Andrei Barborica, Ioana Mindruta, Víctor J. López-Madrona, F-Xavier Alario, Agnès Trébuchon, Cristian Donos, Irina Oane, Constantin Pistol, Felicia Mihai, and Christian G. Bénar.
2023. Frontiers in Human Neuroscience 17: 1154038 — @HAL
Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g. memory). A full validation is possible only when performing simultaneous scalp-SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously-recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. Investigating whether the two I n r e v i e w 2 types of signals carry complementary information that would improve the machine-learning classifier performance, part of the multivariate analysis, revealed that the results are driven by the modality with best separate performance, namely SEEG.