The goal of the research presented is to integrate information from different imaging
modalities in order to obtain estimates of brain activity with optimal spatial and temporal
resolution, and ultimately to relate noninvasive imaging signals to biophysical models of
neuronal circuits. The problem is phrased in a Bayesian framework, in which three
primary forms of information are encoded: 1) the forward models for the different
imaging signals, specifying the coupling of the signals with the physiological variables; 2)
the coupling between different physiological parameters, such as membrane potentials /
synaptic currents and hemodynamics / metabolism; and 3) a priori information about the
spatial patterns and dynamics of electrical activity.
High-resolution structural MRI data is used to obtain detailed models of the anatomy of
the cortex and other brain structures, providing a priori information about the possible
location and orientation of synaptic currents. A combination of multi-spectral structural
MRI and Diffusion Tensor Imaging is used to obtain accurate forward models for
EEG/MEG and optical imaging signals, and fMRI. Finally, the coupling between local
current source density and hemodynamic variables (blood flow, volume, and
oxygenation) is encoded in the form of a probabilistic spatiotemporal transfer function
estimated from simultaneous electrophysiological and hemodynamic recordings.
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