Content
Topics and schedule
Interactive lectures with hands-on demonstrations and work-through sessions, from MRI physics to machine learning, over three full days. Click any session for a detailed outline.
Audience and format
Learn by doing, on your own laptop.
This course is designed for fMRI researchers with beginning to intermediate skill levels. For newcomers, it provides a comprehensive set of foundational tools for acquiring and analyzing fMRI data. For experienced researchers, it offers advanced training in Independent Component Analysis and optimizing the validity of your studies.
The format is interactive lectures with hands-on demonstrations and work-through sessions. Participants work through examples on their own laptops. Registration is first-come, first-served, and enrollment is limited by the interactive nature of the course.
Software used in the course (install instructions and a MATLAB trial are provided to attendees):
- MATLAB
- SPM25
- GIFT
- CANlab Core Tools
- SnPM
- Mediation (M3) toolbox
Sessions
What we cover, session by session
Each card opens a detailed outline of the session: main concepts, key terms, take-aways, figures from the lectures, and — for hands-on sessions — step-by-step cheat sheets.
Day 1 — Acquisition, preprocessing, and the GLM
Wednesday, September 9 · hour-by-hour agendaCourse introduction
Introduces the instructors and the scope of the three-day course: paradigm design, image acquisition, and analysis of fMRI data.
Virtual tours of MRI and acquisition of data, stimulus presentation, behavioral monitoring
A walk through the scanner suite from the participant's point of view: screening and clothing, the magnet room, positioning and comfort, and the control room where stimulus delivery, response logging, and eye tracking are synchronized to the scanner.
fMRI physics, pulse sequences, and reconstruction
Covers the physics needed to understand an fMRI dataset: nuclear spin and the Larmor equation, T1 and T2 relaxation, spin and gradient echoes, spatial encoding with gradients and k-space, and echo-planar imaging.
Install: SPM and toolboxes
Gets every laptop ready for the hands-on sessions: download the course folder, install MATLAB, and add SPM and the other toolboxes to the MATLAB path with add_toolbox_paths.m.
MATLAB basics and orientation
A brief tour of MATLAB for people who will use SPM: the desktop, getting help, variable types, scripts versus functions, and file types.
Reproducible analysis: coding and data-management practices; CANlab tools install check
Lays out what a reproducible, interpretable, well-documented analysis looks like and why it matters: analysis errors are common, and results that others can rerun and build on are more trusted and more useful.
Intro to SPM: data checking, reorienting data
Introduces Statistical Parametric Mapping, its history and why it is widely used, then begins the preprocessing pipeline with the two steps people most often skip: looking at the raw images and reorienting them to resemble the SPM EPI template, and running ArtRepair to find and replace bad volumes.
Spatial preprocessing: realignment, slice timing, unwarp
Explains why head motion is the dominant artifact in fMRI, how much motion is enough to matter, and the four lines of defense: prevention, detection and discarding, prospective correction, and post-processing realignment.
Preprocessing: coregistration, normalization, smoothing
Covers moving each participant's data into a common stereotactic space so results can be compared across people, studies, and labs, and then smoothing the normalized images.
General Linear Model I: principles and fMRI
Introduces the General Linear Model as the workhorse of fMRI analysis: the model, its assumptions, how parameters are estimated, and what leverage and influence mean for design and outliers.
CANlab interactive tools: basic analysis and visualization
A short walkthrough of the CANlab object-oriented tools using a MATLAB live script: load a set of first-level contrast images, check them for outliers, run a one-sample t-test with FDR thresholding, and visualize the results as slice montages, labeled region tables, and surface renderings.
Day 2 — Modeling, connectivity, and ICA
Thursday, September 10 · hour-by-hour agendaGLM model building: predictors and contrasts
This session moves from a single-regressor GLM to designs with multiple event types, and introduces contrasts as the tool for testing differences, averages, and custom hypotheses on the fitted betas.
GLM filtering and nuisance regressors
This session surveys the main sources of noise in fMRI time series (scanner drift, spikes, physiological fluctuations, head motion) and the post-acquisition tools for removing them: high-pass filtering, outlier indicator regressors, physiological models, and motion covariates.
GLM multicollinearity and diagnostics
This session reviews the statistical assumptions behind GLM p-values and what happens when each is violated, then focuses on two practical hazards in fMRI regression: multicollinearity among predictors and high-leverage observations.
SPM GUI for single subjects: explore design, scaling, results
A hands-on session in which participants specify, estimate, and view results for a single-subject auditory oddball model in the SPM GUI, using the course cheat sheet.
Basis sets: flexible hemodynamic modeling
This session explains why a single canonical HRF is often wrong for a given voxel, region, or population, and how temporal basis sets (canonical plus derivatives, finite impulse response) let the GLM fit a range of response shapes.
Derivative boost
A short session on hemodynamic delays that vary across the brain, why adding a temporal derivative to the HRF only partly solves the problem, and how a 'boosted' amplitude estimate combining the HRF and derivative parameters removes latency-induced amplitude bias in group (random-effects) analyses.
Parametric modulators
This session introduces parametric modulation, which models trial-to-trial variation in brain activity as a function of a behavioral or stimulus variable within a person.
Autocorrelation and generalized linear models
This session covers temporal autocorrelation in fMRI noise, why it violates the independence assumption of ordinary least squares, and how generalized least squares with prewhitening addresses it.
Intro to connectivity and mediation; mediation demo/walkthrough
This session situates mediation analysis within the landscape of connectivity methods, then explains the mediation and moderation path models, the a*b indirect effect, and bootstrap inference.
Introduction to ICA: Independent Component Analysis
This session introduces independent component analysis as a data-driven alternative to seed-based and GLM approaches for fMRI.
Day 3 — Design, group inference, and ICA in practice
Friday, September 11 · hour-by-hour agendaExperimental design: psychological and statistical principles
This lecture covers how to design an fMRI task so that participants actually engage the psychological process of interest and so that the resulting brain signals can be detected efficiently.
Group analysis: fixed, random, and mixed effects
This lecture explains why generalizing to a population requires modeling between-subject variance, and compares fixed-effects, two-stage summary-statistics and full mixed-effects approaches.
Group analysis: thresholding and inference
This lecture covers how to threshold statistical maps when testing many voxels: voxel-, cluster- and set-level inference, family-wise error control by Bonferroni, random field theory and permutation, and false discovery rate control.
SPM results: group subjects, plotting, display, small-volume correction
In this hands-on session you run a second-level one-sample t-test in SPM12 on 50 subjects' auditory oddball contrast images, then use the Results interface to threshold, tabulate, plot and display group activations.
ICA II: fMRI
This lecture extends ICA to multi-subject fMRI: how components are combined across subjects, how single-subject maps and time courses are recovered by back-reconstruction, and how group inferences are made on ICA outputs.
ICA of fMRI: implementation
In this hands-on session you run a group ICA in the GIFT toolbox, following the GIFT walkthrough, on the course's auditory oddball data.
Schedule
Three-day agenda
Each day runs 8:00 am – 6:00 pm Eastern Time with breaks (Day 3 ends at 4:00 pm). Download the agenda (PDF).
Day 1 — Acquisition, preprocessing, and the GLM · Wednesday, September 9
| Start | Session | Topic | Instructor |
|---|---|---|---|
| 8:00 am | 0.0 | Course introduction | Kiehl, Calhoun, Wager |
| 8:30 am | 1.1 | Virtual tours of MRI and acquisition of data, stimulus presentation, behavioral monitoring | Kiehl |
| 9:30 am | Break | ||
| 9:45 am | — | Check download and tools install: Dropbox folder with data | All |
| 10:00 am | 1.2 | fMRI physics, pulse sequences, and reconstruction | Calhoun |
| 11:00 am | 1.2b | Install: SPM and toolboxes | Wager |
| 11:20 am | 1.3 | MATLAB basics and orientation | Wager |
| 11:30 am | 1.4 | Reproducible analysis: coding and data-management practices; CANlab tools install check | Wager |
| 12:00 pm | Lunch break | ||
| 12:30 pm | 1.5 | Intro to SPM: data checking, reorienting data | Kiehl |
| 1:30 pm | 1.6 | Spatial preprocessing: realignment, slice timing, unwarp | Kiehl |
| 2:30 pm | Break | ||
| 3:00 pm | 1.7 | Preprocessing: coregistration, normalization, smoothing | Kiehl |
| 4:00 pm | 1.8 | General Linear Model I: principles and fMRI | Wager |
| 5:00 pm | 1.9 | CANlab interactive tools: basic analysis and visualization | Wager |
| 5:30 pm | — | Question and answer session | Kiehl, Wager, Calhoun |
| 6:00 pm | Adjourn | ||
Day 2 — Modeling, connectivity, and ICA · Thursday, September 10
| Start | Session | Topic | Instructor |
|---|---|---|---|
| 8:00 am | — | Review from Day 1: question and answer | Kiehl, Calhoun, Wager |
| 8:30 am | 2.1 | GLM model building: predictors and contrasts | Wager |
| 9:00 am | 2.2 | GLM filtering and nuisance regressors | Wager |
| 9:30 am | 2.3 | GLM multicollinearity and diagnostics | Wager |
| 10:15 am | Break | ||
| 10:30 am | 2.4 | SPM GUI for single subjects: explore design, scaling, results | Kiehl |
| 12:00 pm | Lunch break | ||
| 12:30 pm | 2.5 | Basis sets: flexible hemodynamic modeling | Wager |
| 1:15 pm | 2.6 | Derivative boost | Calhoun |
| 1:30 pm | 2.7 | Parametric modulators | Wager |
| 1:45 pm | 2.8 | Autocorrelation and generalized linear models | Wager |
| 2:30 pm | Break | ||
| 3:00 pm | 2.9 | Intro to connectivity and mediation; mediation demo and walkthrough | Wager |
| 4:00 pm | 2.10 | Introduction to ICA: Independent Component Analysis | Calhoun |
| 5:30 pm | — | Question and answer session | Kiehl, Calhoun, Wager |
| 6:00 pm | Adjourn | ||
Day 3 — Design, group inference, and ICA in practice · Friday, September 11
| Start | Session | Topic | Instructor |
|---|---|---|---|
| 8:00 am | — | Review of Days 1–2 | Kiehl, Calhoun, Wager |
| 8:15 am | 3.1 | Experimental design: psychological and statistical principles | Wager |
| 9:45 am | Coffee break | ||
| 10:00 am | 3.2 | Group analysis: fixed, random, and mixed effects | Wager |
| 10:45 am | 3.3 | Group analysis: thresholding and inference | Wager |
| 11:30 am | 3.4 | SPM results: group subjects, plotting, display, small-volume correction | Kiehl |
| 12:30 pm | Lunch | ||
| 1:00 pm | 3.5 | ICA II: fMRI | Calhoun |
| 2:00 pm | Break | ||
| 2:15 pm | 3.6 | ICA of fMRI: implementation | Calhoun, Kiehl |
| 3:45 pm | — | Final Q&A and farewell | Kiehl, Calhoun, Wager |
| 4:00 pm | Adjourn | ||
Next step
Ready to join us?
Spaces are limited. Reserve yours now, then browse the background readings and software to prepare. The sessions are taught by Vince Calhoun, Kent Kiehl, and Tor Wager.