localized visual areas (FFA, OFA, PPA, EBA, LOC, early visual)

https://studyforrest.org/

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README.md

studyforrest.org Dataset

made-with-datalad PDDL-licensed [No registration or authentication required]() doi

Localization of higher-level visual ROIs

For all participants in the phase2 extension of the studyforrest dataset, the following visual areas were localized:

  • fusiform face area (FFA)
  • occipital face area (OFA)
  • parahippocampal place area (PPA)
  • extrastriate body area (EBA)
  • lateral occipital complex (LOC)
  • early visual cortex (VIS)

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/: all source code to perform the analysis of the block-design localizer fMRI data

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

sub-??/: analysis results per participant

onsets/:

 per-stimulus condition timing in FSL's EV3 format (converted from the BIDS
 event specifications)

2ndlvl.gfeat/:

 Full output folder of the 2nd level fixed-effects analysis aggregating
 1st-level GLM parameters across all four experiment runs. This contains
 thresholded and unthresholded Z-stat maps which were the basis for the
 subsequent titration of ROI masks per participant.

rois/:

 One mask volume per isolated voxel cluster in the results. Each filename
 starts with the ROI label (e.g. lFFA for left fusiform face area). There
 can be more than one file/cluster per ROI per subject, if more than one
 isolated voxel cluster was present in the results.

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.