ArticleNetwork neuroscience (Cambridge, Mass.)2023
Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI.
Article in Network neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 11 citations in OpenAlex.
- Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.Nature communications · 2026Article
- The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction.iScience · 2026Article
- Global topology of brain-wide co-fluctuations links task states, personality, and behavioral symptom dimensions.bioRxiv : the preprint server for biology · 2026Article
- Controlling the human connectome with spatially diffuse input signals.Communications biology · 2026Article
- Social 'envirotyping' the ABCD study contextualizes dissociable brain organization and diverging outcomes.Social cognitive and affective neuroscience · 2026Article
- Variation in high-amplitude events across the human lifespan.Network neuroscience (Cambridge, Mass.) · 2026Article
- NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.Proceedings of machine learning research · 2025Article
- Identification of brain-like complex information architectures in embryonic tissue ofCommunicative & integrative biology · 2025Article
- Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains.Communications biology · 2024Article
- Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry.Frontiers in neurology · 2024Article
- Living on the edge: network neuroscience beyond nodes.Trends in cognitive sciences · 2023Review
- Partial entropy decomposition reveals higher-order information structures in human brain activity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.Communications biology · 2023Article
- Synchronous high-amplitude co-fluctuations of functional brain networks during movie-watching.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder.NeuroImage · 2022Article
- Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder.Human brain mapping · 2022Article
- Mapping high-amplitude fMRI edge time series events across space and time.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multiscale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.
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