Connectivity-informed solution for spatio-temporal M/EEG source reconstruction

Ivana Kojčić (1, 2) , Théodore Papadopoulo (1, 2) , Rachid Deriche (1, 2) , Samuel Deslauriers-Gauthier (1, 2)

1 ATHENA - Computational Imaging of the Central Nervous System
2 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)

Recovering brain activity from M/EEG measurements is an ill–posed problem and prior constraints need to be introduced in order to obtain unique solution [1]. The majority of the methods use spatial and/or temporal constraints, without taking account of long–range connectivity. In this work, we propose a new connectivity–informed spatio–temporal approach to constrain the inverse problem using supplementary information coming from difusion MRI. We present results based on simulated brain activity obtained with realistic subject anatomy from Human Connectome Project [4] dataset.

Mots clés: EEG; MEG; diffusion MRI; source localization; inverse problems

Domaines: Imagerie médicale; Traitement du signal et de l'image [eess.SP]; Modélisation et simulation; Neurosciences

Ivana Kojčić, Théodore Papadopoulo, Rachid Deriche, Samuel Deslauriers-Gauthier. Connectivity-informed solution for spatio-temporal M/EEG source reconstruction. NeuroMod 2019 - First meeting of the NeuroMod Institute, Jul 2019, Fréjus, France. ⟨hal-02279612⟩