Performance Evaluation of a Novel, Bayesian Source Reconstruction Technique for MEG

Abstract

Event Abstract Back to Event Performance Evaluation of a Novel, Bayesian Source Reconstruction Technique for MEG Julia Owen1*, David Wipf1, Hagai Attias2, Kensuke Sekihara3 and Srikantan Nagarajan1 1 University of California, Biomagnetic Imaging Laboratory, United States 2 Golden Metalic Inc., United States 3 Tokyo Metropolitan University, Department of Systems Design and Engineering, Japan Magnetoencephalography (MEG) non-invasively detects brain activity from direct measurements of the magnetic field with an array of sensors. Determining the spatial distribution, orientation, and time courses of the underlying sources is an open inverse problem. We have developed a novel empirical Bayesian scheme that improves upon existing methods of source reconstruction in terms of reconstruction accuracy, robustness, and efficiency. The algorithm derived from this model, Champagne, is designed to estimate the number and location of a sparse set of flexible dipoles that adequately explain the observed sensor data. This method relies on having access to pre- and post-stimulus data, where the pre-stimulus data is thought to contain no stimulus-evoked brain activity. We have shown that Champagne reliably reconstructs a large number of correlated dipoles. We have extended our performance evaluations to include additional simulated data and real datasets. To evaluate Champagne’s performance of simulated data, with real brain noise, we have tested the algorithm with many configurations of sources to obtain aggregate performance metrics using signal detection theory. This was done for both deep sources, sources that are close together, and configurations where there are both deep and superficial sources. We compare the performance of our method to other widely-used methods such as beamforming and minimum-norm algorithms. We have also developed a statistical analysis routine that uses non-parametric permutation testing on real datasets. This method obtains a null distribution from actual brain data by switching the pre- and post-stimulus periods before averaging. With this technique, we can compare Champagne’s performance on real brain data to the performance of other popular methods listed above. Our performance evaluations on real and simulated data provide complimentary information. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Signal proccessing Citation: Owen J, Wipf D, Attias H, Sekihara K and Nagarajan S (2010). Performance Evaluation of a Novel, Bayesian Source Reconstruction Technique for MEG. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00108 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: 23 Mar 2010; Published Online: 23 Mar 2010. * Correspondence: Julia Owen, University of California, Biomagnetic Imaging Laboratory, San Francisco, United States, julia.owen@ucsf.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 Julia Owen David Wipf Hagai Attias Kensuke Sekihara Srikantan Nagarajan Google Julia Owen David Wipf Hagai Attias Kensuke Sekihara Srikantan Nagarajan Google Scholar Julia Owen David Wipf Hagai Attias Kensuke Sekihara Srikantan Nagarajan PubMed Julia Owen David Wipf Hagai Attias Kensuke Sekihara Srikantan Nagarajan 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.