ArticlePLoS computational biology2016
Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging.Psychophysiology · 2023Pooled it
- Developmental and aging changes in brain network switching dynamics revealed by EEG phase synchronization.PLoS computational biology · 2026Article
- The neural correlates of shared and individual experience.Communications biology · 2026Article
- Development of brain metastable dynamics during the equivalent of the third gestational trimester.Developmental cognitive neuroscience · 2025Review
- Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns.Frontiers in computational neuroscience · 2025Article
- Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.Biometrics · 2024Article
- State-switching and high-order spatiotemporal organization of dynamic functional connectivity are disrupted by Alzheimer's disease.Network neuroscience (Cambridge, Mass.) · 2023Article
- A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise.Scientific reports · 2022Article
- From calcium imaging to graph topology.Network neuroscience (Cambridge, Mass.) · 2022Review
- Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics.Human brain mapping · 2021Article
- A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction.Frontiers in neuroscience · 2020Article
- Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns.Frontiers in human neuroscience · 2020Article
- Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox.MethodsX · 2020Article
- Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks.Frontiers in human neuroscience · 2019Article
- Dynamic graph metrics: Tutorial, toolbox, and tale.NeuroImage · 2018Article
- Modeling and interpreting mesoscale network dynamics.NeuroImage · 2018Review
- Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2018Article
- Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.
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.