Combining EEG and fMRI to Study Brain Networks
Author Information
Author(s): Lei Xu, Ostwald Dirk, Hu Jiehui, Qiu Chuan, Porcaro Camillo, Bagshaw Andrew P., Yao Dezhong
Primary Institution: University of Electronic Science and Technology of China
Hypothesis
Can multimodal functional network connectivity (mFNC) effectively reveal the interactions among brain networks using EEG and fMRI data?
Conclusion
The study demonstrates that mFNC can uncover comprehensive relationships among functional brain networks during visual tasks.
Supporting Evidence
- mFNC has the potential to reveal underlying neural networks from EEG and fMRI data.
- Granger causality analysis was used to explore directed influences among functional networks.
- Results showed that EEG and fMRI can provide complementary information about brain activity.
Takeaway
This study shows how two different brain scanning methods, EEG and fMRI, can work together to help us understand how different parts of the brain communicate with each other.
Methodology
The study used independent component analysis (ICA) to extract functional networks from EEG and fMRI data, followed by Granger causality analysis to explore interactions among these networks.
Potential Biases
Potential biases may arise from the assumptions made in the analysis, particularly regarding the independence of the components.
Limitations
The method may be affected by the assumptions of ICA and the potential for non-stationarity in the data.
Participant Demographics
One right-handed male participant aged 28 years.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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