ArticleNetwork neuroscience (Cambridge, Mass.)2017
Spontaneous brain network activity: Analysis of its temporal complexity.
Article in Network neuroscience (Cambridge, Mass.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Energy-Based Phase-Locking State Analysis in Brain State Identification.Human brain mapping · 2026Article
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- Studying Time-Resolved Functional Connectivity via Communication Theory: On the Complementary Nature of Phase Synchronization and Sliding Window Pearson Correlation.Brain connectivity · 2025Article
- Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.bioRxiv : the preprint server for biology · 2025Article
- A method for unsupervised learning of coherent spatiotemporal patterns in multiscale data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.Scientific reports · 2024Article
- Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.PloS one · 2024Article
- Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.Network neuroscience (Cambridge, Mass.) · 2024Article
- Dynamical interactions reconfigure the gradient of cortical timescales.Network neuroscience (Cambridge, Mass.) · 2023Article
- The complexity of the stream of consciousness.Communications biology · 2022Article
- Article
- On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI.Entropy (Basel, Switzerland) · 2022Article
- Edge-based network analysis reveals frequency-specific network dynamics in aberrant anxiogenic processing in rats.Network neuroscience (Cambridge, Mass.) · 2022Article
- Meso-scale network analysis of resting state-fMRI brain network connectivity performs poorly as a prognostic tool in critically ill traumatic brain injury patients.Neuroimage. Reports · 2022Article
- Evaluating phase synchronization methods in fMRI: A comparison study and new approaches.NeuroImage · 2021Article
- Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series.Network neuroscience (Cambridge, Mass.) · 2021Article
- Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.Human brain mapping · 2020Article
- Changes in EEG multiscale entropy and power-law frequency scaling during the human sleep cycle.Human brain mapping · 2019Article
- Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates.Network neuroscience (Cambridge, Mass.) · 2019Article
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5 authors.
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Abstract
The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain's complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the "cliquiness" of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.
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