Dynamics of Brain Function in Preterm-Born Young Adolescents

Preterm birth is a major risk factor for neurodevelopment impairments often only appearing later in life. The brain is still at a high rate of development during adolescence, making this a promising window for intervention. It is thus crucial to understand the mechanisms of altered brain function in this population. The aim of this thesis is to investigate how the brain dynamically reconfigures its own organisation over time in preterm-born young adolescents. Research to date has mainly focused on structural disturbances or in static features of brain function in this population. However, recent studies have shown that brain activity is highly dynamic, both spontaneously and during performance of a task, and that small disruptions in its complex architecture may interfere with normal behaviour and cognitive abilities. This thesis explores the dynamic nature of brain function in preterm-born adolescents in three ...

Freitas, Lorena G. A. — École Polytechnique Fédérale de Lausanne


Automated quantification of preterm brain maturation using electroencephalography

Around 10 percent of all human births is premature, which means that annually about 15 million babies are born before 37 completed weeks of gestation. About one third of the admissions to the Neonatal Intensive Care Unit (NICU) consists of this patient group. Due to complications, 1 million babies die from premature delivery, and it is therefore the most important cause of neonatal death. In general, premature and immature babies have a high risk for neurological abnormalities by maturation in extra-uterine life. Even though improved health care has increased the survival changes of these neonates, they are sensitive to brain damage and consequently, neurocognitive disabilities. Nowadays, critical information about the brain development can be extracted from the electroencephalography (EEG). Clinical experts visually assess evolving EEG characteristics over both short and long periods to evaluate maturation of patients at risk and, ...

Koolen, Ninah — KU Leuven


Unsupervised and semi-supervised Non-negative Matrix Factorization methods for brain tumor segmentation using multi-parametric MRI data

Gliomas represent about 80% of all malignant primary brain tumors. Despite recent advancements in glioma research, patient outcome remains poor. The 5 year survival rate of the most common and most malignant subtype, i.e. glioblastoma, is about 5%. Magnetic resonance imaging (MRI) has become the imaging modality of choice in the management of brain tumor patients. Conventional MRI (cMRI) provides excellent soft tissue contrast without exposing the patient to potentially harmful ionizing radiation. Over the past decade, advanced MRI modalities, such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have gained interest in the clinical field, and their added value regarding brain tumor diagnosis, treatment planning and follow-up has been recognized. Tumor segmentation involves the imaging-based delineation of a tumor and its subcompartments. In gliomas, segmentation plays an important role in treatment planning as well ...

Sauwen, Nicolas — KU Leuven


Dynamic organization of human brain function and its relevance for psychosis vulnerability

The brain is the substrate of a complex dynamic system providing a remarkably varied range of functionalities, going from simple perception to higher-level cognition. Disturbances in its complex dynamics can cause an equally vast variety of mental disorders. One such brain disorder is schizophrenia, a neurodevelopmental disease characterized by abnormal perception of reality that manifests in symptoms like hallucinations or delusions. Even though the brain is known to be affected in schizophrenia, the exact pathophysiology underlying its developmental course is still mostly unknown. In this thesis, we develop and apply methods to look into ongoing brain function measured through magnetic resonance imaging (MRI) and evaluate the potential of these approaches for improving our understanding of psychosis vulnerability and schizophrenia. We focus on patients with chromosome 22q11.2 deletion syndrome (22q11DS), a genetic disorder that comes with a 30fold increased risk for ...

Zöller, Daniela — EPFL (École Polytechnique Fédérale de Lausanne)


Heart rate variability : linear and nonlinear analysis with applications in human physiology

Cardiovascular diseases are a growing problem in today’s society. The World Health Organization (WHO) reported that these diseases make up about 30% of total global deaths and that heart diseases have no geographic, gender or socioeconomic boundaries. Therefore, detecting cardiac irregularities early-stage and a correct treatment are very important. However, this requires a good physiological understanding of the cardiovascular system. The heart is stimulated electrically by the brain via the autonomic nervous system, where sympathetic and vagal pathways are always interacting and modulating heart rate. Continuous monitoring of the heart activity is obtained by means of an ElectroCardioGram (ECG). Studying the fluctuations of heart beat intervals over time reveals a lot of information and is called heart rate variability (HRV) analysis. A reduction of HRV has been reported in several cardiological and noncardiological diseases. Moreover, HRV also has a prognostic ...

Vandeput, Steven — KU Leuven


Central and peripheral mechanisms: a multimodal approach to understanding and restoring human motor control

All human actions involve motor control. Even the simplest movement requires the coordinated recruitment of many muscles, orchestrated by neuronal circuits in the brain and the spinal cord. As a consequence, lesions affecting the central nervous system, such as stroke, can lead to a wide range of motor impairments. While a certain degree of recovery can often be achieved by harnessing the plasticity of the motor hierarchy, patients typically struggle to regain full motor control. In this context, technology-assisted interventions offer the prospect of intense, controllable and quantifiable motor training. Yet, clinical outcomes remain comparable to conventional approaches, suggesting the need for a paradigm shift towards customized knowledge-driven treatments to fully exploit their potential. In this thesis, we argue that a detailed understanding of healthy and impaired motor pathways can foster the development of therapies optimally engaging plasticity. To this ...

Kinany, Nawal — Ecole Polytechnique Fédérale de Lausanne (EPFL)


Development of an automated neonatal EEG seizure monitor

Brain function requires a continuous flow of oxygen and glucose. An insufficient supply for a few minutes during the first period of life may have severe consequences or even result in death. This happens in one to six infants per 1000 live term births. Therefore, there is a high need for a method which can enable bedside brain monitoring to identify those neonates at risk and be able to start the treatment in time. The most important currently available technology to continuously monitor brain function is electroEncephaloGraphy (or EEG). Unfortunately, visual EEG analysis requires particular skills which are not always present round the clock in the Neonatal Intensive Care Unit (NICU). Even if those skills are available it is laborsome to manually analyse many hours of EEG. The lack of time and skill are the main reasons why EEG is ...

Deburchgraeve, Wouter — KU Leuven


Mining the ECG: Algorithms and Applications

This research focuses on the development of algorithms to extract diagnostic information from the ECG signal, which can be used to improve automatic detection systems and home monitoring solutions. In the first part of this work, a generically applicable algorithm for model selection in kernel principal component analysis is presented, which was inspired by the derivation of respiratory information from the ECG signal. This method not only solves a problem in biomedical signal processing, but more importantly offers a solution to a long-standing problem in the field of machine learning. Next, a methodology to quantify the level of contamination in a segment of ECG is proposed. This level is used to detect artifacts, and to improve the performance of different classifiers, by removing these artifacts from the training set. Furthermore, an evaluation of three different methodologies to compute the ECG-derived ...

Varon, Carolina — KU Leuven


Monitoring Infants by Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 2‰ live births, 11‰ for preterm ...

Cattani Luca — University of Parma (Italy)


Three dimensional shape modeling: segmentation, reconstruction and registration

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas, such as biometrics, biomedical imaging, and data mining. It is well known that 3D polar shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This thesis studies several areas involved in 3D shape modeling, including random field models for statistical shape modeling, optimal shape filtering, parametric active contours for object segmentation and surface reconstruction. It also investigates multi-modal image registration with respect to tumor activity quantification. Spherical harmonic expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-Lo´eve (K-L) expansion of any isotropic random field on the unit sphere. Spherical ...

Li, Jia — University of Michigan


Towards an Automated Portable Electroencephalography-based System for Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is a neurodegenerative terminal disorder that accounts for nearly 70% of dementia cases worldwide. Global dementia incidence is projected to 75 million cases by 2030, with the majority of the affected individuals coming from low- and medium- income countries. Although there is no cure for AD, early diagnosis can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using mental status examinations, expensive neuroimaging scans, and invasive laboratory tests, all of which render the diagnosis time-consuming and costly. Notwithstanding, over the last decade electroencephalography (EEG), specifically resting-state EEG (rsEEG), has emerged as an alternative technique for AD diagnosis with accuracies inline with those obtained with more expensive neuroimaging tools, such as magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET). However the use of rsEEG for ...

Cassani, Raymundo — Université du Québec, Institut national de la recherche scientifique


Functional Neuroimaging Data Characterisation Via Tensor Representations

The growing interest in neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has by now been recognized as an effective approach exploiting its inherent multi-way nature. In particular, the advantages of tensorial over matrix-based methods have previously been demonstrated in the context of functional magnetic resonance imaging (fMRI) source localization; the identification of the regions of the brain which are activated at specific time instances. However, such methods can also become ineffective in realistic challenging scenarios, involving, e.g., strong noise and/or significant overlap among the activated regions. Moreover, they commonly rely on the assumption of an underlying multilinear model generating the data. In the first part of this thesis, we aimed at investigating the possible gains from exploiting the 3-dimensional nature of the brain images, through a higher-order tensorization ...

Christos Chatzichristos — National and Kapodistrian University of Athens


Adaptive Nonlocal Signal Restoration and Enhancement Techniques for High-Dimensional Data

The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition process based on the measurement of a physical quantity of interest incident upon an imaging sensor over a specified period of time. The quantity of interest depends on the targeted imaging application. Common imaging sensors measure the number of photons impinging over a dense grid of photodetectors in order to produce an image similar to what is perceived by the human visual system. Different applications focus on the part of the electromagnetic spectrum not visible by the human visual system, and thus require different sensing technologies to form the image. In all cases, even with the advance of ...

Maggioni, Matteo — Tampere University of Technology


Analysis and improvement of quantification algorithms for magnetic resonance spectroscopy

Magnetic Resonance Spectroscopy (MRS) is a technique used in fundamental research and in clinical environments. During recent years, clinical application of MRS gained importance, especially as a non-invasive tool for diagnosis and therapy monitoring of brain and prostate tumours. The most important asset of MRS is its ability to determine the concentration of chemical substances non-invasively. To extract relevant signal parameters, MRS data have to be quantified. This usually doesn¢t prove to be straightforward since in vivo MRS signals are characterized by poor signal-to-noise ratios, overlapping peaks, acquisition related artefacts and the presence of disturbing components (e.g. residual water in proton spectra). The work presented in this thesis aims to improve the quantification in different applications of MRS in vivo. To obtain the signal parameters related to MRS data, different approaches were suggested in the past. Black-box methods, don¢t require ...

Pels, Pieter — Katholieke Universiteit Leuven


Robust Estimation and Model Order Selection for Signal Processing

In this thesis, advanced robust estimation methodologies for signal processing are developed and analyzed. The developed methodologies solve problems concerning multi-sensor data, robust model selection as well as robustness for dependent data. The work has been applied to solve practical signal processing problems in different areas of biomedical and array signal processing. In particular, for univariate independent data, a robust criterion is presented to select the model order with an application to corneal-height data modeling. The proposed criterion overcomes some limitations of existing robust criteria. For real-world data, it selects the radial model order of the Zernike polynomial of the corneal topography map in accordance with clinical expectations, even if the measurement conditions for the videokeratoscopy, which is the state-of-the-art method to collect corneal-height data, are poor. For multi-sensor data, robust model order selection selection criteria are proposed and applied ...

Muma, Michael — Technische Universität Darmstadt

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