.. # For CoSMoMVPA's license terms and conditions, see # # the COPYING file distributed with CoSMoMVPA # .. _nmsm2019_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 `. experience. * fMRI and/or MEEG :ref:`advanced beginner level ` 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 `. * 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 `. + have set paths properly in ``.cosmomvpa.cfg`` (described :ref:`here `) + have :ref:`tested ` 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 `_. 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 `_ .. 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 `_. 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 :doc:`ex_dataset_basics` -------------- --------------------------------------------------------------------------------------------------- 15:30 Coffee break -------------- --------------------------------------------------------------------------------------------------- 16:00-17:30 Split-half correlations. :doc:`ex_splithalf_correlations` -------------- --------------------------------------------------------------------------------------------------- 17:40-18:30 Optional: discuss your data models -------------- --------------------------------------------------------------------------------------------------- Tuesday 09:00 :doc:`ex_classify_lda`, :doc:`ex_nfold_crossvalidation`. -------------- --------------------------------------------------------------------------------------------------- 10:30 Coffee break -------------- --------------------------------------------------------------------------------------------------- 11:00 :doc:`ex_measures` -------------- --------------------------------------------------------------------------------------------------- 12:30 Lunch break -------------- --------------------------------------------------------------------------------------------------- 14:00 :doc:`ex_classify_double_dipping`, :doc:`ex_neighborhood` first part -------------- --------------------------------------------------------------------------------------------------- 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. :doc:`ex_surface_searchlight` -------------- --------------------------------------------------------------------------------------------------- 15:30 Coffee break -------------- --------------------------------------------------------------------------------------------------- 16:00-17:30 Multiple comparison correction. Concluding remarks. :doc:`ex_multiple_comparisons` ============== =================================================================================================== Acknowledgements ++++++++++++++++ Thanks to Felix Ball, Emanuele Porcu, Peter Vavra, Nico Marek, Camila Agostino, Tömme Noesselt for organizing the symposium. Contact +++++++ Please send an email to a@b, b=gmail.com, a=n.n.oosterhof. :ref:`Back to index ` .. include:: links.txt