Extracting the Invisible: Mesial Temporal Source Detection in Simultaneous EEG and SEEG Recordings.

Epileptic source detection relies mainly on visual expertise of scalp EEG signals, but it is recognised that epileptic discharges can escape to this expertise due to a deep localization of the brain sources that induce a very low, even negative, signal to noise ratio. In this methodological study, we aimed to investigate the feasibility of extracting deep mesial temporal sources that were invisible in scalp EEG signals using blind source separation (BSS) methods (infomax ICA, extended infomax ICA, and JADE) combined with a statistical measure (kurtosis). We estimated the effect of different methodological and physiological parameters that could alter or improve the extraction. Using nine well-defined mesial epileptic networks (1949 spikes) obtained from seven patients and simultaneous EEG-SEEG recordings, the first independent component extracted from the scalp EEG signals was validated in mean from 46 to 80% according to the different parameters. The three BSS methods equally performed (no significant difference) and no influence of the number of scalp electrodes used was found. At the opposite, the number and amplitude of spikes included in the averaging before the extraction modified the performance. Anyway, despite their invisibility in scalp EEG signals, this study demonstrates that deep source extraction is feasible under certain conditions and with the use of common signal analysis toolboxes. This finding confirms the crucial need to continue the signal analysis of scalp EEG recordings which contains subcortical signals that escape to expert visual analysis but could be found by signal processing.

Stereo-electroencephalography identifies N2 sleep and spindles in human hippocampus.

OBJECTIVES: To describe the hippocampal stereo-electroencephalogram during sleep according to sleep stages (including N2 sleep) and cycles, together with the hippocampal spindles. METHODS: All patients with drug-resistant focal epilepsy undergoing intra-hippocampal implantation between August 2012 and June 2013 at Nancy University Hospital were screened. Six patients with explored hippocampus devoid of pathological features were analyzed. During one night, we identified continuous periods of successive N2, N3 and REM sleep for two full cycles. We performed a spectral analysis of the hippocampal signal for each labeled sleep period. RESULTS: N2, N3 and REM sleeps were individualized according to their spectral powers, for each frequency band and sleep cycle. Hippocampal spindles showed dynamic intrinsic properties, the 11.5-16Hz frequency band being mainly dominant, whereas the 9-11.5Hz frequency band heightening during the beginning and the end of the transient. For N3 and REM sleep stages, the power of the hippocampal signal was significantly decreased between the first and the second sleep cycle. CONCLUSION: Distinct N2 sleep, fast spindles and homeostatic profile are all common properties shared by hippocampus and cortex during sleep. SIGNIFICANCE: The close functional link between hippocampus and cortex may have various sleep-related substrates.

In-vivo measurements of human brain tissue conductivity using focal electrical current injection through intracerebral multicontact electrodes.

In-vivo measurements of human brain tissue conductivity at body temperature were conducted using focal electrical currents injected through intracerebral multicontact electrodes. A total of 1,421 measurements in 15 epileptic patients (age: 28 ± 10) using a radiofrequency generator (50 kHz current injection) were analyzed. Each contact pair was classified as being from healthy (gray matter, n = 696; white matter, n = 530) or pathological (epileptogenic zone, n = 195) tissue using neuroimaging analysis of the local tissue environment and intracerebral EEG recordings. Brain tissue conductivities were obtained using numerical simulations based on conductivity estimates that accounted for the current flow in the local brain volume around the contact pairs (a cube with a side length of 13 mm). Conductivity values were 0.26 S/m for gray matter and 0.17 S/m for white matter. Healthy gray and white matter had statistically different median impedances (P < 0.0001). White matter conductivity was found to be homogeneous as normality tests did not find evidence of multiple subgroups. Gray matter had lower conductivity in healthy tissue than in the epileptogenic zone (0.26 vs. 0.29 S/m; P = 0.012), even when the epileptogenic zone was not visible in the magnetic resonance image (MRI) (P = 0.005). The present in-vivo conductivity values could serve to create more accurate volume conduction models and could help to refine the identification of relevant intracerebral contacts, especially when located within the epileptogenic zone of an MRI-invisible lesion. Hum Brain Mapp 38:974-986, 2017. © 2016 Wiley Periodicals, Inc.

Catching the invisible: mesial temporal source contribution to simultaneous EEG and SEEG recordings.

Mesial temporal sources are presumed to escape detection in scalp electroencephalographic recordings. This is attributed to the deep localization and infolded geometry of mesial temporal structures that leads to a cancellation of electrical potentials, and to the blurring effect of the superimposed neocortical background activity. In this study, we analyzed simultaneous scalp and intracerebral electroencephalographic recordings to delineate the contribution of mesial temporal sources to scalp electroencephalogram. Interictal intracerebral spike networks were classified in three distinct categories: solely mesial, mesial as well as neocortical, and solely neocortical. The highest and earliest intracerebral spikes generated by the leader source of each network were marked and the corresponding simultaneous intracerebral and scalp electroencephalograms were averaged and then characterized both in terms of amplitude and spatial distribution. In seven drug-resistant epileptic patients, 21 interictal intracerebral networks were identified: nine mesial, five mesial plus neocortical and seven neocortical. Averaged scalp spikes arising respectively from mesial, mesial plus neocortical and neocortical networks had a 7.1 (n = 1,949), 36.1 (n = 628) and 10 (n = 1,471) µV average amplitude. Their scalp electroencephalogram electrical field presented a negativity in the ipsilateral anterior and basal temporal electrodes in all networks and a significant positivity in the fronto-centro-parietal electrodes solely in the mesial plus neocortical and neocortical networks. Topographic consistency test proved the consistency of these different scalp electroencephalogram maps and hierarchical clustering clearly differentiated them. In our study, we have thus shown for the first time that mesial temporal sources (1) cannot be spontaneously visible (mean signal-to-noise ratio -2.1 dB) on the scalp at the single trial level and (2) contribute to scalp electroencephalogram despite their curved geometry and deep localization.

Propagation of electrical field in the brain using electrical intra-cerebral stimulations.

For drug resistant partial epilepsy, intra-cerebral electrical stimulation (Deep Brain Stimulation--DBS) constitutes one of the means to locate epileptic volume. This paper investigates, in the framework of source localization problem, the propagation of the electrical field and current density distribution induced in the brain during in vivo electrical stimulation. There are three objectives in this work: to validate the propagation model for different large frequencies, to highlight the problem of the close field with the DBS source and to show the influence of the proximity to the skull on the results. We compared the Stereo-EEG data, recorded during DBS, with those obtained using: (i) the simplest model, the dipolar model in an infinite homogeneous medium, (ii) a more realistic approach with a numerical method, the Boundary Element Method (BEM). Studies on ten subjects with 234 stimulations showed that the dipole model could be used in the brain far from the skull in direction of dipole moment but that BEM was more appropriate close to the skull.

3D handheld laser scanner based approach for automatic identification and localization of EEG sensors.

This paper describes and assesses for the first time the use of a handheld 3D laser scanner for scalp EEG sensor localization and co-registration with magnetic resonance images. Study on five subjects showed that the scanner had an equivalent accuracy, a better repeatability, and was faster than the reference electromagnetic digitizer. According to electrical source imaging, somatosensory evoked potentials experiments validated its ability to give precise sensor localization. With our automatic labeling method, the data provided by the scanner could be directly introduced in the source localization studies.