Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The limbic network is involved in crucial physiologic functions involving memory, emotion and sleep. Temporal lobe epilepsy is frequently drug-resistant, and people often experience comorbidities related to memory, mood and sleep. Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is an established therapy for temporal lobe epilepsy. However, the optimal stimulation parameters and their impact on memory, mood and sleep comorbidities remain unclear. We used an investigational brain sensing-stimulation implanted device to accurately track seizures, interictal epileptiform spikes (IES), and memory, mood and sleep comorbidities in five ambulatory subjects. Wireless streaming of limbic network local field potentials (LFPs) and subject behaviour were captured on a mobile device integrated with a cloud environment. Automated algorithms applied to the continuous LFPs were used to accurately cataloged seizures, IES and sleep-wake brain state. Memory and mood assessments were remotely administered to densely sample cognitive and behavioural response during ANT-DBS in ambulatory subjects living in their natural home environment. We evaluated the effect of continuous low-frequency and duty cycle high-frequency ANT-DBS on epileptiform activity and memory, mood and sleep comorbidities. Both low-frequency and high-frequency ANT-DBS paradigms reduced seizures. However, continuous low-frequency ANT-DBS showed greater reductions in IES, electrographic seizures and better sleep and memory outcomes. These results highlight the potential of synchronized brain sensing and dense behavioural tracking during ANT-DBS for optimizing neuromodulation therapy. While studies with larger patient numbers are needed to validate the benefits of low-frequency ANT-DBS, these findings are potentially translatable to individuals currently implanted with ANT-DBS systems.

• Klíčová slova
• artificial intelligence and machine learning, electrical brain stimulation, epilepsy comorbidities, intracranial EEG,
• Publikační typ
• časopisecké články MeSH

The network nature of focal epilepsy is exemplified by mesial temporal lobe epilepsy (mTLE), characterized by focal seizures originating from the mesial temporal neocortex, amygdala, and hippocampus. The mTLE network hypothesis is evident in seizure semiology and interictal comorbidities, both reflecting limbic network dysfunction. The network generating seizures also supports essential physiological functions, including memory, emotion, mood, and sleep. Pathology in the mTLE network often manifests as interictal behavioral disturbances and seizures. The limbic circuit is a vital network, and here we review one of the most common focal epilepsies and its comorbidities. We describe two people with drug resistant mTLE implanted with an investigational device enabling continuous hippocampal local field potential sensing and anterior nucleus of thalamus deep brain stimulation (ANT-DBS) who experienced reversible psychosis during continuous high-frequency stimulation. The mechanism(s) of psychosis remain poorly understood and here we speculate that the anti-epileptic effect of high frequency ANT-DBS may provide insights into the physiology of primary disorders associated with psychosis.

• Klíčová slova
• ANT-DBS, Epilepsy, limbic network, psychosis, seizure,
• Publikační typ
• časopisecké články MeSH
• kazuistiky MeSH

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

• Klíčová slova
• brain signals, dynamic threshold, epilepsy, morphological filter, ripples, spikes,
• MeSH
• algoritmy MeSH
• elektroencefalografie * metody   MeSH
• epilepsie * diagnóza   MeSH
• lidé MeSH
• mapování mozku MeSH
• mozek MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

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.

• Klíčová slova
• electrophysiology, epilepsy, machine learning, seizures,
• Publikační typ
• časopisecké články MeSH

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

• Klíčová slova
• SEEG (stereoelectroencephalography), biomarker, deep brain stimulation, electrocorticography (ECoG), epilepsy, major depression (MDD), neuromodulation, psychiatric disorders,
• Publikační typ
• časopisecké články MeSH

Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8-13 Hz) relative to a wide band of 0.5-40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

• Klíčová slova
• alpha rhythm, diagnosis of epilepsy, routine EEG, spectral analysis, visual EEG review,
• Publikační typ
• časopisecké články MeSH

Tuberous sclerosis complex (TSC) is a monogenetic disease that arises due to mutations in either the TSC1 or TSC2 gene and affects multiple organ systems. One of the hallmark manifestations of TSC are cortical malformations referred to as cortical tubers. These tubers are frequently associated with treatment-resistant epilepsy. Some of these patients are candidates for epilepsy surgery. White matter abnormalities, such as loss of myelin and oligodendroglia, have been described in a small subset of resected tubers but mechanisms underlying this phenomenon are unclear. Herein, we analyzed a variety of neuropathologic and immunohistochemical features in gray and white matter areas of resected cortical tubers from 46 TSC patients using semi-automated quantitative image analysis. We observed divergent amounts of myelin basic protein as well as numbers of oligodendroglia in both gray and white matter when compared with matched controls. Analyses of clinical data indicated that reduced numbers of oligodendroglia were associated with lower numbers on the intelligence quotient scale and that lower amounts of myelin-associated oligodendrocyte basic protein were associated with the presence of autism-spectrum disorder. In conclusion, myelin pathology in cortical tubers extends beyond the white matter and may be linked to cognitive dysfunction in TSC patients.

• Klíčová slova
• Cognitive dysfunction, Epilepsy, Myelin, Tuberous sclerosis complex, White matter,
• MeSH
• bílá hmota patologie   MeSH
• lidé MeSH
• mozková kůra patologie   MeSH
• myelinová pochva patologie   MeSH
• oligodendroglie patologie   MeSH
• šedá hmota patologie   MeSH
• tuberózní skleróza patologie   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

OBJECTIVE: To identify people with epilepsy who will not achieve a 12-month seizure remission within 5 years of starting treatment. METHODS: The Standard and New Antiepileptic Drug (SANAD) study is the largest prospective study in patients with epilepsy to date. We applied a recently developed multivariable approach to the SANAD dataset that takes into account not only baseline covariates describing a patient's history before diagnosis but also follow-up data as predictor variables. RESULTS: Changes in number of seizures and treatment history were the most informative time-dependent predictors and were associated with history of neurologic insult, epilepsy type, age at start of treatment, sex, and having a first-degree relative with epilepsy. Our model classified 95% of patients. Of those classified, 95% of patients observed not to achieve remission at 5 years were correctly classified (95% confidence interval [CI] 89.5%-100%), with 51% identified by 3 years and 90% within 4 years of follow-up. Ninety-seven percent (95% CI 93.3%-98.8%) of patients observed to achieve a remission within 5 years were correctly classified. Of those predicted not to achieve remission, 76% (95% CI 58.5%-88.2%) truly did not achieve remission (positive predictive value). The predictive model achieved similar accuracy levels via external validation in 2 independent United Kingdom-based datasets. CONCLUSION: Our approach generates up-to-date predictions of the patient's risk of not achieving seizure remission whenever new clinical information becomes available that could influence patient counseling and management decisions.

• MeSH
• antikonvulziva terapeutické užití   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• multivariační analýza MeSH
• prospektivní studie MeSH
• refrakterní epilepsie diagnóza   farmakoterapie   MeSH
• rizikové faktory MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• randomizované kontrolované studie MeSH
• Názvy látek
• antikonvulziva MeSH