Recurring transient brain-wide co-activation patterns from EEG spatially resembling time-averaged resting-state...

Area of Science:

• Neuroscience
• Brain Imaging
• Cognitive Science

Background:

• Human brains exhibit continuous activity at rest, forming resting-state networks (RSNs).
• Functional magnetic resonance imaging (fMRI) studies suggest RSNs may arise from rapid, recurring neuronal events.
• fMRI's slow temporal resolution limits its ability to capture these fast neuronal dynamics, unlike electroencephalography (EEG).

Purpose of the Study:

• To investigate transient cortical co-activation patterns (CAPs) in resting-state EEG data.
• To determine if these CAPs correspond spatially to known RSNs.
• To explore the potential of CAPs in revealing the fast dynamics of spontaneous brain activity.

Main Methods:

• Utilized resting-state EEG data from human participants.
• Applied a k-means clustering algorithm to identify recurring transient (<100 ms) cortical co-activation patterns (CAPs).
• Analyzed spatial correspondence between CAPs and RSNs derived from both EEG and fMRI literature.

Main Results:

• Identified a set of CAPs that spatially correspond strongly with established RSNs across various functional systems (visual, auditory, motor, default mode, etc.).
• Observed that CAPs share properties with RSNs, including hemispheric symmetry and intersubject variability.
• Demonstrated that CAPs capture fast neuronal dynamics, showing differences in occurrence and lifetime, and exhibit intersubject reproducibility.

Conclusions:

• Classical RSNs may be driven by the recurring transient neuronal activations represented by CAPs.
• CAPs offer a high temporal resolution window (<100 ms) into the neuronal mechanisms underlying spontaneous brain activity.
• CAPs provide a promising avenue for advancing our understanding of large-scale brain network dynamics.