A key feature of information theory is its universality, as it can be applied to study a broad variety of complex systems. However, many information-theoretic measures can vary significantly even across systems with similar properties, making normalisation techniques essential for allowing meaningful comparisons across datasets. Inspired by the framework of Partial Information Decomposition (PID), here we introduce Null Models for Information Theory (NuMIT), a null model-based non-linear normalisation procedure which improves upon standard entropy-based normalisation approaches and overcomes their limitations. We provide practical implementations of the technique for systems with different statistics, and showcase the method on synthetic models and on human neuroimaging data. Our results demonstrate that NuMIT provides a robust and reliable tool to characterise complex systems of interest, allowing cross-dataset comparisons and providing a meaningful significance test for PID analyses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.