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.

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.

This paper deals with the co-registration of an MRI scan with EEG sensors. We set out to evaluate the effectiveness of a 3D handheld laser scanner, a device that is not widely used for co-registration, applying a semi-automatic procedure that also labels EEG sensors. The scanner acquired the sensors' positions and the face shape, and the scalp mesh was obtained from the MRI scan. A pre-alignment step, using the position of three fiducial landmarks, provided an initial value for co-registration, and the sensors were automatically labeled. Co-registration was then performed using an iterative closest point algorithm applied to the face shape. The procedure was conducted on five subjects with two scans of EEG sensors and one MRI scan each. The mean time for the digitization of the 64 sensors and three landmarks was 53 s. The average scanning time for the face shape was 2 min 6 s for an average number of 5,263 points. The mean residual error of the sensors co-registration was 2.11 mm. These results suggest that the laser scanner associated with an efficient co-registration and sensor labeling algorithm is sufficiently accurate, fast and user-friendly for longitudinal and retrospective brain sources imaging studies.

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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.

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