Cognitive manipulation of brain electric microstates

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Brain Activity Analysis

Background:

• Electroencephalography (EEG) studies reveal semi-stable global electrical brain activity patterns during wakeful rest, known as microstates.
• Topographical analyses identify four distinct microstates explaining significant variance, occurring in a repetitive sequence.
• Previous fMRI research linked these microstates to specific functional brain systems activated by cognitive and sensory processes.

Purpose of the Study:

• To investigate how cognitive tasks (serial subtraction vs. rest) and visual input (eyes open vs. closed) affect the spatial and temporal properties of EEG microstates.
• To test the hypothesis that microstate D, linked to the dorsal attention system, would be altered during a cognitive task.
• To explore if microstate sequences preferentially transition to microstate D during tasks and if microstate B is affected by visual input.

Main Methods:

• High-density EEG was employed to analyze microstate properties.
• Participants performed a serial subtraction task under conditions of wakeful rest and varying visual input (eyes open/closed).
• K-means clustering was used to identify microstates, with analyses focusing on duration, coverage, and occurrence parameters.

Main Results:

• Microstate D parameters (duration, coverage, occurrence) increased during the cognitive task compared to rest.
• Microstate C showed decreased duration, coverage, and occurrence during the task.
• Microstate B occurrence and coverage increased with eyes open; microstates A and D durations decreased, while C occurrence increased in the eyes open condition.

Conclusions:

• Cognitive tasks and visual input significantly alter EEG microstate parameters, demonstrating their dynamic nature.
• The relationship between microstates and functional brain systems is complex and sensitive to cognitive and sensory manipulations.
• More than four primary microstates may be necessary to fully explain EEG activity variance.