The teaching is organized as follows:

Learning objectives

The course is composed by two modules: the first aims at covering practical aspects of the most common diagnostic tools and decision support systems in the neuroscience/neurological fields with a view toward their use in clinical settings. This offers the students the possibility to assess various applications of neuroimaging in clinical research, emphasizing the translational potentialities of bioengineering. Moreover, at the end of the course students will be : i) familiar with the functional and anatomical properties of the different brain areas and possible pathological changes; ii) able to critically interpret different brain images; iii) able to identify those image types and protocols that are more relevant for a given clinical scenario, for example for differential diagnosis in common neurological disorders or monitoring of therapeutic treatments. The second, Intelligent neuro-data modeling, aims at providing students with knowledge of proper engineering technologies and methodologies in the neuroengineering field (with focus on neuroimaging) through the analysis of the whole pipeline from problem definition to data acquisition and preprocessing, data analysis and decision making. At the end of the course the students will have deepened the fundamental principles of the most common neuroimaging sequences currently used. Moreover, they will be able to process and analyze different modalities with a critical view of the main steps in the pipeline, including data pre-processing, mathematical modelling and post-hoc analyses, also relying on artificial intelligence-based methods. They will be able to solve inherently interdisciplinary problems including both medical and information engineering aspects. The theory classes will be completed with practical laboratory sessions (MATLAB, Python and most common software for neuroimaging data processing), where examples with real data will be addressed in order to translate the learned methodologies into practical applications and solve real-world neuroimaging problems.