A complex physical system like the human brain can only be comprehended by the use of a combination of various medical imaging techniques, each of which shed light on only a specific aspect of the neural processes that take place beneath the skull. Electroencephalography (EEG) and functional magnetic resonance (fMRI) are two such modalities, which enable the study of brain (dys)function. While the EEG is measured with a limited set of scalp electrodes which record rapid electrical changes resulting from neural activity, fMRI offers a superior spatial resolution at the expense of only picking up slow fluctuations of oxygen concentration that takes place near active brain cells. Hence, combining these very complementary modalities is an appealing, but complicated task due to their heterogeneous nature. In this thesis, we devise advanced signal processing techniques which integrate the multimodal data stemming from ...

Understanding the cognitive processes that are going on in the human brain, requires the combination of several types of observations. For this reason, since several years, neuroscience research started to focus on multimodal approaches. One such multimodal approach is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The non-invasive character of these two modalities makes their combination not only harmless and painless, but also especially suited for widespread research in both clinical and experimental applications. Moreover, the complementarity between the high temporal resolution of the EEG and the high spatial resolution of the fMRI, allows obtaining a more complete picture of the processes under study. However, the combination of EEG and fMRI is challenging, not only on the level of the data acquisition, but also when it comes to extracting the activity of interest and interpreting the ...

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 ...

Epilepsy is a neurological condition that manifests in epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. Epilepsy cannot be cured and in many cases is not controlled by medication either. Surgical resection of the region responsible for generating the epileptic seizures might offer remedy for these patients. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measure the changes of brain activity in time over different locations of the brain. As such, they provide valuable information on the nature, the timing and the spatial origin of the epileptic activity. Unfortunately, both techniques record activity of different brain and artefact sources as well. Hence, EEG and fMRI signals are characterised by low signal to noise ratio. Data quality and the vast amount ...

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 ...

Standard signal processing techniques are implicitly based on the assumption that the signal lies on a regular, homogeneous domain. In practice, however, many signals lie on an irregular or inhomogeneous domain. An application area where data are naturally defined on an irregular or inhomogeneous domain is human brain neuroimaging. The goal in neuroimaging is to map the structure and function of the brain using imaging techniques. In particular, functional magnetic resonance imaging (fMRI) is a technique that is conventionally used in non-invasive probing of human brain function. This doctoral dissertation deals with the development of signal processing schemes that adapt to the domain of the signal. It consists of four papers that in different ways deal with exploiting knowledge of the signal domain to enhance the processing of signals. In each paper, special focus is given to the analysis of ...

Most cognitive processes in the brain are reflected through several aspects simultaneously, allowing us to observe the same process from different biological phenomena. The diverse nature of these biological processes suggests that a better understanding of cerebral activity may be achieved through multimodal measurements. One of the possible multimodal brain recording settings is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which is one of the main topics of this thesis. Two groups of EEG-fMRI integration approaches are possible. The first group, commonly called model-based techniques, are very popular due to the fact that the results from such analyses confirm or disprove a specific hypothesis. However, such hypotheses are not always available, requiring a more explorative approach to analyze the data. This exploration is possible with the second group of approaches, the so-called data-driven methods. The data-driven ...

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 ...

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 ...

Over the last decades, Magnetic Resonance Imaging (MRI) has taken a leading role in the study of human body and it is widely used in clinical diagnosis. In vivo and ex vivo Magnetic Resonance Spectroscopic (MRS) techniques can additionally provide valuable metabolic information as compared to MRI and are gaining more clinical interest. The analysis of MRS data is a complex procedure and requires several preprocessing steps aiming to improve the quality of the data and to extract the most relevant features before any classification algorithm can be successfully applied. In this thesis a new approach to quantify magnetic resonance spectroscopic imaging (MRSI) data and therefore to obtain improved metabolite estimates is proposed. Then an important part is focusing on improving the diagnosis of glial brain tumors which are characterized by an extensive heterogeneity since various intramural histopathological properties such ...

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. ...

A key issue in understanding how the brain functions is the ability to correlate functional information with anatomical localisation. Functional information can be provided by a variety of techniques like positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) or transcranial magnetic stimulation (TMS). All these methods provide different, but complementary, information about the functional areas of the brain. PET and fMRI provide spatially accurate picture of brain regions involved in a given task. TMS permits to infer the contribution of the stimulated brain area to the task under investigation. EEG and MEG, which reflects brain activity directly, have temporal accuracy of the order of a millisecond. TMS, EEG and MEG are offset by their low spatial resolution. In this thesis, we propose two methods to improve the spatial accuracy of method based on TMS and EEG. The ...

Diffusion Magnetic Resonance Imaging (dMRI) is a meaningful technique for white matter (WM) fiber-tracking and microstructural characterization of axonal/neuronal integrity and connectivity. By measuring water molecules motion in the three directions of space, numerous parametric maps can be reconstructed. Among these, fractional anisotropy (FA), mean diffusivity (MD), and axial (λa) and radial (λr) diffusivities have extensively been used to investigate brain diseases. Overall, these findings demonstrated that WM and grey matter (GM) tissues are subjected to numerous microstructural alterations in multiple sclerosis (MS). However, it remains unclear whether these tissue alterations result from global processes, such as inflammatory cascades and/or neurodegenerative mechanisms, or local inflammatory and/or demyelinating lesions. Furthermore, these pathological events may occur along afferent or afferent WM fiber pathways, leading to antero- or retrograde degeneration. Thus, for a better understanding of MS pathological processes like its spatial and ...

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 ...

In the world of Information and communications technology (ICT), strategies for innovation and development are increasingly focusing on applications that require spatial representation and real-time interaction with and within 3D-media environments. One of the major challenges that such applications have to address is user-centricity, reflecting e.g. on developing complexity-hiding services so that people can personalize their own delivery of services. In these terms, multimodal interfaces represent a key factor for enabling an inclusive use of new technologies by everyone. In order to achieve this, multimodal realistic models that describe our environment are needed, and in particular models that accurately describe the acoustics of the environment and communication through the auditory modality are required. Examples of currently active research directions and application areas include 3DTV and future internet, 3D visual-sound scene coding, transmission and reconstruction and teleconferencing systems, to name but ...