431 research outputs found

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy

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    Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.Comment: This paper is currently under review in IEEE Journa

    Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images

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    Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time–frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time–frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification

    Epileptic seizure prediction using machine learning techniques

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    Epileptic seizures affect about 1% of the world’s population, thus making it the fourth most common neurological disease, this disease is considered a neurological disorder characterized by the abnormal activity of the brain. Part of the population suffering from this disease is unable to avail themselves of any treatment, as this treatment has no beneficial effect on the patient. One of the main concerns associated with this disease is the damage caused by uncontrollable seizures. This damage affects not only the patient himself but also the people around him. With this situation in mind, the goal of this thesis is, through methods of Machine Learning, to create an algorithm that can predict epileptic seizures before they occur. To predict these seizures, the electroencephalogram (EEG) will be employed, since it is the most commonly used method for diagnosing epilepsy. Of the total 23 channels available, only 8 will be used, due to their location. When a seizure occurs, besides the visible changes in the EEG signal, at the moment of the seizure, the alterations before and after the epileptic seizure are also noticeable. These stages have been named in the literature: • Preictal: the moment before the epileptic seizure; • Ictal: the moment of the seizure; • Postictal: the moment after the seizure; • Interictal: space of time between seizures. The goal of the predictive algorithm will be to classify the different classes and study different classification problems by using supervised learning techniques, more precisely a classifier. By performing this classification when indications are detected that a possible epileptic seizure will occur, the patient will then be warned so that he can prepare for the seizure.Crises epiléticas afetam cerca de 1% da população mundial, tornando-a assim a quarta doença neurológica mais comum. Esta é considerada uma doença caracterizada pela atividade anormal do cérebro. Parte da população que sofre desta condição não consegue recorrer a qualquer tratamento, pois este não apresenta qualquer efeito benéfico no paciente. Uma das principais preocupações associadas com este problema são os danos causados pelas convulsões imprevisíveis. Estes danos não afetam somente o próprio paciente, como também as pessoas que o rodeiam. Com esta situação em mente, o objetivo desta dissertação consiste em, através de métodos de Machine Learning, criar um algoritmo capaz de prever as crises epiléticas antes da sua ocorrência. Para proceder à previsão destas convulsões, será utilizado o eletroencefalograma (EEG), uma vez que é o método mais usado para o diagnóstico de epilepsia. Serão utilizados apenas 8 dos 23 canais disponíveis, devido à sua localização. Quando ocorre uma crise, além das alterações visíveis no sinal EEG, não só no momento da crise, são também notáveis alterações antes e após a convulsão. A estas fases a literatura nomeou: • Pre-ictal: momento anterior à crise epilética; • Ictal: momento da convulsão; • Pós-ictal: momento posterior à crise; • Interictal: espaço de tempo entre convulsões. O objetivo do algoritmo preditivo será fazer a classificação das diferentes classes e o estudo de diferentes problemas de classificação, através do uso de técnicas de machine learning, mais precisamente um classificador. Ao realizar esta classificação, quando forem detetados indícios de que uma possível crise epilética irá ocorrer, o paciente será então avisado, podendo assim preparar-se para esta

    Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

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    Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Imaging fascicular organisation in mammalian vagus nerve for selective VNS

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    Nerves contain a large number of nerve fibres, or axons, organised into bundles known as fascicles. Despite the somatic nervous system being well understood, the organisation of the fascicles within the nerves of the autonomic nervous system remains almost completely unknown. The new field of bioelectronics medicine, Electroceuticals, involves the electrical stimulation of nerves to treat diseases instead of administering drugs or performing complex surgical procedures. Of particular interest is the vagus nerve, a prime target for intervention due to its afferent and efferent innervation to the heart, lungs and majority of the visceral organs. Vagus nerve stimulation (VNS) is a promising therapy for treatment of various conditions resistant to standard therapeutics. However, due to the unknown anatomy, the whole nerve is stimulated which leads to unwanted off-target effects. Electrical Impedance Tomography (EIT) is a non-invasive medical imaging technique in which the impedance of a part of the body is inferred from electrode measurements and used to form a tomographic image of that part. Micro-computed tomography (microCT) is an ex vivo method that has the potential to allow for imaging and tracing of fascicles within experimental models and facilitate the development of a fascicular map. Additionally, it could validate the in vivo technique of EIT. The aim of this thesis was to develop and optimise the microCT imaging method for imaging the fascicles within the nerve and to determine the fascicular organisation of the vagus nerve, ultimately allowing for selective VNS. Understanding and imaging the fascicular anatomy of nerves will not only allow for selective VNS and the improvement of its therapeutic efficacy but could also be integrated into the study on all peripheral nerves for peripheral nerve repair, microsurgery and improving the implementation of nerve guidance conduits. Chapter 1 provides an introduction to vagus nerve anatomy and the principles of microCT, neuronal tracing and EIT. Chapter 2 describes the optimisation of microCT for imaging the fascicular anatomy of peripheral nerves in the experimental rat sciatic and pig vagus nerve models, including the development of pre-processing methods and scanning parameters. Cross-validation of this optimised microCT method, neuronal tracing and EIT in the rat sciatic nerve was detailed in Chapter 3. Chapter 4 describes the study with microCT with tracing, EIT and selective stimulation in pigs, a model for human nerves. The microCT tracing approach was then extended into the subdiaphragmatic branches of the vagus nerves, detailed in Chapter 5. The ultimate goal of human vagus nerve tracing was preliminarily performed and described in Chapter 6. Chapter 7 concludes the work and describes future work. Lastly, Appendix 1 (Chapter 8) is a mini review on the application of selective vagus nerve stimulation to treat acute respiratory distress syndrome and Appendix 2 is morphological data corresponding to Chapter 4
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