Connectome spectral analysis to track EEG task dynamics on a subsecond scale.

Area of Science:

• Neuroscience
• Graph Signal Processing
• Computational Neuroscience

Background:

• Tracking fast spatiotemporal cortical dynamics is crucial for understanding brain function.
• Integrating white matter connectivity with electroencephalography (EEG) data presents a multimodal challenge.

Purpose of the Study:

• To introduce a novel approach for tracking rapid brain activity using network harmonics.
• To demonstrate the efficiency and functional relevance of network harmonics derived from structural connectivity.

Main Methods:

• Combined white matter connectivity and source-projected EEG data.
• Utilized graph signal processing to derive 'network harmonics' ordered by smoothness.
• Applied network harmonics as a basis for analyzing functional EEG data during a face detection task.

Main Results:

• Network harmonics provide a sparse representation of EEG signals, capturing 90% of signal power with the smoothest 15 harmonics.
• Successfully tracked large-scale cortical activity with 50 ms resolution using only 13 network harmonics.
• Identified known activity in the fusiform face area and novel co-activation patterns in other cortical regions.

Conclusions:

• Network harmonics offer a computationally efficient and theoretically grounded method for multimodal brain data integration.
• This approach facilitates the study of brain structure-function relationships and network tracking across tasks and populations.
• The method shows promise for both theoretical advancements and practical applications in neuroscience research.