Dynamical MEG source modeling with multi-target Bayesian filtering.
We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multi-target Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to automatically estimate the source structure in most cases more accurately than either traditional multi-dipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real somatosensory evoked fields the method reconstructs a source constellation comparable to that obtained by multi-dipole modeling.
CNR-INFM LAMIA, Via Dodecaneso 35, Genoa, Italy. sorrentino@fisica.unige.it
Alberto
A
Sorrentino
Lauri
L
Parkkonen
Annalisa
A
Pascarella
Cristina
C
Campi
Michele
M
Piana
D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D006801:Humans; D016013:Likelihood Functions; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D009778:Occipital Lobe / Q000502:physiology; D010296:Parietal Lobe / Q000502:physiology; D011336:Probability; D013702:Temporal Lobe / Q000502:physiology