ArticleCerebral cortex communications2022
Data-driven discovery of canonical large-scale brain dynamics.
Article in Cerebral cortex communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 8 citations in OpenAlex.
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- System identification and surrogate data analyses imply approximate Gaussianity and non-stationarity of resting-brain dynamics.bioRxiv : the preprint server for biology · 2026Article
- The turbulent brain: Modeling vortex interactions for understanding human cognition.Network neuroscience (Cambridge, Mass.) · 2026Article
- Shifts in brain dynamics and drivers of consciousness state transitions.Frontiers in computational neuroscience · 2026Article
- Multiscale brain modeling: bridging microscopic and macroscopic brain dynamics for clinical and technological applications.Frontiers in cellular neuroscience · 2025Review
- The Hopf whole-brain model and its linear approximation.Scientific reports · 2024Article
- Rest assured: Dynamic functional connectivity and the baseline state of the human brain.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke.Scientific reports · 2023Article
- Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics.Science advances · 2023Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
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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.