CONP Portal | Dataset
Learning Naturalistic Structure: Processed fMRI dataset
Description:
This data was obtained from OpenNeuro as ds001545. We would like to thank the authors for their generosity in sharing their data, and we point interested users towards their paper describing its acquisition:
Aly M, Chen J, Turk-Browne NB, & Hasson U (2018). Learning naturalistic temporal structure in the posterior medial network. Journal of Cognitive Neuroscience, 30(9): 1345-1365.
Experimental design
In this dataset, subjects were scanned while watching repeated presentations of intact and scrambled clips from Wes Anderson's 2014 film, The Grand Budapest Hotel. Scrambled clips were presented in either a 'fixed' (i.e., consistent scrambling from run to run) or 'random' (i.e., random scrambling from run to run) condition. An overview of the experimental design is shown in this figure from Aly and colleagues (2018)
Preprocessing
After downloading from OpenNeuro using DataLad, data was preprocessed using fMRIPrep 1.5.0rc1. A complete transcript of the fMRIPrep processing is available as a README file in the repository. Post-processing was performed using Nilearn. Briefly, functional files were masked with the fMRIPrep-derived brain mask and trimmed to discard non-steady state volumes. Please see the README for further details.
Dataset README information
Learning Naturalistic Structure: Processed fMRI dataset
Crawled from Zenodo
Description
This data was obtained from OpenNeuro as ds001545. We would like to thank the authors for their generosity in sharing their data, and we point interested users towards their paper describing its acquisition:
>
> Aly M, Chen J, Turk-Browne NB, & Hasson U (2018). Learning naturalistic temporal structure in the posterior medial network. Journal of Cognitive Neuroscience, 30(9): 1345-1365.
>
Experimental design
In this dataset, subjects were scanned while watching repeated presentations of intact and scrambled clips from Wes Anderson's 2014 film, The Grand Budapest Hotel. Scrambled clips were presented in either a 'fixed' (i.e., consistent scrambling from run to run) or 'random' (i.e., random scrambling from run to run) condition. An overview of the experimental design is shown in this figure from Aly and colleagues (2018)
Preprocessing
After downloading from OpenNeuro using DataLad, data was preprocessed using fMRIPrep 1.5.0rc1. A complete transcript of the fMRIPrep processing is available as a README file in the repository. Post-processing was performed using Nilearn. Briefly, functional files were masked with the fMRIPrep-derived brain mask and trimmed to discard non-steady state volumes. Please see the README for further details.
The following instructions require a basic understanding of UNIX/LINUX command lines. Future portal functionality may include downloads directly from the web browser. Dataset download is currently enabled through DataLad.
Note: The conp-dataset
requires version >=0.12.5 of DataLad
and version >=8.20200309 of git-annex.
To install DataLad on your system, please refer to the install section of the DataLad Handbook (installation via miniconda is recommended in order to obtain the latest version of DataLad).
1) Initiate the CONP dataset
To initiate the CONP dataset (conp-dataset
), run the following
command in the directory where you want CONP datasets to be installed:
datalad install https://github.com/CONP-PCNO/conp-dataset.git
2) Install the Learning_Naturalistic_Structure__Processed_fMRI_dataset dataset
To install the dataset, go into the created conp-dataset directory and run
datalad install
on the dataset Learning_Naturalistic_Structure__Processed_fMRI_dataset
:
cd conp-dataset
datalad install projects/Learning_Naturalistic_Structure__Processed_fMRI_dataset
3) Download the Learning_Naturalistic_Structure__Processed_fMRI_dataset dataset
Now that the DataLad dataset has been installed, go into the dataset
directory under projects/Learning_Naturalistic_Structure__Processed_fMRI_dataset
.
cd projects/Learning_Naturalistic_Structure__Processed_fMRI_dataset
Note that files visible in the dataset are symlinks and will need to be
downloaded manually using the
datalad get
command in the dataset directory:
datalad get <filepath>
Note, if you run datalad get *
command, all the files present
in the dataset directory will be downloaded.