Fecha: jueves día 16 de Junio de 2011
Lugar: Sala de Conferencias del CITIC (detrás de la Escuela Técnica Superior de las Ingenierías Informatica y Telecomunicacion, ETSIIT)
Horario: 12.00am
Descripción: Conferencia impartida por Martin Luessi, que visita la Unversidad de Granada financiado por CEI BioTIC GENIL, programa GENIL-Young Talented Researchers.
Abstract: In recent years, functional magnetic resonance imaging (fMRI) has become a prominent neuroimaging method, enabling accurate localization of neuronal activity, typically within millimeters. One drawback of fMRI is its limited temporal resolution due to the indirect way themeasurements are related to neuronal activity and due to the repetition time of the MRI scanner. Other functional neuroimaging methods, namely electroencephalography (EEG) and magnetoroencephalography (MEG), provide a direct measure of electrical activity in the brain and can attain a much higher temporal resolution. Unfortunately, the spatial resolution of these modalities is considered low as M/EEG source localization is an ill-posed inverse problem. As of today, no functional neuroimaging method exists that has the spatial resolution of fMRI and the temporal resolution of M/EEG.
In this talk we present a method which improves the spatio-temporal resolution through fusion of EEG and fMRI. The method combines EEG and fMRI by means of a common generative model. We adopt the symmetrical model structure from work by Daunizeau et al. and improve the model using more sophisticated signal priors. More specifically, we use a total variation (TV) prior to model the spatial distribution of the cortical current responses and hemodynamic response functions and utilize spatially adaptive temporal priors to model their temporal shapes. The spatial adaptivity of the prior model allows for adaptation to the local characteristics of the estimated responses and leads to an improved estimation of the cortical current distribution and the hemodynamic response functions. We utilize a Bayesian formulation with a variational Bayesian framework and obtain a full automatic fusion algorithm.
Short bio of Martin Luessi: Martin Luessi received his F.H. (undergraduate) degree from the Hochschule für Technik Rapperswil (HSR) in Switzerland in 2006 and his M.S. degree in electrical engineering from Northwestern University, Evanston IL, USA, in 2007, where he is currently pursuing a Ph.D. degree with the department of Electrical Engineering Computer Science. His primary research is focused on signal processing methods for functional neuroimaging. Specific examples are M/EEG source localization, EEG/fMRI fusion, and methods for inferring connectivity from neuroimaging data.
Other research interests include, Bayesian modeling and inference, compressive sensing, brain-computer interfaces, statistical pattern recognition, computer vision, and image processing. After completing his Ph.D., Martin will start a position as a postdoctoral research fellow at the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH) / Harvard Medical School, where he will perform research on connectivity methods for MEG.
Organiza: Depto. Ciencias de la Computacion e I.A. E.T.S. de Ingenierias Informatica y Telecomunicacion
Más información: Prof. Rafael Molina. Depto. Ciencias de la Computacion e I.A.
E.T.S. de Ingenierias Informatica y Telecomunicacion
Universidad de Granada. 18071 Granada SPAIN.
Phone: +34-958-240594. Fax: +34-958-243317
e-mail: rms@decsai.ugr.es URL: http://decsai.ugr.es/~rms