Tonic Resting State Hubness Supports High Gamma Activity Defined Verbal Memory Encoding Network in Epilepsy

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Systems Neuroscience

Background:

• High gamma activity (HGA) measured via invasive electroencephalography (iEEG) is a key biomarker for verbal memory encoding.
• The functional connectivity of brain networks supporting memory encoding remains incompletely understood, particularly in clinical populations.

Purpose of the Study:

• To investigate the functional connectivity characteristics, specifically network segregation and hubness, of HGA-memory regions.
• To compare these network properties between individuals with drug-resistant epilepsy and healthy controls.
• To determine the predictive relevance of network hubness and segregation for verbal memory encoding.

Main Methods:

• Integration of iEEG-derived HGA with resting-state BOLD fMRI data.
• Estimation of network segregation and hubness for HGA-memory regions in epilepsy patients and controls.
• Correlation analysis between network properties and verbal memory encoding performance.

Main Results:

• HGA-memory regions exhibit unique hubness patterns compared to adjacent regions in both healthy individuals and epilepsy patients.
• Network hubness was a stronger predictor of verbal memory encoding than network segregation.
• The HGA-memory network involves regions from both cognitive control and primary sensory/processing networks.

Conclusions:

• Effective verbal memory encoding relies on the integration of diverse brain functions, not solely a central cognitive core.
• A stable, intrinsic functional connectivity provides the foundation for dynamic, task-related memory processes.
• Altered network hubness in HGA-memory regions may have implications for memory deficits in neurological conditions like epilepsy.