Intracerebral Electrodes Research Articles

P11-F The cortical sources of face selective N170: A simultaneous multi-scale EEG study

The sudden onset of a face image leads to a prominent face-selective response in human scalp electroencephalographic (EEG) recordings, peaking 170 ms after stimulus onset at occipito-temporal (OT) scalp sites: the N170 (or M170 in magnetoencephalography). According to a widely held view, the main cortical source of the N170 lies in the fusiform gyrus (FG), whereas the posteriorly located inferior occipital gyrus (IOG) would rather generate earlier face-selective responses. Here, we report neural responses to upright and inverted faces recorded in a unique patient using multicontact intracerebral electrodes implanted in the right IOG and in the OT sulcus above the right lateral FG (LFG). Simultaneous EEG recordings on the scalp identified the N170 over the right OT scalp region. The latency and amplitude of this scalp N170 were correlated at the single-trial level with the N170 recorded in the lateral IOG, close to the scalp lateral occipital surface. In addition, positive component maximal around the latency of the N170 (a P170) was prominent above the internal LFG, whereas this region typically generates an N170 (or “N200”) over its external/ventral surface. This suggests that electrophysiological responses in the LFG manifest as an equivalent dipole oriented mostly along the vertical axis with likely minimal projection to the lateral OT scalp region. Altogether, these observations provide evidence that the IOG is a major cortical generator of the face-selective scalp N170, qualifying the potential contribution of the FG and questioning a strict serial spatiotemporal organization of the human cortical face network.

High-Frequency Activity During Stereotyped Low-Frequency Events Might Help to Identify the Seizure Onset Zone.

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.Liu S, Gurses C, Sha Z, Quach MM, Sencer A, Bebek N, et al. Brain. 2018;141(3):713-730. doi:10.1093/brain/awx374. PMID: 29394328.High-frequency oscillations in local field potentials recorded with intracranial electroencephalogram are putative biomarkers of seizure-onset zones in epileptic brain. However, localized 80- to 500-Hz oscillations can also be recorded from normal and nonepileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones that limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 patients with epilepsy and 5 control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent of the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step toward the translation of these signature events into valid clinical biomarkers.

Harmonization of the pipeline for seizure detection to phenotype post-traumatic epilepsy in a preclinical multicenter study on post-traumatic epileptogenesis

RationaleThe Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Centre without walls is an NIH funded multicenter consortium. One of EpiBioS4Rx projects is a preclinical post-traumatic epileptogenesis biomarker study that involves three study sites: The University of Eastern Finland, Monash University (Melbourne) and the University of California Los Angeles. Our objective is to create a platform for evaluating biomarkers and testing new antiepileptogenic treatments for post-traumatic epilepsy (PTE) using the lateral fluid percussion injury (FPI) model in rats. As only 30–50% of rats with severe lateral FPI develop PTE by 6 months post-injury, prolonged video-EEG monitoring is crucial to identify animals with PTE. Our objective is to harmonize the surgical and data collection procedures, equipment, and data analysis for chronic EEG recording in order to phenotype PTE in this rat model across the three study sites. MethodsTraumatic brain injury (TBI) was induced using lateral FPI in adult male Sprague-Dawley rats aged 11–12 weeks. Animals were divided into two cohorts: a) the long-term video-EEG follow-up cohort (Specific Aim 1), which was implanted with EEG electrodes within 24 h after the injury; and b) the magnetic resonance imaging (MRI) follow-up cohort (Specific Aim 2), at 5 months after lateral FPI. Four cortical epidural screw electrodes (2 ipsilateral, 2 contralateral) and three intracerebral bipolar electrodes were implanted (septal CA1 and the dentate gyrus, layers II and VI of the perilesional cortex both anterior and posterior to the injury site). During the 7th post-TBI month, animals underwent 4 weeks of continuous video-EEG recordings to diagnose of PTE. ResultsAll centers harmonized the induction of TBI and surgical procedures for the implantation of EEG recordings, utilizing 4 or more EEG recording channels to cover areas ipsilateral and contralateral to the brain injury, perilesional cortex and the hippocampus and dentate gyrus. Ground and reference screw electrodes were implanted. At all sites the minimum sampling rate was 512 Hz, utilizing a finite impulse response (FIR) and impedance below 10 KΩ through the entire recording. As part of the quality control criteria we avoided electrical noise, and monitoring changes in impedance over time and the appearance of noise on the recordings. To reduce electrical noise, we regularly checked the integrity of the cables, stability of the EEG recording cap and the appropriate connection of the electrodes with the cables. Following the pipeline presented in this article and after applying the quality control criteria to our EEG recordings all of the sites were successful to phenotype seizure in chronic EEG recordings of animals after TBI. DiscussionDespite differences in video-EEG acquisition equipment used, the three centers were able to consistently phenotype seizures in the lateral fluid-percussion model applying the pipeline presented here. The harmonization of methodology will help to improve the rigor of preclinical research, improving reproducibility of pre-clinical research in the search of biomarkers and therapies to prevent antiepileptogenesis.

Robot-Assisted Responsive Neurostimulator System Placement in Medically Intractable Epilepsy: Instrumentation and Technique.

The management of medically refractory epilepsy patients who are not surgical candidates has remained challenging. Closed loop-or responsive-neurostimulation (RNS) is now an established therapy for the treatment of epilepsy with specific indications. The RNS® system (NeuroPace Inc, Mountainview, California) has recently been shown to be effective in reducing the seizure frequency of partial onset seizures. The electrode design consists of either intracerebral depth electrodes or subdural strip electrodes, and stereotaxis is typically used to guide placement into the EZ. Details on the operative techniques used to place these electrodes have been lacking. To address the advantage of using a robotic-assisted technique to place depth electrodes for RNS® system placement compared to the typical frame-based or frameless stereotactic systems. We retrospectively reviewed our single center, technical operative experience with RNS® system placement using robotic assistance from 2014 to 2016 via chart review. Twelve patients underwent RNS® system placement using robotic assistance. Mean operative time was 121 min for a median of 2 depth electrodes with mean deviation from intended target of ∼3 mm in x, y, and z planes. Two patients developed wound infections, 1 of whom was reimplanted. Seizures were reduced by ∼40% at 2 yr, similar to the results seen in the open label portion of the pivotal RNS trial. Robotic-assisted stereotaxis can be used to provide a stable and accurate stereotactic platform for insertion of intracerebral RNS electrodes, representing a safe, efficient and accurate procedure.

Cortico-striatal synchronization in human focal seizures.

Although a number of experimental and clinical studies have pointed out participation or an even more prominent role of basal ganglia in focal seizures, the mode of interaction between cortical and striatal signals remains unclear. In the present study, we took stereoelectroencephalographic (SEEG) recordings in drug-resistant epilepsy patients, to qualitatively and quantitatively analyse the ictal striatum activity as well as its synchronization with cerebral cortex. Eleven patients who underwent SEEG evaluation were prospectively included if they fulfilled two inclusion criteria: (i) at least one orthogonal intracerebral electrode contact explored the basal ganglia, in either their putaminal or caudate part; and (ii) at least two SEEG seizures were recorded. Cortical and subcortical regions of interest were defined and different periods of interest were analysed. SEEG was visually inspected and h2 non-linear correlation analysis performed to study functional connectivity between cortical region of interest and striatum. Six correlation indices were calculated. Two main patterns of striatal activation were recorded: the most frequent was characterized by an early alpha/beta activity that started within the first 5 s after seizure onset, sometimes concomitant with it. The second one was characterized by late, slower, theta/delta activity. A significant difference in h2 correlation indices was observed during the preictal and seizure onset period compared to background for global striatal index, mesio-temporal/striatal index, latero-temporal/striatal index, insular/striatal index, prefrontal/striatal index. In addition, a significant difference in h2 correlation indices was observed during the seizure termination period compared to all the other periods of interest for the six indices calculated. These results indicate that cortico-striatal synchronization can arise from the start of focal seizures. Depending on the ictal frequency pattern, desynchronization can occur later, but a late and terminal hypersynchronization progressively takes over. These changes in synchronization level between cortical and striatal activity might be part of an endogenous mechanism controlling the duration of abnormal oscillations within the striato-thalamo-cortical loop and thereby their termination. Pathophysiology of basal ganglia in focal seizures appears to be much more interlinked with the cortex than expected. Beyond the stereotypical features they could imprint to seizure semiology, their role in strengthening mechanisms underlying cessation of ictal propagation should inspire new rationales for deep brain stimulation in patients with intractable focal epilepsies.

Two main focal seizure patterns revealed by intracerebral electroencephalographic biomarker analysis.

Long-term recording with intracerebral electrodes is commonly utilized to identify brain areas responsible for seizure generation (epileptogenic zone) and to tailor therapeutic surgical resections in patients with focal drug-resistant epilepsy. This invasive diagnostic procedure generates a wealth of data that contribute to understanding human epilepsy. We analyze intracerebral signals to identify and classify focal ictal patterns. We retrospectively analyzed stereo-electroencephalographic (EEG) data in a cohort of patients either cryptogenic (magnetic resonance imaging negative) or presenting with noncongruent anatomoelectroclinical data. A computer-assisted method based on EEG signal analysis in frequency and space domains was applied to 467 seizures recorded in 105 patients submitted to stereo-EEG presurgical monitoring. Two main focal seizure patterns were identified. P-type seizures, typical of neocortex, were observed in 73 patients (69.5%), lasted 22±13seconds (mean+SD), and were characterized by a sharp-onset/sharp-offset transient superimposed on low-voltage fast activity (126±19Hz). L-type seizures were observed in 43 patients (40.9%) and consistently involved mesial temporal structures; they lasted longer (93±48second), started with 116±21Hz low-voltage fast activity superimposed on a slow potential shift, and terminated with large-amplitude, periodic bursting activity. In 23 patients (21.9%), the L-type seizure was preceded by a P seizure. Spasmlike and unclassifiable EEG seizures were observed in 11.4% of cases. The proposed computer-assisted approach revealed signal information concealed to visual inspection that contributes to identifying two principal seizure patterns typical of the neocortex and of mesial temporal networks.

Amygdala enlargement in mesial temporal lobe epilepsy: an alternative imaging presentation of limbic epilepsy.

To assess imaging, clinical, and pathological features of mesial temporal lobe epilepsy (mTLE) patients with amygdala enlargement (AE) in comparison with those with mesial temporal sclerosis (MTS). Clinical, imaging, and pathologic features were retrospectively reviewed in 40 mTLE patients with postoperative follow-up (10 with AE and 30 with MTS). The volumes and signal intensity of the amygdala and hippocampus were assessed in 10 AE, 10 age- and sex-matched MTS patients, and 12 controls (HC). AE patients had a lower rate of concordant FDG PET (p< 0.05) and required more frequently intracerebral electrodes compared to MTS patients (p< 0.05). AE had larger ipsilateral amygdala (p< 0.0001) and hippocampus volumes (p< 0.0001) compared to MTS and to HC, with no significant differences for other brain structures. Normalized FLAIR signal was higher in the ipsilateral than contralateral amygdala in both AE and MTS (p< 0.001 and p< 0.05, respectively) and higher in the ipsilateral amygdala compared to HC (p< 0.05). In MTS, ADC in the ipsilateral amygdala (867mm2/s) was higher compared to the contralateral one (804.8 × 10-6mm2/s, p< 0.01), compared to HC (773 × 10-6mm2/s, p< 0.01) and compared to the ipsilateral amygdala in AE (813.7 × 10-6mm2/s, p< 0.05). AE patients had dysplasia (50%) or astrocytic gliosis (50%) of the amygdala extending to the hippocampus and temporal isocortex, and only 2/10 cases had pathologic findings of MTS. AE patients have distinct imaging and pathologic features compared to MTS, and require more extensive preoperative workup. Recognition of AE may improve preoperative assessment in TLE surgical candidates.

An intracerebral exploration of functional connectivity during word production.

Language is mediated by pathways connecting distant brain regions that have diverse functional roles. For word production, the network includes a ventral pathway, connecting temporal and inferior frontal regions, and a dorsal pathway, connecting parietal and frontal regions. Despite the importance of word production for scientific and clinical purposes, the functional connectivity underlying this task has received relatively limited attention, and mostly from techniques limited in either spatial or temporal resolution. Here, we exploited data obtained from depth intra-cerebral electrodes stereotactically implanted in eight epileptic patients. The signal was recorded directly from various structures of the neocortex with high spatial and temporal resolution. The neurophysiological activity elicited by a picture naming task was analyzed in the time-frequency domain (10-150 Hz), and functional connectivity between brain areas among ten regions of interest was examined. Task related-activities detected within a network of the regions of interest were consistent with findings in the literature, showing task-evoked desynchronization in the beta band and synchronization in the gamma band. Surprisingly, long-range functional connectivity was not particularly stronger in the beta than in the high-gamma band. The latter revealed meaningful sub-networks involving, notably, the temporal pole and the inferior frontal gyrus (ventral pathway), and parietal regions and inferior frontal gyrus (dorsal pathway). These findings are consistent with the hypothesized network, but were not detected in every patient. Further research will have to explore their robustness with larger samples.

Probabilistic functional tractography of the human cortex revisited

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

PB14. Fully automated segmentation of intracerebral depth electrode contacts with subsequent separation into distinct electrodes in patients with pharmacoresistant epilepsy

Introduction The localization of the seizure onset zone, being mandatory prior to epilepsy surgery, frequently requires the use of invasive depth electrodes. The interpretation of the electrophysiological recordings then critically depends on the visualization of the electrode positions in 3D MRI scans of these patients. We developed a fully automated software pipeline to segment the depth electrode contacts and to achieve a separation into distinct electrodes. The pipeline uses pre- and post-implantation cranial computerized tomography as well as post-implantation 3D MRI. It consists of standard pre-processing routines for co-registering and masking of images and a custom-made image analysis procedure. Prior spatial information about the implanted electrodes is not required. Methods In a first step, both the pre-implantation CT (preOP-CT) and the post-implantation CT (OP-CT) were co-registered to a post-implantation 3D MRI. Then masks of brain and skull bone were extracted from the preOP-CT, and an automated search for intensity clusters was performed in the brain masked OP-CT. The detected clusters were finally separated into electrode contacts. The only requirements for this method were a voxel size of 1 × 1 × 1 mm or better for the OP-CT and an equidistant spacing of the contacts of all implanted electrodes. Results The method was tested in 28 patients with pharmacoresistant focal epilepsy (161 electrodes and 1608 contacts altogether). A comparison between automatic segmentation and visual identification of the electrode contacts showed that 1597 contacts (99.3%) were correctly detected. Only 9 contacts, which were all located within or outside the skull bone, remained undetected. In 2 patients, one single contact was falsely detected and had to be corrected manually. The pre-processing routines (co-registration and masking) took about 30 min per patient. The image analysis procedure (cluster search and electrode separation) took less than 30 s per patient. Conclusions The implemented method is, to the best of our knowledge, the first fully automated procedure for the segmentation of intracerebral depth electrode contacts with subsequent separation of the detected contacts into distinct electrodes, which can be directly superimposed on 3D MRI. The method does not require any a priori information about the implanted electrodes and is able to identify the loci of depth electrode contacts in a fast and reliable manner.

S127. Computer aided evaluation of intracerebral depth electrode recordings by automatic seizure detection

Introduction Preoperative evaluation of patients with medically intractable epilepsies requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters. Methods The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate. Results The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity. Conclusion We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic.

F08. Automatic spike detection in intracerebral depth electrode recordings

Introduction Intracranial recordings, like intracerebral depth electrode recordings are considered to be the best choice for preoperative invasive evaluation when standard electro-clinical examinations are not conclusive. These recordings reflect a vast amount of interictal epileptic discharges, the so called spikes, which are in general abundant compared to seizure activity. Manually reviewing these recordings to find the spatial origin of seizures and spikes is still state of the art, which is a very exhausting task due to the large number of signals recorded. In order to enable a time-efficient evaluation of such recordings, we developed a spike detection algorithm which performs automatic detection, spatial clustering and smart visualization of spike clusters. Methods The automatic spike detection algorithm for depth electrode recordings is based on our existing spike detection algorithm for surface EEG. The spike detection algorithm assesses morphological and topographical potential field features. Both feature groups had to be adapted for depth electrode recordings. In these recordings spikes are shorter in duration, often have higher amplitude and the properties of the potential field compared to surface EEG are very different. Due to the morphological similarity of individual waves of the posterior alpha rhythm and spikes, the detection within such rhythmic periods is suppressed. In order to speed up the review, we developed a spatial clustering and a smart visualization of spike clusters. In the so-called overlay plot the signals of all individual spikes of each cluster are displayed one above the other in one figure, allowing for quickly distinguishing between true spikes and artifacts. In order to assess the sensitivity and precision of the developed algorithms, 1400 spikes in 3 datasets from different patients recorded at the Academic Center of Epileptology, Kempenhaeghe/MUMC were annotated by an experienced biomedical assistant. Results The automatic spike detection algorithm detected 54% of all spikes on average. The overall precision was 39%. Sensitivity values reach from 18% to 81%. However, also for the patient with 18% sensitivity the algorithm was able to detect several spikes on all those electrodes where also the human reviewer detected them. For one patient a very low precision of 6% was measured. This small precision value came from repetitive artifacts which are very similar to spikes and are even difficult to distinguish for human experts. Removing the most evident artifact clusters by using the overlay plot, the precision increases from 6% to 30%. Doing the same for all patients an average precision of 68% is achieved. Conclusion Our automatic method was able to detect spikes with a high sensitivity on average. Moreover, our algorithm always detected spikes on all the electrodes where the expert also identified them allowing for a profound diagnosis. The smart combination of detection, clustering and visualization potentially enables a more time-efficient review.

An image registration protocol to integrate electrophysiology, MRI and neuropathology data in epileptic patients explored with intracerebral electrodes

BackgroundSeveral attempts have been made to coregister in vivo MRI with the histopathology of surgical samples, aiming to validate new MRI biomarkers and improve the detection of epileptogenic lesions. As a further implementation, we propose a method to reconstruct the anatomical localization of the intracerebral electrodes on the histological sections, developing a coregistration protocol to match the in vivo MRI onto the ex vivo MRI obtained from the surgical specimen. New methodSince the ex vivo MRI is natively in geometrical correspondence with histology slices, the goal of the coregistration process is to compute the transform function mapping the clinical MRI space to the ex vivo MRI. Electrodes and leads, identified in CT-MRI, can then be segmented and translated onto the histological slices. ResultsStep-by-step, qualitative visual inspection showed an improved matching of the anatomical structures or boundaries and electrodes positions between the two modalities. The quantitative evaluation of the coregistration protocol reported a mean error ranging between 0.82 and 1.27 mm when a sufficient number of landmarks, particularly in the core of the specimen, were clearly identified. Comparison with existing methodsBecause histology was performed according to ex vivo MRI geometry we chose to transform the in vivo onto the ex vivo MRI, differently from other methods. ConclusionsInteresting applications of the method will include correlating the locally-generated pathological electrical activity with the subtle morphological alterations of the tissue, and histologically validating the origin of signal alterations or quantitative parameter variations in MRI studies.

Insulo-opercular cortex generates oroalimentary automatisms in temporal seizures.

Oroalimentary automatisms (OAAs) resembling normal alimentary behavior are stereotyped complex movements that may occur during epileptic seizures. They are considered common clinical signs in temporal lobe seizures, but their anatomofunctional mechanisms are not established. We took the opportunity of presurgical intracerebral recordings to study the relations between the occurrence of OAAs and temporal/spatial features of ictal activities. We retrospectively reviewed patients with medically intractable medial temporal lobe epilepsy who underwent stereoelectroencephalography (SEEG) at Cleveland Clinic between 2009 and 2016. Patients presenting oroalimentary automatisms during seizures, with intracerebral electrodes spanning temporal and extratemporal areas, were selected. SEEG-clinical correlations with latency measurements were done. Coherence analyses were performed on regions of interest as defined by the areas involved at the onset of oroalimentary automatisms. Fifteen patients (115 seizures) were analyzed. Sixty-nine seizures exhibited overt oroalimentary automatisms. Only insulo-opercular cortex ictal involvement was consistently related to the occurrence of OAAs, with a linear correlation between OAA onset and ictal oscillatory activity onset in the insulo-opercular cortex. SEEG signal processing showed an increase in theta coherence preceding oroalimentary automatism onset between mediobasal-temporal structures and insulo-opercular cortex, as well as between the 2 insulo-opercular regions. The underlying mechanism for the production of oroalimentary automatisms in medial temporal seizures is based on temporal-insulo-opercular theta coherence leading to a synchronous state generating rhythmic patterned outputs from the cortical masticatory area.

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers.awx374media15721572971001.

Spike-related haemodynamic responses overlap with high frequency oscillations in patients with focal epilepsy

Simultaneous scalp EEG/functional MRI measures non-invasively haemodynamic responses to interictal epileptic discharges, which are related to the epileptogenic zone. High frequency oscillations are also an excellent indicator of this zone, but are primarily recorded from intracerebral EEG. We studied the spatial overlap of these two important markers in patients with drug-resistant epilepsy to assess if their combination could help better define the extent of the epileptogenic zone. We included patients who underwent EEG-functional MRI and later intracerebral EEG. Based on intracerebral EEG findings, we separated patients with unifocal seizures from patients with multifocal or unknown onset seizures. Haemodynamic t-maps were coregistered with the intracerebral electrode positions. Each EEG channel was classified as pertaining to one of the following categories: primary haemodynamic cluster (maximum t-value), secondary cluster (t-value > 90% of the primary cluster) or outside the primary and secondary clusters. We marked high frequency oscillations (ripples: 80-250 Hz; fast ripples: 250-500 Hz) during 1 h of slow wave sleep, and compared their rates in each haemodynamic category. After classifying channels as high- or low-rate, the proportion of high-rate channels within the primary or primary plus secondary clusters was compared to the proportion expected by chance. Twenty-five patients, 11 with unifocal and 14 with multifocal/unknown seizure onsets, were studied. We found a significantly higher median high frequency oscillation rate in the primary cluster compared to secondary cluster and outside these two clusters for the unifocal group (P < 0.0001), but not for the multifocal/unknown group. For the unifocal group, the number of high-rate channels within the primary or primary plus secondary clusters was significantly higher than expected by chance. This held only for the high-ripple-rate channels in the multifocal/unknown group. At the patient level, most patients (18/25, or 72%) had at least one high-rate channel within a primary cluster. In patients with unifocal epilepsy, the maximum haemodynamic response (primary cluster) related to scalp interictal discharges overlaps with the tissue generating high frequency oscillations at high rates. If intracranial EEG is warranted, this response should be explored. As a tentative clinical use of the combination of these techniques we propose that higher high frequency oscillation rates inside than outside the maximum response indicates that the patient has indeed a focal epileptogenic zone demarcated by this response, whereas similar rates inside and outside may indicate a widespread epileptogenic zone or an epileptogenic zone not covered by the implantation.

SEEG is a Safe Procedure for a Comprehensive Anatomic Exploration of the Insula: A Retrospective Study of 108 Procedures Representing 254 Transopercular Insular Electrodes.

The exploration of the insula in pre-surgical evaluation of epilepsy is considered to be associated with a high vascular risk resulting in an incomplete exploration of the insular cortex. To report a retrospective observational study of insular exploration using stereoelectroencephalography (sEEG) with transopercular and parasagittal oblique intracerebral electrodes from January 2008 to January 2016. The first purpose of this study was to evaluate the surgical risks of insular cortex sEEG exploration. The second purpose was to define the ability of placing intracerebral contacts in the whole insular cortex. Ninety-nine patients underwent 108 magnetic resonance imaging (MRI)-guided stereotactic implantations of intracerebral electrodes in the context of preoperative assessment of drug-resistant epilepsy, including at least 1 electrode placed in the insular cortex. On postoperative computed tomography images co-registered with MRI, followed by MRI segmentation and application of a transformation matrix, intracerebral contact coordinates of the insular electrodes' contacts were anatomically localized in the Talairach space. Finally, dispersion and clustering analysis was performed. There was no morbidity, in particular hemorrhagic complications, or mortality related to insular electrodes. Statistical comparison of intracerebral contact positions demonstrated that whole insula exploration is possible on the left and right sides. In addition, the clustering analysis showed the homogeneous distribution of the electrodes within the insular cortex. In the presurgical evaluation of drug-resistant epilepsy, the insular cortex can be explored safely and comprehensively using transopercular sEEG electrodes. Parasagittal oblique trajectories may also be associated to achieve an optimal exploration.

A fingerprint of the epileptogenic zone in human epilepsies.

Defining a bio-electrical marker for the brain area responsible for initiating a seizure remains an unsolved problem. Fast gamma activity has been identified as the most specific marker for seizure onset, but conflicting results have been reported. In this study, we describe an alternative marker, based on an objective description of interictal to ictal transition, with the aim of identifying a time-frequency pattern or 'fingerprint' that can differentiate the epileptogenic zone from areas of propagation. Seventeen patients who underwent stereoelectroencephalography were included in the study. Each had seizure onset characterized by sustained gamma activity and were seizure-free after tailored resection or laser ablation. We postulated that the epileptogenic zone was always located inside the resection region based on seizure freedom following surgery. To characterize the ictal frequency pattern, we applied the Morlet wavelet transform to data from each pair of adjacent intracerebral electrode contacts. Based on a visual assessment of the time-frequency plots, we hypothesized that a specific time-frequency pattern in the epileptogenic zone should include a combination of (i) sharp transients or spikes; preceding (ii) multiband fast activity concurrent; with (iii) suppression of lower frequencies. To test this hypothesis, we developed software that automatically extracted each of these features from the time-frequency data. We then used a support vector machine to classify each contact-pair as being within epileptogenic zone or not, based on these features. Our machine learning system identified this pattern in 15 of 17 patients. The total number of identified contacts across all patients was 64, with 58 localized inside the resected area. Subsequent quantitative analysis showed strong correlation between maximum frequency of fast activity and suppression inside the resection but not outside. We did not observe significant discrimination power using only the maximum frequency or the timing of fast activity to differentiate contacts either between resected and non-resected regions or between contacts identified as epileptogenic versus non-epileptogenic. Instead of identifying a single frequency or a single timing trait, we observed the more complex pattern described above that distinguishes the epileptogenic zone. This pattern encompasses interictal to ictal transition and may extend until seizure end. Its time-frequency characteristics can be explained in light of recent models emphasizing the role of fast inhibitory interneurons acting on pyramidal cells as a prominent mechanism in seizure triggering. The pattern clearly differentiates the epileptogenic zone from areas of propagation and, as such, represents an epileptogenic zone 'fingerprint'.awx306media15687076823001.

40. Event related potentials recorded by intracerebral electrodes

Preoperative evaluation, by means of intracerebral electrodes, in patients presenting with symptomatic drug resistant epilepsy, provides an opportunity to explore neural generators of P300, Mismatch Negativity (MMN) and also N140. We studied 5 pediatric patients with drug resistant epilepsy. ERPs and SEPs (to median and tibial nerve stimulation) were recorded from the intracerebral electrode contacts referred to the earlobe ipsilateral to the stimulation. P300 was recorded by electrodes located in hippocampus with a clear inversion of signals in posterior hippocampus; MMN was also well recorded by electrodes placed in opercular-insular cortex without a clear inversion of signal; N140, increased in amplitude during attention condition, showed the maximum amplitude in the frontal electrode contacts and in the opercular-insular traces. Numerous investigations revealed that generators of Event Related Potentials are independent and also with a cortical-subcortical distribution. In this short case series, we could show some brain areas clearly involved in the generation of P300, MMN and N140 components.

27. Sural and tibial nerve SEPs are mediated by the same spinal pathway

Scalp somatosensory evoked potentials (SEPs) are later in latency after sural than after tibial nerve stimulation. This led to hypothesize that different spinal pathways could be involved in the scalp SEP generation (Rossini et al., 1986). Our aim was to investigate the SEP latencies at different subcortical sites after sural and tibial nerve stimulation. We studied 7 patients. Four patients with Parkinson’s disease (PD) were implanted with intracerebral electrodes for DBS: one in the thalamus bilaterally (Vim nucleus), and 3 in the Pedunculopontine nucleus (PPTg). The remaining 3 patients were implanted with cervical epidural electrodes for intractable pain. SEPs were recorded from the scalp and the deep recording electrodes after both tibial and sural nerve stimulation. In all our patients, the scalp SEP latencies were later after sural than after tibial nerve stimulation. On the contrary, SEPs recorded at all subcortical sites showed similar latencies for both sural and tibial nerve stimulation. In conclusion, sural and tibial nerve scalp SEPs are mediated by the same spinal pathway. The difference in latency observed between the scalp SEPs after sural and tibial nerve stimulation is probably due to the orientation of the dipolar sources generating these components.