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Related Experiment Video

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Infraslow Electroencephalographic and Dynamic Resting State Network Activity.

Joshua K Grooms1, Garth J Thompson2, Wen-Ju Pan1

  • 11 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia .

Brain Connectivity
|May 3, 2017
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Summary

This study links infraslow electrical activity, measured by direct current electroencephalography (DC EEG), to blood oxygenation level dependent (BOLD) signal fluctuations in humans. These findings reveal infraslow brain activity

Keywords:
DC-EEGfunctional connectivityinfraslowresting state MRIsliding window correlation

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Electrophysiology

Background:

  • Previous research linked traditional electroencephalography (EEG) bands to the blood oxygenation level dependent (BOLD) signal.
  • The relationship between BOLD signals and infraslow (<1 Hz) EEG activity, crucial for large-scale network dynamics in rodents, remained understudied in humans.

Purpose of the Study:

  • To investigate the link between infraslow electrical activity and brain network dynamics in humans.
  • To determine if infraslow EEG signals correlate with BOLD signal fluctuations and resting-state networks.

Main Methods:

  • Simultaneous acquisition of direct current (DC) EEG and resting-state functional magnetic resonance imaging (fMRI) data.
  • Correlation analysis between DC EEG signals and BOLD signals.
  • Dynamic analysis of the time-varying correlation between DC EEG and BOLD signals.

Main Results:

  • DC EEG signals showed correlations with BOLD signals that mirrored known resting-state networks.
  • The correlation between DC EEG and BOLD signals exhibited significant temporal variability within subjects.
  • This variability suggests DC EEG reflects the dynamic contributions of different resting-state networks over time.

Conclusions:

  • Infraslow electrical activity is demonstrably linked to BOLD signal fluctuations in the human brain.
  • Infraslow EEG activity may underpin the large-scale organization of brain networks, similar to findings in animal models.
  • This research opens new avenues for understanding brain network dynamics using infraslow electrical signals.