Abstract
In the last decade, an increasing interest has arisen in investigating the relationship between the electrophysiological and hemodynamic measurements of brain activity, such as EEG and (BOLD) fMRI. In particular, changes in BOLD have been shown to be associated with changes in the spectral profile of neuronal activity, rather than with absolute neural power. On the other hand, though, recent findings showed that different EEG rhythms are independently related to changes in the BOLD signal: therefore, it would be important to distinguish between the contributions of the different EEG rhythms to BOLD fluctuations when modeling the relationship between EEG and BOLD. Here we proposed a novel method to perform EEG-informed fMRI analysis, so that the EEG regressors take into account both the changes in the spectral profile and the rhythms distinction. We applied it to EEG-fMRI data during a bimanual motor task in healthy subjects, and compared the results with those obtained by regressing fMRI data onto a single regressor covering the entire range of frequencies, ignoring the distinction between different EEG rhythms. Our results showed that the proposed method better captures the correlations between BOLD signal and EEG rhythms modulations, identifying task-related well localized activated volumes.
Original language | English |
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Title of host publication | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
Pages | 4712-4715 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2012 |
Event | 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States Duration: Aug 28 2012 → Sept 1 2012 |
Other
Other | 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 |
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Country/Territory | United States |
City | San Diego, CA |
Period | 8/28/12 → 9/1/12 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics