Graph properties of synchronized cortical networks during visual working memory maintenance

Area of Science:

• Neuroscience
• Cognitive Science
• Network Science

Background:

• Neuronal oscillatory synchronization is crucial for brain communication and cognition.
• Non-invasive imaging techniques like magnetoencephalography (MEG) and electroencephalography (EEG) allow study of brain activity.
• The large-scale network structure of synchronized cortical activity during cognitive tasks remains largely uncharacterized.

Purpose of the Study:

• To investigate the structure of synchronized cortical networks during visual working memory (VWM) maintenance.
• To characterize the frequency-dependent network properties of the human brain.
• To explore the relationship between network structure and cognitive load.

Main Methods:

• Simultaneous MEG and EEG (MEEG) recordings were used.
• Minimum-norm-estimate-based inverse modeling was applied to image cortical activity.
• Inter-areal phase synchrony was quantified across the entire cortical surface in different frequency bands (delta/theta, alpha, beta, gamma).

Main Results:

• Alpha and beta band networks exhibited higher clustering and small-world properties compared to delta/theta and gamma bands.
• Alpha and beta networks showed truncated power-law degree distributions and high k-core numbers.
• Human cortical networks in alpha and beta bands displayed a memory-load dependent, scale-free small-world structure with core-like hubs during VWM.

Conclusions:

• Synchronized brain networks during cognitive states exhibit distinct, frequency-dependent structures.
• Alpha and beta band network structures during VWM maintenance are scale-free and small-world like.
• These distinct network structures may support specific functional roles in cognition.