ArticleProceedings of the National Academy of Sciences of the United States of America2023
Partial entropy decomposition reveals higher-order information structures in human brain activity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed.
- High-order brain interactions in ketamine during rest and task: a double-blinded cross-over design using portable EEG on male participants.Translational psychiatry · 2024Trial
- Sampling bias corrections for discrete and Gaussian partial information decompositions.Patterns (New York, N.Y.) · 2026Article
- Fundamental limits incorporating logical reasoning into Shannon's information theory.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Accurately modeling resting-brain functional connectivity using hypergraph neural field-Fourier deep neural network.Scientific reports · 2026Article
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains.Nature human behaviour · 2026Article
- Emergent multiscale organisation of neural dynamics fragments in anaesthesia.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Interpreting Performance of Deep Neural Networks with Partial Information Decomposition.Entropy (Basel, Switzerland) · 2025Article
- The topology of synergy: Linking topological and information-theoretic approaches to higher-order interactions in complex systems.PLoS computational biology · 2025Article
- Null models for comparing information decomposition across complex systems.PLoS computational biology · 2025Article
- Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation.PLoS computational biology · 2025Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning.Nature communications · 2025Article
- Benchmarking methods for mapping functional connectivity in the brain.Nature methods · 2025Article
- Leveraging multivariate information for community detection in functional brain networks.Communications biology · 2025Article
- Emergence of a synergistic scaffold in the brains of human infants.Communications biology · 2025Article
- High-Order Information Analysis of Epileptogenesis in the Pilocarpine Rat Model of Temporal Lobe Epilepsy.eNeuro · 2025Article
- Revealing non-trivial information structures in aneural biological tissues via functional connectivity.PLoS computational biology · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
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5 authors.
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Abstract
The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic structure is dynamic in time and likely will illuminate interesting links between brain and behavior. Beyond brain-specific application, the PED provides a very general approach for understanding higher-order structures in a variety of complex systems.
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