Relating Brain Imaging Signals to Biophysical Models of Neuronal Circuits
Anders Dale
Massachusetts General Hospital
Harvard Medical School
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 formulated within 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 physiological variables; (2) the coupling between different physiological parameters, such as membrane potentials and synaptic currents, and hemodynamics and metabolism; and (3) a priori information about the spatial patterns and dynamics of electrical activity.
High-resolution structural MRI data are 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 multispectral structural MRI and Diffusion Tensor Imaging is used to obtain accurate forward models for EEG/MEG, 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.