Brain-Computer Interfaces for augmented communication: Asynchronous and adaptive algorithms and evaluation with end users

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.

Probiotics for the prevention/treatment of human diseases and ecological study of the intestinal microbiota

The interest in human intestinal microbiota has increased in the last 20 years and significant advances have been achieved with regard to its composition and functions. The gut microbiota contributes to the maintenance of the host health status and, since alterations in the gut microbiota have been involved in the pathogenesis/progression of some diseases, several studies have focused on the manipulation of its composition. Probiotics are a strategy to maintain/restore the correct balance of gut microbial population and to prevent/treat diseases. The aim of this thesis was to explore the possibility of probiotic supplementation for the prevention/treatment of human diseases and the related study of the intestinal microbial environment. After reviewing studies concerning the use of Bifidobacterium breve as probiotic in paediatric diseases, the effectiveness of a probiotic formulation consisting of two strains of B. breve was assessed in paediatric subjects for the prevention or alleviation of gastrointestinal disorders, including coeliac disease and paediatric obesity. As the emerging role of gut microbiota in neurological diseases, the intestinal microbial environment in amyotrophic lateral sclerosis patients compared to healthy controls and the effects of a probiotic administration were examined. Considering the role of viruses in shaping gut microbiota, gut bacteriophages and bacterial community of preterm infants were investigated. The results evidenced differences in gut microbial composition of healthy controls and diseased subjects in coeliac and amyotrophic lateral sclerosis patients. The probiotic approach was effective in restoring the microbial composition in the former, whereas, in the latter, the influence was focused only on some microbial groups. The probiotic intervention was effective in improving the glyco-insulinemic profile in obese children and in preventing gastrointestinal disorders in healthy newborns. The study of the bacterial and phage composition in preterm infants suggested a transkingdom interplay between bacteria and viruses with a reciprocal influence on their composition.

Quantitative susceptibility mapping as biomarker of neurodegeneration: methodological models and clinical applications

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance technique that can quantify in vivo biomarkers of pathology, such as alteration in iron and myelin concentration. It allows for the comparison of magnetic susceptibility properties within and between different subject groups. In this thesis, QSM acquisition and processing pipeline are discussed, together with clinical and methodological applications of QSM to neurodegeneration. In designing the studies, significant emphasis was placed on results reproducibility and interpretability. The first project focuses on the investigation of cortical regions in amyotrophic lateral sclerosis. By examining various histogram susceptibility properties, a pattern of increased iron content was revealed in patients with amyotrophic lateral sclerosis compared to controls and other neurodegenerative disorders. Moreover, there was a correlation between susceptibility and upper motor neuron impairment, particularly in patients experiencing rapid disease progression. Similarly, in the second application, QSM was used to examine cortical and sub-cortical areas in individuals with myotonic dystrophy type 1. The thalamus and brainstem were identified as structures of interest, with relevant correlations with clinical and laboratory data such as neurological evaluation and sleep records. In the third project, a robust pipeline for assessing radiomic susceptibility-based features reliability was implemented within a cohort of patients with multiple sclerosis and healthy controls. Lastly, a deep learning super-resolution model was applied to QSM images of healthy controls. The employed model demonstrated excellent generalization abilities and outperformed traditional up-sampling methods, without requiring a customized re-training. Across the three disorders investigated, it was evident that QSM is capable of distinguishing between patient groups and healthy controls while establishing correlations between imaging measurements and clinical data. These studies lay the foundation for future research, with the ultimate goal of achieving earlier and less invasive diagnoses of neurodegenerative disorders within the context of personalized medicine.