Cortex parcellation via diffusion data as prior knowledge for the MEG inverse problem

In this paper, we present a new approach to the recovery of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) imaging. This method consists in introducing prior knowledge regarding the anatomical connectivity in the brain to this ill-posed inverse problem. Thus, we perform cortex parcellation via structural information coming from diffusion MRI (dMRI), the only non-invasive modality allowing to have access to the structure of the WM tissues. Then, we constrain, in the MEG inverse problem, sources in the same diffusion parcel to have close magnitude values. Results of our method on MEG simulations are presented and favorably compared with classical source reconstruction methods.