Skip to content
Permalink
Browse files

Merge branch 'nmsm_workshop'

  • Loading branch information...
nno committed Jul 19, 2019
2 parents 872c912 + 951e14b commit b21a15928bbba68c128390b38139c16d99d8d399
@@ -1,5 +1,6 @@
History of exercises:

- 2019 :ref:`NMSM2019`.
- 2017 :ref:`LABMAN 2017, the second Latin American Brain Mapping Network Meeting <labman2017>`.
- 2016 :ref:`PRNI 2016, the 6th International Workshop on Pattern Recognition in Neuroimaging workshop<prni2016>`.
- 2016 :ref:`CIMeC 2016 Hands-on Methods <cimec2016>`.
@@ -28,7 +28,7 @@ CoSMoMVPA
get_started
download
documentation
labman2017
nmsm2019
tips
faq
contact
@@ -48,7 +48,7 @@ CoSMoMVPA
.. image:: _static/icon_documentation.png
:target: documentation.html
.. image:: _static/icon_exercises.png
:target: labman2017.html
:target: nmsm2019.html
.. image:: _static/icon_tips.png
:target: tips.html
.. image:: _static/icon_faq.png
@@ -3,6 +3,7 @@
News
----
- CoSMoMVPA will be presented at :ref:`nmsm2019`, 22--26 July 2019.
- our CoSMoMVPA manuscript has been published (:cite:`OCH16`): Oosterhof, N. N., Connolly, A. C., and Haxby, J. V. (2016). CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave. Frontiers in Neuroinformatics, :doi:`10.3389/fninf.2016.00027`.


@@ -0,0 +1,56 @@
.. # For CoSMoMVPA's license terms and conditions, see #
# the COPYING file distributed with CoSMoMVPA #
.. _nmsm2019:

NMSM 2019, Noesselt’s lab 3rd Modelling Symposium, University of Magdeburg
=========================================================================
Noesselt’s lab 3rd Modelling Symposium, University of Magdeburg

*presented by Nick Oosterhof*

Location: Magdeburg, Germany; Universitätsplatz campus, Gebäude 28, room 27.

.. include:: all_exercises_ever_toc.txt

Contents
--------

.. toctree::
:maxdepth: 2

nmsm2019_intro
nmsm2019_ex_toc

.. toctree::
:maxdepth: 1

matindex
matindex_hdr
matindex_skl

matindex_run
matindex_skl_run

Matlab outputs: matlab_pb_toc_

Miscellaneous
-------------

.. toctree::
:maxdepth: 1

matindex_test
matindex_demo

Indices and tables
==================

* :ref:`genindex`
* :ref:`matindex`
* :ref:`search`

.. _matlab_pb_toc: _static/publish/index.html

.. include:: links.txt

@@ -0,0 +1,34 @@
.. # For CoSMoMVPA's license terms and conditions, see #
# the COPYING file distributed with CoSMoMVPA #
.. _nmsm2019_ex_toc:
.. _ex_toc:

**Note: this page is under construction. Information is subject to change**

Exercises
=========

Contents:

.. toctree::
:maxdepth: 1
:titlesonly:

ex_dataset_basics
ex_splithalf_correlations
ex_classify_lda
ex_classify_double_dipping
ex_nfold_crossvalidation
ex_measures
ex_neighborhood
ex_searchlight_measure
ex_meeg_searchlight
ex_meeg_time_generalization
ex_rsa_tutorial
ex_multiple_comparisons



:ref:`Back to index <nmsm2019>`

@@ -0,0 +1,219 @@
.. # For CoSMoMVPA's license terms and conditions, see #
# the COPYING file distributed with CoSMoMVPA #
.. _labman2017_intro:

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

Overview of the workshop
++++++++++++++++++++++++
After an introductory presentation, it starts with basic operations of reading, writing, selecting, and aggregating dataset structures. This is followed by MVPA correlation and classficiation analysis of fMRI data in a region of interest. Subsequently, this is extended to exploratory searchlight analysis, representational similarity analysis, MEEG analysis in the space and time dimensions, and surface-based searchlights . Finally approaches to multiple comparison are discussed.


Format
++++++
In this workshop, all material is present on the website. Each exercise part of the workshop has three parts:

- short presentation and introduction to exercise
- time to work on the exercise
- presentation of a possible solution to the exercise

Exercises are provided in the form of code skeletons, with part of the code left out as an exercise. Full solutions for all exercises are provided on the website.

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

* Matlab / Octave :ref:`advanced beginner level <matlab_experience>`. experience.
* fMRI and/or MEEG :ref:`advanced beginner level <cogneuro_experience>` analysis experience.
* Working Matlab_ or Octave_ installation.
* Working FieldTrip_ installation (required for MEEG analysis).
* MRI data viewer, such as MRIcron_ (strongly recommended).
* :ref:`CoSMoMVPA source code and tutorial data <get_code_and_example_data>`.
* It is recommended, prior to the course, to:

+ read the CoSMoMVPA manuscript (:doi:`10.1101/047118`, citation :cite:`OCH16`).
+ have the most recent CoSMoMVPA code (see :ref:`download`).
+ have a recent version of the :ref:`tutorial data <get_tutorial_data>`.
+ have set paths properly in ``.cosmomvpa.cfg`` (described :ref:`here <set_cosmovmpa_cfg>`)
+ have :ref:`tested <test_local_setup>` that you can load and save data from and to the paths in ``.cosmomvpa.cfg``.

Goals of this course
++++++++++++++++++++

* Learn typical MVPA approaches (correlation analysis, classification analysis, representational similarity analysis).
* Learn how these approaches can be applied to both fMRI and MEEG data.
* 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.
* Learn multiple-comparison approaches.
* 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 / MEEG data
* Learning to use Matlab / Octave
* Dataset types other than volumetric and surface-based fMRI data and MEEG time-locked data. (Not covered: source-space MEEG)
* How to become a CoSMoMVPA developer

Datasets
++++++++
For most of the course we will be using the AK6 dataset and the MEG obj6 dataset (described below). Although these can be downloaded separately, it is recommended however to use the full tutorial dataset.

Download link: `full tutorial data <datadb-v0.3.zip>`_.

AK6 dataset
-----------
This dataset is used for exercises shown on the website (with answers), and you can use it to learn MVPA. It 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.

Download link: `tutorial data with AK6 data only <datadb-ak6-v0.3.zip>`_

.. 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.


MEG obj6 dataset
----------------
This dataset is used for both the tutorial and for the assignments.

It contains MEG data from a single participant viewing images of six categories; for details see the README file.

Download link: `tutorial data with MEEG obj6 data only <datadb-meg_obj6-v0.3.zip>`_.




Tentative schedule
++++++++++++++++++

When:

- 22.07.2019 - 26.07.2019.

Where:

- Magdeburg, Germany; Universitätsplatz campus, Gebäude 28, room 27

For dinner and other information, see: https://www.noesseltlab.org/events-presentations/3rd-modelling-symposium-1/



============== ===================================================================================================
Date and time Description
============== ===================================================================================================
Monday
09:00 General introduction presentation
-------------- ---------------------------------------------------------------------------------------------------
10:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
11:00 Getting started. :doc:`get_started`; :doc:`ex_dataset_basics`
-------------- ---------------------------------------------------------------------------------------------------
12:30 Lunch break
-------------- ---------------------------------------------------------------------------------------------------
14:00 Split-half correlations. :doc:`ex_splithalf_correlations`
-------------- ---------------------------------------------------------------------------------------------------
15:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
16:00-17:00 Classification analysis. :doc:`ex_classify_lda`. Optional :doc:`ex_classify_double_dipping`
-------------- ---------------------------------------------------------------------------------------------------
17:40-18:30 Optional: discuss your data models
-------------- ---------------------------------------------------------------------------------------------------
Tuesday
09:00 Classification with cross-validation. :doc: `ex_nfold_crossvalidation`
-------------- ---------------------------------------------------------------------------------------------------
10:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
11:00 CoSMoMPVA measures part 1. :doc: `ex_measures`
-------------- ---------------------------------------------------------------------------------------------------
12:30 Lunch break
-------------- ---------------------------------------------------------------------------------------------------
14:00 CoSMoMPVA measures part 2. :doc: `ex_measures`
-------------- ---------------------------------------------------------------------------------------------------
15:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
16:00-17:30 Neighborhoods and searchlight basics. :doc:`ex_neighborhood`
-------------- ---------------------------------------------------------------------------------------------------
17:40-18:30 Optional: discuss your data models
-------------- ---------------------------------------------------------------------------------------------------
Wednesday Free day
-------------- ---------------------------------------------------------------------------------------------------
Thursday
09:00 Whole-brain fMRI searchlight. :doc:`ex_searchlight_measure`
-------------- ---------------------------------------------------------------------------------------------------
10:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
11:00 M/EEG searchlight part 1: :doc:`ex_meeg_searchlight`
-------------- ---------------------------------------------------------------------------------------------------
12:30 Lunch break
-------------- ---------------------------------------------------------------------------------------------------
14:00 M/EEG searchlight part 2: :doc:`ex_meeg_searchlight`
-------------- ---------------------------------------------------------------------------------------------------
15:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
16:00-17:30 M/EEG time generalization: :doc:`ex_meeg_time_generalization`
-------------- ---------------------------------------------------------------------------------------------------
17:40-18:30 Optional: discuss your data models
-------------- ---------------------------------------------------------------------------------------------------
Friday
09:00 Present your data
-------------- ---------------------------------------------------------------------------------------------------
10:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
11:00 Representational similarity analysis :doc:`ex_rsa_tutorial`
-------------- ---------------------------------------------------------------------------------------------------
12:30 Lunch break
-------------- ---------------------------------------------------------------------------------------------------
14:00 Surface-based searchlight. (Exercise to be written)
-------------- ---------------------------------------------------------------------------------------------------
15:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
16:00-17:30 Multiple comparison correction. Concluding remarks. :doc:`ex_multiple_comparisons`
============== ===================================================================================================



Contact
+++++++
Please send an email to a@b, b=gmail.com, a=n.n.oosterhof.

:ref:`Back to index <prni2016>`

.. include:: links.txt

0 comments on commit b21a159

Please sign in to comment.
You can’t perform that action at this time.