ArticleNature communications2019
Brain songs framework used for discovering the relevant timescale of the human brain.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Awakening: Predicting external stimulation to force transitions between different brain states.Proceedings of the National Academy of Sciences of the United States of America · 2019Trial
- Shared spatial and temporal principles govern connectome dynamics across timescales.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Non-duality in brain and experience of advanced meditators-key role for intrinsic neural timescales.Communications biology · 2026Article
- Enhancing time scalability of spiking neural networks with dynamic time constants.Scientific reports · 2026Article
- Modeling macroscopic brain dynamics with brain-inspired computing architecture.Nature communications · 2025Article
- Large-scale cortical functional networks are organized in structured cycles.Nature neuroscience · 2025Article
- A mechanism for the emergence of low-dimensional structures in brain dynamics.NPJ systems biology and applications · 2025Article
- The lifelong nonlinear development of spatial variability of brain signals.Communications biology · 2025Article
- Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.Cognitive neurodynamics · 2024Article
- A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.Entropy (Basel, Switzerland) · 2024Article
- Insomnia disorder characterized by probabilistic metastable substates using blood-oxygenation-level-dependent (BOLD) phase signals.Sleep & breathing = Schlaf & Atmung · 2024Article
- A synergetic turn in cognitive neuroscience of brain diseases.Trends in cognitive sciences · 2024Review
- Dendrites contribute to the gradient of intrinsic timescales encompassing cortical and subcortical brain networks.Frontiers in cellular neuroscience · 2024Article
- Multi-modal and multi-model interrogation of large-scale functional brain networks.NeuroImage · 2023Article
- A systematic approach to brain dynamics: cognitive evolution theory of consciousness.Cognitive neurodynamics · 2023Article
- Assessing the coupling between local neural activity and global connectivity fluctuations: Application to human intracranial electroencephalography during a cognitive task.Human brain mapping · 2023Article
- Toward naturalistic neuroscience: Mechanisms underlying the flattening of brain hierarchy in movie-watching compared to rest and task.Science advances · 2023Article
- Dynamic reconfiguration of frequency-specific cortical coactivation patterns during psychedelic and anesthetized states induced by ketamine.NeuroImage · 2022Article
- Telling functional networks apart using ranked network features stability.Scientific reports · 2022Article
- Computational Models in Electroencephalography.Brain topography · 2022Review
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3 authors.
Funding
Abstract
A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below 0.1 Hz), other neuroimaging modalities such as MEG and EEG suggest that much faster timescales may be equally or more relevant for discovering spatiotemporal structure. Here, we introduce a novel way to generate whole-brain neural dynamical activity at the millisecond scale from fMRI signals. This method allows us to study the different timescales through binning the output of the model. These timescales can then be investigated using a method (poetically named brain songs) to extract the spacetime motifs at a given timescale. Using independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we show that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data.
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