Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

MAPPING LOW-FREQUENCY FIELD POTENTIALS IN BRAIN CIRCUITS WITH HIGH-RESOLUTION CMOS ELECTRODE ARRAY RECORDINGS

Neurotechnologies based on microelectronic active electrode array devices are on the way to provide the capability to record electrophysiological neural activity from a thousands of closely spaced microelectrodes. This generates increasing volumes of experimental data to be analyzed, but also offers the unprecedented opportunity to observe bioelectrical signals at high spatial and temporal resolutions in large portions of brain circuits. The overall aim of this PhD was to study the application of high-resolution CMOS-based electrode arrays (CMOS-MEAs) for electrophysiological experiments and to investigate computational methods adapted to the analysis of the electrophysiological data generated by these devices. A large part of my work was carried out on cortico-hippocampal brain slices by focusing on the hippocampal circuit. In the history of neuroscience, a major technological advance for hippocampal research, and also for the field of neurobiology, was the development of the in vitro hippocampal slice preparation. Neurobiological principles that have been discovered from work on in vitro hippocampal preparations include, for instance, the identification of excitatory and inhibitory synapses and their localization, the characterization of transmitters and receptors, the discovery of long-term potentiation (LTP) and long-term depression (LTD) and the study of oscillations in neuronal networks. In this context, an initial aim of my work was to optimize the preparation and maintenance of acute cortico-hippocampal brain slices on planar CMOS-MEAs. At first, I focused on experimental methods and computational data analysis tools for drug-screening applications based on LTP quantifications. Although the majority of standard protocols still use two electrodes platforms for quantifying LTP, in my PhD I investigate the potential advantages of recording the electrical activity from many electrodes to spatiotemporally characterized electrically induced responses. This work also involved the collaboration with 3Brain AG and a CRO involved in drug-testing, and led to a software tool that was licensed for developing its exploitation. In a second part of my work I focused on exploiting the recording resolution of planar CMOS-MEAs to study the generation of sharp wave ripples (SPW-Rs) in the hippocampal circuit. This research activity was carried out also by visiting the laboratory of Prof. A. Sirota (Ludwig Maximilians University, Munich). In addition to set-up the experimental conditions to record SPW-Rs from planar CMOS-MEAs integrating 4096 microelectrodes, I also explored the implementation of a data analysis pipeline to identify spatiotemporal features that might characterize different type of in-vitro generated SPW-R events. Finally, I also contributed to the initial implementation of high-density implantable CMOS-probes for in-vivo electrophysiology with the aim of evaluating in vivo the algorithms that I developed and investigated on brain slices. With this aim, in the last period of my PhD I worked on the development of a Graphical User Interface for controlling active dense CMOS probes (or SiNAPS probes) under development in our laboratory. I participated to preliminary experimental recordings using 4-shank CMOS-probes featuring 1024 simultaneously recording electrodes and I contributed to the development of a software interface for executing these experiments

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Multimodal assessment of neonatal pain

Pain assessment is critical to prevent suffering and harm in infants admitted to the neonatal care unit. As pain is a subjective experience, its assessment in nonverbal infants relies on surrogate measures. Current infant pain assessment tools that are based on behaviour and autonomic nervous system measurements lack face validity — they are unlikely to reflect pain in all its dimensions. In recent years, EEG-derived measures of pain have been developed in late preterm and term infants. Multimodal tools which include these cerebral measurements are conceptually more appropriate to measure pain. Yet, their use is still limited to specific research applications. This thesis focuses on outstanding questions that need to be addressed in order to advance the development of multimodal pain assessment tools that incorporate cerebral measurements. In the first part of this thesis, I focus on the characterisation of preterm infants’ noxious-evoked responses and their development. Across several modalities, premature infants have dampened or altered responsiveness compared to term infants, and it is uncertain if these responses can be reliably discriminated from tactile-evoked responses. In particular, a discriminative pattern of noxious-evoked EEG activity that is present in term infants, is unlikely to be present in preterm infants. In addition, it is unclear how noxious-evoked responses, especially brainderived responses, change with age. In this thesis, I use a classification model to show that infants aged 28–40 weeks postmenstrual age display discriminable multimodal responses to a noxious clinical procedure and a tactile control procedure, and I provide examples of how a such a model could be used in clinical trials of analgesics. I show that noxious-evoked responses change magnitude and morphology across this age range, and that discriminative brain activity emerges in early prematurity. In the second part of this thesis, I focus on improving the neuroscientific validity of a noxious-evoked EEG response measured at the cot-side, as the spatial neural correlates of these responses are still poorly understood. I present an EEG-fMRI pilot study to investigate the spatial neural correlates of inter-individual differences in noxious-evoked EEG responses and provide recommendations for a larger follow-up study. Overall, this thesis provides a characterisation of infants’ noxious-evoked responses and their development across multiple modalities, a crucial next step in improving multimodal neonatal pain assessment

Spectral and coherence estimates on electroencephalogram recordings during arithmetical tasks

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédic

Localising epileptiform activity and eloquent cortex using magnetoencephalography

In patients with drug resistant epilepsy, the surgical resection of epileptogenic cortex allows the possibility for seizure freedom, provided that epileptogenic and eloquent brain tissue can be accurately identified prior to surgery. This is often achieved using various techniques including neuroimaging, electroencephalographic (EEG), neuropsychological and invasive measurements. Over the last 20 years, magnetoencephalography (MEG) has emerged as a non-invasive tool that can provide important clinical information to patients with suspected neocortical epilepsy being considered for surgery. The standard clinical MEG analyses to localise abnormalities are not always successful and therefore the development and evaluation of alternative methods are warranted. There is also a continuous need to develop MEG techniques to delineate eloquent cortex. Based on this rationale, this thesis is concerned with the presurgical evaluation of drug resistant epilepsy patients using MEG and consists of two themes: the first theme focuses on the refinement of techniques to functionally map the brain and the second focuses on evaluating alternative techniques to localise epileptiform activity. The first theme involved the development of an alternative beamformer pipeline to analyse Elekta Neuromag data and was subsequently applied to data acquired using a pre-existing and a novel language task. The findings of the second theme demonstrated how beamformer based measures can objectively localise epileptiform abnormalities. A novel measure, rank vector entropy, was introduced to facilitate the detection of multiple types of abnormal signals (e.g. spikes, slow waves, low amplitude transients). This thesis demonstrates the clinical capacity of MEG and its role in the presurgical evaluation of drug resistant epilepsy patients