Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns

Background: Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. New method: We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. Results: Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 +/- 4.44 min to label the 30.19 +/- 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. Comparison with existing methods: Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. Conclusions: Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method

Biomarkers of Sudden Unexpected Death in Epilepsy (SUDEP)

La SUDEP (Sudden Unexpected Death in Epilepsy) è una complicanza devastante dell’epilessia e rappresenta la più comune causa di mortalità prematura in epilessia. Studi volti alla definizione di fattori di rischio clinici hanno permesso di identificare gruppi ad alto rischio. Tuttavia al momento non esistono validati biomarkers genomici, elettrofisiologici o strutturali predittivi di aumentato rischio di SUDEP. Al fine di definire la base genetica della SUDEP, abbiamo condotto una analisi di sequenziamento esomico per esaminare la prevalenza di varianti con effetto deleterio in soggetti deceduti per SUDEP rispetto a pazienti epilettici non deceduti e controlli con altre patologie. Abbiamo riscontrato una prevalenza significativamente aumentata di varianti deleterie diffuse a livello dell’intero genoma nei soggetti deceduti per SUDEP in confronto agli altri gruppi. Un secondo studio di neuroimaging è stato dedicato alla valutazione di anomalie regionali del volume della sostanza grigia in soggetti deceduti per SUDEP, confrontati con soggetti epilettici viventi rispettivamente ad alto e basso rischio per SUDEP, e controlli sani. Abbiamo riscontrato un aumento del volume della sostanza grigia in emisfero destro a livello di amigdala, parte anteriore dell’ippocampo e paraippocampo nei soggetti deceduti per SUDEP e nei soggetti ad alto rischio, rispetto ai soggetti a basso rischio ed ai controlli. Sia il sequenziamento esomico sia il neuroimaging strutturale hanno fornito dati significativi per il profilo di rischio di SUDEP. La definizione dei meccanismi eziologici della SUDEP è fondamentale. La traslazione di tali dati in algoritmi predittivi di rischio individuale consente di promuovere la ‘medicina personalizzata’, allo scopo di adottare strategie preventive e ridurre il rischio individuale di SUDEP in pazienti con epilessia.SUDEP (Sudden Unexpected Death in Epilepsy) is the most devastating outcome in epilepsy and the commonest cause of epilepsy-related premature mortality. Studies of clinical risk factors have allowed identifying high-risk populations. However no genomic, electrophysiological or structural features have emerged as established biomarkers of an increased SUDEP risk. To elucidate the genetic architecture of SUDEP, we used an unbiased whole-exome sequencing approach to examine overall burden and over-representation of deleterious variants in people who died of SUDEP compared to living people with epilepsy and non-epilepsy disease controls. We found significantly increased genome-wide polygenic burden per individual in the SUDEP cohort when compared to epilepsy and non-epilepsy disease controls. The polygenic burden was driven both by the number of variants per individual, and overrepresentation of variants likely to be deleterious in the SUDEP cohort. To elucidate which brain regions may be implicated in SUDEP, we investigated whether regional abnormalities in grey matter volume appear in those who died of SUDEP, compared to subjects at high and low risk for SUDEP, and healthy controls. We identified increased grey matter volume in the right anterior hippocampus/amygdala and parahippocampus in SUDEP cases and people at high risk, when compared to those at low risk and controls. Compared to controls, posterior thalamic grey matter volume, an area mediating oxygen regulation, was reduced in SUDEP cases and subjects at high risk. It is fundamental to understand the range of SUDEP aetiological mechanisms. Our results suggest that both exome sequencing data and structural imaging features may contribute to generate SUDEP risk estimates. Translation of this knowledge into predictive algorithms of individual risk and preventive strategies would promote stratified medicine in epilepsy, with the aim of reducing an individual patient's risk of SUDEP

Neural Anomalies Monitoring: Applications to Epileptic Seizure Detection and Prediction

There have been numerous efforts in the field of electronics with the aim of merging the areas of healthcare and technology in the form of low power, more efficient hardware. However one area of development that can aid in the bridge of healthcare and emerging technology is in Information and Communication Technology (ICT). Here, databasing and analysis systems can help bridge the wealth of information available (blood tests, genetic information, neural data) into a common framework of analysis. Also, ICT systems can integrate real-time processing from emerging technological solutions, such as developed low-power electronics. This work is based on this idea, merging technological solutions in the form of ICT with the need in healthcare to identify normality in a patients’ health profile. In this work we develop this idea and explain the concept more thoroughly. We then go on to explore two applications under development. The first is a system designed around monitoring neural activity and identifying, through a processing algorithm, what is normal activity, such that we can identify anomalies, or abnormalities in the signal. We explore Epilespy with seizure detection and prediction as an application case study to show the potential of this method. The motivation being that current methods of prediction have proven to be unsuccessful. We show that using our algorithm we can achieve significant success in seizure prediction and detection, above and beyond current methods. The second application explores the link between genetic information and standard tests (blood, urine etc...) and how they link in together to define a personalised benchmark. We show how this could work and the steps that have been made towards developing such a database

The Molecular Genetic Investigation of Epilepsy of Infancy with Migrating Focal Seizures

Epilepsy of infancy with migrating focal seizures (EIMFS) is characterised by the onset of frequent focal seizures in the first 6 months of life, a typical migratory EEG pattern and severe developmental delay. In this thesis, I report a cohort of patients with EIMFS, delineate clinical features and investigate the molecular genetic basis of this syndrome. In 2012, heterozygous mutations in the sodium-gated potassium channel KCNT1, were described in patients with EIMFS. Using a variety of genetic techniques, I have identified 12 patients with mutations in this gene. Four are novel, previously unreported mutations. Functional investigations, including protein homology modelling and electrophysiology in a xenopus oocyte model showed that all novel KCNT1 variants were gain-of-function mutations. In addition, I describe a new genetic cause of EIMFS. Within my cohort, I identified a consanguineous family with two affected children. Autozygosity mapping and whole exome sequencing revealed a novel, homozygous mutation in SLC12A5. SLC12A5 encodes KCC2, the neuronal potassium chloride co-transporter that determines the direction and polarity of GABA-mediated signalling. Through international collaboration, I found a second family with two affected children harbouring compound heterozygous SLC12A5 mutations. All three SLC12A5 variants were investigated using an overexpression HEK293 cell model. Immunoblotting and immunohistochemistry revealed decreased cell surface expression of mutant KCC2. Electrophysiology experiments showed a depolarization of the chloride reversal potential and a delayed response to chloride loading. Taken together, these results indicate that loss of KCC2 function is likely to result in abnormal neuronal inhibition in this form of EIMFS. The genetic heterogeneity in EIMFS is strong evidence that a wide variety of different pathogenic mechanisms can result in the severe epilepsy and abnormal neurodevelopment observed in this condition. Further elucidation of causative genes in both animal and cell models is needed to identify novel therapeutic targets for this devastating disorder

ManyDG: Many-domain Generalization for Healthcare Applications

The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients data and evaluated on new patients. Many of them might suffer from poor generalizability. One key reason can be overfitting due to the unique information related to patient identities and their data collection environments, referred to as patient covariates in the paper. These patient covariates usually do not contribute to predicting the targets but are often difficult to remove. As a result, they can bias the model training process and impede generalization. In healthcare applications, most existing domain generalization methods assume a small number of domains. In this paper, considering the diversity of patient covariates, we propose a new setting by treating each patient as a separate domain (leading to many domains). We develop a new domain generalization method ManyDG, that can scale to such many-domain problems. Our method identifies the patient domain covariates by mutual reconstruction and removes them via an orthogonal projection step. Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.Comment: The paper has been accepted by ICLR 2023, refer to https://openreview.net/forum?id=lcSfirnflpW. We will release the data and source codes here https://github.com/ycq091044/ManyD

Emotion and motor function: a clinical and developmental perspective

The idea that emotions and physical actions are strongly intertwined has been widely accepted for quite some time. Yet surprisingly, in both the affective neuroscience and movement neuroscience literature, relatively little empirical attention has been paid to the (psycho)neurophysiological processes underpinning emotion-motor interactions. This body of work provides new insights into emotion-motor interactions by furthering our understanding of the temporal relationship between emotion and motor preparation and motor output during different stages of brain maturation as well as the neurobiological correlates of abnormal motor output in the form of non-epileptic seizures

Cortical circuits for visual processing and epileptic activity propagation

The thesis focuses on the relationship between cortical connectivity and cortical function. The first part investigates how the fine scale connectivity between visual neurons determines their functional responses during physiological sensory processing. The second part ascertains how the mesoscopic scale connectivity between brain areas constrains the spread of abnormal activity during the propagation of focal cortical seizures. Part 1: Neurons in the primary visual cortex (V1) are tuned to retinotopic location, orientation and direction of motion. Such selectivity stems from the integration of inputs from hundreds of presynaptic neurons distributed across cortical layers. Yet, the functional principles that organize such presynaptic networks have only begun to be understood. To uncover them, I used monosynaptic rabies virus tracing to target a single pyramidal neuron in L2/3 (starter neuron) and trace its presynaptic partners. I combined this approach with two-photon microscopy in V1 to investigate the relationship between the activity of the starter cell, its presynaptic neurons and the surrounding excitatory population across cortical layers in awake animals. Part 2: Focal epilepsy involves excessive and synchronous cortical activity that propagates both locally and distally. Does this propagation follow the same functional circuits as normal cortical activity? I induced focal seizures in primary visual cortex (V1) of awake mice, and compared their propagation to the retinotopic organization of V1 and higher visual areas. I measured activity through simultaneous local field potential recordings and widefield calcium imaging, and observed prolonged seizures that were orders of magnitude larger than normal visual responses. I demonstrate that seizure start as standing waves (synchronous elevated activity in the focal V1 region and in corresponding retinotopic locations in higher areas) and then propagate both locally and into distal regions. These regions matched each other in retinotopy. I conclude that seizure propagation respects the connectivity underlying normal visual processing

Delineation of the genetic causes of complex epilepsies in South African pediatric patients

Background Sub-Saharan Africa bears the highest burden of epilepsy worldwide. A proportion is presumed to be genetic, but this aetiology is buried under the burden of infections and perinatal insults, in a setting of limited awareness and few options for testing. Children with developmental and epileptic encephalopathies (DEEs), are most severely affected by this diagnostic gap, as the rate of actionable findings is highest in DEE-associated genes. This research study investigated the genetic architecture of epilepsy in South African (SA) children clinically diagnosed with DEE, highlighting the clinical utility of informative genetic findings and relevance to precision medicine for DEEs in a resource-constrained setting. Methods A group of 234 genetically naïve SA children with drug-resistant epilepsy and a diagnosis or suspicion of DEE, were recruited between 2016 and 2019. All probands were genetically tested using a DEE gene panel of 71 genes. Of the panel-negative probands, 78 were tested with chromosomal microarray and 20 proband/parent trios underwent exome sequencing. Statistical comparison of electroclinical features in children with and without candidate variants was performed to identify characteristics most likely predictive of a positive genetic finding. Results Pathogenic/likely pathogenic (P/LP) variants were identified in 41/234(17.5%) * probands. Of these, 29/234(12.4%) * were sequence variants in epilepsy-associated genes and 12/234(5.1%) * were genomic copy number variants (CNVs). Sixteen variants of uncertain significance (VUS) were detected in 12 patients. Of the 41 children with P/LP variants, 26/234(11%) had variants supporting precision therapy. Multivariate regression modelling highlighted neonatal or infantile-onset seizures with movement abnormalities and attention difficulties as predictive of a positive genetic finding. This, coupled with an emphasis on precision medicine outcomes, was used to propose the pragmatic “Think-Genetics” decision tree for early recognition of a possible genetic aetiology, pragmatic testing, and multidisciplinary consultation. Conclusion The findings presented here emphasise the relevance of an early genetic diagnosis in DEEs and highlight the importance of access to genetic testing. The “Think-Genetics” strategy was designed for early recognition, appropriate interim management, and genetic testing for DEEs in resource constrained settings. The outcomes of this study emphasise the pressing need for augmentation of the local genetic laboratory services, to incorporate gene panels and exome sequencing. *These percentages were rounded off to whole numbers in the published articles included in this thesis (i.e., rounded off to 18%, 12% and 5%, respectively)