Analyzing brain oscillations with EEG/fMRI fusion

Abstract

Event Abstract Back to Event Analyzing brain oscillations with EEG/fMRI fusion P. Valdés-Sosa1* 1 Cuban Neuroscience Center, Cuba This presentation reviews progress and challenges in analyzing brain oscillations with EEG/fMRI fusion, the principled combination of information from both recording modalities to achieve images with simultaneously high spatial and temporal resolution. Fusion methods may be either data driven or model driven. At a data driven level, mapping the covariation between EEG alpha power and BOLD, or between primary current density and net metabolic demand, show positive relations between frontal cortices and thalamus and negative ones for the occipital region. A simple heuristic model argues that these findings may be due to changes in synchronization of EEG generators. Model driven approaches to fusion repeatedly generate EEG/fMRI simulations and maximize their likelihood given the data by adjusting the parameters of neural mass oscillators coupled at each voxel to the balloon model (modeled by Random Differential equations RDE). However, the estimation procedure is critical in order to preserves the dynamical properties of the continuous time models. An approach with optimal properties is 1) Integration of RDE by local linearization (assuming the Jacobian of the system locally constant in time; 2) Using innovations obtained by Kalman filtering to evaluate the likelihood. This LL-innovation method has been already applied to models with a modest number of voxels. More realistically sized models involve integrating large sets of RDE and therefore depend critically on algorithmic advances. Simulations of hundreds of voxels can be based on Krylov subspace approximations to the required matrix exponential of the Jacobian. Even larger models of tens of thousands of voxels may be addressed by decoupling connectivity/delay parameters to form a RDE-delay algebraic system which my may be solved semi-analytically. Both types of methods produce simulations in good concordance with observations and the heuristic model. A remaining obstacle is the filtering step in these large models which we discuss may be tackled either by ensemble or variational approach which may be extended to Bayesian estimation. Conference: 10th International Conference on Cognitive Neuroscience, Bodrum, Türkiye, 1 Sep - 5 Sep, 2008. Presentation Type: Oral Presentation Topic: Symposium 14: Multimodal Studies of Oscillations Citation: Valdés-Sosa P (2008). Analyzing brain oscillations with EEG/fMRI fusion. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.070 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: 01 Dec 2008; Published Online: 01 Dec 2008. * Correspondence: P. Valdés-Sosa, Cuban Neuroscience Center, Havana, Cuba, pedro.valdes.sosa@gmail.com 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 P. Valdés-Sosa Google P. Valdés-Sosa Google Scholar P. Valdés-Sosa PubMed P. Valdés-Sosa 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.