151 research outputs found

    An alternative approach for assessing drug induced seizures, using non-protected larval zebrafish

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    As many as 9% of epileptic seizures occur as a result of drug toxicity. Identifying compounds with seizurogenic side effects is imperative for assessing compound safety during drug development, however, multiple marketed drugs still have clinical associations with seizures. Moreover, current approaches for assessing seizurogenicity, namely rodent EEG and behavioural studies, are highly resource intensive. This being the case, alternative approaches have been postulated for assessing compound seizurogenicity, including in vitro, in vivo, and in silico methods. In this thesis, experimental work is presented supporting the use of larval zebrafish as a candidate model organism for developing new seizure liability screening approaches. Larval zebrafish are translucent, meaning they are highly amenable to imaging approaches while offering a more ethical alternative to mammalian research. Zebrafish are furthermore highly fecund facilitating capacity for both high replication and high throughput. The primary goal of this thesis was to identify biomarkers in larval zebrafish, both behavioural and physiological, of compounds that increase seizure liability. The efficacy of this model organism for seizure liability testing was assessed through exposure of larval zebrafish to a mechanistically diverse array of compounds, selected for their varying degrees of seizurogenicity. Their central nervous systems were monitored using a variety of different techniques including light sheet microscopy, local field potential recordings, and behavioural monitoring. Data acquired from these measurements were then analysed using a variety of techniques including frequency domain analysis, clustering, functional connectivity, regression, and graph theory. Much of this analysis was exploratory in nature and is reflective of the infancy of the field. Experimental findings suggest that larval zebrafish are indeed sensitive to a wide range of pharmacological mechanisms of action and that drug actions are reflected by behavioural and direct measurements of brain activity. For example, local field potential recordings revealed electrographic responses akin to pre-ictal, inter-ictal and ictal events identified in humans. Ca2+ imaging using light sheet microscopy found global increases in fluorescent intensity and functional connectivity due to seizurogenic drug administration. In addition, [2] further functional connectivity and graph analysis revealed macroscale network changes correlated with drug seizurogenicity and mechanism of action. Finally, analysis of swimming behaviour revealed a strong correlation between high speed swimming behaviours and administration of convulsant compounds. In conclusion, presented herein are data demonstrating the power of functional brain imaging, LFP recordings, and behavioral monitoring in larval zebrafish for assessing the action of neuroactive drugs in a highly relevant vertebrate model. These data help us to understand the relevance of the 4 dpf larval zebrafish for neuropharmacological studies and reveal that even at this early developmental stage, these animals are highly responsive to a wide range of neuroactive compounds across multiple primary mechanisms of action. This represents compelling evidence of the potential utility of larval zebrafish as a model organism for seizure liability testing

    The Value of Seizure Semiology in Epilepsy Surgery: Epileptogenic-Zone Localisation in Presurgical Patients using Machine Learning and Semiology Visualisation Tool

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    Background Eight million individuals have focal drug resistant epilepsy worldwide. If their epileptogenic focus is identified and resected, they may become seizure-free and experience significant improvements in quality of life. However, seizure-freedom occurs in less than half of surgical resections. Seizure semiology - the signs and symptoms during a seizure - along with brain imaging and electroencephalography (EEG) are amongst the mainstays of seizure localisation. Although there have been advances in algorithmic identification of abnormalities on EEG and imaging, semiological analysis has remained more subjective. The primary objective of this research was to investigate the localising value of clinician-identified semiology, and secondarily to improve personalised prognostication for epilepsy surgery. Methods I data mined retrospective hospital records to link semiology to outcomes. I trained machine learning models to predict temporal lobe epilepsy (TLE) and determine the value of semiology compared to a benchmark of hippocampal sclerosis (HS). Due to the hospital dataset being relatively small, we also collected data from a systematic review of the literature to curate an open-access Semio2Brain database. We built the Semiology-to-Brain Visualisation Tool (SVT) on this database and retrospectively validated SVT in two separate groups of randomly selected patients and individuals with frontal lobe epilepsy. Separately, a systematic review of multimodal prognostic features of epilepsy surgery was undertaken. The concept of a semiological connectome was devised and compared to structural connectivity to investigate probabilistic propagation and semiology generation. Results Although a (non-chronological) list of patients’ semiologies did not improve localisation beyond the initial semiology, the list of semiology added value when combined with an imaging feature. The absolute added value of semiology in a support vector classifier in diagnosing TLE, compared to HS, was 25%. Semiology was however unable to predict postsurgical outcomes. To help future prognostic models, a list of essential multimodal prognostic features for epilepsy surgery were extracted from meta-analyses and a structural causal model proposed. Semio2Brain consists of over 13000 semiological datapoints from 4643 patients across 309 studies and uniquely enabled a Bayesian approach to localisation to mitigate TLE publication bias. SVT performed well in a retrospective validation, matching the best expert clinician’s localisation scores and exceeding them for lateralisation, and showed modest value in localisation in individuals with frontal lobe epilepsy (FLE). There was a significant correlation between the number of connecting fibres between brain regions and the seizure semiologies that can arise from these regions. Conclusions Semiology is valuable in localisation, but multimodal concordance is more valuable and highly prognostic. SVT could be suitable for use in multimodal models to predict the seizure focus

    Source-sink connectivity: A novel interictal EEG marker of the epileptic brain network

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    Epilepsy affects over 60 million people worldwide. Epilepsy diagnosis depends on abnormalities in scalp electroencephalography (EEG) signals but their presence varies from 29-55%, resulting in a delayed diagnosis. Additionally, artifacts mimicking abnormalities and conditions imitating epileptic seizures contribute to a misdiagnosis rate of 30%. Antiepileptic drugs (AEDs) are the mainstay of epilepsy treatment, but around 30% of patients do not respond to AEDs. Surgical treatment is a hopeful alternative but outcomes depend on precise identification of the epileptogenic zone (EZ), the brain region(s) where seizures originate, and success rates range from 20-80%. Localization of the EZ requires visual inspection of intracranial EEG (iEEG) recordings during seizures which is costly and time-consuming and, in the end, clinicians ignore most of the data captured. Diagnosis and management of epilepsy rely on detecting sporadic EEG signatures. Thus, there is a great need to more quickly and accurately identify the underlying cause and location of seizures in the brain. We developed and tested the source-sink index (SSI) as an interictal (between seizures) EEG marker of epileptogenic activity. We hypothesized that seizures are suppressed when the EZ is inhibited by neighboring regions. We developed an algorithm that identifies two groups of nodes from the EEG network: those inhibiting their neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, dynamical network models were estimated from EEG data and their connectivity properties revealed top sources and sinks in the network. We tested and validated a twofold application of SSI, as: i) an iEEG marker of the EZ, and ii) a scalp EEG marker of epilepsy. We found that SSI highly agreed with the annotated EZ in successful outcome patients but identified untreated regions in failure patients. Further, SSI outperformed high frequency oscillations, a frequently proposed interictal EZ marker, in predicting surgical outcomes. When used to predict diagnostic outcomes, SSI showed significant improvement over the gold standard's reported sensitivity and specificity. Our results suggest that SSI captures the characteristics of regions responsible for seizure initiation. As such, it is a promising marker of epileptogenicity that could significantly improve the speed and outcomes of epilepsy management and diagnosis

    Temporal Characteristics of High-Frequency Oscillations as a Biomarker of Human Epilepsy

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    Epilepsy is a debilitating neurological disorder characterized by recurrent spontaneous seizures. While seizures themselves adversely affect physiological function for short time periods relative to normal brain states, their cumulative impact can significantly decrease patient quality of life in myriad ways. For many, anti-epileptic drugs are effective first-line therapies. One third of all patients do not respond to chemical intervention, however, and require invasive resective surgery to remove epileptic tissue. While this is still the most effective last-line treatment, many patients with ‘refractory’ epilepsy still experience seizures afterward, while some are not even surgical candidates. Thus, a significant portion of patients lack further recourse to manage their seizures – which additionally impacts their quality of life. High-frequency oscillations (HFOs) are a recently discovered electrical biomarker with significant clinical potential in refractory human epilepsy. As a spatial biomarker, HFOs occur more frequently in epileptic tissue, and surgical removal of areas with high HFO rates can result in improved outcomes. There is also limited preliminary evidence that HFOs change prior to seizures, though it is currently unknown if HFOs function as temporal biomarkers of epilepsy and imminent seizure onset. No such temporal biomarker has ever been identified, though if it were to exist, it could be exploited in online seizure prediction algorithms. If these algorithms were clinically implemented in implantable neuromodulatory devices, improvements to quality of life for refractory epilepsy patients might be possible. Thus, the overall aim of this work is to investigate HFOs as potential temporal biomarkers of seizures and epilepsy, and further to determine whether their time-varying properties can be exploited in seizure prediction. In the first study we explore population-level evidence for the existence of this temporal effect in a large clinical cohort with refractory epilepsy. Using sophisticated automated HFO detection and big-data processing techniques, a continuous measure of HFO rates was developed to explore gradual changes in HFO rates prior to seizures, which were analyzed in aggregate to assess their stereotypical response. These methods resulted in the identification of a subset of patients in whom HFOs from epileptic tissue gradually increased before seizures. In the second study, we use machine learning techniques to investigate temporal changes in HFO rates within individuals, and to assess their potential usefulness in patient-specific seizure prediction. Here, we identified a subset of patients whose predictive models sufficiently differentiated the preictal (before seizure) state better than random chance. In the third study, we extend our prediction framework to include the signal properties of HFOs. We explore their ability to improve the identification of preictal periods, and additionally translate their predictive models into a proof-of-concept seizure warning system. For some patients, positive results from this demonstration show that seizure prediction using HFOs could be possible. These studies overall provide convincing evidence that HFOs can change in measurable ways prior to seizure start. While this effect was not significant in some individuals, for many it enabled seizures to be predicted above random chance. Due to data limitations in overall recording duration and number of seizures captured, these findings require further validation with much larger high-density intracranial EEG datasets. Still, they provide a preliminary framework for the eventual use of HFOs in patient-specific seizure prediction with the potential to improve the lives of those with refractory epilepsy.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168079/1/jaredmsc_1.pd

    Advanced Invasive Neurophysiological Methods to Aid Decision Making in Paediatric Epilepsy Surgery

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    For patients with drug-resistant focal epilepsy, surgery is the most effective treatment to attain seizure freedom. Intracranial electroencephalogram investigations succeed in defining the seizure onset zone (SOZ) where non-invasive methods fail to identify a single seizure generator. However, resection of the SOZ does not always lead to a surgical benefit and, in addition, eloquent functions like language might be compromised. The aim of this thesis was to use advanced invasive neurophysiological methods to improve pre-surgical planning in two ways. The first aim was to improve delineation of the pathological tissue, the SOZ using novel quantitative neurophysiological biomarkers: high gamma activity (80–150Hz) phase-locked to low frequency iEEG discharges (phase-locked high gamma, PLHG) and high frequency oscillations called fast ripples (FR, 250–500Hz). Resection of contacts containing these markers were recently reported to lead to an improved seizure outcome. The current work shows the first replication of the PLHG metric in a small adult pilot study and a larger paediatric cohort. Furthermore, I tested whether surgical removal of PLHG- and/or FR-generating brain areas resulted in better outcome compared to the current clinical SOZ delineation. The second aim of this work was to aid delineation of eloquent language cortex. Invasive event-related potentials (iERP) and spectral changes in the beta and gamma frequency bands were used to determine cortical dynamics during speech perception and production across widespread brain regions. Furthermore, the relationship between these cortical dynamics and the relationship to electrical stimulation responses was explored. For delineation of pathological tissue, the combination of FRs and SOZ proved to be a promising biomarker. Localising language cortex showed the highest level of activity around the perisylvian brain regions with a significantly higher occurrence rate of iERPs compared to spectral changes. Concerning electrical stimulation mapping beta and high gamma frequency bands represented the most promising markers

    Three projects in applied mathematics: discovering underlying features amid large, noisy data

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    In many scientific fields, we are faced with extremely large, noisy datasets. Features of interest in these datasets may be difficult to explicitly define, obscured by noise, or simply lost in the magnitude of the dataset. Uncovering these features often necessitates the development of novel mathematical and statistical modeling approaches, and the utilization of powerful analysis tools. In this work, we present three distinct projects, all of which develop specific mathematical and statistical analysis to find features of interest amid large, noisy data. The first project measures cross-frequency coupling (CFC), i.e., the extent to which signals in different frequency bands interact, amid large, noisy neural voltage recordings. We use generalized linear models (GLMs) to define an accurate measure with confidence intervals and significance values. We show in simulation how this measure improves upon existing approaches, and apply this measure to analyze CFC during a human seizure. The second project develops a fully-automated detector of spike ripples, a powerful biomarker of epilepsy, which occur sparingly in long duration neural voltage recordings. The method applies convolutional neural networks (CNNs) to spectrogram data, and performs comparably to gold-standard expert classifications. We apply this measure to a population of patients with childhood epilepsy, and effectively separate them into high and low seizure risk groups. The final project studies the COVID-19 epidemic, modeling infections and deaths over time from large quantities of noisy, incomplete state-level observations. We use a statistical, data-driven analysis to estimate the basic reproduction number (R0), and use this estimate in multiple compartmental models, fitting unknown parameters for death and recovery rates using an ensemble Markov chain Monte Carlo (MCMC) method. We show consistent estimates of dynamics and parameters across multiple compartmental models, in alignment with our current epidemiological understanding of the disease. In all projects, we are able to uncover key features of interest amid the large, noisy data, providing key insights backed by mathematical and statistical rigor

    Détection automatique multi-échelle et de grande envergure d'oscillations intracérébrales pathologiques dans l'épilepsie par réseaux de neurones artificiels

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    Environ un tiers des patients épileptiques sont résistants aux médicaments. La seule solution pour les guérir est de retirer la zone cérébrale à l'origine des crises, appelée zone épileptogène (ZE). Pour localiser cette zone, il est parfois nécessaire des mener des explorations par stéréo-électroencéphalographie (SEEG). L'analyse du signal EEG par les neurologues est une étape déterminante du diagnostic, mais la quantité de données générée est colossale. Ainsi, seule une petite partie des enregistrements peut être analysée par les équipes médicales qui se concentrent principalement sur l'activité durant les crises et celle juste autour. Pour caractériser l'étendue et la dynamique du réseau épileptogène, les neurologues étudient aussi des marqueurs intercritiques. Mais certains de ces biomarqueurs sont strictement invisibles à l'œil nu. Le premier objectif de ce travail de thèse interdisciplinaire consistait à établir de nouvelles méthodes pour détecter efficacement et automatiquement les marqueurs intercritiques, à savoir les pointes épileptiques intercritiques (PEIs) et en particulier les fast ripples (FRs). Le second objectif visait à définir et décrire l'intérêt d'enregistrements des marqueurs physiopathologiques de l'épilepsie par l'intermédiaire de micro-électrodes, alors que la plupart des études jusqu'à présent utilisaient des macro-électrodes classiques. Enfin, le troisième objectif était focalisé sur les FRs, avec pour idée de mieux comprendre leur origine, leur émergence et leur implication dans la pathologie. Nos équipes utilisent des électrodes hybrides permettant un enregistrement multi échelle du signal cérébral des patients. Ces électrodes sont constituées de macro-canaux permettant d'enregistrer l'activité de larges populations neuronales et de micro-canaux capables de capturer des signaux plus focaux, pouvant aller jusqu'à l'échelle du neurone unitaire. Nous avons construit un détecteur automatique de PEIs basé sur une nouvelle méthode de traitement du signal que nous avons baptisée Convolutional Kernel Density Estimation (CKDE). Nous avons également élaboré un détecteur automatique de FRs basé sur une approche écologique en trois étapes, imitant le travail du neurologue. Tous ces outils ont été incorporés à des interfaces graphiques utilisateurs (GUI) combinant les différentes fonctionnalités pour en permettre l'utilisation facile et efficiente. La détection des PEIs par CKDE offre la preuve de concept qu'une analyse orientée pixels de l'activité EEG peut être utilisée comme stratégie pour détecter des marqueurs intercritiques. Nous avons évalué cette méthode sur 10 minutes d'enregistrements chez un patient. Quinze PEIs ont été détectées automatiquement parmi lesquelles 13 vrais positifs et 2 faux positifs. Nos résultats principaux concernent toutefois la détection des FRs qui auraient à ce jour le plus grand potentiel dans le diagnostic des épilepsies pharmacorésistantes. Pour entraîner le CNN qui est une pièce maîtresse de notre détecteur, nous avons constitué une base de données de 4 954 FRs détectés manuellement chez 13 patients. Ce détecteur de FRs a été incorporé au logiciel que nous avons imaginé et créé, baptisé Ladybird, utilisé chez 29 patients pour détecter et traiter plusieurs milliers de FRs. Les avancées techniques et théoriques réalisées au cours de ce travail de thèse nous permettent d'envisager une utilisation à grande échelle de nos outils. Notre objectif est que les équipes médicales puissent en bénéficier directement, dans leur routine diagnostic. Un brevet a été déposé en vue d'un processus d'industrialisation.Almost a third of epileptic patients are resistant to medication. The only way to cure them is to remove the area of the brain that causes the seizures, called the epileptogenic zone (EZ). To locate this area, it is sometimes necessary to carry out stereo-electroencephalography (SEEG) investigations. SEEG consists of implanting intracerebral electrodes in the patient, who remains in hospital for about ten to fifteen days. During this period, the patient's intracerebral activity is continuously recorded on more than a hundred recording channels distributed in the brain structures suspected of being involved in the epileptogenic network. The analysis of the EEG signal by neurologists is a crucial step in the diagnosis, but the amount of data generated is tremendous. As a result, only a small fraction of the recordings can be analyzed by medical teams, who focus mainly on activity during and immediately surrounding seizures. To characterise the extent and dynamics of the epileptogenic network, neurologists also study interictal markers. But some of these biomarkers are impossible to detect manually. The first objective of this interdisciplinary thesis work was to establish new methods to efficiently and automatically detect intercritical markers, namely interictal epileptic discharges (IEDs) and in particular fast ripples (FRs). The second objective was to define and describe the interest of recording pathophysiological markers of epilepsy using micro-electrodes, whereas most studies until now used classical macro-electrodes. Finally, the third objective was focused on FRs, with the idea to better understand their origin, emergence and involvement in the pathology. Our teams use hybrid electrodes that allow for a multi-scale recording of the brain signal of patients. These electrodes are made up of macro-channels allowing the recording of the activity of large neuronal populations and micro-channels capable of capturing much more focal signals, down to the scale of single neuron activity. We have built an automatic IED detector based on a new method of processing the image-transformed signal using a technique we call Convolutional Kernel Density Estimation (CKDE). We also developed an automatic FR detector based on a three-step, CNN-based, ecological approach, mimicking the work of the neurologist. All these tools have been incorporated into graphical user interfaces (GUIs) that combine the different functionalities for easy and efficient use. The detection of IEDs by CKDE offers proof of concept that a pixel-oriented analysis of EEG activity can be used as a strategy to detect interictal markers. We evaluated this method on 10 minutes of recordings in a patient. Fifteen IEDs were automatically detected, of which 13 were true positives and 2 false positives. However, our main results concern the detection of FRs, which would have the greatest potential in the diagnosis of drug-resistant epilepsies. To train the CNN, which is a key component of our detector, we built a database of 4,954 manually detected FRs in 13 patients at both the EEG-macro and the EEG-micro scales. This multi-scale FR detector was incorporated into the software we designed, called Ladybird, which was used in 29 patients to detect and treat several thousand FRs. The technical and theoretical advances made during this thesis allow us to consider a large-scale use of our tools. Our objective is that medical teams can benefit directly from them, in their diagnostic routine. A patent has been filed in view of an industrialization process

    Machine Learning for Understanding Focal Epilepsy

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    The study of neural dysfunctions requires strong prior knowledge on brain physiology combined with expertise on data analysis, signal processing, and machine learning. One of the unsolved issues regarding epilepsy consists in the localization of pathological brain areas causing seizures. Nowadays the analysis of neural activity conducted with this goal still relies on visual inspection by clinicians and is therefore subjected to human error, possibly leading to negative surgical outcome. In absence of any evidence from standard clinical tests, medical experts resort to invasive electrophysiological recordings, such as stereoelectroencephalography to assess the pathological areas. This data is high dimensional, it could suffer from spatial and temporal correlation, as well as be affected by high variability across the population. These aspects make the automatization attempt extremely challenging. In this context, this thesis tackles the problem of characterizing drug resistant focal epilepsy. This work proposes methods to analyze the intracranial electrophysiological recordings during the interictal state, leveraging on the presurgical assessment of the pathological areas. The first contribution of the thesis consists in the design of a support tool for the identification of epileptic zones. This method relies on the multi-decomposition of the signal and similarity metrics. We built personalized models which share common usage of features across patients. The second main contribution aims at understanding if there are particular frequency bands related to the epileptic areas and if it is worthy to focus on shorter periods of time. Here we leverage on the post-surgical outcome deriving from the Engel classification. The last contribution focuses on the characterization of short patterns of activity at specific frequencies. We argue that this effort could be helpful in the clinical routine and at the same time provides useful insight for the understanding of focal epilepsy
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