Opportunities for improving animal welfare in rodent models of epilepsy and seizures

Animal models of epilepsy and seizures, mostly involving mice and rats, are used to understand the pathophysiology of the different forms of epilepsy and their comorbidities, to identify biomarkers, and to discover new antiepileptic drugs and treatments for comorbidities. Such models represent an important area for application of the 3Rs (replacement, reduction and refinement of animal use). This report provides background information and recommendations aimed at minimising pain, suffering and distress in rodent models of epilepsy and seizures in order to improve animal welfare and optimise the quality of studies in this area. The report includes practical guidance on principles of choosing a model, induction procedures, in vivo recordings, perioperative care, welfare assessment, humane endpoints, social housing, environmental enrichment, reporting of studies and data sharing. In addition, some model-specific welfare considerations are discussed, and data gaps and areas for further research are identified. The guidance is based upon a systematic review of the scientific literature, survey of the international epilepsy research community, consultation with veterinarians and animal care and welfare officers, and the expert opinion and practical experience of the members of a Working Group convened by the United Kingdom's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs)

Phase Synchronization Operator for On-Chip Brain Functional Connectivity Computation

This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters, and adders. The processor, fabricated in a 0.18- μ m CMOS process, only occupies 0.05 mm 2 and consumes 15 nW from a 0.5 V supply voltage at a signal input rate of 1024 S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research (USA) N00014-19-1-215

Exploring machine learning techniques in epileptic seizure detection and prediction

Epilepsy is the most common neurological disorder, affecting between 0.6% and 0.8% of the global population. Among those affected by epilepsy whose primary method of seizure management is Anti Epileptic Drug therapy (AED), 30% go on to develop resistance to drugs which ultimately leads to poor seizure management. Currently, alternative therapeutic methods with successful outcome and wide applicability to various types of epilepsy are limited. During an epileptic seizure, the onset of which tends to be sudden and without prior warning, sufferers are highly vulnerable to injury, and methods that might accurately predict seizure episodes in advance are clearly of value, particularly to those who are resistant to other forms of therapy. In this thesis, we draw from the body of work behind automatic seizure prediction obtained from digitised Electroencephalography (EEG) data and use a selection of machine learning and data mining algorithms and techniques in an attempt to explore potential directions of improvement for automatic prediction of epileptic seizures. We start by adopting a set of EEG features from previous work in the field (Costa et al. 2008) and exploring these via seizure classification and feature selection studies on a large dataset. Guided by the results of these feature selection studies, we then build on Costa et al's work by presenting an expanded feature-set for EEG studies in this area. Next, we study the predictability of epileptic seizures several minutes (up to 25 minutes) in advance of the physiological onset. Furthermore, we look at the role of the various feature compositions on predicting epileptic seizures well in advance of their occurring. We focus on how predictability varies as a function of how far in advance we are trying to predict the seizure episode and whether the predictive patterns are translated across the entire dataset. Finally, we study epileptic seizure detection from a multiple-patient perspective. This entails conducting a comprehensive analysis of machine learning models trained on multiple patients and then observing how generalisation is affected by the number of patients and the underlying learning algorithm. Moreover, we improve multiple-patient performance by applying two state of the art machine learning algorithms

Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders.

Distinctive features in sensory event-related potentials (ERPs) are endophenotypic biomarkers of psychiatric disorders, widely studied using electroencephalographic (EEG) methods in humans and model animals. Despite the popularity and unique significance of the mouse as a model species in basic research, existing EEG methods applicable to mice are far less powerful than those available for humans and large animals. We developed a new method for multi-channel epidural ERP characterization in behaving mice with high precision, reliability and convenience and report an application to time-domain ERP feature characterization of the Sp4 hypomorphic mouse model for schizophrenia. Compared to previous methods, our spatio-temporal ERP measurement robustly improved the resolving power of key signatures characteristic of the disease model. The high performance and low cost of this technique makes it suitable for high-throughput behavioral and pharmacological studies

Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces

Brain Tumour Classification Using Deep Features from Structural MRI

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Non-linear classifiers applied to EEG analysis for epilepsy seizure detection

This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions. (C) 2017 Elsevier Ltd. All rights reserved

Dog electroencephalogram for early safety seizure liability assessments and investigation of species-specific sensitivity for neurological symptoms

Preclinical safety is an important part of drug development in animals and humans. In toxicology studies, seizure liability can be detected at high doses as convulsions. Non-convulsive seizures induce only subtle behavioral changes and their assessment in animals is challenging. Electroencephalography (EEG) is the only method to correlate animal behavior to seizure activity and video-EEG is the current gold-standard for preclinical seizure liability assessments (Authier et al., 2014b). In most cases there are no clear premonitory signs that forewarn of convulsions but epileptiform EEG activity prior to clinical manifestation has been reported during a period potentially sufficient for prophylactic anticonvulsive treatment (Dürmüller et al., 2007). Aim of this thesis was investigation of a study design for assessment of neurological symptoms in dogs. This design should optimize detection of neurological signs while minimizing study duration and animal numbers. Video-EEG was used to increase symptom detection rate and to explore the possibility to refine seizure liability testing by enabling EEG-based anticonvulsive treatment. For establishment of the EEG system in our facility, reference substances were tested first. Then, three in-house drug candidates with different modes of action and known neurological side effects were chosen. Two telemetered beagle dogs were used per experiment. Substance effects on clinical symptoms and on the EEG were investigated. CSF and blood samples for analysis of drug exposure and biomarkers were collected simultaneous to symptoms. Results were compared to previous toxicological studies thereby enabling evaluation of non-rodent species differences in sensitivity for neurological symptoms. Results showed that combination of implants for CSF collection and EEG recording is possible. In this study design, intravenous administration was superior to oral dosing as it led to a reduced variability in exposure levels. Also, experimental time was significantly reduced compared to standard toxicology studies while the same neurological symptoms were induced. This shortened duration enabled continuous clinical observations for a better evaluation of CNS effects and immediate veterinary assistance in the spirit of animal welfare. The EEG was not superior to clinical observations in forewarning of convulsion risk and did not enable convulsion prevention. This was due first to the short latency between onset of abnormal EEG activity and convulsions which was below one minute with in-house compounds. Secondly, accurate interpretation of the unfiltered EEG signal was limited, especially differentiation of artefacts and epileptiform activity. In conclusion, a study design using intravenous infusions is suitable for the characterization of neurological symptoms. All the symptoms, which were already known from studies with a longer duration, were also seen. This allowed better correlation of neurological symptoms to exposure and immediate veterinarian treatments. For substances with a high risk to induce severe neurological symptoms, such studies can guide dose selection for longer regulatory toxicological studies to prevent occurrence of severe neurological symptoms.Im Rahmen der Entwicklung von Human- und Veterinärarzneimitteln wird die Anwendersicherheit neuer Medikamente in präklinischen Sicherheitsstudien erforscht. Zentralnervöse Nebenwirkungen werden häufig erst in toxikologischen Prüfungen erkannt, wenn bei hohen Dosierungen Krampfanfälle bei den Versuchstieren auftreten. Epileptische Anfälle können allerdings auch subtilere Symptome, deren Erkennen in Tieren schwierig ist, verursachen. Die Elektroenzephalographie (EEG) bietet in Tierstudien die einzige Möglichkeit, nicht-konvulsive Anfälle zu diagnostizieren. Daher ist die Kombination von Videoüberwachung und EEG in der präklinischen Arzneimittelentwicklung gegenwärtig der Goldstandard für die Sicherheitsbewertung einer Substanz im Hinblick auf ihr Risiko, Anfälle auszulösen (Authier et al., 2014b). Meist gibt es keine klinischen Warnzeichen vor dem Auftreten von Krampfanfällen. Allerdings wurde das Auftreten epileptiformer EEG-Aktivität vor klinischen Symptomen beobachtet. Das beschriebene Zeitfenster ist potentiell ausreichend für prophylaktische antikonvulsive Behandlung (Dürmüller et al., 2007). Ziel dieser Arbeit war es, in Pilotstudien ein neues Studiendesign für die Charakterisierung neurologischer Nebenwirkungen zu evaluieren. Dieses Studiendesign sollte die Erkennungsrate neurologischer Nebenwirkungen optimieren und dabei gleichzeitig eine Reduktion der dazu nötigen Tiere und der Studiendauer ermöglichen. Der Einsatz von EEG und Videoüberwachung sollte es ermöglichen, Substanz-induzierte Anfälle im Frühstadium zu erkennen und ihr klinisches Auftreten zu verhindern. Um das EEG-System in der Forschungseinrichtung neu zu etablieren und um zu evaluieren, ob Implantate für Liquor-Entnahme und EEG-Aufzeichnung kompatibel sind, wurden zuerst Referenzsubstanzen getestet. Zur Beantwortung der eigentlichen Fragestellung wurden drei Arzneimittelkandidaten mit unterschiedlichen Wirkmechanismen ausgewählt, von denen bekannt war, dass sie neurologische Symptome verursachen. Je Substanztest wurden zwei Hunde mit implantierten EEG-Sendern verwendet. Zwei der Substanzen wurden in eskalierenden intravenösen Dosen verabreicht, die dritte wurde als einzelne orale Dosis gegeben. Effekte der Substanzen auf klinische Symptome und auf das EEG wurden evaluiert. Parallel wurden Blut- und Liquor-Proben zur Bestimmung der Substanzspiegel und potentieller Biomarker genommen. Die Auswahl der Substanzen bot zusätzlich die Möglichkeit, die Empfindlichkeit der beiden regelmäßig in Arzneimittelprüfungen verwendeten Nicht-Nager Spezies Hund und Affe für neurologische Symptome vergleichend zu bewerten. Die Ergebnisse zeigen, dass die Kombination von Implantaten für EEG-Aufzeichnung und CSF-Probennahme möglich ist. Die intravenöse Applikation war der oralen Substanzgabe vorzuziehen, da die Variabilität der Substanz-Plasmaspiegel geringer war. Alle Symptome, die aus früheren toxikologischen Studien mit längerer Dauer bekannt waren, wurden ebenso beobachtet. Durch das Dosierungsschema war ihr Auftreten allerdings auf eine verkürzte Zeitspanne reduziert. Die kurze Studiendauer ermöglichte durchgehende klinische Beobachtung, somit die Erkennung aller Symptome und zeitnahe veterinärmedizinische Behandlungen, was im Sinne des Tierschutzes einen Vorteil darstellt. Für eine frühzeitige Erkennung von Krampfanfällen war das EEG nicht besser geeignet als klinische Beobachtung, da die Interpretation des ungefilterten EEG Signals durch das Auftreten von Artefakten erschwert war. Das Studiendesign, in dem das EEG angewendet wurde, ist zur Charakterisierung neurologischer Nebenwirkungen geeignet, da alle Symptome, die aus Studien mit längerer Dauer bekannt waren, ebenso beobachtet wurden. Durch die verkürzte Dauer wurde ermöglicht, Symptome und Substanzplasmaspiegel zu korrelieren und zeitnahe tierärztliche Behandlungen durchzuführen. Bei Substanzen, die ein hohes Risiko für neurologische Nebenwirkungen haben, kann dieses Studiendesign genutzt werden um im Vorfeld von behördlich geforderten toxikologischen Studien Dosierungen zu bestimmen, bei denen keine schweren neurologischen Nebenwirkungen zu erwarten sind