Localization of neural activity from neuromagnetic data using varying-support sources

Magnetoencephalography (MEG) and Electroencephalography (EEG) measure in a non-invasive way the magnetic field and the electrical potential, respectively, induced by cerebral activity. These instruments have an outstanding temporal resolution and the recorded data could provide interesting insights of the dynamics of neural currents. In order to get reliable information on the unknown neural currents from the data, we need to solve a ill-posed inverse problem: the operator involved in the formulation of the model linking the neural activity and the measured data is such that the solution of the inversion problem is not unique and does not depend continuously on the data. Moreover, the choice of the model for the source deeply affects the representation of the solution: a distributed model for neural currents can encompass complex, spread brain activity but could be not accurate in the representation of focal brain activity, while the representation with dipolar sources could not represent properly the activity generated by patches of cortex. In this work we propose a modified version of the Particle Filter we employed so far for MEG data analysis: in this implementation the support of the sources is not more fixed to be a dipole but can change back and forth to be distributed, adapting itself among the time samples to the best configuration. We test the method with ad hoc synthetic data.