Session 1. Foundations of fMRI Processing with SPM
From raw functional images to analyzable brain maps
- Introduction to fMRI preprocessing
- Hands-on practice with real fMRI data using SPM
- Key outputs for downstream analysis
Session 2. From fMRI Matrix to Machine Learning in Parkinson's Disease
Transforming imaging features into clinical prediction
- Feature extraction from fMRI matrices
- Building simple machine-learning models
- Application: detection of cognitive impairment in Parkinson's disease
Session 3. From PSG Signals to Deep Learning: An AI-Assisted Hands-on Approach
Using time-series neurophysiological signals to build and interpret deep learning models
- Introduction to 1-D biomedical signal processing
- Hands-on AI-assisted coding: building deep learning pipeline interactively
- Application: dentification of RBD as a prodromal marker of PD
