Comparison of network analysis approaches on EEG connectivity in beta during Visual Short-term Memory binding tasks

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Visual Short-Term Memory (VSTM) tasks are crucial for assessing cognitive function, particularly in preclinical Alzheimer's disease.
• Electroencephalogram (EEG) signals in the beta band reflect working memory processes.
• Network analysis offers a powerful approach to understanding brain functional connectivity.

Purpose of the Study:

• To compare the efficacy of Maximum Spanning Trees (MSTs) versus arbitrary connection thresholds for analyzing VSTM network functional differences.
• To investigate the sensitivity of network analysis methods in detecting cognitive task variations.
• To explore the role of contralateral brain activity in VSTM task performance.

Main Methods:

• Analysis of beta-band EEG signals from healthy young volunteers performing VSTM tasks.
• Comparison of network analysis using MSTs against 20% and 25% connection thresholds.
• Assessment of network differences between various VSTM tasks, considering left and right visual field presentation.

Main Results:

• Threshold-based network analysis significantly outperformed MSTs in detecting functional network differences.
• MSTs failed to identify significant differences between task conditions.
• Threshold analyses revealed significant differences between shape and shape-colour binding tasks on the left display side, but not the right, indicating contralateral effects.

Conclusions:

• Arbitrary connection thresholds are more effective than MSTs for detecting cognitive differences in VSTM network activity.
• Contralateral brain activity plays a significant role in the sensitivity of detecting cognitive task variations.
• These findings have implications for using EEG network analysis in clinical assessments, including Alzheimer's disease diagnostics.