The social cognition of medical knowledge, with special reference to childhood epilepsy

This paper arose out of an engagement in medical communication courses at a Gulf university. It deploys a theoretical framework derived from a (critical) sociocognitive approach to discourse analysis in order to investigate three aspects of medical discourse relating to childhood epilepsy: the cognitive processes that are entailed in relating different types of medical knowledge to their communicative context; the types of medical knowledge that are constituted in the three different text types analysed; and the relationship between these different types of medical knowledge and the discursive features of each text type. The paper argues that there is a cognitive dimension to the human experience of understanding and talking about one specialized from of medical knowledge. It recommends that texts be studied in medical communication courses not just in terms of their discrete formal features but also critically, in terms of the knowledge which they produce, transmit and reproduce

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.

OBJECTIVE: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. METHODS: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort\u27s data were provided in a different format and harmonized using the two versions of the HPO. RESULTS: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals\u27 seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures. SIGNIFICANCE: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020-12-07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data

TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS

The 2016 Epilepsy Innovation Institute (Ei2) community survey reported that unpredictability is the most challenging aspect of seizure management. Effective and precise detection, prediction, and localization of epileptic seizures is a fundamental computational challenge. Utilizing epilepsy data from multiple epilepsy monitoring units can enhance the quantity and diversity of datasets, which can lead to more robust epilepsy data analysis tools. The contributions of this dissertation are two-fold. One is the implementation of a temporal query for epilepsy data; the other is the machine learning approach for seizure detection, seizure prediction, and seizure localization. The three key components of our temporal query interface are: 1) A pipeline for automatically extract European Data Format (EDF) information and epilepsy annotation data from cross-site sources; 2) Data quantity monitoring for Epilepsy temporal data; 3) A web-based annotation query interface for preliminary research and building customized epilepsy datasets. The system extracted and stored about 450,000 epilepsy-related events of more than 2,497 subjects from seven institutes up to September 2019. Leveraging the epilepsy temporal events query system, we developed machine learning models for seizure detection, prediction, and localization. Using 135 extracted features from EEG signals, we trained a channel-based eXtreme Gradient Boosting model to detect seizures on 8-second EEG segments. A long-term EEG recording evaluation shows that the model can detect about 90.34% seizures on an existing EEG dataset with 961 hours of data. The model achieved 89.88% accuracy, 92.32% sensitivity, and 84.76% AUC based on the segments evaluation. We also introduced a transfer learning approach consisting of 1) a base deep learning model pre-trained by ImageNet dataset and 2) customized fully connected layers, to train the patient-specific pre-ictal and inter-ictal data from our database. Two convolutional neural network architectures were evaluated using 53 pre-ictal segments and 265 continuous hours of inter-ictal EEG data. The evaluation shows that our model reached 86.79% sensitivity and 3.38% false-positive rate. Another transfer learning model for seizure localization uses a pre-trained ResNext50 structure and was trained with an image augmentation dataset labeling by fingerprint. Our model achieved 88.22% accuracy, 34.99% sensitivity, 1.02% false-positive rate, and 34.3% positive likelihood rate

TGFβ signaling is associated with changes in inflammatory gene expression and perineuronal net degradation around inhibitory neurons following various neurological insults.

Brain damage due to stroke or traumatic brain injury (TBI), both leading causes of serious long-term disability, often leads to the development of epilepsy. Patients who develop post-injury epilepsy tend to have poor functional outcomes. Emerging evidence highlights a potential role for blood-brain barrier (BBB) dysfunction in the development of post-injury epilepsy. However, common mechanisms underlying the pathological hyperexcitability are largely unknown. Here, we show that comparative transcriptome analyses predict remodeling of extracellular matrix (ECM) as a common response to different types of injuries. ECM-related transcriptional changes were induced by the serum protein albumin via TGFβ signaling in primary astrocytes. In accordance with transcriptional responses, we found persistent degradation of protective ECM structures called perineuronal nets (PNNs) around fast-spiking inhibitory interneurons, in a rat model of TBI as well as in brains of human epileptic patients. Exposure of a naïve brain to albumin was sufficient to induce the transcriptional and translational upregulation of molecules related to ECM remodeling and the persistent breakdown of PNNs around fast-spiking inhibitory interneurons, which was contingent on TGFβ signaling activation. Our findings provide insights on how albumin extravasation that occurs upon BBB dysfunction in various brain injuries can predispose neural circuitry to the development of chronic inhibition deficits

Differential regulation of gene expression pathways with dexamethasone and ACTH after early life seizures.

Early-life seizures (ELS) are associated with persistent cognitive deficits such as ADHD and memory impairment. These co-morbidities have a dramatic negative impact on the quality of life of patients. Therapies that improve cognitive outcomes have enormous potential to improve patients\u27 quality of life. Our previous work in a rat flurothyl-induction model showed that administration of adrenocorticotropic hormone (ACTH) at time of seizure induction led to improved learning and memory in the animals despite no effect on seizure latency or duration. Administration of dexamethasone (Dex), a corticosteroid, did not have the same positive effect on learning and memory and has even been shown to exacerbate injury in a rat model of temporal lobe epilepsy. We hypothesized that ACTH exerted positive effects on cognitive outcomes through beneficial changes to gene expression and proposed that administration of ACTH at seizure induction would return gene-expression in the brain towards the normal pattern of expression in the Control animals whereas Dex would not. Twenty-six Sprague-Dawley rats were randomized into vehicle- Control, and ACTH-, Dex-, and vehicle- ELS. Rat pups were subjected to 60 flurothyl seizures from P5 to P14. After seizure induction, brains were removed and the hippocampus and PFC were dissected, RNA was extracted and sequenced, and differential expression analysis was performed using generalized estimating equations. Differential expression analysis showed that ACTH pushes gene expression in the brain back to a more normal state of expression through enrichment of pathways involved in supporting homeostatic balance and down-regulating pathways that might contribute to excitotoxic cell-damage post-ELS

Application of Modelling Methods from Informatics in Evidence Based Health Care

The paper introduces the idea to use modelling methods from informatics for the further development of the Evidence Based Health Care (EBHC) in terms of both: its technology support as well as an aid for the development of its methodology. In the paper we briefly introduce the contents of the EBHC and its predecessor Evidence Based Medicine (EBM), their evolution, current state, its main limitations and consequent challenges. We also briefly introduce the principle of modelling in informatics and those semi-formal modelling methods and techniques which we regard as relevant for the given purpose, namely conceptual modelling, object life cycle modelling, and business process modelling. Using the example, based on existing EBHC practical guidelines for febrile seizures, we then illustrate the possibilities of the use of modelling techniques for an improvement of the EBHC processes and also the related possibilities of the exploitation of modelling techniques from informatics for further development of the EBHC methodology. In conclusions section we then summarize main opportunities of the application of modelling techniques from informatics in the field of EBHC and outline the needed future work