The partial epileptic seizures are often considered to be caused by a wrong balance between inhibitory and excitatory interneuron connections within a focal brain area. These abnormal balances are likely to result in loss of functional connectivities between remote brain structures, while functional connectivities within the incriminated zone are enhanced. The identification of the epileptic networks underlying these hypersynchronies are expected to contribute to a better understanding of the brain mechanisms responsible for the development of the seizures. In this objective, threshold strategies are commonly applied, based on synchrony measurements computed from recordings of the electrophysiologic brain activity. However, such methods are reported to be prone to errors and false alarms. In this paper, we propose a hidden Markov chain modeling of the synchrony states with the aim to develop a reliable machine learning methods for epileptic network inference. The method is applied on a real Stereo-EEG recording, demonstrating consistent results with the clinical evaluations and with the current knowledge on temporal lobe epilepsy.
Subtraction ictal single photon emission computed tomography (SPECT) images, provided by filtered back-projection (FBP), may exhibit a confusing high level of noise. This study was aimed at assessing an optimized three-dimensional ordered subset expectation maximization (3D-OSEM) iterative reconstruction in this setting.On phantom images, parameters of 3D-OSEM reconstruction were selected as those providing the higher signal/noise ratio but a high enough spatial resolution, equivalent to that of conventional FBP reconstruction (full width at half-maximum = 11 mm). Thereafter, subtraction ictal ethylene cysteine dimer-SPECT coregistered to MRI and reconstructed with either FBP or 3D-OSEM were compared in 21 patients with well-characterized temporal epilepsy foci (subsequent successful surgical treatment).On subtraction images, the use of the selected 3D-OSEM reconstruction (five iterations, 16 subsets and a 9 mm Gauss filter) instead of FBP was associated with: (i) marked reductions in background activity (0.05 ± 0.09 vs. 0.25 ± 0.18 cps, P < 0.001) and in the size of temporal foci (10 ± 7 vs. 14 ± 11 cm, P = 0.01) and (ii) a trend toward higher accuracy in identifying the involved temporal lobes (86 vs. 76%).Localization of temporal epilepsy foci by subtraction ictal SPECT is likely to be enhanced by using 3D-OSEM rather than FBP reconstruction because of marked reductions in background activity and in the size of detected foci.
Les pieges de l’EEG en cas de suspicion de crise (s) epileptique (s) chez les sujets âges et en matiere de suivi d’une epilepsie ne se resument pas aux seuls aspects morphologiques habituellement decrits et dont l’analyse est proposee par les auteurs en les distinguant en vrais faux positifs typiques et atypiques. Les pieges les plus frequents en pratique courante tiennent aux conceptions que se fait le clinicien de l’interet de l’EEG, de ses connaissances cliniques et des questions qu’il se pose au cours de situations cliniques ou les pathologies et therapeutiques sont tres souvent intriquees ; a ces pieges que les auteurs denomment « electro-cliniques » s’ajoutent les pieges lies aux difficultes d’acces et de realisation de la technique EEG par un personnel qualifie, en situation d’urgence et sous forme d’enregistrement EEG-video.
Observability of electrical potentials from deep brain sources to surface EEG remains unclear and debated among the neuroscience community. This question is particularly crucial in the temporal lobe epilepsies investigations because they involve complex (mesial and/or lateral) epileptogenic networks (Maillard et al., 2004; Bartolomei et al, 2008). At present, when mesial structures are supposed to be epileptogenic only clinical indirect evidences are used to diagnose mesial temporal lobe (MTL) epilepsy. Based on this methodology and on drug resistance evidence, surgical treatment can be proposed without the need of invasive intracerebral investigation. Reported results of this surgery demonstrate an incomplete success (70-80%; McIntosh et al. 2012) which indicate that indirect evidences of the contribution of mesial sources are not sufficient. Seven patients undergoing pre-surgical evaluation of drug resistant epilepsy were selected from a prospective series of twenty eight patients in whom simultaneous depth and surface EEG recordings had been performed since 2009. Above these patients, three had right temporal lobe (TLE) epilepsy and four left TLE. Simultaneous SEEG-EEG signals were recorded using 128 channels placed on the same acquisition system that avoids the need to synchronize both signals. Intracerebral interictal spikes (IIS) were selected on depth EEG signals blinded to EEG signals. These IIS were triggered as temporally known (T0) brain sources due to their specific waveform and the high signal to noise ratio. Then, after IIS characterization and classification, EEG signals were automatically averaged according to the T0 markers. Averaged EEG signals were finally characterized (3D mapping, duration, amplitude and statistics) and clustered using hierarchical clustering method. Overview of the data collection and analysis process is presented in figure 1. In mean in our population, 9 depth EEG electrodes and 16 surface EEG electrodes were simultaneously used. 684±186 IIS were selected by patient for a total number of spikes in our population of 4787. According to the anatomical distribution of the IIS, 21 foci were defined and classified according to three categories: mesial (limbic structures plus collateral fissure; M, 9 foci), mesial and neocortical (M+NC, 5 foci) and neocortical part of the temporal lobe (NC, 7 foci). Comparison between SEEG spikes and averaged EEG spikes on the most activated electrode at T0 was presented in table 1. Concerning 3D Map amplitude, negative pole were always seen in the temporo-basal region for both M, M+NC and NC foci and positive pole were only observed for M+NC and NC foci. Using Walsh statistical test, 8 EEG channels in mean was presented averaged amplitude at t0 statistically different of the averaged background activity. Three different clusters were fund using the hierarchical clustering method on averaged EEG signals: 1) all patients included in the M foci class and 2) all patients included in the M+NC and NC foci class and 3) one patient with an atypical brain source. Observability of deep sources with surface EEG recordings is possible. Electrical sources from mesial temporal lobe cannot be considered as closed electrical field structures. The main problem to observe signals from these deep structures concern the signal to noise ratio. Indeed, spontaneous surface spikes originated from mesial structures cannot be seen without averaging. Hierarchical clustering method and 3D map amplitude of average EEG signals at t0 seems to indicate that M contributions was different to M+NC and NC contributions. So ICA method associated with a predetermined topography constraint should detect (without the need of simultaneous depth EEG) the mesial contribution in raw EEG signals.
L’objectif de ce travail etait d’etudier avec les potentiels evoques la question de la specialisation hemispherique au cours de la reconnaissance, en tenant compte de la tâche et surtout des differentes etapes qui jalonnent la transition perceptivo mnesique de la voie visuelle ventrale vers le lobe temporal medial. La premiere etude des potentiels evoques de surface chez des volontaires sains a identifie une composante negative culminant autour de 270 ms (appelee N270) evoquee a la fois par la categorisation et la reconnaissance des mots et des images abstraites, qui etait fonctionnellement et anatomiquement distincte de la N400. La N270 refletait un processus asymetrique en fonction du materiel, qui n’etait pas modulee par l’effet de repetition et dont les generateurs etaient spatialement plus localises (moins distribues) que ceux de la N400, avec une source majeure au niveau du cortex rhinal. Dans la seconde etude nous avons montre a partir d’enregistrements intra cerebraux que les cortex ento et peri-rhinal se differenciaient par leur dynamique temporelle, et par des proprietes fonctionnelles distinctes. L’activite du cortex peri-rhinal etait caracterisee par deux potentiels successifs N230 puis N400 modules par l’effet de repetition-suppression. L’activite du cortex ento-rhinal etait caracterisee par un potentiel N250, module par la nature du materiel et par un effet de repetition-amplification qui etait egalement observe dans l’hippocampe. Ces donnees confortent l’hypothese selon laquelle la N270 de scalp resulte de la sommation des potentiels N230 peri-rhinal et N250 ento-rhinal. Enfin, dans la troisieme etude, nous avons etudie la reorganisation des reseaux perceptivo-mnesiques induite par l’epilepsie temporale en a l���aide des potentiels evoques de reconnaissance de surface. Nos resultats suggerent que ce potentiel pourrait refleter la fonctionnalite residuelle des structures temporales internes ipsilaterales a l’epilepsie.
A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance.However, BCI systems that are applicable in real-time and outside clinical settings require high performance.Therefore, we have to improve the current methods for feature extraction.In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer 1 .Beamformers allow for easy incorporation of anatomical data and are applicable in real-time.A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm.We optimized a synchronous system for two untrained subjects and investigated two aspects.First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method 2 (FDM) head model including white and gray matter, CSF, scalp and skull tissue.Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed.On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here.On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan.Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found.Lastly, a real-time application was made.These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications.From the real-time application, the information transfer rate 3 (ITR) was also determined.An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%.We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model.Including fMRI spatial priors did not improve accuracy.Personal finetuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects.A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min.We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems.
