Comparing the efficacy of anti-seizure medications using matched cohorts on a large insurance claims database.

Epilepsy is a severe chronic neurological disease affecting 60 million people worldwide. Primary treatment is with anti-seizure medicines (ASMs), but many patients continue to experience seizures. We used retrospective insurance claims data on 280,587 patients with uncontrolled epilepsy (UE), defined as status epilepticus, need for a rescue medicine, or admission or emergency visit for an epilepsy code. We conducted a computational risk ratio analysis between pairs of ASMs using a causal inference method, in order to match 1034 clinical factors and simulate randomization. Data was extracted from the MarketScan insurance claims Research Database records from 2011 to 2015. The cohort consisted of individuals over 18 years old with a diagnosis of epilepsy who took one of eight ASMs and had more than a year of history prior to the filling of the drug prescription. Seven ASM exposures were analyzed: topiramate, phenytoin, levetiracetam, gabapentin, lamotrigine, valproate, and carbamazepine or oxcarbazepine (treated as the same exposure). We calculated the risk ratio of UE between pairs of ASM after controlling for bias with inverse propensity weighting applied to 1034 factors, such as demographics, confounding illnesses, non-epileptic conditions treated by ASMs, etc. All ASMs exhibited a significant reduction in the prevalence of UE, but three drugs showed pair-wise differences compared to other ASMs. Topiramate consistently was associated with a lower risk of UE, with a mean risk ratio range of 0.68-0.93 (average 0.82, CI: 0.56-1.08). Phenytoin and levetiracetam were consistently associated with a higher risk of UE with mean risk ratio ranges of 1.11 to 1.47 (average 1.13, CI 0.98-1.65) and 1.15 to 1.43 (average 1.2, CI 0.72-1.69), respectively. Large-scale retrospective insurance claims data - combined with causal inference analysis - provides an opportunity to compare the effect of treatments in real-world data in populations 1,000-fold larger than those in typical randomized trials. Our causal analysis identified the clinically unexpected finding of topiramate as being associated with a lower risk of UE; and phenytoin and levetiracetam as associated with a higher risk of UE (compared to other studied drugs, not to baseline). However, we note that our data set for this study only used insurance claims events, which does not comprise actual seizure frequencies, nor a clear picture of side effects. Our results do not advocate for any change in practice but demonstrate that conclusions from large databases may differ from and supplement those of randomized trials and clinical practice and therefore may guide further investigation.

Quantization of events in the event-universe and the emergence of quantum mechanics.

Quantum mechanics (QM) is derived based on a universe composed solely of events, for example, outcomes of observables. Such an event universe is represented by a dendrogram (a finite tree) and in the limit of infinitely many events by the p-adic tree. The trees are endowed with an ultrametric expressing hierarchical relationships between events. All events are coupled through the tree structure. Such a holistic picture of event-processes was formalized within the Dendrographic Hologram Theory (DHT). The present paper is devoted to the emergence of QM from DHT. We used the generalization of the QM-emergence scheme developed by Smolin. Following this scheme, we did not quantize events but rather the differences between them and through analytic derivation arrived at Bohmian mechanics. We remark that, although Bohmian mechanics is not the main stream approach to quantum physics, it describes adequately all quantum experiments. Previously, we were able to embed the basic elements of general relativity (GR) into DHT, and now after Smolin-like quantization of DHT, we can take a step toward quantization of GR. Finally, we remark that DHT is nonlocal in the treelike geometry, but this nonlocality refers to relational nonlocality in the space of events and not Einstein's spatial nonlocality. By shifting from spatial nonlocality to relational we make Bohmian mechanics less exotic.

Rao-Fisher information geometry and dynamics of the event-universe views distributions.

We developed the novel mathematical model for event-universe by representing events as branches of dendrograms (finite trees) expressing the hierarchic relation between events. At the ontic level we operate with infinite trees. Algebraically such mathematical structures are represented as p-adic numbers. We call this kind of event mechanics Dendrogramic Holographic theory (DHT). It can be considered as a fundamental theory generating both GR and QM. In this paper we endower DHT with Rao-Cramer's information geometry. Following Smolin's derivation of QM from the event-universe, we introduce views from one event to others and by using their probability distributions we invent stochastic geometry. The important mathematical result is that all such views' distributions can be parametrized by four real parameters that are a part of the shape complexity measure introduced by Barbour in his particle shape dynamics theory - adapted to DHT. Hence, within DHT all possible event-universes can be embedded in four-dimensional real space. Asin GR, we introduce proper time. This "proper time" depends only on the change between one distribution of an observer to the other. The linkage of time to change is highlighted in the ideology of Rovelli and Barbour's shape dynamics.

Anti-epileptogenic effect of FC99 and resveratrol.

Toll-like receptor 3 (TLR3), plays an important role in the development of epilepsy after brain insults. Previously, TLR3 deficiency in a pilocarpine model of temporal lobe epilepsy (TLE) was shown to reduce mortality, spontaneous recurrent seizures (SRS) and neuroinflammation. We hypothesized that pharmacological inhibition of TLR3 would reduce epileptogenesis following status epilepticus. We show that Resveratrol and FC99, two TLR3 blockers, demonstrate anti-epileptogenic effects in a pilocarpine model of TLE. While both Resveratrol and FC99 were previously shown to benefit in other pathologies, neither of these blockers had been proposed for the treatment of epilepsy. Our results provide substantial evidence to the importance of TLR3 inhibition in the prevention of epilepsy and specifically highlighting FC99 as a promising novel anti-epileptic drug. We anticipate our data to be a starting point for further studies assessing the anti-epileptogenic potential of FC99 and other TLR3 blockers, paving the way for pharmacological interventions that prevent epileptogenesis.

Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning.

Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.

Epilepsy following herpes simplex encephalitis - A case series.

BACKGROUND: Herpes simplex encephalitis (HSE) is associated with severe mortality and morbidity. Its incidence is estimated at 1:250 000, and the typical symptomatology of acute disease including headaches, mental state disturbances, confusion, sleepiness, and seizures. The chronic phase of the disease is occasionally characterized by epilepsy and neurological deficits. STUDY RATIONALE: The present retrospective single-center study aims to identify risk factors for predicting the development of epilepsy (epileptogenesis) following HSE. METHODS: Medical records were screened for patients older than 18 years, hospitalized between January 2005 and September 2019 with a diagnosis of "encephalitis" and "herpes simplex virus, HSV" infection. HSE diagnosis was based on an analysis of the cerebrospinal fluid with positive HSV testing results. RESULTS: Twenty-three patients fit our inclusion criteria: fever and behavioral changes, followed by seizures, were reported in 58.3 % of patients. On follow-up (59.7 ± 38.8 months), eight patients (34.8 %) developed epilepsy. Pathological imaging and EEG were correlated with acute symptomatic seizures (ASS). ASS was associated with an 8-fold risk increase to develop post-encephalitis epilepsy (PE). PE was associated with younger age but not with CSF results, imaging, or EEG. CONCLUSION: Our retrospective single-center study on PE, following HSE, shows that younger age and ASS were associated with PE. Brain imaging, CSF analysis, and EEG were not associated with the development of epilepsy following HSE.

[CHANGING CONCEPTS IN THE CLINICAL APPROACH FOR EPILEPSY SURGERY].

INTRODUCTION: Epilepsy is a common disease state, occurring in approximately 1% of the population worldwide, including both pediatric and adult populations. It is characterized by recurrent episodes of unpredictable pathologic cortical brain activity. One-third of patients develop drug intractability and experience recurrent seizures, despite optimal treatment. These result in cognitive decline, behavioral changes, decreased quality of life, and increased risk for trauma and death (SUDEP- sudden unprovoked death from epilepsy). Therefore, the international league against epilepsy (ILAE) recommends referral of intractable patients to highly specialized epilepsy centers, for further evaluation for epilepsy surgery.

EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia.

The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.

Corticotropin Releasing Factor Mediates KCa3.1 Inhibition, Hyperexcitability, and Seizures in Acquired Epilepsy.

Temporal lobe epilepsy (TLE), the most common focal seizure disorder in adults, can be instigated in experimental animals by convulsant-induced status epilepticus (SE). Principal hippocampal neurons from SE-experienced epileptic male rats (post-SE neurons) display markedly augmented spike output compared with neurons from nonepileptic animals (non-SE neurons). This enhanced firing results from a cAMP-dependent protein kinase A-mediated inhibition of KCa3.1, a subclass of Ca2+-gated K+ channels generating the slow afterhyperpolarizing Ca2+-gated K+ current (IsAHP). The inhibition of KCa3.1 in post-SE neurons leads to a marked reduction in amplitude of the IsAHP that evolves during repetitive firing, as well as in amplitude of the associated Ca2+-dependent component of the slow afterhyperpolarization potential (KCa-sAHP). Here we show that KCa3.1 inhibition in post-SE neurons is induced by corticotropin releasing factor (CRF) through its Type 1 receptor (CRF1R). Acute application of CRF1R antagonists restores KCa3.1 activity in post-SE neurons, normalizing KCa-sAHP/IsAHP amplitudes and neuronal spike output, without affecting these variables in non-SE neurons. Moreover, pharmacological antagonism of CRF1Rs in vivo reduces the frequency of spontaneous recurrent seizures in post-SE chronically epileptic rats. These findings may provide a new vista for treating TLE.SIGNIFICANCE STATEMENT Epilepsy, a common neurologic disorder, often develops following a brain insult. Identifying key cellular mechanisms underlying acquired epilepsy is critical for developing effective antiepileptic therapies. In an experimental model of acquired epilepsy, principal hippocampal neurons manifest hyperexcitability because of downregulation of KCa3.1, a subtype of Ca2+-gated K+ ion channels. We show that KCa3.1 downregulation is mediated by corticotropin releasing factor (CRF) acting through its Type 1 receptor (CRF1R). Congruently, acute application of selective CRF1R antagonists restores KCa3.1 channel activity, leading to normalization of neuronal excitability. In the same model, injection of a CRF1R antagonist to epileptic animals markedly decreases the frequency of electrographic seizures. Therefore, targeting CRF1Rs may provide a new strategy in the treatment of acquired epilepsy.

Computational normal mode analysis accurately replicates the activity and specificity profiles of CRISPR-Cas9 and high-fidelity variants.

The CRISPR-Cas system has transformed the field of gene-editing and created opportunities for novel genome engineering therapeutics. The field has significantly progressed, and recently, CRISPR-Cas9 was utilized in clinical trials to target disease-causing mutations. Existing tools aim to predict the on-target efficacy and potential genome-wide off-targets by scoring a particular gRNA according to an array of gRNA design principles or machine learning algorithms based on empirical results of large numbers of gRNAs. However, such tools are unable to predict the editing outcome by variant Cas enzymes and can only assess potential off-targets related to reference genomes. Here, we employ normal mode analysis (NMA) to investigate the structure of the Cas9 protein complexed with its gRNA and target DNA and explore the function of the protein. Our results demonstrate the feasibility and validity of NMA to predict the activity and specificity of SpyCas9 in the presence of mismatches by comparison to empirical data. Furthermore, despite the absence of their exact structures, this method accurately predicts the enzymatic activity of known high-fidelity engineered Cas9 variants.

Towards Unification of General Relativity and Quantum Theory: Dendrogram Representation of the Event-Universe.

Following Smolin, we proceed to unification of general relativity and quantum theory by operating solely with events, i.e., without appealing to physical systems and space-time. The universe is modelled as a dendrogram (finite tree) expressing the hierarchic relations between events. This is the observational (epistemic) model; the ontic model is based on p-adic numbers (infinite trees). Hence, we use novel mathematics: not only space-time but even real numbers are not in use. Here, the p-adic space (which is zero-dimensional) serves as the base for the holographic image of the universe. In this way our theory is connected with p-adic physics; in particular, p-adic string theory and complex disordered systems (p-adic representation of the Parisi matrix for spin glasses). Our Dendrogramic-Holographic (DH) theory matches perfectly with the Mach's principle and Brans-Dicke theory. We found a surprising informational interrelation between the fundamental constants, h, c, G, and their DH analogues, h(D), c(D), G(D). DH theory is part of Wheeler's project on the information restructuring of physics. It is also a step towards the Unified Field theory. The universal potential V is nonlocal, but this is relational DH nonlocality. V can be coupled to the Bohm quantum potential by moving to the real representation. This coupling enhances the role of the Bohm potential.

In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease.

TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype-phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype-phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype-phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

Paroxysmal slow wave events predict epilepsy following a first seizure.

OBJECTIVE: Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG-based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure. METHODS: We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow-up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs). RESULTS: Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%. SIGNIFICANCE: The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure.

Non-infectious mechanisms of neurological damage due to infection.

Infections of the nervous system is a growing aspect of clinical neurology. Accumulating knowledge in early diagnosis, course, therapy and prognosis is enlarging the clinical tools required for effective therapy. Of special importance is the ability to differentiate between proper infections, where anti-microbial agents, when available, should be introduced and used and post infectious conditions where therapy is mainly directed against the host immune system. The two conditions sometimes overlap, a situation that requires the ability to combine clinical skills with the use of laboratory tools such as polymerase chain reaction (PCR), serology, and antigenic detection. In the era of the SARS-CoV-2 pandemic, the need to make this distinction is emphasized as correct diagnosis of post infectious conditions and expedited therapy is important and sometimes lifesaving. We here attempt to present several infectious agents and their possible indirect damage to the nervous system causing in some cases significant neurological deficits. We try to limit our focus on those mechanisms which do not involve the direct tissue damage by the infectious agents but rather are connected to para- and post-infectious mechanisms. We attempt to delineate the features that will enable to tailor the correct diagnosis and following the effective therapy.

Herpes simplex encephalitis in patients receiving chemotherapy and whole-brain radiation therapy.

Herpes simplex encephalitis (HSE) is a very severe infection of the central nervous system (CNS) caused mainly by herpes simplex virus type 1 (HSV-1) and occasionally by herpes simplex virus type 2 (HSV-2). After relapse or drug-resistant to chemotherapy, whole-brain radiation therapy (WBRT) is a mainstay of treatment in patients with both identifiable brain metastases and CNS lymphoma. Although HSV-1 encephalitis predominantly affects immunocompetent host, HSV encephalitis may be more common in immune-suppressed patients than is currently recognized. Disease presentation may be atypical including lack of pleocytosis in cerebrospinal fluid (CSF). We report four patients diagnosed with HSE following chemotherapy and WBRT. The occurrence of HSE in patients with cancer seems not to be increased compared to the general population, but as our case series shows, a high level of suspicion is needed by the treating physician to diagnose HSE early in patients presenting with new neurological symptoms following WBRT.

Dendrogramic Representation of Data: CHSH Violation vs. Nonergodicity.

This paper is devoted to the foundational problems of dendrogramic holographic theory (DH theory). We used the ontic-epistemic (implicate-explicate order) methodology. The epistemic counterpart is based on the representation of data by dendrograms constructed with hierarchic clustering algorithms. The ontic universe is described as a p-adic tree; it is zero-dimensional, totally disconnected, disordered, and bounded (in p-adic ultrametric spaces). Classical-quantum interrelations lose their sharpness; generally, simple dendrograms are "more quantum" than complex ones. We used the CHSH inequality as a measure of quantum-likeness. We demonstrate that it can be violated by classical experimental data represented by dendrograms. The seed of this violation is neither nonlocality nor a rejection of realism, but the nonergodicity of dendrogramic time series. Generally, the violation of ergodicity is one of the basic features of DH theory. The dendrogramic representation leads to the local realistic model that violates the CHSH inequality. We also considered DH theory for Minkowski geometry and monitored the dependence of CHSH violation and nonergodicity on geometry, as well as a Lorentz transformation of data.

EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline.

No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer's disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.