Introduction¶
Prerequisites¶
- Matlab / Octave Advanced beginner level.
- fMRI analysis advanced beginner level.
- Working Matlab or Octave installation.
- CoSMoMVPA source code and tutorial data.
Goals of this course¶
- For fMRI data, describe the typical MVPA approaches (correlation analysis, classification analysis, representational similarity analysis) applied to both regions of interest and across the whole brain in a searchlight approach.
- Learn how to use CoSMoMVPA to perform these analyses:
- Understand the dataset structure to represent both the data itself (e.g. raw measurements or summary statistics) and its attributes (e.g. labels of conditions (targets), data acquisition run (chunks).
- See how parts of the data can be selected using slicing and splitting, and combined using stacking.
- Introduce measures that compute summaries of the data (such as correlation differences, classification accuracies, similarity to an a prior defined representational simillarity matrix) that can be applied to both a single ROI or in a searchlight.
- Make yourself an independent user, so that you can apply the techniques learnt here to your own datasets.
Not covered in this course¶
- Preprocessing of fMRI data
- Learning to use Matlab / Octave
- Other dataset types than volumetric fMRI data (MEEG, surface-based fMRI)
- How to become a CoSMoMVPA developer
Sample Dataset¶
The dataset used here contains preprocessed data for 8 subjects from [CGG+12]. In this experiment, participants were presented with categories of six animals: 2 primates: monkeys and lemurs; 2 birds: mallard ducks and yellow-throated warblers; and 2 bugs: ladybugs and luna moths.

For each participant, the following data is present in the ak6
(for Animal Kingdom, 6 species) directory:
- s0[1-8]/ This directory contains fMRI data from 8 of the 12
participants studied in the experiment reported in
Connolly et al. 2012 (Code-named 'AK6' for animal
kingdom, 6-species). Each subject's subdirectory
contains the following data:
- glm_T_stats_perrun.nii A 60-volume file of EPI-data preprocessed using
AFNI up to and including fitting a general linear
model using 3dDeconvolve. Each volume contains the
t-statistics for the estimated response to a one
of the 6 stimulus categories. These estimates were
calculated independently for each of the 10 runs
in the experiment.
- glm_T_stats_even.nii Data derived from glm_T_stats_perrun.nii.
- glm_T_stats_odd.nii Each is a 6-volume file with the T-values averaged
across even and odd runs for each category.
- brain.nii Skull-stripped T1-weighted anatomical brain image.
- brain_mask.nii Whole-brain mask in EPI-space/resolution.
- vt_mask.nii Bilateral ventral temporal cortex mask similar to
that used in Connolly et al. 2012.
- ev_mask.nii Bilateral early visual cortex mask.
Also present are model similarity structures, which you can see here:

This data is stored in the models
directory:
- models
- behav_sim.mat Matlab file with behavioural similarity ratings.
- v1_model.mat Matlab file with similarity values based on
low-level visual properties of the stimuli.
To cover in this workshop¶
CoSMoMVPA concepts and dataset structure.
Basic operations on datasets.
Introduction to common MVPA measures:
- correlation difference
- classification accuracy
- representational similarity matching
Common MVPA techniques:
- ROI analysis
- Searchlight analysis
Tentative schedule¶
The following schedule is tentative and can change any moment depending on user, organizer and/or presenter needs.
Date and time | Description |
---|---|
We 10:00-12:00 | General intro: Get your computer ready to run CoSMoMVPA and use the tutorial dataset |
We 12:00-14:00 | Lunch break |
We 14:00-17:00 | Pratical exercises: Basic dataset operations |
Th 10:00-13:00 | Practical exercises: Correlation analysis, basic classification analysis |
Th 13:00-14:00 | Lunch break |
Th 14:00-17:00 | Pratical exercises: Classification with cross-validation, measures, neighborhoods |
Th 18:00-??:00 | Social dinner at Hugo’s bar and grill, 72 High Street, Egham |
Fr 10:00-13:00 | Practical exercises: Searchlight, RSA visualization and ROI analysis |
Fr 13:00-14:00 | Lunch break |
Fr 14:00-17:00 | RSA ROI and searchlight, multiple comparison correction, analysis decisions, concluding remarks |