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He works with James Haxby at the CIMeC, and he is currently supported by the ATTEND
project. His research focuses on the cognitive and neural representation of actions and attention. In this line of research data is acquired with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) and analyzed using multi-variate pattern analysis (MVPA). 
Besides emperical work, He is also involved in the development of the PyMVPA (Python) and CoSMoMVPA (Matlab) toolboxes.

Nikolaas Oosterhof

PRNI 2016 - TUTORIAL 3

CoSMoMVPA: A multi-modal/variate pattern analysis toolbox in Matlab

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, and MEEG analysis in the space and time dimensions. Finally approaches to multiple comparison are discussed.

Note: although MEEG analysis is covered only on day 2, basic concepts and functionality for MEEG analysis is discussed on day 1. Also for those who are mainly interested in MEEG analysis (and less so in fMRI) it is still recommended to attend both days.

MONDAY, JUNE 20th

08:30 :: Registration

09:00 :: Introduction

11:00 :: Get your computer ready to run CoSMoMVPA; practical exercises: fMRI basic dataset operations

10:30 :: Coffee Break

12:30 :: Lunch Break

13:30 :: Correlation Analysis

15:00 :: Coffee Break

15:30 :: Classification Analysis

TUESDAY, JUNE 21st

09:00 :: Neighborhoods, Searchlight

11:00 :: MEEG basics, classification, searchlight

10:30 :: Coffee Break

12:30 :: Lunch Break

13:30 :: MEEG time generalization; fMRI RSA

15:00 :: Coffee Break

15:30 :: Multiple comparison correction; concluding remarks

Setting up your system

It is recommended, prior to the tutorial, to:

  • read the CoSMoMVPA manuscript: http://dx.doi.org/10.1101/047118

  • have the most recent CoSMoMVPA code http://www.cosmomvpa.org/download.html

  • have a recent version of the tutorial data 

  • have set paths properly in .cosmomvpa.cfg (described :ref:`here <set_cosmovmpa_cfg>`)

  • have tested your local setup  that you can load and save data from and to the paths in .cosmomvpa.cfg.

Please visit the repository of tutorial materials at http://bit.ly/prni2016cosmomvpa

Goals of the tutorial

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.

Objectives not covered by this tutorial

  • Preprocessing of fMRI / MEEG data

  • Learning to use Matlab / Octave

  • Dataset types other than volumetric fMRI data and MEEG time-locked data
    (Not covered: surface-based fMRI, source-space MEEG)

  • How to become a CoSMoMVPA developer

For additional information you may contact tutorial3@prni.org

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