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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
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Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI
Raziyeh Mosayebi1, Amin Dehghani1, Gholam-Ali Hossein-Zadeh1,2
1School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Frontiers in Human Neuroscience
|October 3, 2022
Summary
Correlated Coupled Tensor Matrix Factorization (CCMTF) effectively integrates electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. This method reveals shared brain activity patterns and dynamic functional connectivity, particularly in the 1-15 Hz range, during emotion regulation.
Area of Science:
- Neuroscience
- Cognitive Science
- Data Analysis
Background:
- Joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offers deeper insights into brain mechanisms.
- Emotion regulation paradigms, particularly those involving neurofeedback, are crucial for understanding brain function.
Purpose of the Study:
- To apply the Correlated Coupled Tensor Matrix Factorization (CCMTF) method for analyzing simultaneous EEG and fMRI data during an emotion regulation task.
- To investigate shared covariations and dynamic functional connectivity between EEG and fMRI signals.
Main Methods:
- Utilized the Correlated Coupled Tensor Matrix Factorization (CCMTF) method to analyze joint EEG and fMRI data.
- Performed dynamic functional connectivity (dFC) analysis on CCMTF-estimated regions.
- Compared CCMTF results with a Normalized Mutual Information (NMI) based approach.
Main Results:
- CCMTF revealed similar temporal covariations between EEG and fMRI during transitions from resting to task states, showing an increasing trend.
- fMRI spatial components showed activations in limbic, DLPFC, OFC, and VLPC regions, correlating with EEG frequencies (1-15 Hz) (r ≈ 0.75).
- Both CCMTF and NMI analyses indicated that the primary relationship between EEG and fMRI data lies within the 1-15 Hz frequency range, encompassing brain activations and dFC.
Conclusions:
- The CCMTF method is effective for extracting shared information between EEG and fMRI data without requiring EEG inverse solutions or separate frequency band analyses.
- CCMTF can uncover novel information regarding brain functions and their connectivity patterns.
- The study highlights the importance of the 1-15 Hz frequency range for understanding the relationship between EEG and fMRI during emotion regulation.
