EEG and fMRI coupling and decoupling based on joint independent component analysis (jICA)

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Systems Neuroscience

Background:

• Integrating functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) is crucial for understanding neural activity.
• Assessing the coupling and decoupling between fMRI and EEG signals is essential for meaningful data integration.

Purpose of the Study:

• To introduce and validate a novel method, fMRI/EEG-jICA, for quantifying the coupling and decoupling between fMRI and EEG signals.
• To demonstrate the utility of fMRI/EEG-jICA in identifying distinct brain networks and enhancing the functional characterization of fMRI signals.

Main Methods:

• Joint independent component analysis (jICA) was employed to separate fMRI and EEG signals into distinct components.
• The proposed fMRI/EEG-jICA method quantifies signal coupling based on the jICA mixing matrix.
• fMRI multiple regression analysis was used for comparison.

Main Results:

• jICA successfully separated fMRI and EEG data into two distinct components related to auditory processing.
• A primary component showed increased amplitude with syllable presentation rate (obligatory response), while a secondary component decreased (attentional response).
• fMRI/EEG-jICA demonstrated greater sensitivity in detecting spatiotemporally distinct brain networks compared to fMRI multiple regression analysis.

Conclusions:

• fMRI/EEG-jICA effectively reveals distinct brain networks and leverages EEG signals to improve fMRI data interpretation.
• The method shows promise for studying neurovascular coupling, particularly in the context of neurovascular disorders.