Symmetrical EEG/fMRI Fusion with Spatially Adaptive Priors
Speaker: Martin Luessi , Northwestern University
Date: February 24 2011
Time: 11:30AM to 12:30PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, x38005, polina@mit.edu
Relevant URL: 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 the
measurements 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 fully
automatic fusion algorithm.
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