Journal
BRAIN TOPOGRAPHY
Volume 32, Issue 4, Pages 704-719Publisher
SPRINGER
DOI: 10.1007/s10548-018-0691-2
Keywords
EEG; Source space activity; Dipole orientation; Visual evoked potentials; Epilepsy
Categories
Funding
- Swiss National Science Foundation [CRSII5-170873, 320030_159705, PP00P1_157420, 320030-169198]
- National Centre of Competence in Research (NCCR) SYNAPSY-The Synaptic Basis of Mental Diseases (NCCR Synapsy) [51NF40-158776]
- Foundation Gertrude Von Meissner
- European Union [660230]
- Swiss National Science Foundation (SNF) [320030_169198] Funding Source: Swiss National Science Foundation (SNF)
- Marie Curie Actions (MSCA) [660230] Funding Source: Marie Curie Actions (MSCA)
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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 singular-value 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 (similar to 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.
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