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Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

Gustavo Deco1, Patric Hagmann2, Anthony G Hudetz3

  • 1Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona 08010, Spain.

Cerebral Cortex (New York, N.Y. : 1991)
|July 13, 2013
PubMed
Summary

Brain activity transitions gradually from wake to sleep. Local slow waves emerge during wakefulness, and resting-state networks reorganize as sleep deepens, eventually merging into one synchronized network.

Keywords:
fMRI BOLDmodelingresting state

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sleep Research

Background:

  • The transition from wakefulness to sleep involves significant changes in brain activity and consciousness.
  • While traditionally viewed as abrupt, neural dynamics suggest a more gradual shift, with local slow waves appearing in wakefulness and sleep slow waves rarely being global.
  • Existing research indicates changes in resting-state functional connectivity (FC) between wake and sleep, but the evolution of resting-state networks during this transition remains unclear.

Purpose of the Study:

  • To investigate how resting-state networks change during the transition from wakefulness to sleep.
  • To model the effects of decreasing arousal-promoting neuromodulation on brain activity and network organization.

Main Methods:

  • Utilized large-scale modeling of human cortico-cortical anatomical connectivity.
  • Parametrically decreased cholinergic neuromodulation in the model to simulate the process of falling asleep.
  • Analyzed the emergence of local slow waves and changes in resting-state functional connectivity.

Main Results:

  • Decreasing cholinergic neuromodulation led to the appearance of local slow waves without altering the overall organization of resting-state networks.
  • These local slow waves were found to be macroscopically structured into networks resembling established resting-state networks.
  • At very low neuromodulator levels, slow waves became global, causing resting-state networks to merge into a single, undifferentiated, broadly synchronized network.

Conclusions:

  • The transition to sleep involves a gradual emergence of local slow waves that are organized within network structures similar to resting-state networks.
  • As sleep deepens and neuromodulation significantly decreases, these networks merge, leading to a global synchronization characteristic of deep sleep.
  • This study provides a computational model explaining the dynamic changes in brain network organization during the wake-to-sleep transition.