Section: New Results

Coupling functional and structural models

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

Participants : Anne-Charlotte Philippe, Maureen Clerc, Théodore Papadopoulo, Rachid Deriche.

In this work, a new approach is presented for 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. Towards this goal, a cortex parcellation is performed using structural information coming from diffusion MRI (dMRI), the only non-invasive modality allowing to have access to the structure of the WM tissues. Then, sources in the same diffusion parcel are constrained, in the MEG inverse problem, to have close magnitude values. Results of our method on MEG simulations are presented and favorably compared with classical source reconstruction methods.

This work has been published in [32] , and is part of A.C. Philippe's Ph.D thesis [12] .

Diffusion-Weighted Imaging tractography-based parcellation of the human cortex as regularization term for the MEG inverse problem

Participants : Anne-Charlotte Philippe, Maureen Clerc, Théodore Papadopoulo, Rachid Deriche.

The purpose of this work is to advocate the use of structural connectivity information to regularize the ill-posed MEG inverse problem. Diffusion MRI being the only non invasive modality allowing to have access to the connectivity profile of cortical sources, the proposed method called Diff-MNE consists in the introduction of a cortex parcellation based on diffusion data regularization term to the MEG inverse problem. Our method is tested on synthetic and real human brain data and compared to the classical minimum-norm method. Results show that a diffusion-based cortex parcellation as a regularization term for the MEG inversion process improves the source reconstruction. This proves the interest of merging diffusion MRI and MEG data.

This work is under submission to a Neuroimage and is part of A.C. Philippe's Ph.D thesis  [12]

Propagation of epileptic spikes revealed by diffusion-based constrained MEG source reconstruction

Participants : Anne-Charlotte Philippe, Maureen Clerc, Théodore Papadopoulo, Rachid Deriche.

In this work, we study the propagation of an epileptic spike (from single event data). As in the two previous sections, a cortex parcellation is performed using structural information coming from diffusion MRI Then, a MEG inverse problem is defined on a parcellated source space which imposes constant activity on each parcel. This inverse problem is applied separately for measurements obtained in a given time range. The most active parcels over the time range are located and their time course are displayed. This allowed the study of the propagation of an epileptic spike via those active parcels. Results on real data shows varying spatial propagations of an epileptic spike for the same subject.

This work has been published in [40] , and is part of A.C. Philippe's Ph.D thesis [12] .