MEG-based identification of the epileptogenic zone in occult peri-insular epilepsy

AbstractIntroductionPresurgical work-ups of patients with pharmacoresistant epileptic seizures can require multiple diagnostic methods if magnetic resonance imaging (MRI) combined with video-EEG monitoring fails to show an epileptogenic lesion. Yet, the added value of available methods is not clear. In particular, only a minority of epilepsy centres apply magnetoencephalography (MEG). This study explores the potential of MEG for patients whose previous sophisticated work-ups missed deep-seated, peri-insular epileptogenic lesions.Patients and methodsThree patients with well documented, frequent, stereotypical hypermotor seizures without clear focus hypotheses after repeated presurgical work-ups including video-EEG-monitoring, 3Tesla (3T) magnetic resonance imaging (MRI), morphometric MRI analysis, PET and SPECT were referred to MEG source localisation.ResultsIn two out of three patients, MEG source localisation identified very subtle morphological abnormalities formerly missed in MRI or classified as questionable pathology. In the third patient, MEG was not reliable due to insufficient detection of epileptic patterns. Here, a 1mm×1mm×1mm 3T fluid-attenuated inversion recovery (FLAIR) MRI revealed a potential epileptogenic lesion. A minimal invasive work-up via lesion-focused depth electrodes confirmed the intralesional seizure onset in all patients, and histology revealed dysplastic lesions. Seizure outcomes were Engel 1a in two patients, and Engel 1d in the third.DiscussionMEG can contribute to the identification of epileptogenic lesions even when multiple previous methods failed, and when the lesions are located in deep anatomical structures such as peri-insular cortex. For epilepsy centres without MEG capability, referral of patients with cryptogenic focal epilepsies to centres with MEG systems may be indicated

Influence of phase composition on electrophysical properties of barium titanate piezoelectric ceramics

In the present study, we investigated the influence of phase transformations and dielectric properties of barium titanate ceramics. The obtained ceramic samples were sintered at 950 ° C and 1350 ° C. It was revealed that sintering of ceramics at 1350 ° C allows obtaining a stable tetragonal phase at room temperature

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

Objective: To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. Methods: Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation. Results: The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients. Conclusions: Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data. Significance: The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.Peer reviewe

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

Telemedizin in der Epilepsieversorgung: Arzt-zu-Arzt-Anwendungen - Teil II: Aktuelle Projekte in Deutschland

Background During the last 10 years several German epilepsy centers (Bochum, Erlangen, Greifswald, Berlin Brandenburg, Frankfurt Rhein-Main) developed telemedicine projects, which offer doc-to-doc applications in the field of epilepsy care. Objective To give an overview of the currently running telemedical projects in epilepsy care in Germany. Material and methods Project leaders present their work using a predefined schematic. Results and discussion All projects achieved technical solutions for the telemedical doc-to-doc application in the field of epileptology. The presented projects partly differ with regards to their goals and implementation, partly they share similarities. All projects aim to use their experience in the individual projects to develop a common strategy for the facilitation of epileptological telemedicine and its transfer into standard care.Hintergrund In den vergangenen 10 Jahren wurden an verschiedenen Epilepsiezentren in Deutschland (Bochum, Erlangen, Greifswald, Berlin Brandenburg, Frankfurt Rhein-Main) Projekte entwickelt, die sich mit telemedizinischen Arzt-zu-Arzt-Anwendungen im Bereich der Epilepsieversorgung beschäftigen. Ziel der Arbeit Im Folgenden wird ein Überblick über die aktuell laufenden telemedizinischen Projekte in der Epilepsieversorgung in Deutschland gegeben. Material und Methoden Die Verantwortlichen der einzelnen Projekte stellen ihr Projekt anhand einer vorgegebenen Struktur dar. Ergebnisse und Diskussion In allen Projekten konnte gezeigt werden, dass eine technische Lösung für die telemedizinische Arzt-zu-Arzt Anwendung im Bereich Epileptologie geschaffen werden kann. Die dargestellten Projekte unterscheiden sich zum Teil hinsichtlich des Zieles und der Umsetzung, zum Teil zeigen sich Übereinstimmungen. Perspektivisches Ziel ist es, aus den Erfahrungen der einzelnen Projekte eine gemeinsame Strategie zur Förderung epileptologischer Telemedizin und ihrer Überführung in die Regelversorgung zu entwickeln

Telemedizin in der Epilepsieversorgung: Arzt-zu-Arzt-Anwendungen - Teil I: State-of-the-Art, Herausforderungen, Perspektiven

Telemedical doc-to-doc applications in epilepsy care can help to provide the special expertise of adult or pediatric epileptologists area wide, as they make it possible to provide medical services across spatial distances. Various solutions are being developed both nationally and internationally for this purpose; however, there are organizational, technical, legal and economic challenges. The long-term perspective of the various current approaches is unclear. Ultimately, business models will have to be developed in which all players (consultation providers and requesting physician, patients, health insurers, operators of telemedical platforms and, if necessary, the respective professional associations) weigh up the specific benefits and risks.Telemedizinische Arzt-zu-Arzt-Anwendungen in der Epilepsieversorgung können helfen, die spezielle Expertise von neurologischen oder pädiatrischen EpileptologInnen flächendeckend vorzuhalten, da sie es ermöglichen, medizinische Leistung über Distanzen hinweg zu erbringen. Sowohl national als auch international werden hierzu verschiedene Lösungsansätze entwickelt. Herausforderungen begegnet man auf organisatorischer, technischer, rechtlicher und ökonomischer Ebene, sodass die langfristige Perspektive der einzelnen aktuellen Lösungsansätze noch unklar ist. Letztendlich bedarf es der Entwicklung von Betriebsmodellen, bei denen alle Akteure (Konsilgeber, Konsilanforderer, Patient, Kostenträger, Betreiber der telemedizinischen Plattform und ggf. auch die jeweilige Fachgesellschaft) jeweils den spezifischen Nutzen und die Risiken abwägen

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called "SpikeDeep-Classifier" is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that "SpikeDeep-Classifier" possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting

Method of analysis the main probability‐time characteristics of the computing system architectures using analytical modeling

В статье рассматриваются вопросы создания аналитических моделей высокопроизводительных вычислительных систем на основе одноядерных и многоядерных процессоров с помощью аппарата стохастических сетей массового обслуживания. Проводится краткий обзор технологии многоядерных процессоровThe article deals with the issues of creating analytical models of high‐performance computing systems based on single‐core and multi‐core processes using stochastic queueing networks. A brief review of multi‐core processor technology is carried out

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation