fMRI Course

Materials

Readings and software

No specific preparation is required — the course is designed as an introduction to fMRI. If you would like a head start, begin with the books and chapters below and install the software before day one.

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Books

Start here

Two textbooks by course instructor Tor Wager and Martin Lindquist that cover the concepts taught in the course, from acquisition to inference.

Cover of Elements of Functional Magnetic Resonance Imaging
New · MIT Press

Elements of Functional Magnetic Resonance Imaging

Tor D. Wager & Martin A. Lindquist

A comprehensive, current treatment of fMRI — physics and acquisition, experimental design, preprocessing, the general linear model, group analysis and multiple comparisons, connectivity, and multivariate prediction. The closest companion to what we teach.

View at MIT Press
Cover of Principles of fMRI
Leanpub

Principles of fMRI

Tor D. Wager & Martin A. Lindquist

The earlier e-book that provides comprehensive coverage of the key concepts involved in fMRI acquisition and analysis. Concise and readable — a good first pass before the course.

Get it on Leanpub

Methods chapters

Handbook chapters

Comprehensive chapters by the instructors that cover many aspects of fMRI analysis. They overlap in their core material, but each has some different sections and topics, so it is worth skimming more than one. The 2017 and 2014 chapters are the most recent starting points.

  • Fundamentals of functional neuroimaging
    Geuter, S., Lindquist, M. A., & Wager, T. D. (2017). In Handbook of Psychophysiology (4th ed.). Cambridge University Press.
    PDF
  • Principles of functional magnetic resonance imaging
    Lindquist, M. A., & Wager, T. D. (2014). In Handbook of Neuroimaging Data Analysis (pp. 3–48). Chapman & Hall / CRC.
    PDF
  • Essentials of functional magnetic resonance imaging
    Wager, T. D., & Lindquist, M. A. (2011). In The Oxford Handbook of Social Neuroscience. Oxford University Press.
    PDF Publisher
  • Essentials of functional neuroimaging
    Wager, T. D., Lindquist, M. A., & Hernandez, L. (2009). In Handbook of Neuroscience for the Behavioral Sciences. Wiley.
    PDF
  • Elements of functional neuroimaging
    Wager, T. D., Hernandez, L., Jonides, J., & Lindquist, M. A. (2007). In Handbook of Psychophysiology (3rd ed., pp. 19–55). Cambridge University Press.
    PDF

Review articles

Key papers

Peer-reviewed reviews on ICA, statistical inference, reproducibility, and brain signatures. Links go to the full text (PDF, PubMed Central, or arXiv).

  • Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery
    Calhoun, V. D., & Adalı, T. (2012). IEEE Reviews in Biomedical Engineering, 5, 60–73.
    Full text
  • Comparison of multi-subject ICA methods for analysis of fMRI data
    Erhardt, E. B., Rachakonda, S., Bedrick, E. J., Allen, E. A., Adalı, T., & Calhoun, V. D. (2011). Human Brain Mapping, 32, 2075–2095.
    PDF PMC
  • The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery
    Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). Neuron, 84, 262–274.
    PDF PMC
  • A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data
    Calhoun, V. D., Liu, J., & Adalı, T. (2009). NeuroImage, 45, S163–S172.
    PDF PMC
  • The statistical analysis of fMRI data
    Lindquist, M. A. (2008). Statistical Science, 23, 439–464.
    Full text
  • Zen and the art of multiple comparisons
    Lindquist, M. A., & Mejia, A. (2015). Psychosomatic Medicine, 77, 114–125.
    Full text
  • Best practices in data analysis and sharing in neuroimaging using MRI
    Nichols, T. E., Das, S., Eickhoff, S. B., et al. (2017). Nature Neuroscience, 20, 299–303.
    Full text
  • Building better biomarkers: brain models in translational neuroimaging
    Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Nature Neuroscience, 20, 365–377.
    PDF PMC
  • Representation, pattern information, and brain signatures: from neurons to neuroimaging
    Kragel, P. A., Koban, L., Barrett, L. F., & Wager, T. D. (2018). Neuron, 99, 257–273.
    PDF PMC

Online courses and websites

Go deeper

Free video courses by Martin Lindquist and Tor Wager, plus reference sites for SPM, GIFT, and study design.

Gallery of ICA components estimated by two group-ICA methods — Lecture 3.5 slides (Calhoun)

Software

Install before day one

All course toolboxes are free and open source. Registered attendees receive sample datasets, walkthrough cheat sheets, short demonstration videos, and a MATLAB trial license.

SPM25 Statistical Parametric Mapping

The core package used throughout the course for preprocessing, GLM estimation, and inference. SPM 25 runs in MATLAB; a standalone build that does not need a MATLAB license is also available.

Used in

1.2b Install: SPM and toolboxes1.5 Intro to SPM: data checking, reorienting data1.6 Spatial preprocessing: realignment, slice timing, unwarp1.7 Preprocessing: coregistration, normalization, smoothing2.4 SPM GUI for single subjects: explore design, scaling, results3.4 SPM results: group subjects, plotting, display, small-volume correction

GIFT Group ICA of fMRI Toolbox

Vince Calhoun's MATLAB toolbox for group ICA and IVA, including dynamic functional network connectivity, constrained ICA with the NeuroMark template, and source-based morphometry. Requires only base MATLAB.

Used in

2.10 Introduction to ICA: Independent Component Analysis3.5 ICA II: fMRI3.6 ICA of fMRI: implementation

CANlab tools Cognitive and Affective Neuroscience Lab

Tor Wager's object-oriented MATLAB tools for interactive analysis and visualization of neuroimaging data, plus repositories of brain signature patterns and atlases, robust regression, and multilevel mediation. Core Tools and Neuroimaging Pattern Masks are meant to be installed together.

Used in

1.4 Reproducible analysis: coding and data-management practices; CANlab tools install check1.9 CANlab interactive tools: basic analysis and visualization2.9 Intro to connectivity and mediation; mediation demo/walkthrough

SnPM Statistical nonParametric Mapping

Permutation-based inference for SPM by Tom Nichols and Andrew Holmes. SnPM13 runs inside the SPM batch system and provides voxel- and cluster-level nonparametric multiple-comparison correction.

Used in

3.3 Group analysis: thresholding and inference

FSL randomise Permutation inference in FSL

FSL's command-line tool for nonparametric permutation inference, including threshold-free cluster enhancement (TFCE). A useful complement to SnPM if you work in the FSL ecosystem.

Used in

3.3 Group analysis: thresholding and inference

MATLAB Required for SPM, GIFT, CANlab, SnPM

The course toolboxes run in MATLAB. Registered attendees receive a trial license before the course; many universities also provide MATLAB through campus-wide licenses.

Used in

1.3 MATLAB basics and orientation1.2b Install: SPM and toolboxes

Keep exploring

Where to next

Content and scheduleWhere each toolbox and topic appears in the three days.InstructorsThe authors of several of the readings above.EnrollSeptember 9–11, 2026 · live online. Reserve a space.HomeBack to the course overview.