ArticleNature communications2021
High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Effects of a dissociative drug on fronto-limbic resting-state functional connectivity in individuals with posttraumatic stress disorder: a randomized controlled pilot study.Psychopharmacology · 2024Trial
- Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.Nature communications · 2026Article
- Sparse Distributed Archetypes Reveal Compressible Network Motifs Underlying Naturalistic Cognition.bioRxiv : the preprint server for biology · 2026Article
- Global topology of brain-wide co-fluctuations links task states, personality, and behavioral symptom dimensions.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal complexity in the psychotic brain.Molecular psychiatry · 2026Article
- Charting brain functional development from birth to 6 years of age.Nature human behaviour · 2025Article
- Brain network dynamics predict moments of surprise across contexts.Nature human behaviour · 2025Article
- High-level cognition is supported by information-rich but compressible brain activity patterns.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Preliminary evidence for altered brain-heart coherence during anxiogenic movies.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Living on the edge: network neuroscience beyond nodes.Trends in cognitive sciences · 2023Review
- Anxiety Shapes Amygdala-Prefrontal Dynamics During Movie Watching.Biological psychiatry global open science · 2023Article
- Leveraging edge-centric networks complements existing network-level inference for functional connectomes.NeuroImage · 2022Article
- NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.Network neuroscience (Cambridge, Mass.) · 2022Article
- Edges in brain networks: Contributions to models of structure and function.Network neuroscience (Cambridge, Mass.) · 2022Review
- A Neuroscientific and Cognitive Literary Approach to the Treatment of Time in Calderón'sFrontiers in integrative neuroscience · 2022Article
- Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture.Nature neuroscience · 2020Article
- A Gaussian Process Model of Human Electrocorticographic Data.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Mapping high-amplitude fMRI edge time series events across space and time.Imaging neuroscience (Cambridge, Mass.)Article
- The Comet Toolbox: Improving robustness in network neuroscience through multiverse analysis.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain's functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.
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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.