Régularisation du problème inverse MEG par IRM de diffusion. (MEG inverse problem regularization via diffusion MRI)
نویسنده
چکیده
Magnetoencephalography (MEG) is a functional non-invasive modality which provides information on the temporal succession of cognitive processes with an excellent time resolution. Unfortunately, spatial resolution is limited due to the illposed nature of the MEG inverse problem for estimating source currents from the electromagnetic measurement. Therefore, prior information on the source current is essential to reconstruct cerebral activity. Cortex parcellation into regions sharing functional features is a classical approach in neuroscience. This information constitutes a relevant spatial regularization for the MEG inverse problem. It has been demonstrated that the anatomical connectivity profile of a cortical area can serve as an indicator of its functional contribution to the overall system. So we propose a whole cortex parcellation method based on the anatomical connectivity mapped by diffusion MRI. In order to reduce source space, Brodmann atlas is used to pre-cluster the cortical surface. Inside each Brodmann area, the correlation matrix between connectivity profiles is clustered. The cortex parcellation is then updated testing the similarity of diffusion data on both sides of pre-parcellation boundaries. MEG inverse problem is constrained from this result. Two methods have been developed. The first one is based on the subdivision of source space regarding the parcellation. The cortical activity is obtained on a set of parcels and its analysis is simplified. Not to force sources to have exactly the same value inside a cortical area, we develop an alternative method. We introduce a new regularization term in the MEG inverse problem which constrains sources in a same region to have close values. Our methods are applied on simulated and real subjects. Clinical application is also performed on epileptic data. Each contribution takes part of a pipeline, each step of which is detailed to make our work reproducible.
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