Petr Jezdik

Predictors of seizure-free outcome after epilepsy surgery for pediatric tuberous sclerosis complex.

Variable predictors of postsurgical seizure outcome have been reported in children with tuberous sclerosis complex (TSC). We analyzed a large surgical series of pediatric TSC patients in order to identify prognostic factors crucial for selection of subjects for epilepsy surgery.

Detection of Interictal Epileptiform Discharges Using Signal Envelope Distribution Modelling: Application to Epileptic and Non-Epileptic Intracranial Recordings

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.

Automatic detection and spatial clustering of interictal discharges in invasive recordings

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.

Automatic detection of high-frequency oscillations in invasive recordings

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.

Comparison of algorithms for detection of high frequency oscillations in intracranial EEG

High frequency oscillations (HFOs) are novel biomarkers of epileptogenic tissue. Visual identification of HFO in long-term EEG recordings is time consuming due to low HFOs rate, low signal-to-noise ratio and presence of biological and technical artifacts. In this study, we have examined several algorithms of HFOs detection to facilitate analysis of intracranial recordings and increase their diagnostic yield. We have evaluated three newly designed and three published HFOs detectors. Detectors were applied on datasets containing HFOs labeled by experienced readers and their performance evaluated. Results of the detection and properties of the algorithms are reviewed and discussed in respect to clinical practice and their possible utilization during the diagnostic workup in patients with epilepsy.

The Sub-Regional Functional Organization of Neocortical Irritative Epileptic Networks in Pediatric Epilepsy

Between seizures, irritative network generates frequent brief synchronous activity, which manifests on the EEG as interictal epileptiform discharges (IEDs). Recent insights into the mechanism of IEDs at the microscopic level have demonstrated a high variance in the recruitment of neuronal populations generating IEDs and a high variability in the trajectories through which IEDs propagate across the brain. These phenomena represent one of the major constraints for precise characterization of network organization and for the utilization of IEDs during presurgical evaluations. We have developed a new approach to dissect human neocortical irritative networks and quantify their properties. We have demonstrated that irritative network has modular nature and it is composed of multiple independent sub-regions, each with specific IED propagation trajectories and differing in the extent of IED activity generated. The global activity of the irritative network is determined by long-term and circadian fluctuations in sub-region spatiotemporal properties. Also, the most active sub-region co-localizes with the seizure onset zone in 12/14 cases. This study demonstrates that principles of recruitment variability and propagation are conserved at the macroscopic level and that they determine irritative network properties in humans. Functional stratification of the irritative network increases the diagnostic yield of intracranial investigations with the potential to improve the outcomes of surgical treatment of neocortical epilepsy.

Intraoperative thermography in safety control of the electrical stimulation mapping

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.

Circadian Dynamics of High Frequency Oscillations in Patients with Epilepsy

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.

Case study of intracranial EEG records of patients with focal cortical dysplasia type I and II

In this study we try to find out if it is possible to differentiate type of focal cortical dysplasia by features obtained from intracranial EEG. We compare occurrence and rates of three biomarkers present in epilepsy in patients with focal cortical dysplasia type I and II. Case study is made on long term night records of 6 pediatric patients. Detection of interictal epileptiform discharges and high-frequency oscillations is made by automated algorithms, delta brush are marked visually. Position of lesion and electrodes inside were obtained from MRI. In individual rates were not found difference on significant level. No major significance were found, but as promising seem to be ratio inside to outside rates of high-frequency oscillations and presence of delta brush, which were found only in patients with focal cortical dysplasia type II.

Analysis of high-frequency activity in electroencephalogram of animal epilepsy model

High frequency oscillations (HFO) are believed to be a new specific biomarker of epileptogenic tissue. According to the reported findings, we hypothesized that HFOs occur more specific to the epileptogenic tissue. To test this hypothesis, we analyzed intracranial electroencephalograms (iEEG) of three subjects of animal epilepsy model. Overall 180 minutes of iEEG records were processed by the automatic high frequency activity detector and further analyzed. The dominant frequency of each segment was determined and categorized as ripples (80-200 Hz) or fast ripples (200-1000 Hz). Even though overall number of HFO detections predominate in the hemisphere where epileptic focus is located, in comparison of each brain structure separately our hypothesis cannot be fully confirmed.