Anca Nica

Risk factors of postictal generalized EEG suppression in generalized convulsive seizures

Objective: To identify the clinical determinants of occurrence of postictal generalized EEG suppression (PGES) after generalized convulsive seizures (GCS). Methods: We reviewed the video-EEG recordings of 417 patients included in the REPO2MSE study, a multicenter prospective cohort study of patients with drug-resistant focal epilepsy. According to ictal semiology, we classified GCS into 3 types: tonic-clonic GCS with bilateral and symmetric tonic arm extension (type 1), clonic GCS without tonic arm extension or flexion (type 2), and GCS with unilateral or asymmetric tonic arm extension or flexion (type 3). Association between PGES and person-specific or seizure-specific variables was analyzed after correction for individual effects and the varying number of seizures. Results: A total of 99 GCS in 69 patients were included. Occurrence of PGES was independently associated with GCS type (p < 0.001) and lack of early administration of oxygen (p < 0.001). Odds ratio (OR) for GCS type 1 in comparison with GCS type 2 was 66.0 (95% confidence interval [CI 5.4–801.6]). In GCS type 1, risk of PGES was significantly increased when the seizure occurred during sleep (OR 5.0, 95% CI 1.2–20.9) and when oxygen was not administered early (OR 13.4, 95% CI 3.2–55.9). Conclusion: The risk of PGES dramatically varied as a function of GCS semiologic characteristics. Whatever the type of GCS, occurrence of PGES was prevented by early administration of oxygen.

Identification of Interictal Epileptic Networks from Dense-EEG.

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r2, h2, PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data

Electric Source Imaging (ESI) and Magnetic Source Imaging (MSI) of EEG and MEG signals are widely used to determine the origin of interictal epileptic discharges during the pre-surgical evaluation of patients with epilepsy. Epileptic discharges are detectable on EEG/MEG scalp recordings only when associated with a spatially extended cortical generator of several square centimeters, therefore it is essential to assess the ability of source localization methods to recover such spatial extent. In this study we evaluated two source localization methods that have been developed for localizing spatially extended sources using EEG/MEG data: coherent Maximum Entropy on the Mean (cMEM) and 4th order Extended Source Multiple Signal Classification (4-ExSo-MUSIC). In order to propose a fair comparison of the performances of the two methods in MEG versus EEG, this study considered realistic simulations of simultaneous EEG/MEG acquisitions taking into account an equivalent number of channels in EEG (257 electrodes) and MEG (275 sensors), involving a biophysical computational neural mass model of neuronal discharges and realistically shaped head models. cMEM and 4-ExSo-MUSIC were evaluated for their sensitivity to localize complex patterns of epileptic discharges which includes (a) different locations and spatial extents of multiple synchronous sources, and (b) propagation patterns exhibited by epileptic discharges. Performance of the source localization methods was assessed using a detection accuracy index (Area Under receiver operating characteristic Curve, AUC) and a Spatial Dispersion (SD) metric. Finally, we also presented two examples illustrating the performance of cMEM and 4-ExSo-MUSIC on clinical data recorded using high resolution EEG and MEG. When simulating single sources at different locations, both 4-ExSo-MUSIC and cMEM exhibited excellent performance (median AUC significantly larger than 0.8 for EEG and MEG), whereas, only for EEG, 4-ExSo-MUSIC showed significantly larger AUC values than cMEM. On the other hand, cMEM showed significantly lower SD values than 4-ExSo-MUSIC for both EEG and MEG. When assessing the impact of the source spatial extent, both methods provided consistent and reliable detection accuracy for a wide range of source spatial extents (source sizes ranging from 3 to 20cm2 for MEG and 3 to 30cm2 for EEG). For both EEG and MEG, 4-ExSo-MUSIC localized single source of large signal-to-noise ratio better than cMEM. In the presence of two synchronous sources, cMEM was able to distinguish well the two sources (their location and spatial extent), while 4-ExSo-MUSIC only retrieved one of them. cMEM was able to detect the spatio-temporal propagation patterns of two synchronous activities while 4-ExSo-MUSIC favored the strongest source activity. Overall, in the context of localizing sources of epileptic discharges from EEG and MEG data, 4-ExSo-MUSIC and cMEM were found accurately sensitive to the location and spatial extent of the sources, with some complementarities. Therefore, they are both eligible for application on clinical data.

High‐frequency oscillations are not better biomarkers of epileptogenic tissues than spikes

High‐frequency oscillations (HFOs) in intracerebral EEG (stereoelectroencephalography; SEEG) are considered as better biomarkers of epileptogenic tissues than spikes. How this can be applied at the patient level remains poorly understood. We investigated how well HFOs and spikes can predict epileptogenic regions with a large spatial sampling at the patient level.

Classification of high frequency oscillations in epileptic intracerebral EEG

High Frequency Oscillations (HFOs 40-500 Hz), recorded from intracerebral electroencephalography (iEEG) in epileptic patients, are categorized into four distinct sub-bands (Gamma, High-Gamma, Ripples and Fast Ripples). They have recently been used as a reliable biomarker of epileptogenic zones. The objective of this paper is to investigate the possibility of discriminating between the different classes of HFOs which physiological/pathological value is critical for diagnostic but remains to be clarified. The proposed method is based on the definition of a relevant feature vector built from energy ratios (computed using Wavelet Transform-WT) in a-priori-defined frequency bands. It makes use of a multiclass Linear Discriminant Analysis (LDA) and is applied to iEEG signals recorded in patients candidate to epilepsy surgery. Results obtained from bootstrap on training/test datasets indicate high performances in terms of sensitivity and specificity.

Interpretation of SEEG recordings

The interpretation of SEEG recordings is a crucial step. It must be carried out by an epileptologist/neurophysiologist with sufficient training and qualification in this field. The objectives of the interpretation are to define the brain topography of interictal activities (irritative zone) and the epileptogenic zone, defined as the site of primary organization of ictal discharges. Several patterns of seizure onset are possible, the most typical including fast discharges. The interpretation of the SEEG is based on the recording of spontaneous activity but also on the results of intracerebral electrical stimulation. It must be done with accurate anatomical information on the location of the electrodes in terms of the patients anatomy. Quantification of interictal activities (spikes, high frequency oscillations) and ictal activity (epileptogenicity index) is recommended. The interpretation of the SEEG must also take into account functional data and will be the basis for the final decision on the operability and type of intervention chosen.

Stereoelectroencephalography and surgical outcome in polymicrogyria-related epilepsy: A multicentric study: SEEG and Epilepsy Surgery in PMG

We aimed to (1) assess the concordance between various polymicrogyria (PMG) types and the associated epileptogenic zone (EZ), as defined by stereoelectroencephalography (SEEG), and (2) determine the postsurgical seizure outcome in PMG‐related drug‐resistant epilepsy.

A Page-Hinkley based method for HFOs detection in epileptic depth-EEG

Interictal High Frequency Oscillations, (HFOs [30600 Hz]), recorded from intracerebral electroencephalo-graphy (iEEG) in epileptic brain, showed to be potential biomarkers of epilepsy. Hence, their automatic detection has become a subject of high interest. So far, all detection algorithms consisted of comparing HFOs energy, computed in bands of interest, to a threshold. In this paper, a sequential technique was investigated. Detection was based on a variant of the Cumulative Sum (CUSUM) test, the so-called Page-Hinkley algorithm showing optimal results for detecting abrupt changes in the mean of a normal random signal. Experiments on simulated and real datasets showed the good performance of the method in terms of sensitivity and false detection rate. Compared to the classical thresholding, Page-Hinkley showed better performance.

Estimating the dominant frequency of High Frequency Oscillations in depth-EEG signals

Pathological high-frequency oscillations (HFOs, 200–600 Hz) observed in depth-EEG and on scalp EEG recordings are recognized to be potentially valuable biomarkers of the epileptogenic zone responsible for generating seizures. Many research studies have been dedicated to detect, classify, simulate and understand the underlying mechanisms responsible for their generation. However, broadly classifying the HFOs into classes of wide frequency bands may negatively impact the quality of information carried by these electrophysiological biomarkers. In this paper, we perform a comparative study of various signal processing methods for estimating the dominant frequency of HFOs. The novelty is to make use of a physiologically-plausible computational model in which the HFO frequency can be tuned a priori. Results indicate that non-parametric methods best estimate the frequency of the low-amplitude fast oscillations characteristic of HFOs.

Brain (Hyper)Excitability Revealed by Optimal Electrical Stimulation of GABAergic Interneurons

BACKGROUND Neurological disorders are often characterized by an excessive and prolonged imbalance between neural excitatory and inhibitory processes. An ubiquitous finding among these disorders is the disrupted function of inhibitory GABAergic interneurons. OBJECTIVE The objective is to propose a novel stimulation procedure able to evaluate the efficacy of inhibition imposed by GABAergic interneurons onto pyramidal cells from evoked responses observed in local field potentials (LFPs). METHODS Using a computational modeling approach combined with in vivo and in vitro electrophysiological recordings, we analyzed the impact of electrical extracellular, local, bipolar stimulation (ELBS) on brain tissue. We implemented the ELBS effects in a neuronal population model in which we can tune the excitation-inhibition ratio and we investigated stimulation-related parameters. Computer simulations led to sharp predictions regarding: i) the shape of evoked responses as observed in local field potentials, ii) the type of cells (pyramidal neurons and interneurons) contributing to these field responses and iii) the optimal tuning of stimulation parameters (intensity and frequency) to evoke meaningful responses. These predictions were tested in vivo (mouse). Neurobiological mechanisms were assessed in vitro (hippocampal slices). RESULTS Appropriately-tuned ELBS allows for preferential activation of GABAergic interneurons. A quantitative neural network excitability index (NNEI) is proposed. It is computed from stimulation-induced responses as reflected in local field potentials. NNEI was used in four patients with focal epilepsy. Results show that it can readily reveal hyperexcitable brain regions. CONCLUSION Well-tuned ELBS and NNEI can be used to locally probe brain regions and quantify the (hyper)excitability of the underlying brain tissue.