PURPOSE: The aim of this study was to evaluate seizure outcome in children with hematological malignancies and PRES and to identify prognostic factors that could help manage the syndrome. METHOD: We retrospectively reviewed the report data of 21 patients diagnosed with hematological malignancy or aplastic anemia and PRES between 2008 and 2018. Basic demographic data, oncology treatment, presymptomatic hypertension before PRES manifestation, neurological status, seizure type, and EEG and MRI findings at PRES onset and at the one-year follow-up visit were studied. Patients who developed remote symptomatic seizures or epilepsy were identified. RESULTS: We included 21 children (11 females and 10 males) in the study. Sixteen patients (76.2%) were diagnosed with ALL and the rest individually with AML, CML, T-lymphoma, Burkitt lymphoma, and severe aplastic anemia. Presymptomatic hypertension (PSH) was evaluated in 19 patients and was present in 18 (94.7%). The duration was 9 h and more in 16 patients (88.8%); the severity was grade II in 12 patients (66.7%). Seizures as the initial symptom of PRES were present in 17 patients (80.9%). Four patients (19.0%) were assessed with remote symptomatic seizures. Two of them (9.5%) had ongoing seizures at the one-year follow-up visit and were diagnosed with epilepsy. The presence of gliosis on follow-up MRI indicated worse outcome with development of epilepsy (without statistical significance). CONCLUSIONS: PRES syndrome has an overall good prognosis and the evolution to epilepsy is rare. The severity and duration of PSH or seizure severity and EEG findings at PRES onsetwere not associated with worse neurological outcomes in this study.

• Klíčová slova
• Children, MRI, Oncology, PRES, Prognosis, Seizure,
• MeSH
• dítě MeSH
• elektroencefalografie metody   MeSH
• hematologické nádory komplikace   diagnostické zobrazování   patofyziologie   MeSH
• kohortové studie MeSH
• lidé MeSH
• mladiství MeSH
• následné studie MeSH
• předškolní dítě MeSH
• prognóza MeSH
• retrospektivní studie MeSH
• syndrom zadní leukoencefalopatie komplikace   diagnostické zobrazování   patofyziologie   MeSH
• syndromy selhání kostní dřeně komplikace   diagnostické zobrazování   patofyziologie   MeSH
• záchvaty komplikace   diagnostické zobrazování   patofyziologie   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Epilepsy is a disorder that affects around 1% of the population. Approximately one third of patients do not respond to anti-convulsant drugs treatment. To understand the underlying biological processes involved in drug resistant epilepsy (DRE), a combination of proteomics strategies was used to compare molecular differences and enzymatic activities in tissue implicated in seizure onset to tissue with no abnormal activity within patients. Label free quantitation identified 17 proteins with altered abundance in the seizure onset zone as compared to tissue with normal activity. Assessment of oxidative protein damage by protein carbonylation identified additional 11 proteins with potentially altered function in the seizure onset zone. Pathway analysis revealed that most of the affected proteins are involved in energy metabolism and redox balance. Further, enzymatic assays showed significantly decreased activity of transketolase indicating a disruption of the Pentose Phosphate Pathway and diversion of intermediates into purine metabolic pathway, resulting in the generation of the potentially pro-convulsant metabolites. Altogether, these findings suggest that imbalance in energy metabolism and redox balance, pathways critical to proper neuronal function, play important roles in neuronal network hyperexcitability and can be used as a primary target for potential therapeutic strategies to combat DRE. SIGNIFICANCE: Epileptic seizures are some of the most difficult to treat neurological disorders. Up to 40% of patients with epilepsy are resistant to first- and second-line anticonvulsant therapy, a condition that has been classified as refractory epilepsy. One potential therapy for this patient population is the ketogenic diet (KD), which has been proven effective against multiple refractory seizure types However, compliance with the KD is extremely difficult, and carries severe risks, including ketoacidosis, renal failure, and dangerous electrolyte imbalances. Therefore, identification of pathways disruptions or shortages can potentially uncover cellular targets for anticonvulsants, leading to a personalized treatment approach depending on a patient's individual metabolic signature.

• Klíčová slova
• Drug resistant epilepsy, Protein carbonylation, Proteomics, Seizure,
• MeSH
• antikonvulziva terapeutické užití   MeSH
• energetický metabolismus MeSH
• epilepsie * farmakoterapie   MeSH
• lidé MeSH
• oxidace-redukce MeSH
• záchvaty * farmakoterapie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• Názvy látek
• antikonvulziva MeSH

PURPOSE: The study aim was to evaluate pharmacotherapy effects and long-term seizure outcomes in patients with juvenile absence epilepsy (JAE) during a five-year follow-up period. The secondary aim was to identify factors from patient history and determine their influence on seizure control. METHOD: We retrospectively studied 46 patients with JAE in the period between 2006 and 2011. The age at seizure onset, onset seizure type, family history of epilepsy, status epilepticus in history, medication history, and the rate of seizure control were studied. RESULTS: There were 30 females (65.2%) and 16 males (34.8%) in the study. The mean age at seizure onset was 12.9±5.6 years (ranged from 3 to 28 years). In 30 patients (65.2%), seizure onset was with absences, in 15 patients (32.6%) with generalized tonic-clonic seizure (GTCS), and in 1 patient (2.2%) with absence status. In 43 patients (93.5%), GTCS occurred in the course of the disease. Family history for epilepsy was positive in 10 patients (21.7%). In the five-year follow-up period, seizure freedom (Group 1) was achieved in 7 patients (15.2%). In total, 22 patients (47.8%) were classified into the groups involving very poor seizure control and antiepileptic drug resistance (Groups 5 and 6). The mean number of antiepileptic drugs (AEDs) used in the course of the disease in appropriate therapeutic doses was 3.8±2.3 (1-10 AEDs). CONCLUSION: The study results show that almost half of JAE patients have poor seizure control with a high rate of pharmacoresistance. The outcome of JAE can be very uncertain.

• Klíčová slova
• Epilepsy, JAE, Juvenile absence epilepsy, Outcome, Seizure control, Therapy,
• MeSH
• absentní epilepsie farmakoterapie   patofyziologie   MeSH
• antikonvulziva terapeutické užití   MeSH
• centra terciární péče MeSH
• dítě MeSH
• dospělí MeSH
• kombinovaná farmakoterapie MeSH
• léková rezistence MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• následné studie MeSH
• předškolní dítě MeSH
• retrospektivní studie MeSH
• výsledek terapie MeSH
• záchvaty farmakoterapie   patofyziologie   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• antikonvulziva MeSH

There is a paucity of data to guide anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) with brain sensing. The clinical Medtronic Percept DBS device provides constrained brain sensing power within a frequency band (power-in-band [PIB]), recorded in 10-min averaged increments. Here, four patients with temporal lobe epilepsy were implanted with an investigational device providing full bandwidth chronic intracranial electroencephalogram (cEEG) from bilateral ANT and hippocampus (Hc). ANT PIB-based seizure detection was assessed. Detection parameters were cEEG PIB center frequency, bandwidth, and epoch duration. Performance was evaluated against epileptologist-confirmed Hc seizures, and assessed by area under the precision-recall curve (PR-AUC). Data included 99 days of cEEG, and 20, 278, 3, and 18 Hc seizures for Subjects 1-4. The best detector had 7-Hz center frequency, 5-Hz band width, and 10-s epoch duration (group PR-AUC = .90), with 75% sensitivity and .38 false alarms per day for Subject 1, and 100% and .0 for Subjects 3 and 4. Hc seizures in Subject 2 did not propagate to ANT. The relative change of ANT PIB was maximal ipsilateral to seizure onset for all detected seizures. Chronic ANT and Hc recordings provide direct guidance for ANT DBS with brain sensing.

• Klíčová slova
• chronic brain recordings, deep brain stimulation, neuromodulation, seizure detection,
• MeSH
• epilepsie * terapie   MeSH
• hipokampus diagnostické zobrazování   MeSH
• hluboká mozková stimulace * MeSH
• lidé MeSH
• nuclei anteriores thalami * fyziologie   MeSH
• thalamus MeSH
• záchvaty diagnóza   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

We demonstrate the feasibility of lowering the seizure threshold using a combined approach of electroconvulsive therapy and transcranial magnetic stimulation. High-frequency transcranial magnetic stimulation of the supplementary motor area shortly before each electroconvulsive treatment session resulted in a reduction of the seizure threshold by half in a male patient with a severe psychotic depressive episode of bipolar affective disorder.

• Klíčová slova
• Adverse effect, Lowering seizure threshold, RUL ECT, TMS pre-stimulation, Ultra-brief pulse,
• MeSH
• bipolární porucha komplikace   terapie   MeSH
• elektrokonvulzívní terapie metody   MeSH
• lidé MeSH
• senioři MeSH
• transkraniální magnetická stimulace metody   MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• dopisy MeSH
• kazuistiky MeSH
• práce podpořená grantem MeSH

OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy. APPROACH: The system was trained and tested on 75 seizures collected over 1608 d utilizing a genetic algorithm to optimize forecasting hyper-parameters (prediction horizon (PH), median filter window length, and probability threshold) for each subject-specific seizure forecasting model. The trained CNN models were deployed on a hand-held tablet computer and tested on testing iEEG datasets from four canines. The results from the iEEG testing datasets were compared with Monte Carlo simulations using a Poisson random predictor with equal time in warning to evaluate seizure forecasting performance. MAIN RESULTS: The results show the CNN models forecasted seizures at rates significantly above chance in all four dogs (p < 0.01, with mean 0.79 sensitivity and 18% time in warning). The deep learning method presented here surpassed the performance of previously reported methods using computationally expensive features with standard machine learning methods like logistic regression and support vector machine classifiers. SIGNIFICANCE: Our findings principally support the feasibility of deploying trained CNN models on a hand-held computational device (Mayo Epilepsy Assist Device) that analyzes streaming iEEG data for real-time seizure forecasting.

• MeSH
• deep learning * MeSH
• elektrokortikografie přístrojové vybavení   metody   MeSH
• epilepsie diagnóza   patofyziologie   MeSH
• implantované elektrody * MeSH
• předpověď MeSH
• psi MeSH
• záchvaty diagnóza   patofyziologie   MeSH
• zvířata MeSH
• Check Tag
• psi MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.

• MeSH
• algoritmy MeSH
• biologické markery MeSH
• elektrokortikografie MeSH
• epilepsie parciální diagnóza   MeSH
• lidé MeSH
• modely neurologické MeSH
• mozek patofyziologie   MeSH
• rizikové faktory MeSH
• záchvaty diagnóza   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• kazuistiky MeSH
• práce podpořená grantem MeSH
• Názvy látek
• biologické markery MeSH

OBJECTIVE: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS: This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS: For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE: Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

• Klíčová slova
• circadian, forecasting, seizure, sleep, weather,
• MeSH
• Bayesova věta MeSH
• časové faktory * MeSH
• dospělí MeSH
• elektrokortikografie MeSH
• epilepsie patofyziologie   MeSH
• hodnocení rizik MeSH
• lidé středního věku MeSH
• lidé MeSH
• počasí * MeSH
• rizikové faktory MeSH
• spánek * MeSH
• záchvaty epidemiologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.

• Klíčová slova
• Open Data Ecosystem for the Neurosciences, ensemble methods, epilepsy, intracranial EEG, refractory epilepsy, seizure prediction,
• MeSH
• algoritmy * MeSH
• crowdsourcing MeSH
• elektroencefalografie MeSH
• elektrokortikografie metody   MeSH
• epilepsie parciální diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• prediktivní hodnota testů MeSH
• refrakterní epilepsie diagnóza   MeSH
• reprodukovatelnost výsledků MeSH
• senzitivita a specificita MeSH
• spánek MeSH
• strojové učení MeSH
• studie proveditelnosti MeSH
• záchvaty diagnóza   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.

• MeSH
• algoritmy MeSH
• crowdsourcing metody   MeSH
• dospělí MeSH
• elektroencefalografie metody   MeSH
• epilepsie patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mozek diagnostické zobrazování   patofyziologie   MeSH
• prediktivní hodnota testů MeSH
• předpověď metody   MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH