Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.

The COVID-19 outbreak impacted almost all the aspects of ordinary life. In this context, social networks quickly started playing the role of a sounding board for the content produced by people. Studying how dramatic events affect the way people interact with each other and react to poorly known situations is recognized as a relevant research task. Since automatically identifying country-based COVID-19 social posts on generalized social networks, like Twitter and Facebook, is a difficult task, in this work we concentrate on Reddit megathreads, which provide a unique opportunity to study focused reactions of people by both topic and country. We analyze specific reactions and we compare them with a “normal” period, not affected by the pandemic; in particular, we consider structural variations in social posting behavior, emotional reactions under the Plutchik model of basic emotions, and emotional reactions under unconventional emotions, such as skepticism, particularly relevant in the COVID-19 context.