.. #   For CoSMoMVPA's license terms and conditions, see   #
   #   the COPYING file distributed with CoSMoMVPA         #

.. intro

Introduction
============

Prerequisites
+++++++++++++

    * Matlab / Octave :ref:`Advanced beginner level <matlab_experience>`.
    * fMRI analysis `:ref:`advanced beginner level <cogneuro_experience>`.
    * Working Matlab_ or Octave_ installation.
    *  :ref:`the source code and the example data <get_code_and_example_data>`.


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 :cite:`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.

.. image:: _static/fmri_design.png
    :width: 400px

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:

.. image:: _static/sim_sl.png
    :width: 600px

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


.. include:: links.txt