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Contributions to sparse source localization for MEG/EEG brain imaging

Abstract : Understanding the full complexity of the brain has been a challenging research project for decades, yet there are many mysteries that remain unsolved. Being able to model how the brain represents, analyzes, processes, and transforms information of millions of different tasks in a record time is primordial for both cognitive and clinical studies. These tasks can go from language, perception, memory, attention, emotion, to reasoning and creativity. Magnetoencephalography (MEG) and Electroencephalography (EEG) allow us to non-invasively measure the brain activity with high temporal and good spatial resolution using sensors positioned all over the head, in order to be analyzed. For a given magnetic-electric field outside the head, there are an infinite number of electrical current source distributed inside of the brain that could have created it. This means that the M/EEG inverse problem is ill-posed, having many solutions to the single problem. This constrains us to make assumptions about how the brain might work. This thesis investigated the assumption of having sparse source estimate, i.e. only few sources are activated for each specific task. This is modeled as a penalized regression with a spatio-temporal regularization term. The aim of this thesis was to use outstanding methodologies from machine learning field to solve the three steps of the M/EEG inverse problem. The first step is to model the problem in the time frequency domain with a multi-scale dictionary to take into account the mixture of non-stationary brain sources, i.e. brain regions share information resulting in brain activity alternating from a source to another. This is done by formulating the problem as a penalized regression with a data fit term and a spatio-temporal regularization term, which has an extra hyperparameter. This hyperparameter is mostly tuned by hand, which makes the analysis of source brain activity not objective, but also hard to generalize on big studies. The second contribution is to automatically estimate this hyperparameter under some conditions, which increase the objectivity of the solvers. However, these state-of-the-art solvers have a main problem that their source localization solver gives one solution, and does not allow for any uncertainty quantification. We investigated this question by studying new techniques as done by a Bayesian community involving Markov Chain Monte Carlo (MCMC) methods. It allows us to obtain uncertainty maps over source localization estimation, which is primordial for a clinical study, e.g. epileptic activity. The last main contribution is to have a complete comparison of state-of-the-art solvers on phantom dataset. Phantom is an artificial object that mimics the brain activity based on theoretical description and produces realistic data corresponding to complex spatio-temporal current sources. In other words, all solvers have been tested on an almost real dataset with a known ground truth for a real validation.
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Submitted on : Friday, October 29, 2021 - 2:36:12 PM
Last modification on : Wednesday, November 3, 2021 - 3:59:39 AM
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  • HAL Id : tel-03409068, version 1


yousra Bekhti. Contributions to sparse source localization for MEG/EEG brain imaging. Medical Imaging. Télécom ParisTech, 2018. English. ⟨NNT : 2018ENST0017⟩. ⟨tel-03409068⟩



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