Modelli statistici avanzati in neuroscienze

The course aims to provide students with advanced skills in the application and interpretation of statistical models for the analysis of neuroscientific data. Specifically, it will provide the foundation for:
- Developing critical skills in the use of advanced statistical models in clinical and experimental settings.
- Deepening the analysis of neuropsychological and neuroscientific data through multivariate statistical methods and machine learning.
- Integrating theoretical and practical knowledge in the processing of complex datasets.
- Developing practical skills for the rigorous interpretation of data and the effective communication of scientific results.

At the end of the course, students will be able to:
- Understand the theoretical principles underlying the main advanced statistical models, including linear and nonlinear models, hierarchical models, and multivariate analysis.
- Identify the appropriate statistical techniques for specific types of neuroscientific data (fMRI, EEG, MEG).
- Apply advanced statistical analysis tools to neuroscientific datasets.
- Develop predictive and interpretive models based on neuroscientific data to address clinical and experimental questions.
- Critically assess the quality and robustness of statistical models applied to neuroscientific studies.
- Formulate statistical hypotheses and interpret results with attention to methodological limitations.
- Present the results of advanced statistical analyses clearly and effectively, adapting them to scientific publications.