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Whole-brain propagating patterns in human resting-state brain activities.

Yusuke Takeda1, Nobuo Hiroe2, Okito Yamashita1

  • 1Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan.

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Summary

Researchers discovered whole-brain propagating activities in resting-state brain activity. These activities transiently exhibit multiple resting-state networks (RSNs) across different frequencies, suggesting information integration across brain networks.

Keywords:
Electroencephalography (EEG)Magnetoencephalography (MEG)Resting-stateResting-state networkSpatiotemporal patternWhole-brain propagating activity

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Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • Repetitive propagating activities are observed in resting-state brain activity across species and regions.
  • These activities are hypothesized to reflect embedded past experiences and potentially integrate distributed information, like visual and motor data.

Purpose of the Study:

  • To reveal and characterize whole-brain propagating activities in human resting-state data.
  • To investigate the relationship between these activities and known resting-state networks (RSNs).

Main Methods:

  • Simultaneous recording of magnetoencephalography (MEG) and electroencephalography (EEG) in humans.
  • Estimation of source currents and extraction of repetitive spatiotemporal patterns using a novel algorithm.
  • Analysis of frequency components within the extracted patterns and comparison with fMRI-based RSNs.

Main Results:

  • Whole-brain propagating activities were identified in human resting-state MEG/EEG data.
  • Extracted spatiotemporal patterns comprised multiple frequency components.
  • Each frequency component transiently exhibited frequency-specific resting-state networks (RSNs), including default mode and sensorimotor networks.
  • Simulation suggested patterns reflect phase alignment of oscillators along anatomical connectivity.

Conclusions:

  • Whole-brain propagating activities transiently manifest multiple RSNs across their frequency components.
  • These findings suggest that resting-state propagating activities integrate information across different frequencies and brain networks.