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 ...

Preterm birth is a major pediatric health problem that perturbs the genetically determined program of corticogenesis of the developing brain. As a consequence, prematurity has been strongly associated with adverse long-term neurodevelopmental outcome that may persist even into adulthood. Early characterization of the underlying neuronal mechanisms and early identification of infants at risk is of paramount importance since it allows better development of early therapeutic interventions aiming to prevent adverse outcomes through resilience. This dissertation aims to investigate the consequences of preterm birth on brain function and structure and their relation to adverse neurodevelopmental outcome, as well as to unveil the effect of an early music intervention on brain function. Research to date has mainly focused on the effect of early interventions on the long-term outcome but not on the effect of those interventions on brain function in preterm populations. ...

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 ...

Magnetic Resonance Imaging (MRI) constitutes a non-invasive medical imaging technique that allows the exploration of the inner anatomy, tissues, and physiological processes of the body. Among the different MRI applications, functional Magnetic Resonance Imaging (fMRI) has slowly become an essential tool for investigating the brain behavior and, nowadays, it plays a fundamental role in clinical and neurophysiological research. Due to its particular nature, specialized signal processing techniques are required in order to analyze the fMRI data properly. Among the various related techniques that have been developed over the years, the General Linear Model (GLM) is one of the most widely used approaches, and it usually appears as a default in many specialized software toolboxes for fMRI. On the other end, Blind Source Separation (BSS) methods constitute the most common alternative to GLM, especially when no prior information regarding the brain ...

In this thesis, the power of Machine Learning (ML) algorithms is combined with brain connectivity patterns, using Magnetic Resonance Imaging (MRI), for classification and prediction of Multiple Sclerosis (MS). White Matter (WM) as well as Grey Matter (GM) graphs are studied as connectome data types. The thesis addresses three main research objectives. The first objective aims to generate realistic brain connectomes data for improving the classification of MS clinical profiles in cases of data scarcity and class imbalance. To solve the problem of limited and imbalanced data, a Generative Adversarial Network (GAN) was developed for the generation of realistic and biologically meaningful connec- tomes. This network achieved a 10% better MS classification performance compared to classical approaches. As second research objective, we aim to improve classification of MS clinical profiles us- ing morphological features only extracted from GM brain tissue. ...

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 ...

Every year, an estimated 15 million babies are born preterm, that is, before 37 weeks of gestation. This number is rising in all countries and currently represents more than 1 in 10 babies, affecting families all over the world. During the last decades, the survival rate of prematurely born neonates has steadily increased, mainly as a result of medical and technical progress in neonatal intensive care. The very preterm infants, which represent up to 10% of the preterm infants in the EU, remain at risk for adverse outcome and neurodevelopmental disability. These maladaptive outcomes have a severe effect on the children’s quality of life and a huge economic impact on society. In order to reduce this burden and improve neonatal care in general, appropriate tools need to be developed to identify the neonates with a higher risk of adverse outcomes. ...

This thesis focuses on wearables for health status monitoring, covering applications aimed at emergency solutions to the COVID-19 pandemic and aging society. The methods of ambient assisted living (AAL) are presented for the neurodegenerative disease Parkinson’s disease (PD), facilitating ’aging in place’ thanks to machine learning and around wearables - solutions of mHealth. Furthermore, the approaches using machine learning and wearables are discussed for early-stage COVID-19 detection, with encouraging accuracy. Firstly, a publicly available dataset containing COVID-19, influenza, and healthy control data was reused for research purposes. The solution presented in this thesis is considering the classification problem and outperformed the state-of-the-art methods, whereas the original paper introduced just anomaly detection and not shown the specificity of the created models. The proposed model in the thesis for early detection of COVID-19 achieved 78 % for the k-NN classifier. Moreover, a ...

Worldwide approximately 11% of the babies are born before 37 weeks of gestation. The survival rates of these prematurely born infants have steadily increased during the last decades as a result of the technical and medical progress in the neonatal intensive care units (NICUs). The focus of the NICUs has therefore gradually evolved from increasing life chances to improving quality of life. In this respect, promoting and supporting optimal brain development is crucial. Because these neonates are born during a period of rapid growth and development of the brain, they are susceptible to brain damage and therefore vulnerable to adverse neurodevelopmental outcome. In order to identify patients at risk of long-term disabilities, close monitoring of the neurological function during the first critical weeks is a primary concern in the current NICUs. Electroencephalography (EEG) is a valuable tool for continuous noninvasive ...

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, ...

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 ...

Localizing the bioelectric phenomena originating from the cerebral cortex and evoked by auditory and somatosensory stimuli are clear objectives to both understand how the brain works and to recognize different pathologies. Diseases such as Parkinson's, Alzheimer's, schizophrenia and epilepsy are intensively studied to find a cure or accurate diagnosis. Epilepsy is considered the disease with major prevalence within disorders with neurological origin. The recurrent and sudden incidence of seizures can lead to dangerous and possibly life-threatening situations. Since disturbance of consciousness and sudden loss of motor control often occur without any warning, the ability to predict epileptic seizures would reduce patients' anxiety, thus considerably improving quality of life and safety. The common procedure for epilepsy seizure detection is based on brain activity monitorization via electroencephalogram (EEG) data. This process consumes a lot of time, especially in the case of long ...

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 ...

This thesis contributes to motion characterization through inertial and physiological signals captured by wearable devices and analyzed using signal processing and deep learning techniques. This research leverages the possibilities of motion analysis for three main applications: to know what physical activity a person is performing (Human Activity Recognition), to identify who is performing that motion (user identification) or know how the movement is being performed (motor anomaly detection). Most previous research has addressed human motion modeling using invasive sensors in contact with the user or intrusive sensors that modify the user’s behavior while performing an action (cameras or microphones). In this sense, wearable devices such as smartphones and smartwatches can collect motion signals from users during their daily lives in a less invasive or intrusive way. Recently, there has been an exponential increase in research focused on inertial-signal processing to ...

Magnetic Resonance Spectroscopy (MRS) is a technique which has evolved rapidly over the past 15 years. It has been used specifically in the context of brain tumours and has shown very encouraging correlations between brain tumour type and spectral pattern. In vivo MRS enables the quantification of metabolite concentrations non-invasively, thereby avoiding serious risks to brain damage. While Magnetic Resonance Imaging (MRI) is commonly used for identifying the location and size of brain tumours, MRS complements it with the potential to provide detailed chemical information about metabolites present in the brain tissue and enable an early detection of abnormality. However, the introduction of MRS in clinical medicine has been difficult due to problems associated with the acquisition of in vivo MRS signals from living tissues at low magnetic fields acceptable for patients. The low signal-to-noise ratio makes accurate analysis of ...