retinotopic-maps

studyforrest.org Dataset

[]()

Retinotopic Mapping

All participants in the phase2 extension of the studyforrest dataset underwent retinotopic mapping with standard flickering checkerboard stimulus (ring and wedges). More information on the procedure and the results can be found in:

Ayan Sengupta, Falko R. Kaule, J. Swaroop Guntupalli, Michael B. Hoffmann, Christian Häusler, Jörg Stadler, Michael Hanke. An extension of the studyforrest dataset for vision research. (submitted for publication)

For further information about the project visit: http://studyforrest.org

Content

code/: source code for retinotopic mapping analysis.

• The main script is process_retmap and a Python based GUI easyret_gui to call it from an easy to use front end. The process_retmap script calls the Python scripts RetMap_phaseshift for post processing phase shift (if required) and combine_volumes for combining the clw/ccw maps and ecc/con maps together.

src/: links to repositories containing all inputs for the analysis

sub-??/: analysis results per participant

surface_maps/:

 contains eccentricity and polar angle maps of left and right hemispheres
 of a particular participant's cortical surface in *MGH* format

post_processing/:

 contains the post-processed/combined compressed *NIfTI* files in a
 participant's bold3Tp2 image template space
 (see ``src/templatetransforms``), before it is aligned to the
 *T1 structural* and represented on cortical surfaces.

qa/: contains the pyretmap_subjQuali.ods file which details the quality of the participant-wise retinotopic maps produced by the processing pipeline.

How to obtain the data files

This repository is a DataLad dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, DataLad is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and git-annex to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at handbook.datalad.org/en/latest/intro/installation.html.

Get the dataset

A DataLad dataset can be cloned by running

datalad clone <url>

Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual content of the (sometimes large) data files.

Retrieve dataset content

After cloning a dataset, you can retrieve file contents by running

datalad get <path/to/directory/or/file>

This command will trigger a download of the files, directories, or subdatasets you have specified.

DataLad datasets can contain other datasets, so called subdatasets. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run

datalad get -n <path/to/subdataset>

Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with datalad get. If you use datalad get <path/to/subdataset>, all contents of the subdataset will be downloaded at once.

Stay up-to-date

DataLad datasets can be updated. The command datalad update will fetch updates and store them on a different branch (by default remotes/origin/master). Running

datalad update --merge

will pull available updates and integrate them in one go.

More information

More information on DataLad and how to use it can be found in the DataLad Handbook at handbook.datalad.org. The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.