title: Source cancellation profiles of electroencephalography and magnetoencephalography.
abstract: Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.
authors.affiliation: Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 635 Charles E Young Drive South, Suite 225, Los Angeles, CA 90095, USA. andrei.irimia@loni.ucla.edu
authors.forename: Andrei
authors.initials: A
authors.lastname: Irimia
authors.forename: John Darrell
authors.initials: JD
authors.lastname: Van Horn
authors.forename: Eric
authors.initials: E
authors.lastname: Halgren
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology; D002555:Cerebrospinal Fluid / Q000502:physiology; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006257:Head / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D008953:Models, Anatomic; D012535:Scalp / Q000033:anatomy & histology; D059629:Signal-To-Noise Ratio; D012886:Skull / Q000033:anatomy & histology