NOVEL GRAPHICAL MODEL AND NEURAL NETWORK FRAMEWORKS FOR AUTOMATED SEIZURE DETECTION, TRACKING, AND LOCALIZATION IN FOCAL EPILEPSY

Epilepsy is a heterogenous neurological disorder characterized by recurring and unprovoked seizures. It is estimated that 60% of epilepsy patients suffer from focal epilepsy, where seizures originate from one or more discrete locations within the brain. After onset, focal seizure activity spreads, involving more regions in the cortex. Diagnosis and therapeutic planning for patients with focal epilepsy crucially depends on being able to detect epileptic activity as it starts and localize its origin. Due to the subtlety of seizure activity and the complex spatio-temporal propagation patterns of seizure activity, detection and localization of seizure by visual inspection is time-consuming and must be done by highly trained neurologists. In this thesis, we detail modeling approaches to identify and capture the spatio-temporal ictal propagation of focal epileptic seizures. Through novel multi-scale frameworks, information fusion between signal paths, and hybrid architectures, models that capture the underlying seizure propagation phenomena are developed. The first half relies on graphical modeling approaches to detect seizures and track their activity through the space of EEG electrodes. A coupled hidden Markov model approach to seizure propagation is described. This model is subsequently improved through the addition of convolutional neural network based likelihood functions, removing the reliance on hand designed feature extraction. Through the inclusion of a hierarchical switching chain and localization variables, the model is revised to capture multi-scale seizure onset and spreading information. In the second half of this thesis, end-to-end neural network architectures for seizure detection and localization are developed. First, combination convolutional and recurrent neural networks are used to identify seizure activity at the level of individual EEG channels. Through novel aggregation, the network is trained to recognize seizure activity, track its evolution, and coarsely localize seizure onset from lower resolution labels. Next, a multi-scale network capable of analyzing the global and electrode level signals is developed for challenging task of end-to-end seizure localization. Onset location maps are defined for each patient and an ensemble of weakly supervised loss functions are used in a multi-task learning framework to train the architecture

Electrophysiological evidence for memory schemas in the rat hippocampus

According to Piaget and Bartlett, learning involves both assimilation of new memories into networks of preexisting knowledge and alteration of existing networks to accommodate new information into existing schemas. Recent evidence suggests that the hippocampus integrates related memories into schemas that link representations of separately acquired experiences. In this thesis, I first review models for how memories of individual experiences become consolidated into the structure of world knowledge. Disruption of consolidated memories can occur during related learning, which suggests that consolidation of new information is the reconsolidation of related memories. The accepted role of the hippocampus during memory consolidation and reconsolidation suggests that it is also involved in modifying appropriate schemas during learning. To study schema development, I trained rats to retrieve rewards at different loci on a maze while recording hippocampal calls. About a quarter of cells were active at multiple goal sites, though the ensemble as a whole distinguished goal loci from one another. When new goals were introduced, cells that had been active at old goal locations began firing at the new locations. This initial generalization decreased in the days after learning. Learning also caused changes in firing patterns at well-learned goal locations. These results suggest that learning was supported by modification of an active schema of spatially related reward loci. In another experiment, I extended these findings to explore a schema of object and place associations. Ensemble activity was influenced by a hierarchy of task dimensions which included: experimental context, rat's spatial location, the reward potential and the identity of sampled objects. As rats learned about new objects, the cells that had previously fired for particular object-place conjunctions generalized their firing patterns to new conjunctions that similarly predicted reward. In both experiments, I observed highly structured representations for a set of related experiences. This organization of hippocampal activity counters key assumptions in standard models of hippocampal function that predict relative independence between memory traces. Instead, these findings reveal neural mechanisms for how the hippocampus develops a relational organization of memories that could support novel, inferential judgments between indirectly related events

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Emergent Phenomena From Dynamic Network Models: Mathematical Analysis of EEG From People With IGE

In this thesis mathematical techniques and models are applied to electroencephalographic (EEG) recordings to study mechanisms of idiopathic generalised epilepsy (IGE). First, we compare network structures derived from resting-state EEG from people with IGE, their unaffected relatives, and healthy controls. Next, these static networks are combined with a dynamical model describing the ac- tivity of a cortical region as a population of phase-oscillators. We then examine the potential of the differences found in the static networks and the emergent properties of the dynamic network as individual biomarkers of IGE. The emphasis of this approach is on discerning the potential of these markers at the level of an indi- vidual subject rather than their ability to identify differences at a group level. Finally, we extend a dynamic model of seizure onset to investigate how epileptiform discharges vary over the course of the day in ambulatory EEG recordings from people with IGE. By per- turbing the dynamics describing the excitability of the system, we demonstrate the model can reproduce discharge distributions on an individual level which are shown to express a circadian tone. The emphasis of the model approach is on understanding how changes in excitability within brain regions, modulated by sleep, metabolism, endocrine axes, or anti-epileptic drugs (AEDs), can drive the emer- gence of epileptiform activity in large-scale brain networks. Our results demonstrate that studying EEG recordings from peo- ple with IGE can lead to new mechanistic insight on the idiopathic nature of IGE, and may eventually lead to clinical applications. We show that biomarkers derived from dynamic network models perform significantly better as classifiers than biomarkers based on static network properties. Hence, our results provide additional ev- idence that the interplay between the dynamics of specific brain re- gions, and the network topology governing the interactions between these regions, is crucial in the generation of emergent epileptiform activity. Pathological activity may emerge due to abnormalities in either of those factors, or a combination of both, and hence it is essential to develop new techniques to characterise this interplay theoretically and to validate predictions experimentally

Genetic predictors for epilepsy development, treatment response and dosing

Antiepileptic drug (AED) treatment is the first line strategy for seizure control in the majority of individuals with epilepsy but remains challenging, not least because of interindividual variability in efficacy, tolerability and dosing. The studies presented in this thesis set out to explore that variability from a genomic perspective in patients with newly diagnosed epilepsy from across the UK. Single nucleotide polymorphisms (SNPs) in genes encoding drug metabolising enzymes (DMEs) may be associated with the dose of carbamazepine (CBZ) required for seizure control. A cohort of 159 individuals who were seizure-free for 12 months on a stable dose of CBZ monotherapy was genotyped for 51 SNPs across six DMEs. Haplotype analysis identified 8 haplotype blocks across the genes. No single SNPs or haplotype blocks were associated with CBZ dose. Thus, it is unlikely that genetic variability in DMEs accounts for the individual differences in CBZ dose requirement. A splice site SNP (rs3812718) in the SCN1A gene was previously shown to influence maximum doses of AEDs. This SNP was genotyped in 817 patients and tested for association with maximum and maintenance doses of several AEDs. An association was identified between rs3812718 and maximum AED dose, with an interaction analysis suggestive of a drug specific effect. These findings suggest that this SCN1A variant contributes to variability in the limit of tolerability to AEDs. Response to AED treatment is multifactorial and likely to be influenced by multiple genes. Five SNPs previously reported to predict treatment outcome in epilepsy were genotyped in 772 patients and the resulting data, together with data from an Australian cohort, incorporated into a predictive algorithm. The algorithm failed to predict treatment outcome in general but was partially successful in identifying responders to CBZ and valproate. These five SNPs may be relevant to the prognosis of epilepsy, particularly when treated with specific AEDs. Primary generalised epilepsies (PGEs) are highly heritable and believed to be polygenic in origin. Predictive algorithms were employed to explore genetic influences on seizure (absence vs. myoclonus) and epilepsy (PGE vs. focal) type using 1,840 SNP genotypes available from 436 patients with PGE. Although the algorithms failed to distinguish PGE patients on the basis of genetic variants, they showed improved association over univariate methods of analysis. Such an approach may be suitable for future investigations using large genomic datasets. A recent genome-wide association study identified multiple genetic variants that approached genome-wide significance for association with 12 month remission from seizures. Five of these SNPs were genotyped in an independent cohort of 424 patients and tested for association with remission and time to remission. No significant associations were found, questioning the validity of the original observation or the method of replication. Further work is required to understand this outcome. In conclusion, the genetic bases of epilepsy, AED response and AED dose requirement are multigenic and thus far undetectable using traditional association studies in modestly-sized patient cohorts. Further advances in genomic, bioinformatics and statistical methodologies are required before the genetic contribution to heterogeneity in epilepsy-related phenotypes can be translated into improved clinical care

Investigation of the multiple-demand network at multiple spatial scales

This dissertation investigates the frontoparietal ‘multiple-demand’ (MD) network that is involved in the processing of diverse cognitive demands. This network is active when the task at hand is made more demanding, in a variety of different tasks including working memory, task switching, inhibition, math, language etc. While the different MD regions have partly different functions, they are highly interconnected allowing them to function together as a network. The experiment in Chapter 2 looked at the interplay between functional differences as well as co-recruitment within this multiple-demand network. Quantitative differences between regions were more prominent in simple tasks. A strong co-recruitment was seen with increased challenge or incentive. In Chapter 3, task preferences were studied at the voxel level. MD regions were equally well localised in single-subjects using any of three task demands. Voxels localised by all three tasks also captured the underlying neural representations to a similar level in a separate criterion task. Chapter 4 investigated if task representations, as measured by multi-voxel patterns, were modified due to external motivation. The effect was limited to the cue phase and did not extend to the stimulus processing phase where the stimulus is integrated with the cue to arrive at the response. Chapter 5 examined neural representations in frontal and parietal regions more directly through single unit activity and local field potentials (LFPs), during a spatial working memory task. While single neurons showed dynamic coding of target information rather than persistent coding, LFPs held this information constant through time. The impact of reference voltages on LFP data was further investigated. Together, these results explore the functional differences between and within the MD regions, and provide evidence for flexible task representations at the voxel and neuronal level.Funded by Gates Cambridg

Serotonergic modulation of the ventral pallidum by 5HT1A, 5HT5A, 5HT7 AND 5HT2C receptors

Introduction: Serotonin's involvement in reward processing is controversial. The large number of serotonin receptor sub-types and their individual and unique contributions have been difficult to dissect out, yet understanding how specific serotonin receptor sub-types contribute to its effects on areas associated with reward processing is an essential step. Methods: The current study used multi-electrode arrays and acute slice preparations to examine the effects of serotonin on ventral pallidum (VP) neurons. Approach for statistical analysis: extracellular recordings were spike sorted using template matching and principal components analysis, Consecutive inter-spike intervals were then compared over periods of 1200 seconds for each treatment condition using a student’s t test. Results and conclusions: Our data suggests that excitatory responses to serotonin application are pre-synaptic in origin as blocking synaptic transmission with low-calcium aCSF abolished these responses. Our data also suggests that 5HT1a, 5HT5a and 5HT7 receptors contribute to this effect, potentially forming an oligomeric complex, as 5HT1a antagonists completely abolished excitatory responses to serotonin application, while 5HT5a and 5HT7 only reduced the magnitude of excitatory responses to serotonin. 5HT2c receptors were the only serotonin receptor sub-type tested that elicited inhibitory responses to serotonin application in the VP. These findings, combined with our previous data outlining the mechanisms underpinning dopamine's effects in the VP, provide key information, which will allow future research to fully examine the interplay between serotonin and dopamine in the VP. Investigation of dopamine and serotonins interaction may provide vital insights into our understanding of the VP's involvement in reward processing. It may also contribute to our understanding of how drugs of abuse, such as cocaine, may hijack these mechanisms in the VP resulting in sensitization to drugs of abuse

Surgical Management of Gastroesophageal Reflux in Children: Risk Stratification and Prediction of Outcomes

Introduction: Since the 1980s fundoplication, an operation developed for adults with hiatus hernia and reflux symptoms, has been performed in children with gastroesophageal reflux disease (GORD). When compared to adult outcomes, paediatric fundoplication has resulted in higher failure and revision rates. In the first chapter we explore differences in paradigm, patient population and outcomes. Firstly, symptoms are poorly defined and are measured by instruments of varying quality. Secondly, neurological impairment (NI), prematurity and congenital anomalies (oesophageal atresia, congenital diaphragmatic hernia) are prevalent in children. / Purpose: To develop methods for stratifying paediatric fundoplication risk and predicting outcomes based on symptom profile, demographic factors, congenital and medical history. / Methods: Study objectives are addressed in three opera: a symptom questionnaire development (TARDIS:REFLUX), a randomised controlled trial (RCT) and a retrospective database study (RDS). TARDIS: REFLUX: In the second chapter, digital research methods are used to design and validate a symptom questionnaire for paediatric GORD. The questionnaire is a market-viable smartphone app hosted on a commercial platform and trialed in a clinical pilot study. / RCT: In the third chapter, the REMOS trial is reported. The trial addresses the subset of children with NI and feeding difficulties. Participants are randomized to gastrostomy with or without fundoplication. Notably, pre- and post-operative reflux is quantified using pH-impedance. / RDS: In the fourth chapter, data mining and machine learning strategies are applied to a retrospective paediatric GORD database. Predictive modelling techniques applied include logistic regression, decision trees, random forests and market basket analysis. / Results and conclusion: This work makes two key contributions. Firstly, an effective methodology for development of digital research tools is presented here. Secondly, a synthesis is made of literature, the randomised controlled trial and retrospective database modelling. The resulting product is an evidence-based algorithm for the surgical management of children with GORD