MEG and EEG Source Estimation: from Surface Mapping to Multimodal Imaging

Abstract

Event Abstract Back to Event MEG and EEG Source Estimation: from Surface Mapping to Multimodal Imaging Matti Hamalainen1* 1 Massachusetts General Hospital, United States Estimation of the sources underlying the MEG/EEG signals is challenging because of the ill-posed electromagnetic inverse problem: there are current distributions invisible to either MEG or EEG or both and the estimates are sensitive to noise in the data. Therefore, early MEG and EEG studies were solely relying on analysis of the measured signals and their spatial distribution. The introduction of the current dipole model was the first significant advance towards understanding the actual sources of both MEG and EEG. When anatomical MRI data became routinely available during the 1980s, it became possible to visualize the source locations in the anatomical context. The use of boundary-element and finite-element approaches in forward modeling also became feasible with MRI segmentation algorithms capable of delineating the boundaries of different tissue compartments. Since the principal sources of MEG and EEG signals are on the cortex and oriented perpendicular to the cortical mantle, a reconstruction of the individual cortical geometry from MRI can be used as an anatomical constraint in distributed source estimates. The standard l2-norm regularizer results in a widespread current estimate but has the benefit of a closed-form solution. It is well known that an l1-norm regularizer favors sparsity but the source waveforms at a given location usually exhibit unrealistic jerky behavior. We have recently proposed to mitigate this problem by employing an l1 norm over space and an l2 norm among suitable temporal basis functions. MEG/EEG and fMRI can be used in combination to obtain activity estimates with higher temporal and spatial resolution than provided by one type of data alone. One popular method is to employ fMRI to guide cortically constrained minimum-norm solutions. More sophisticated approaches like our fMRI-Informed Regional EEG/MEG source Estimation (FIRE) can be employed to better account for possible discrepancies between the activity detected by MEG/EEG and fMRI. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Oral Presentation Topic: MEG Modeling Citation: Hamalainen M (2010). MEG and EEG Source Estimation: from Surface Mapping to Multimodal Imaging. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00016 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Mar 2010; Published Online: 19 Mar 2010. * Correspondence: Matti Hamalainen, Massachusetts General Hospital, Boston, United States, msh@nmr.mgh.harvard.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Matti Hamalainen Google Matti Hamalainen Google Scholar Matti Hamalainen PubMed Matti Hamalainen Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.