Prefrontal causal roles in bistable perception are dynamically changing and determined by the brain state to which the whole-brain activity pattern belongs.

A generative-model-based, unsupervised learning toolbox for characterizing oscillatory bursting and brain network dynamics in univariate or multivariate time series.

Human brains evolved to have neural dynamics that facilitate brain-wide integration supporting complex behavior.

The variability of the pupil–fMRI relationship was characterized through a combination of clustering and prediction methods, which revealed brain state-specific subcortical and neuromodulatory activation patterns reflected in pupil dynamics.

The HMM-Fisher kernel approach leverages individual signatures of brain dynamics for prediction, which can be used, for example, to search for brain dynamics-informed biomarkers of neuropsychiatric disease or predict treatment response.

Characterization of redox-active cysteines illuminates the understudied regulatory mechanisms of brain-selective kinases.

BrainPy presents a general-purpose programming framework that enables efficient implementation, simulation, training, and analysis of brain dynamics models across multiple organization scales, ultimately facilitating the understanding of the complex neural mechanisms underlying brain functions.

Hidden Markov Modeling to fMRI data reveals how language comprehension arises from the dynamic interplay of large-scale brain networks.

Fast fluctuations of oxygen, linked to behavioral and neural dynamics in vivo, cause phasic transients is amperometry-measured cholinergic signals via the mechanism of non-steady-state enzyme kinetics in the choline-oxidase-based biosensor.

The fast spreading of neural activity across the brain has a complex structure, imposed by the white-matter bundles.