Myotonic syndromes: analysis of factors with pathogenetic and prognostic significance

Background: The natural history of Myotonic Dystrophy type 1 is largely unclear, longitudinal studies are lacking. Objectives: to collect clinical and laboratory data, to evaluate sleep disorders, somatic and autonomic skin fibres, neuropsychological and neuroradiological aspects in DM1 patients. Methods: 72 DM1 patients underwent a standardized clinical and neuroradiological evaluation performed by a multidisciplinary team during 3 years of follow-up. Results: longer disease duration was associated with higher incidence of conduction disorders and lower ejection fraction; higher CVF values were predictors for a reduced risk of cardiopathy. Lower functional pulmonary values were associated with class of expansion and were negatively associated with disease duration; arterial blood gas parameters were not associated with expansion size, disease duration nor with respiratory function test. Excessive daytime sleepiness was not associated with class of expansion nor with any of the clinical parameters examined. We detected apnoea in a large percentage of patients, without differences between the 3 genetic classes; higher CVF values were predictors for a reduced risk of apnoea. Skin biopsies demonstrated the presence of a subclinical small fibre neuropathy with involvement of the somatic fibres. The pupillometry study showed lower pupil size at baseline and a lower constriction response to light. The most affected neuropsychological domains were executive functions, visuoconstructional, attention and visuospatial tasks, with a worse performance of E1 patients in the visuoperceptual ability and social cognition tasks. MRI study demonstrated a decrease in the volumes of frontal, parietal, temporal, occipital cortices, accumbens, putamen nuclei and a more severe volume reduction of the isthmus cingulate, transverse temporal, superior parietal and temporal gyri in E2 patients. Discussion: only some clinical parameters could predict the risk of cardiopathy, pulmonary syndrome and sleep disorders, while other clinical aspects proved to be unpredictable, confirming the importance of periodic clinical follow-up of these patients.

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.