Estimating EEG source dipole orientation based on singular-value decomposition for connectivity analysis

Article Article scientifique Article Estimating EEG source dipole orientation based on singular-value decomposition for connectivity analysis eng 2019 author Rubega Maria 871424 author Carboni Margherita 868568 author Seeber Martin 871287 author Pascucci D. author Tourbier S. author Toscano Giacomo 822799 author Van Mierlo Pieter 848644 author Hagmann P. author Plomp G. author Vulliemoz Serge 0000-0002-1877-8625 712268 author Michel Christoph 91629 704-719 10.1007/s10548-018-0691-2 30511174 0896-0267 http://link.springer.com/10.1007/s10548-018-0691-2 In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singularvalue decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth. EEG Source space activity Dipole orientation Visual evoked potentials Epilepsy Dewey 616.8 Swiss National Science Foundation Exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging : application to physiological and epileptic networks 170873 CH Programmes Swiss National Science Foundation Temporal dynamics of whole-brain neuronal networks 159705 CH Project funding Swiss National Science Foundation Dynamic Networks of Perception, Cognition and Action 157420 CH Careers Swiss National Science Foundation Local and global brain connectivity alterations in focal epilepsy: Effects of disease, surgery and neuromodulation 169198 CH Project funding Swiss National Science Foundation NCCR SYNAPSY: The synaptic bases of mental diseases (phase II) 158776 CH European Commission Functional brain networks in epilepsy 660230 EPICONNECT EU H2020 53 Département de neurosciences fondamentales 21A 61 Département des neurosciences cliniques 226 false 1279 Epilepsie et réseaux cérébraux 1002 1284 NCCR Synapsy 118 Organisation et plasticité des réseaux neuronaux cérébraux 148 Brain Topography 32 4 19c56b7f-711b-45af-b95b-00633468464d Article (Published version) Restricted Rubega2019_Article_EstimatingEEGSourceDipoleOrien.pdf 4678289 application/pdf 3cebdab3-5216-4273-bfe1-f817605342bd Article (Accepted version) Public Rubega_et_al_BrTop_BTOP-D-18_00059R3.pdf 4851307 application/pdf Restricted 2019-12-14 openAccess Faculté de médecine / Section de médecine fondamentale / Département de neurosciences fondamentales Faculté de médecine / Section de médecine clinique / Département des neurosciences cliniques

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Estimating EEG source dipole orientation based on singular-value decomposition for connectivity analysis	Brain Topography			2019	481	274

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