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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.

Networked optical wireless communication, also denoted as LiFi, is expected to play an important role in so-called smart hospitals. In this paper, we present the first experimental study of LiFi in an operating room at Motol University Hospital in Prague, Czech Republic. First, we perform one-to-one measurements using an optical transmitter (Tx) and receiver (Rx) and observe that channels with a free LOS provide sufficient signal strength for mobile communication inside the operating room. Then we combine the individual LOS links into a multiple-input multiple-output (MIMO) link using four distributed transmitters representing a wireless infrastructure, and six distributed receivers representing medical devices. In this configuration, at least two strong singular values of the MIMO channel matrix are observed which allow spatial multiplexing. By appropriately clustering the transmitters and selecting the users, mobile devices can be served in parallel. For data transmission, several multiplexing schemes such as time-division multiple access (TDMA) with one and two best links, TDMA with spatial reuse, space-division multiple access (SDMA) with two best links with and without zero forcing (ZF) are considered. Results show that SDMA with ZF increases the data rate by 2.7 times compared to baseline TDMA, resulting in a total data rate of 600 Mbit/s.

Human perception and cognition are based predominantly on visual information processing. Much of the information regarding neuronal correlates of visual processing has been derived from functional imaging studies, which have identified a variety of brain areas contributing to visual analysis, recognition, and processing of objects and scenes. However, only two of these areas, namely the parahippocampal place area (PPA) and the lateral occipital complex (LOC), were verified and further characterized by intracranial electroencephalogram (iEEG). iEEG is a unique measurement technique that samples a local neuronal population with high temporal and anatomical resolution. In the present study, we aimed to expand on previous reports and examine brain activity for selectivity of scenes and objects in the broadband high-gamma frequency range (50-150 Hz). We collected iEEG data from 27 epileptic patients while they watched a series of images, containing objects and scenes, and we identified 375 bipolar channels responding to at least one of these two categories. Using K-means clustering, we delineated their brain localization. In addition to the two areas described previously, we detected significant responses in two other scene-selective areas, not yet reported by any electrophysiological studies; namely the occipital place area (OPA) and the retrosplenial complex. Moreover, using iEEG we revealed a much broader network underlying visual processing than that described to date, using specialized functional imaging experimental designs. Here, we report the selective brain areas for scene processing include the posterior collateral sulcus and the anterior temporal region, which were already shown to be related to scene novelty and landmark naming. The object-selective responses appeared in the parietal, frontal, and temporal regions connected with tool use and object recognition. The temporal analyses specified the time course of the category selectivity through the dorsal and ventral visual streams. The receiver operating characteristic analyses identified the PPA and the fusiform portion of the LOC as being the most selective for scenes and objects, respectively. Our findings represent a valuable overview of visual processing selectivity for scenes and objects based on iEEG analyses and thus, contribute to a better understanding of visual processing in the human brain.

OBJECTIVEResective epilepsy surgery is an established treatment method for children with focal intractable epilepsy, but the use of this method introduces the risk of postsurgical motor deficits. Electrical stimulation mapping (ESM), used to define motor areas and pathways, frequently fails in children. The authors developed and tested a novel ESM protocol in children of all age categories.METHODSThe ESM protocol utilizes high-frequency electric cortical stimulation combined with continuous intraoperative motor-evoked potential (MEP) monitoring. The relationships between stimulation current intensity and selected presurgical and surgery-associated variables were analyzed in 66 children (aged 7 months to 18 years) undergoing 70 resective epilepsy surgeries in proximity to the motor cortex or corticospinal tracts.RESULTSESM elicited MEP responses in all children. Stimulation current intensity was associated with patient age at surgery and date of surgery (F value = 6.81, p < 0.001). Increase in stimulation current intensity predicted postsurgical motor deficits (F value = 44.5, p < 0.001) without effects on patient postsurgical seizure freedom (p > 0.05).CONCLUSIONSThe proposed ESM paradigm developed in our center represents a reliable method for preventing and predicting postsurgical motor deficits in all age groups of children. This novel ESM protocol may increase the safety and possibly also the completeness of epilepsy surgery. It could be adopted in pediatric epilepsy surgery centers.

The cortical Electric Stimulation Mapping (ESM) procedure is used as a standard approach to localize and continuously monitor function of the eloquent cortex and corticospinal tract during neurosurgical intervention. However, eliciting motor responses using standard ESM paradigm is frequently difficult to young children. We have thus developed and tested a novel EMS protocol, which uses intense, high frequency and short stimulation pulses. However, the intense stimulation peak-peak current (up to 100 mA) possess the potential risk of tissue damage.

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector's specificity and having important implications for future development of spike detectors in general.

High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity. It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO detection in the analysis of long-term intracranial recordings in humans. Obtained results can foster practical implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease human labour and increase the information yield of HFOs.

Interictal epileptiform discharges (spikes) represent electrographic marker of epileptogenic brain tissue. Besides ictal onsets, localization of interictal epileptiform discharges provides additional information to plan resective epilepsy surgery. The main goals of this study were: 1) to develop a reliable automatic algorithm to detect high and low amplitude interictal epileptiform discharges in intracranial EEG recordings and 2) to design a clustering method to extract spatial patterns of their propagation. For detection, we used a signal envelope modeling technique which adaptively identifies statistical parameters of signals containing spikes. Application of this technique to human intracranial EEG data demonstrated that it was superior to expert labeling and it was able to detect even small amplitude interictal epileptiform discharges. In the second task, detected spikes were clustered by principal component analysis according to their spatial distribution. Preliminary results showed that this unsupervised approach is able to identify distinct sources of interictal epileptiform discharges and has the potential to increase the yield of presurgical examination by improved delineation of the irritative zone.

High-frequency oscillations (HFOs) represent relatively new electrographic marker of epileptogenic tissue. It is starting to be used in presurgical examination to better plan surgical resection and to improve outcome of epilepsy surgery. Development of new techniques of unsupervised HFOs detection is required to further investigate the role of HFO in the pathophysiology of epilepsy and to increase the yield of presurgical examination. In this study we applied an envelope distribution modelling technique on experimental and human invasive data to detect HFOs. Application to experimental microelectrode recordings demonstrated satisfactory results with sensitivity 89.9% and false positive rate 2.1 per minute. Application of this algorithm to human invasive recordings achieved sensitivity 80%. High numbers of false positive detections required utilization of postprocessing steps to eliminate the majority of them. This study shows that envelope distribution modelling represents a promising approach to detect HFOs in intracranial recordings. Advantages of this approach are quick adjustments to changes in background activity and resistance to signal nonstationarities. However, successful application to clinical practice requires development of secondary processing steps that will decrease the rate of false positive detections.

This paper presents the possibility of early detection and localization of epileptogenic focus in the iEEG (intracranial Electroencephalography) signal using a method based on multidimensional autoregressive models. The work provides the first results of the method in the iEEG signal, and discusses technical aspects in terms of the suitability of the sampling frequency, AR model order and segmentation step.

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).

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.