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Using diffusion MR information to reconstruct networks of brain activations from MEG and EEG measurements

Abstract : Understanding how brain regions interact to perform a given task is a very challenging task. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive functional imaging modalities used to record brain activity with high temporal resolution. As estimating brain activity from these measurements is an ill-posed problem, We thus must set a prior on the sources to obtain a unique solution. It has been shown in previous studies that structural homogeneity of brain regions could reflect their functional homogeneity. One of the main goals of this work is to use this structural information to define priors to constrain more anatomically the MEG/EEG source reconstruction problem. This structural information is obtained using diffusion magnetic resonance imaging (dMRI), which is, as of today, the unique non-invasive structural imaging modality that provides an insight on the structural organization of white matter. This makes its use to constrain the EEG/MEG inverse problem justified. In our work, dMRI information is used to reconstruct brain activation in two ways: 1- In a spatial method which uses brain parcels to constrain the sources activity. These parcels are obtained by our whole brain parcellation algorithm which computes cortical regions with the most structural homogeneity with respect to a similarity measure. 2- In a spatio-temporal method that makes use of the anatomical connections computed from dMRI to constrain the sources' dynamics. These different methods are validated using synthetic and real data.
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Contributor : Brahim Belaoucha Connect in order to contact the contributor
Submitted on : Thursday, June 8, 2017 - 12:54:50 PM
Last modification on : Monday, October 12, 2020 - 10:28:54 AM


Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : tel-01534876, version 1



Brahim Belaoucha. Using diffusion MR information to reconstruct networks of brain activations from MEG and EEG measurements. Computer Science [cs]. Université Côte d'Azur, 2017. English. ⟨tel-01534876v1⟩



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