Spatial filters and automated spike detection based on brain topographies improve sensitivity of EEG-fMRI studies in focal epilepsy.
The ballistocardiogram (BCG) represents one of the most prominent sources of artifacts that contaminate the electroencephalogram (EEG) during functional MRI. The BCG artifacts may affect the detection of interictal epileptiform discharges (IED) in patients with epilepsy, reducing the sensitivity of the combined EEG-fMRI method. In this study we improved the BCG artifact correction using a multiple source correction (MSC) approach. On the one hand, a source analysis of the IEDs was applied to the EEG data obtained outside the MRI scanner to prevent the distortion of EEG signals of interest during the correction of BCG artifacts. On the other hand, the topographies of the BCG artifacts were defined based on the EEG recorded inside the scanner. The topographies of the BCG artifacts were then added to the surrogate model of IED sources and a combined source model was applied to the data obtained inside the scanner. The artifact signal was then subtracted without considerable distortion of the IED topography. The MSC approach was compared with the traditional averaged artifact subtraction (AAS) method. Both methods reduced the spectral power of BCG-related harmonics and enabled better detection of IEDs. Compared with the conventional AAS method, the MSC approach increased the sensitivity of IED detection because the IED signal was less attenuated when subtracting the BCG artifacts. The proposed MSC method is particularly useful in situations in which the BCG artifact is spatially correlated and time-locked with the EEG signal produced by the focal brain activity of interest.
Siniatchkin
Michael
M
Christian-Albrechts-University, University Hospital of Pediatric Neurology, Schwanenweg 20, D-24105 Kiel, Germany. m.siniatchkin@pedneuro.uni-kiel.de
Moeller
Friederike
F
Jacobs
Julia
J
Stephani
Ulrich
U
Boor
Rainer
R
Wolff
Stephan
S
Jansen
Olav
O
Siebner
Hartwig
H
Scherg
Michael
M
D000200:Action Potentials; D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004828:Epilepsies, Partial / Q000175:diagnosis* / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity