title: A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem.
abstract: In this paper, we present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution, at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA).
authors.affiliation: Unité de Psychophysiologie Cognitive, Hôpital de la Salpêtrière, CNRS URA 654, LENA-Université Paris VI, France. lenasba@ext.jussieu.fr
authors.forename: S
authors.initials: S
authors.lastname: Baillet
authors.forename: L
authors.initials: L
authors.lastname: Garnero
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping; D003071:Cognition / Q000502:physiology; D004566:Electrodes; D004569:Electroencephalography; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography; D008959:Models, Neurological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D013499:Surface Properties