Introduction

Prerequisites

Goals of this course

  • 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) described in the literature.

  • 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

Code and data needed for this workshop

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.

_images/fmri_design.png

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:

_images/sim_sl.png

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 course

  • 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