Modelli statistici avanzati in neuroscienze

A.Y. 2025/2026
6
Max ECTS
42
Overall hours
SSD
M-PSI/03
Language
Italian
Learning objectives
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.
Expected learning outcomes
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.
Single course

This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.

Course syllabus and organization

Single session

Course syllabus
Introduction to Statistics in Neurosciences
· Fundamentals of descriptive and inferential statistics.
· Basic concepts of variability, correlation, and regression.

Basic Statistical Models
· Introduction to linear statistical models.
· Analysis of variance (ANOVA) and basic statistical tests (t-test).

Advanced Statistical Models
· Generalized linear models (GLM) and their extensions.
· Multivariate analysis of variance (MANOVA) and mixed models.

Statistical Methods for Neuroscientific Data
· Analysis of behavioral and neuropsychological data.
· Analysis of EEG/MEG signals and functional neuroimaging.
· Statistics for longitudinal and clinical studies.

Machine Learning Techniques in Neurosciences
· Introduction to machine learning: basic concepts and applications.
· Classification and regression in neuroscientific data.
Prerequisites for admission
Basic knowledge of descriptive statistics (measures of central tendency and variability); fundamentals of inferential statistics. Familiarity with simple regression, correlation, and t-test.
Teaching methods
Theoretical and practical lectures, reading of scientific articles, analysis of research studies from psychological literature, in-class discussions on data analysis, practical exercises.
Teaching Resources
Bibliography will be provided during the course and uploaded to the MyAriel website.
Assessment methods and Criteria
The exam will consist of an oral and/or written test, with specific modalities to be communicated by the instructor at the beginning of the course.
M-PSI/03 - PSYCHOMETRICS - University credits: 6
Lessons: 42 hours