Abstract Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
We present LiFi channel measurements in a neurosurgery room of Motol University Hospital in Prague. Individual channels are combined into a virtual multiuser MIMO link. We report achievable data rates for different LiFi transmission schemes.
Resective 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.The 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.ESM 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).The 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.
Abstract Objective Epilepsy surgery in the operculoinsular cortex is challenging due to the difficult delineation of the epileptogenic zone and the high risk of postoperative deficits. Methods Pre‐ and postsurgical data from 30 pediatric patients who underwent operculoinsular cortex surgery at the Motol Epilepsy Center Prague from 2010 to 2022 were analyzed. Results Focal cortical dysplasia (FCD; n = 15, 50%) was the predominant cause of epilepsy, followed by epilepsy‐associated tumors ( n = 5, 17%) and tuberous sclerosis complex ( n = 2, 7%). In eight patients where FCD was the most likely etiology, the histology was negative. Seven patients (23%) displayed normal magnetic resonance imaging results. Seizures exhibited diverse semiology and propagation patterns (frontal, perisylvian, and temporal). The ictal and interictal electroencephalographic (EEG) findings were mostly extensive. Multimodal imaging and advanced postprocessing were frequently used. Stereo‐EEG was used for localizing the epileptogenic zone and eloquent cortex in 23 patients (77%). Oblique electrodes were used as guides for better neurosurgeon orientation. The epileptogenic zone was in the dominant hemisphere in 16 patients. At the 2‐year follow‐up, 22 patients (73%) were completely seizure‐free, and eight (27%) experienced a seizure frequency reduction of >50% (International League Against Epilepsy class 3 and 4). Fourteen patients (47%) underwent antiseizure medication tapering; treatment was completely withdrawn in two (7%). Nineteen patients (63%) remained seizure‐free following the definitive outcome assessment (median = 6 years 5 months, range = 2 years to 13 years 5 months postsurgery). Six patients (20%) experienced corona radiata or basal ganglia ischemia; four (13%) improved to mild and one (3%) to moderate hemiparesis. Two patients (7%) operated on in the anterior insula along with frontotemporal resection experienced major complications: pontine ischemia and postoperative brain edema. Significance Epilepsy surgery in the operculoinsular cortex can lead to excellent patient outcomes. A comprehensive diagnostic approach is crucial for surgical success. Rehabilitation brings a great chance for significant recovery of postoperative deficits.
Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in 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.
The present thesis is focused on identification of offenders based on tracks with biomechanical content. The work is divided into theoretical and practical part. The theoretical part is an introduction to the solved problems, there are briefly introduced possibilities of identifying offenders from the perspective of biometric identification and description of dynamic stereotype of human locomotion. The practical part consists of a description of the nature of identifying people by dynamic stereotype of proposals to ensure buildings guarded commercial security industry companies to deploy camera to obtain forensically applicable kinogram, expected developments in this the final summary.
We comprehensively characterized a large pediatric cohort with focal cortical dysplasia (FCD) type 1 to expand the phenotypic spectrum and to identify predictors of postsurgical outcomes.
