Overview
This graduate course provides a thorough grounding in the principles and practice of functional MRI. Students gain hands-on experience with analysis pipelines (FSL, SPM, nilearn) and understand the statistical foundations of each step. The lab component uses real datasets from the ACL@NCU.
Prerequisites: Graduate standing; basic statistics and programming background helpful.
Part 1: MRI Physics and the BOLD Signal
- Proton magnetic resonance basics
- Pulse sequences: EPI, gradient echo, spin echo
- The BOLD signal: neural coupling, hemodynamic response function
- Signal-to-noise and field strength considerations
Lab resources
Part 2: Experimental Design
- Block, event-related, and mixed designs
- Design efficiency and counterbalancing
- Sample size and power in neuroimaging
- Pre-registration and registered reports
Part 3: Preprocessing Pipeline
- BIDS formatting and data organization
- Slice-timing and motion correction
- Spatial normalization to MNI space
- Spatial smoothing and temporal filtering
- fMRIPrep: a standardized preprocessing workflow
Part 4: The General Linear Model
- Design matrix construction and HRF convolution
- Contrast specification and statistical maps
- Multiple comparison correction (FWE, FDR, cluster-based)
- Random effects and second-level analysis
Part 5: Multivariate Pattern Analysis
- The rationale for MVPA
- Cross-validation schemes and bias avoidance
- Searchlight analysis
- Representational Similarity Analysis (RSA)
Part 6: Connectivity and Advanced Methods
- Seed-based functional connectivity
- Psychophysiological interaction (PPI)
- Dynamic causal modelling (DCM)
- Resting-state ICA and dual regression
Part 7: Reproducibility and Open Science
- Data sharing: OpenNeuro, OSF
- Code sharing and containerization (Docker, Singularity)
- Effect size estimation in neuroimaging
- Replication crisis and solutions