Willeke Staljanssens

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.

EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy

Electrical source imaging (ESI) from interictal scalp EEG is increasingly validated and used as a valuable tool in the presurgical evaluation of epilepsy as a reflection of the irritative zone. ESI of ictal scalp EEG to localize the seizure onset zone (SOZ) remains challenging. We investigated the value of an approach for ictal imaging using ESI and functional connectivity analysis (FC). Ictal scalp EEG from 111 seizures in 27 patients who had Engel class I outcome at least 1 year following resective surgery was analyzed. For every seizure, an artifact-free epoch close to the seizure onset was selected and ESI using LORETA was applied. In addition, the reconstructed sources underwent FC using the spectrum-weighted Adaptive Directed Transfer Function. This resulted in the estimation of the SOZ in two ways: (i) the source with maximal power after ESI, (ii) the source with the strongest outgoing connections after combined ESI and FC. Next, we calculated the distance between the estimated SOZ and the border of the resected zone (RZ) for both approaches and called this the localization error ((i) LEpow and (ii) LEconn respectively). By comparing LEpow and LEconn, we assessed the added value of FC. The source with maximal power after ESI was inside the RZ (LEpow = 0 mm) in 31% of the seizures and estimated within 10 mm from the border of the RZ (LEpow ≤ 10 mm) in 42%. Using ESI and FC, these numbers increased to 72% for LEconn = 0 mm and 94% for LEconn ≤ 10 mm. FC provided a significant added value to ESI alone (p < 0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy.

EEG beamforming to extract better features of motor imagery in a two-class real-time BCI

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. 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 (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 (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 fine- tuning 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.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.

A new insight into sentence comprehension: The impact of word associations in sentence processing as shown by invasive EEG recording

ABSTRACT The effect of word association on sentence processing is still a matter of debate. Some studies observe no effect while others found a dependency on sentence congruity or an independent effect. In an attempt to separate the effects of sentence congruity and word association in the spatio‐temporal domain, we jointly recorded scalp‐ and invasive‐EEG (iEEG). The latter provides highly localized spatial (unlike scalp‐EEG) and high temporal (unlike fMRI) resolutions. We recorded scalp‐ and iEEG in three patients with refractory epilepsy. The stimuli consisted of 280 sentences with crossed factors of sentence congruity and within sentence word‐association. We mapped semantic retrieval processes involved in sentence comprehension onto the left temporal cortex and both hippocampi, and showed for the first time that certain localized regions participate in the processing of word‐association in sentence context. Furthermore, simultaneous recording of scalp‐ and iEEG gave us a direct overview of signal change due to its propagation across the head tissues. HIGHLIGHTSThe brain mapping of sentence comprehension is still in progress.Invasive EEG provides the combination of high spatial and temporal resolutions.We mapped semantic processing of sentences on the left temporal cortex.Certain brain areas process word‐associations independent of sentence meaning.Scalp and invasive EEGs combined shows propagation of EEG signal through tissues.

Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25–35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.