Multimodal EEG Recordings, Psychometrics and Behavioural Analysis.

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Simultaneous high spatial and temporal resolution measurements of neuronal activity are crucial for comprehensive brain research.
• Multimodal electroencephalography (EEG) offers a powerful approach to integrate different measures of brain function.

Purpose of the Study:

• To provide an overview of multimodal EEG approaches for studying brain activity.
• To explore the application of multimodal EEG in both resting-state and task-based paradigms.
• To discuss the potential of multimodal EEG in understanding aging, neuropharmacology, and neuropsychiatric disorders.

Main Methods:

• Review and synthesis of existing research on multimodal EEG.
• Analysis of alpha rhythm properties and data-driven pattern identification (e.g., default mode network).
• Integration of electrophysiological data with hemodynamic measures (blood oxygenation) and behavioral data.

Main Results:

• High-resolution neuronal metabolic indices correlate with distributed cortical regions and subcortical nuclei.
• Event-related potentials combined with hemodynamic measures reveal insights into target-related networks during tasks.
• Electrical propagation in serial tasks involves interconnected pathways, including dorsal and ventral streams.

Conclusions:

• Multimodal EEG provides a robust framework for investigating brain function with high spatial and temporal resolution.
• The approach has demonstrated utility in studying aging, neuropharmacology, and offers promise for personalized neuropsychiatric marker strategies.