4.7 Article

Improved artifact correction for combined electroencephalography/functional MRI by means of synchronization and use of vectorcardiogram recordings

Journal

JOURNAL OF MAGNETIC RESONANCE IMAGING
Volume 27, Issue 3, Pages 607-616

Publisher

WILEY
DOI: 10.1002/jmri.21277

Keywords

fMRl; EEG; synchronization; gradient artifact; pulse artifact

Funding

  1. Medical Research Council [G9900259] Funding Source: researchfish
  2. MRC [G9900259] Funding Source: UKRI
  3. Medical Research Council [G990259, G9900259] Funding Source: Medline
  4. NCI NIH HHS [CA 082923] Funding Source: Medline

Ask authors/readers for more resources

Purpose: To demonstrate that two methodological developments (synchronization of the MR scanner and electroencephalography [EEG) clocks and use of the scanner's vectorcardiogram [VCG]) improve the quality of EEG data recorded in combined EEG/functional MRI experiments in vivo. Materials and Methods: EEG data were recorded using a 32-channel system, during simultaneous multislice EPI acquisition carried out on a 3 Testa scanner. Recordings were made on three subjects in the resting state and on five subjects using a block paradigm involving visual stimulation with a 10-Hz flashing checkerboard. Results: Gradient artifacts were significantly reduced in the EEG data recorded in vivo when synchronization and a TR equal to a multiple of the EEG clock period were used. This was evident from the greater attenuation of the signal at multiples of the slice acquisition frequency. Pulse artifact correction based on R-peak markers derived from the VCG was shown to offer a robust alternative to the conventionally used ECG-based method, Driven EEG responses at frequencies of up to 60 Hz due to the visual stimulus could be more readily detected in data recorded with EEG and MR scanner clock synchronization. Conclusion: Synchronization of the scanner and EEG clocks, along with VCG-based R-peak detection is advantageous in removing gradient and pulse artifacts in combined EEG/fMRI recordings. This approach is shown to allow the robust detection of high frequency driven activity in the EEG data.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available