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Connectivity-informed M/EEG inverse problem

Abstract : Information between brain regions is transferred through white matter fibers with delays that are measurable with magnetoencephalography and electroencephalography (M/EEG) due to its millisecond temporal resolution. Therefore, a useful representation of the brain is that of a graph where its nodes are the cortical areas and edges are the physical connections between them: either local (between adjacent vertices on the cortical mesh) or non-local (long-range white matter fibers). These long-range anatomical connections can be obtained by diffusion MRI (dMRI) tractography, thus giving us an insight on interaction delays of the macroscopic brain network. A fundamental role in shaping the rich temporal structure of functional connectivity is played by the structural connectivity [6] that places constraints on which functional interactions occur in the network. In the context of regularizing the dynamics of M/EEG and recovering electrical activity of the brain from M/EEG measurements, traditional linear inverse methods deploy different constraints such as minimum norm, maximum-smoothness in space and/or time along the cortical surface. However, they usually do not take into account the structural connectivity and very few include delays supported by dMRI as a prior information [1]. The goal of this work is to include these delays into the MEG source reconstruction process by imposing temporal smoothness in structurally connected sources, with the corresponding delays. We propose to encapsulate delays provided by dMRI in a graph representation and show their potential in improving the MEG source reconstruction when compared to a state-of-the-art approach [4].
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Submitted on : Monday, November 2, 2020 - 11:44:39 PM
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Ivana Kojčić, Théodore Papadopoulo, Rachid Deriche, Samuel Deslauriers-Gauthier. Connectivity-informed M/EEG inverse problem. GRAIL 2020 - MICCAI Workshop on GRaphs in biomedicAl Image anaLysis, Oct 2020, Lima, Peru. ⟨hal-02986430⟩

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