Bayesian Meets Wavelets in spacetime – BMWs

Magneto and Electroencephalography (M/EEG) are brain imaging modalities used to measure the electrical activity of the brain. Thanks to their very high temporal resolutions, these are the only techniques that allow direct monitoring of neural activity over time. M/EEG brain imaging is used clinically for patients with epileptic seizures.

The EEG (electro-encephalography) and MEG (magneto-encephalography) source reconstruction problems are strongly ill-conditioned. Current approaches suffer from severe limitations, among which a poor spatial resolution, the absence of modeling of temporal dynamics, and the difficulty to fuse modalities. In addition, these only provide source estimates (or statistical maps from which activity is inferred), while a more complete probabilistic description of source activation would be desirable.
The main objective of BMWs is to develop and combine for the first time several state-of-the art techniques to estimate the fine spatio-temporal dynamics of brain networks with unprecedented precision and with quantification of uncertainties, and to implement the developed approach within MNE-Python, a common, open source, software platform.

Within BMWs, we will develop Bayesian frameworks for joint processing of EEG/MEG data within a common space-time formulation, and obtain not only quantitative spacetime source estimates but also activation probabilities and uncertainty measures for brain regions of interest. For that we will rely on state of the art models (Bernoulli-Gauss and variants) that have proven their efficiency in signal and image processing applications.
We will also combine wavelet expansions in time and space with sparse and group sparse priors to obtain better localized source estimates. While temporal wavelets are routinely used in EEG/MEG data analysis, the use of spatial wavelets, namely wavelets defined on the cortical surface (designed specifically for that purpose) is original and can be expected to improve the relevance of models.
Last, we will exploit combined surface (EEG and MEG) and intracerebral (SEEG) measurements. The latter will provide a ground truth of unprecedented precision, that will be used to compare prior models and if possible to constrain the EEG/MEG based reconstruction and therefore obtain improved source estimates. For validation we will benefit of unique recordings of MEG, EEG and SEEG performed in patients in presurgical evaluation of epilepsy.

The outcomes of the project will be made available to the neuroimaging community thanks to their integration in the open source software platform MNE-Python, and a challenge on space-time source reconstruction based upon joint EEG/MEG/SEEG data obtained within the project will be set up.