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Object Epilepsy surgery is an effective treatment for selected patients with focal intractable epilepsy. Complete removal of the epileptogenic zone significantly increases the chances for postoperative seizure-freedom. In complex surgical candidates, delineation of the epileptogenic zone requires a long-term invasive video/EEG from intracranial electrodes. It is especially challenging to achieve a complete resection in deep brain structures such as opercular-insular cortex. We report a novel approach utilizing intraoperative visual detection of stereotactically implanted depth electrodes to inform and guide the extent of surgical resection. Methods We retrospectively reviewed data of pediatric patients operated in Motol Epilepsy Center between October 2010 and June 2020 who underwent resections guided by intraoperative visual detection of depth electrodes following SEEG. The outcome in terms of seizure- and AED-freedom was assessed individually in each patient. Results Nineteen patients (age at surgery 2.9–18.6 years, median 13 years) were included in the study. The epileptogenic zone involved opercular-insular cortex in eighteen patients. The intraoperative detection of the electrodes was successful in seventeen patients and the surgery was regarded complete in sixteen. Thirteen patients were seizure-free at final follow-up including six drug-free cases. The successful intraoperative detection of the electrodes was associated with favorable outcome in terms of achieving complete resection and seizure-freedom in most cases. On the contrary, the patients in whom the procedure failed had poor postsurgical outcome. Conclusion The reported technique helps to achieve the complete resection in challenging patients with the epileptogenic zone in deep brain structures.

Epilepsy surgery fails in 30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis.The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation.Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p 0.005) - ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p 0.012).The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone.Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.

Objective To assess whether available algorithms of quantitative EEG (qEEG) could practically help in localizing epileptogenic zone (EZ) and modify surgical planning in patients with focal intractable epilepsy. Methods We will present a case report of a 7-year-old boy with catastrophic epilepsy caused by focal cortical dysplasia located in the operculo-insular region of the right hemisphere. Due to the challenging localization of the presumed EZ, uncertain surgical borders and expected significant risks of the resection, the patient was stereotactically implanted with oblique depth electrodes. Intracranial EEG (iEEG) signal was analyzed using different qEEG methods. Our originally developed interictal epileptiform discharges (IED) detecting algorithm, which also extracts repetitive propagation patterns, was applied to localize sources of IED. We also used own network connectivity algorithm to analyze ictal (seizure) iEEG activity in detail. Employing results of qEEG analyses, 2D and 3D dynamic reconstructions of both interictal and ictal iEEG epileptiform changes were created and used to guide surgical approach. Results Both qEEG algorithms clearly proved the EZ localization in the dorso-caudal insular cortex of the right hemisphere and demonstrated sparing of initially suspected frontal opercular area. The dorso-caudal insular cortex generated 89% of IED; remaining 11% IED originated from the primary motor cortex. Surgical approach was adjusted to this qEEG-based hypothesis. Oblique depth electrodes were preserved intraoperatively to help precise targeting of the lesion. Continuous intraoperative motor-evoked potential monitoring was used to preserve motor functions. The boy has been followed for more than one year postoperatively. He is seizure-free with no motor deficit; with normal cognitive functions. Conclusions The case report demonstrates that currently available qEEG methods could help in guiding resective epilepsy surgery in complicated patients indicated for iEEG studies. We suggest our approach could increase patients’ chance to obtain seizure-free outcomes without new deficits and thus ultimately improve their quality of life. Supported by MH CZ–DRO, University Hospital Motol, Prague, Czech Republic 00064203 and IGA NT/11460-4.

Objective Interictal epileptiform discharges (IEDs) are electrographic hallmark of epilepsy. Information about the spatiotemporal distribution of IEDs in intracranial EEG is utilized to localize epileptogenic zone during the presurgical evaluation and plan the resection. Visual evaluation of long-term multi-channel intracranial recordings is extremely difficult and prone to bias. Clinicians usually assess only high-amplitude (high signal to noise ratio) discharge and low-amplitude IEDs can be overlooked or considered clinically insignificant. The goal of our study was to develop reliable automatic IED detectors to facilitate analysis of long-term recordings and increase the information yield of intracranial recordings. Methods Seven intracranial EEG recordings were randomly selected from our database. Samples of five minutes duration from fifteen high-rate IED channels (525 min in total) were presented to three experienced EEG specialists for spike labelling. The readers independently reviewed the data and classified IEDs into two groups: obvious and ambiguous. The inter-reader agreement was evaluated and IEDs labelled by at least two readers were considered as a gold standard (GS). We have developed, tested and optimized novel IED detector using GS datasets and compared its performance with published detectors. Our detecting approach estimates the signal envelope distribution to discriminate IEDs from background activity. Results Readers together labelled 6518 IEDs (53 ± 21% obvious, 47 ± 21% ambiguous). The reader’s maximal match was 58% in pair and agreement of all three readers was only 30% (Cohen’s kappa 0.14 ± 0.11). Detector’s performance was characterized by sensitivity 91 ± 12% and 8 ± 7 false positives per min and per channel. Its performance was 1.4× better than published detector. Examination of false positives revealed that substantial proportion had shape of reminiscent of IEDs, but with lower amplitude. More than 50% false positives were reclassified by readers as IEDs. In addition, regression analysis showed positive relationship between IEDs marked by readers and number of false positives. Conclusion The inter-reader agreement in visual IED evaluation is poor. Even experienced readers can identify approximately 40% of IED, especially those with high signal-to-noise ratio. In contrast, automatic detector is 2.5× more sensitive and can identify also low-amplitude IEDs. Areas generating not only high- but also low-amplitude IEDs can be crucial for epileptogenic zone localization. Supported by grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012).

Novel and quantitative methods EEG signal analysis are being developed by close multidisciplinary collaborations between epilepsy specialists, biomedical engineers and mathematicians. Quantitative analysis of the long-term monitoring from intracranial electrodes is expected to provide precise and objective results. High performance computational algorithms will be presented, not only from technical point of view, but also to demonstrate that output of these techniques can provide quantitative and clinically relevant diagnostic information. Three types of automatic and semi-automatic algorithms of quantitative EEG analysis will be presented and their benefits for epilepsy surgery planning discussed. Interictal epileptiform discharges and high-frequency oscillations represent electrographic markers of epileptic tissue. Methods of their automatic detection can substantially facilitate analysis of multi-channel long-term intracranial recordings and extract unbiased meaningful information about spatiotemporal and morphological properties of these markers. Visual identification of seizure onset zone in intracranial recordings is challenging and prone to bias. Methods of seizure onset identification represent one of the main research directions of intracranial signal processing. It has been demonstrated that introduction of causality measures and network analysis can provide useful information about epileptic network organization. These techniques are capable to identify the seizure onset zone in both ictal and interictal recordings. Application of average Directed Transfer Function and Granger’s causality to intracranial recordings demonstrate that seizure onset zone is characterized by the disconnection from the rest of the epileptic network. Increased information yield and quantitative results lead to increased integration of the above mentioned methods into presurgical diagnosis. These methods of intracranial signal analysis can improve guiding of resective surgery in difficult-to-treat cases and offer surgery to patients formerly classified as not suitable for surgery. Supported by grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012).

Background High frequency oscillations (HFOs) represent new electrographic marker of epileptogenic tissue and they are considered as a surrogate marker of seizure onset and epileptogenic zones. HFOs are recorded mainly in intracranial recordings. Visual analysis of HFOs in long-term recordings is extremely difficult due to the low signal-to-noise ratio of HFOs. Successful integration of HFOs into presurgical evaluation requires development of reliable methods of automatic HFO detection and quantification. We aimed to examine performance of three new HFO detecting algorithms and compared their performance with published detectors. Methods We implemented three published detectors which utilize RMS, line length or Hilbert transform approach to detect HFOs. We have developed additional three types of detectors which utilize short time energy estimation, Hilbert envelope and Bayesian evidence. All HFO detecting algorithms were applied to gold standard datasets and their performance quantified. Results Line length and Hilbert detectors detected the highest number of HFOs. The lowest number of the detections was achieved by RMS and energy estimating detectors. According to the results, the detectors can be divided into two groups. One group is characterized by high sensitivity. These algorithms detect nearly all the labeled HFOs events, but suffer from the high false positive detection rate. Second group of detectors have high positive prediction value but lower sensitivity. Our Hilbert envelope detector demonstrated the best performance of all evaluated detectors. Conclusions To improve the performance of detectors with high sensitivity will require to develop additional post-processing steps to remove the majority of false detections. Meanwhile detectors with low sensitivity will detect only high-amplitude HFOs. Future selection of the most appropriate algorithm for HFO detection in intracranial recordings will require detail understanding of the clinical significance of low-amplitude HFOs and major sources of false positive detections. Supported by Grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012).

Rationale The irritative zone is an area of the brain generating interictal epileptiform discharges (IEDs) that is used together with other results when planning epilepsy surgery. This zone possesses relatively low diagnostic value due to lack of a specific marker that would identify IEDs generated within the epileptogenic tissue. To increase diagnostic yield of irritative zone, we examined its functional organization. Methods Intracranial EEG recordings from 14 patients with refractory neocortical epilepsy were analysed using an algorithm that separates IEDs according to their spatial distribution into clusters. Analysis of cluster properties enabled to determine: (1) activity – percentage contribution to all IEDs in the given recording; (2) origins – the contacts where cluster initiates and (3) active region – the area of the most common propagation. Results On average 16,868 ± 16,813 IEDs per patient were analysed. The results demonstrated that in all patients the irritative zone was composed of multiple clusters with an average number of 12.5 ± 4.7 clusters per patient. The strongest cluster generated 43.5 ± 18.9% of all IEDs Average size of active region was 4.5 ± 4.5 contacts and contained 2.2 ± 1.7 origins. Evaluation of the cluster resection with the outcome suggested that the topology of the irritative zone may determine the success of the resection. Conclusion This study demonstrates the complex organization and modular nature of the neocortical irritative zone in epilepsy patients. It can be stratified into functional components, each with distinct pathophysiological and clinical significance. The ability to identify the key components of this network and its topology has potential to improve the results of epilepsy surgery. Supported by grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 10/2012).

Purpose Patients with hippocampal sclerosis associated with focal cortical dysplasia can have a higher risk of seizure recurrence if both of these pathologies are not removed. The aim of our study was to determine the role of intraoperative electrocorticography in detection of this dual pathology. Methods Intraoperative electrocorticography recordings were obtained in patients who underwent anteromedial temporal lobe resection. Patients with histopathologically proven hippocampal sclerosis and temporal pole available for analysis were included and were divided into two groups according to histopathology: isolated hippocampal sclerosis ( n = 23) and hippocampal sclerosis associated with focal cortical dysplasia – FCD IIIa ( n = 23).Cortical activity was measured prior to the resection using two six-contact strips (sampling from latero-basal and temporo-polar regions respectively) and one four contact strip sampling from mesio-basal temporal cortex. Occurrence of isolated mesial and independent neocortical (basal or lateral) spike activity was evaluated. Data analysis was performed by raters blinded to histopathology. Results Independent neocortical spikes were identified more frequently in patients with dual pathology (sixteen patients with FCD IIIa vs. four patients with isolated hippocampal sclerosis; p = 0.01). On the contrary, isolated mesial spikes occurred more often in patients with isolated hippocampal sclerosis (19 patients vs. six patients with FCD IIIa). In one patient with FCD IIIa no spikes were recorded. Conclusion Independent latero-basal temporal spikes recorded during intraoperative electrocorticography in patients with hippocampal sclerosis suggest associated dysplastic tissue in neocortex, i.e. dual pathology. Support: IGA MZ CR NT14489–3.