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Related Experiment Video

Updated: Dec 24, 2025

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Modeling regional changes in dynamic stability during sleep and wakefulness.

Ignacio Perez Ipiña1, Patricio Donnelly Kehoe2, Morten Kringelbach3

  • 1Department of Physics, University of Buenos Aires, Argentina.

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This study models brain states during sleep and wakefulness using functional magnetic resonance imaging (fMRI) and connectivity data. It reveals distinct neural dynamics in different brain regions, offering a multidimensional view of consciousness.

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Area of Science:

  • Computational neuroscience
  • Neuroimaging
  • Sleep science

Background:

  • Global brain states are often simplified to a single continuum from unconsciousness to wakefulness.
  • A multidimensional, mechanistic approach using computational models offers a more nuanced understanding of neural dynamics.

Purpose of the Study:

  • To develop a semi-empirical model characterizing brain states by regional activation and functional connectivity.
  • To explore the multidimensional nature of consciousness beyond a simple wake-sleep continuum.

Main Methods:

  • Combined functional magnetic resonance imaging (fMRI) data with in vivo structural connectivity estimates.
  • Developed an anatomically-informed computational model to constrain regional activation.
  • Simulated external perturbations to identify regions critical for arousal from sleep.

Main Results:

  • The model, constrained by functionally coherent networks, divided the cortex into regions with opposing dynamics.
  • Frontoparietal regions exhibited dynamics approaching a fixed point, while sensorimotor regions approached oscillations.
  • Sleep onset showed subcortical deactivation and low correlation, reversing in deeper sleep stages.

Conclusions:

  • Sleep is characterized by diminished perceptual gating but retains capacity for arousal.
  • The model provides a multidimensional framework for consciousness, interpretable through anatomically-informed priors.
  • This approach moves beyond simple stable/unstable dynamics to a richer characterization of brain states.