Method for Spatial Overlap Estimation of Electroencephalography and Functional Magnetic Resonance Imaging Responses
Journal of Neuroscience Methods
Simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measurements may represent activity from partially divergent neural sources, but this factor is seldom modeled in fMRI-EEG data integration.
This paper proposes an approach to estimate the spatial overlap between sources of activity measured simultaneously with fMRI and EEG. Following the extraction of task-related activity, the key steps include, 1) distributed source reconstruction of the task-related ERP activity (ERP source model), 2) transformation of fMRI activity to the ERP spatial scale by forward modelling of the scalp potential field distribution and backward source reconstruction (fMRI source simulation), and 3) optimization of fMRI and ERP thresholds to maximize spatial overlap without a priori constraints of coupling (overlap calculation).
FMRI and ERP responses were recorded simultaneously in 15 subjects performing an auditory oddball task. A high degree of spatial overlap between sources of fMRI and ERP responses (in 9 or more of 15 subjects) was found specifically within temporoparietal areas associated with the task. Areas of non-overlap in fMRI and ERP sources were relatively small and inconsistent across subjects.
Comparison with existing method
The ERP and fMRI sources estimated with solely jICA overlapped in just 4 of 15 subjects, and strictly in the parietal cortex.
The study demonstrates that the new fMRI-ERP spatial overlap estimation method provides greater spatiotemporal detail of the cortical dynamics than solely jICA. As such, we propose that it is a superior method for the integration of fMRI and EEG to study brain function.
Heugel, N.; Liebenthal, E.; and Beardsley, Scott A., "Method for Spatial Overlap Estimation of Electroencephalography and Functional Magnetic Resonance Imaging Responses" (2019). Biomedical Engineering Faculty Research and Publications. 614.
ADA Accessible Version
Accepted version. Journal of Neuroscience Methods, Vol. 328 (December 2019): 108401. DOI. © 2019 Elsevier. Used with permission.