|Le 26/02/2016 - 10H30 - Collegium Lorraine INP à Brabois|
|"Estimation de sources corticales: du montage Laplacien aux solution parcimonieuses"|
|(Thèse Gundars KORATS)|
Cortical Source Imaging plays an important role for understanding the functional and pathological brain mechanisms. It links the activation of certain cortical areas in response to a given cognitive stimulus, and allows one to study the co-activation of the underlying functional networks. Among the available acquisition modality, electroencephalographic measurements (EEG) have the great advantage of providing a time resolution of the order of the millisecond, at the scale of the dynamic of the studied process, while being a non-invasive technique often used in clinical routine. However the identification of the activated sources from EEG recordings remains an extremely difficult task because of the low spatial resolution this modality provides, of the strong filtering effect of the cranial bones and errors inherent to the used propagation model.
In this work different approaches for the estimation of cortical activity from surface EEG have been explored. The simplest cortical imaging methods are based only on the geometrical characteristics of the head. The computational load is greatly reduced and the used models are easy to implement. However, such approaches do not provide accurate information about the neural generators and on their spatiotemporal properties. To overcome such limitations, more sophisticated techniques can be used to build a realistic propagation model, and thus to reach better source reconstruction by its inversion. However, such inversion problem is severely ill-posed, and constraints have to be imposed to reduce the solution space.
We began by reconsidering the cortical source imaging problem by relying mostly on the observations provided by the EEG measurements, when no anatomical modeling is available. The developed methods are based on simple but universal considerations about the head geometry as well as the physiological propagation of the sources. Full-rank matrix operators are applied on the data, similarly as done by Surface Laplacian methods, and are based on the assumption that the surface can be explained by a mixture of linear radial basis functions produced by the underlying sources.
In the second part of the thesis, we relax the full-rank constraint by adopting a distributed dipole model constellating the cortical surface. The inversion is constrained by an hypothesis of sparsity, based on the physiological assumption that only a few cortical sources are active simultaneously Such hypothesis is particularly valid in the context of epileptic sources or in the case of cognitive tasks. To apply this regularization, we consider simultaneously both spatial and temporal domains. We propose two combined dictionaries of spatio-temporal atoms, the first based on a principal components analysis of the data, the second using a wavelet decomposition, more robust to noise and well suited to the non-stationary nature of these electrophysiological data.
All of the proposed methods have been tested on simulated data and compared to conventional approaches of the literature. The obtained performances are satisfactory and show good robustness to the addition of noise. We have also validated our approach on real epileptic data provided by neurologists of the University Hospital of Nancy affiliated to the project. The estimated locations are consistent with the epileptogenic zone identification obtained by intracerebral exploration based on Stereo-EEG measurements.