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Updated: Dec 17, 2025

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
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Connectivity dynamics from wakefulness to sleep.

Eswar Damaraju1, Enzo Tagliazucchi2, Helmut Laufs3

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.

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|June 21, 2020
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Summary
This summary is machine-generated.

Time-resolved functional connectivity in fMRI can be accurately classified by sleep states using shorter sliding windows. This approach reveals distinct brain states during wakefulness, independent of sleep or motion.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Sleep Science

Background:

  • Time-resolved functional connectivity analysis in fMRI is crucial for understanding dynamic brain processes.
  • The sliding windows approach is commonly used, but optimal window parameters and influences of sleep and motion are debated.
  • Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer a powerful method to investigate brain dynamics across states.

Purpose of the Study:

  • To evaluate the effectiveness of different sliding window approaches for time-resolved fMRI connectivity analysis.
  • To investigate the influence of sleep states and head motion on resting-state brain dynamics.
  • To identify and characterize distinct brain connectivity states during wakefulness.

Main Methods:

  • Utilized an independent component analysis (ICA)-based pipeline on concurrent EEG/fMRI data across wakefulness and sleep stages.
  • Applied sliding window correlation analysis to resting-state functional network time courses.
  • Clustered connectivity states and compared fixed tapered windows with adaptive dynamic conditional correlation methods.

Main Results:

  • Connectivity states derived from sliding window correlations accurately classified EEG-defined sleep states.
  • Shorter sliding windows (e.g., 30 seconds) captured transition dynamics better than longer, non-overlapping windows.
  • Head motion was primarily associated with specific connectivity states, not uniformly distributed.
  • A fixed tapered sliding window approach outperformed adaptive dynamic conditional correlation.
  • Identified and accurately classified multiple distinct connectivity states within wakefulness, separate from sleep or motion effects.

Conclusions:

  • Time-resolved fMRI connectivity analysis, particularly with shorter sliding windows, effectively differentiates sleep states and reveals complex brain dynamics.
  • Distinct wakeful brain states exist, characterized by time-varying connectivity patterns independent of sleep or motion.
  • Findings provide guidance on optimal technical choices for dynamic functional connectivity research and highlight avenues for future investigation into wakeful brain states.