Using voxel-specific hemodynamic response function in EEG-fMRI data analysis.
Most existing analytical techniques for EEG-fMRI data need specific assumptions about the hemodynamic response function (HRF). These assumptions may not be appropriate when the HRF varies from subject to subject or from region to region. In this article, we introduce a deconvolution method for EEG-fMRI activation detection, which can be implemented with voxel-specific HRFs. A comparison of performance is made between three fixed HRFs and the deconvolution method under the framework of the general linear model. The main results are as follows: (1) the volume of detected regions from the deconvolved HRFs is larger. (2) In some subjects, the deconvolution technique can find areas of activation that have not been detected with the three fixed HRFs at our threshold of significance. (3) Deconvolution obtained higher adjusted coefficients of multiple determination compared to those obtained with the three fixed HRFs. The results suggest that the fixed HRF methods may not be the most appropriate for the analysis of epileptic activity with EEG-fMRI, and the deconvolution method may be a better choice.
Lu
Yingli
Y
Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, Canada H3A 2B4.
Bagshaw
Andrew P
AP
Grova
Christophe
C
Kobayashi
Eliane
E
Dubeau
François
F
Gotman
Jean
J
D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology / Q000502:physiology*; D001931:Brain Mapping; D002560:Cerebrovascular Circulation / Q000502:physiology*; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000473:pathology / Q000503:physiopathology*; D004827:Epilepsy / Q000145:classification / Q000473:pathology / Q000503:physiopathology; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D012016:Reference Values