Mapping Executive Function Performance Based on Resting-State EEG in Healthy Individuals: A Systematic and...

Area of Science:

• Neuroscience
• Cognitive Science
• Psychophysiology

Background:

• Resting-state EEG (rsEEG) offers a scalable measure of trait-like executive readiness.
• Previous findings on rsEEG and executive function (EF) are fragmented due to task impurity and varied analysis pipelines.
• Understanding reliable rsEEG markers across development and aging is crucial for clinical applications.

Purpose of the Study:

• To synthesize rsEEG features that reliably track EF in healthy individuals across the lifespan.
• To evaluate moderators like cognitive reserve that influence the rsEEG-EF relationship.
• To establish criteria for reproducible rsEEG research and potential clinical translation.

Main Methods:

• Systematic review following PRISMA 2020 guidelines, searching multiple databases (MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, IEEE Xplore).
• Inclusion criteria focused on human participants, rsEEG (eyes-closed/eyes-open), spectral, aperiodic, connectivity, topology, microstate, and LRTC features, and behavioral EF outcomes.
• Sixty-three studies were included, spanning childhood to old age; risk of bias was assessed using ROBINS-I.

Main Results:

• A multi-feature EEG signature, including faster alpha frequency, specific aperiodic 1/f parameters, efficient alpha-band network topology, and frontal beta/gamma coherence, reliably tracks EF.
• Faster alpha pace and steeper aperiodic slope correlate with processing speed and working memory, moderated by age and cognitive reserve.
• Connectivity and topology measures outperform local power; efficient networks predict better performance, while global over-synchrony is linked to sluggishness.

Conclusions:

• rsEEG does not independently diagnose EF; a multi-feature signature is necessary for reliable tracking.
• Age and cognitive reserve are critical moderators, influencing the interpretation of rsEEG markers.
• Clinical translation should focus on stratification and monitoring, considering developmental and aging trajectories.