Multivariate Pattern Analysis (MVPA) of Neuroimaging Data with PyMVPA
March 6 - 7, 2014
Venue:
Hanse-Wissenschaftskolleg
Lehmkuhlenbusch 4
27753 Delmenhorst
Germany
Organizer:
Dr. Sebastian Puschmann
Carl von Ossietzky Universität Oldenburg
Lectures:
The course will be taught by the authors of the PyMVPA software (Yaroslav Halchenko, Dartmouth College and Michael Hanke, University of Magdeburg). Course language will be English.
Multivariate Pattern Analysis (MVPA) of Neuroimaging Data with PyMVPA
March 6 - 7, 2014
Venue:
Hanse-Wissenschaftskolleg
Lehmkuhlenbusch 4
27753 Delmenhorst
Germany
Organizer:
Dr. Sebastian Puschmann
Carl von Ossietzky Universität Oldenburg
Lectures:
The course will be taught by the authors of the PyMVPA software (Yaroslav Halchenko, Dartmouth College and Michael Hanke, University of Magdeburg). Course language will be English.
Multivariate pattern analysis (MVPA) approaches have gained great importance in cognitive neuroscience in recent years. This workshop aims to convey the basic concepts of MVPA and its practical application on neuroimaging data. It features lectures covering topics from basic principles of MVPA to various specific methodologies currently found in the literature. The majority of the time will be spent on hands-on sessions, in which participants will be introduced to PyMVPA (www.pymvpa.org), a free software package providing all tools necessary to conduct MVPA.
During the hands-on sessions, step-by-step tutorials will guide through all steps of MVPA. An updated version of the extensive PyMVPA web-tutorial (http://www.pymvpa.org/tutorial.html) will serve as the base course material for the practical sessions. New topics will include a command line interface to PyMVPA with minimal requirements of Python programming skills and well as an introduction to the increasingly popular representational similarity analysis.
Participants will be provided with computers, software and example datasets on site. In addition, course materials and data will be freely available for download after the end of the workshop.
The workshop will be focused on fMRI data analysis, but the majority of the covered strategies are also applicable to other data modalities such as EEG and MEG, and examples will be given.
Multivariate pattern analysis (MVPA) approaches have gained great importance in cognitive neuroscience in recent years. This workshop aims to convey the basic concepts of MVPA and its practical application on neuroimaging data. It features lectures covering topics from basic principles of MVPA to various specific methodologies currently found in the literature. The majority of the time will be spent on hands-on sessions, in which participants will be introduced to PyMVPA (www.pymvpa.org), a free software package providing all tools necessary to conduct MVPA.
During the hands-on sessions, step-by-step tutorials will guide through all steps of MVPA. An updated version of the extensive PyMVPA web-tutorial (http://www.pymvpa.org/tutorial.html) will serve as the base course material for the practical sessions. New topics will include a command line interface to PyMVPA with minimal requirements of Python programming skills and well as an introduction to the increasingly popular representational similarity analysis.
Participants will be provided with computers, software and example datasets on site. In addition, course materials and data will be freely available for download after the end of the workshop.
The workshop will be focused on fMRI data analysis, but the majority of the covered strategies are also applicable to other data modalities such as EEG and MEG, and examples will be given.
Programme
Thursday, March 6
09:00 - 09:30 Lecture “A very short introduction to multivariate pattern analysis
(MVPA) for neuroscience”
09:30 - 11:00 Hands-on “Data representation in PyMVPA”
11:00 - 11:30 Coffee break
11:30 - 12:30 Hands-on “PyMVPA building blocks and the command line interface”
12:30 - 13:30 Lunch
13:30 - 14:00 Lecture “Basic MVPA strategies”
14:00 - 15:00 Hands-on “Classification and cross-validation”
15:00 - 15:30 Coffee break
15:30 - 17:00 Hands-on “Searchlights”
18:00 Dinner
Friday, March 7
09:00 - 09:30 Lecture “PyMVPA and the larger scientific software eco-system”
09:30 - 11:00 Hands-on “Feature extraction and preprocessing”
11:00 - 11:30 Coffee break
11:30 - 12:30 Hands-on “Connecting building blocks into analysis workflows”
12:30 - 13:30 Lunch
13:30 - 14:00 Lecture “Advanced methods, other data modalities and
current developments”
14:00 - 15:00 Hands-on “Group analyses”
15:00 - 15:30 Coffee break
15:30 - 16:30 Hands-on “Statistical evaluation”
16:30 - 17:00 Q&A
17:00 End of workshop
Programme
Thursday, March 6
09:00 - 09:30 Lecture “A very short introduction to multivariate pattern analysis
(MVPA) for neuroscience”
09:30 - 11:00 Hands-on “Data representation in PyMVPA”
11:00 - 11:30 Coffee break
11:30 - 12:30 Hands-on “PyMVPA building blocks and the command line interface”
12:30 - 13:30 Lunch
13:30 - 14:00 Lecture “Basic MVPA strategies”
14:00 - 15:00 Hands-on “Classification and cross-validation”
15:00 - 15:30 Coffee break
15:30 - 17:00 Hands-on “Searchlights”
18:00 Dinner
Friday, March 7
09:00 - 09:30 Lecture “PyMVPA and the larger scientific software eco-system”
09:30 - 11:00 Hands-on “Feature extraction and preprocessing”
11:00 - 11:30 Coffee break
11:30 - 12:30 Hands-on “Connecting building blocks into analysis workflows”
12:30 - 13:30 Lunch
13:30 - 14:00 Lecture “Advanced methods, other data modalities and
current developments”
14:00 - 15:00 Hands-on “Group analyses”
15:00 - 15:30 Coffee break
15:30 - 16:30 Hands-on “Statistical evaluation”
16:30 - 17:00 Q&A
17:00 End of workshop
Application
The workshop is limited to 30 participants. A participation fee of 150 EUR for Postdocs/ 75 EUR for PhD students will be charged. All meals indicated in the program (lunch, coffee, refreshments, and dinner) will be complimentary.
Please apply by January 15, 2014 using the application form (link for download below).
Downloads
<link fileadmin dateien tagungen pymvpa application_form.doc download>Application form (doc)