CONP Portal | Dataset


PREVENT-AD open data in BIDS format
Creators: StoP-AD Center - Douglas Mental Health University Institute
Contact: Jennifer Tremblay-Mercier, Research Coordinator, jennifer.tremblay-mercier@douglas.mcgill.ca, 514-761-6131 #3329
Licenses: https://openpreventad.loris.ca/images/Open_PREVENT-AD_Terms_of_Use.png
Version: 2.1
Modalities: MRI basic demographics
Formats: BIDS NIfTI TSV JSON
Size: 277.0 GB
No of Files: 53061
No of Subjects: 308
Input Datasets: preventad-open
Primary Publication: Rationale and Structure for a new Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD). John C.S. Breitner et al. The Journal of Prevention of Alzheimer's Disease - JPAD Volume 3, Number 4, 2016. http://dx.doi.org/10.14283/jpad.2016.121
Project Landing Page: https://www.centre-stopad.com/en/
Metadata file: DATS.json
Dimensions: age at MRI, gender, test language, handedness interpretation, handedness score, structural MRI, functional MRI
Is About: Alzheimer's disease, Homo sapiens, older adult
Acknowledges: Fonds de Recherche du Québec - Santé, McGill University, Pfizer Canada, Douglas Hospital Research Centre and Foundation, Fondation Lévesque, Canada Fund for Innovation
Spatial Coverage: Québec (province)
Other Dates: Release Date: 2020-05-30 -- Last Update Date: 2021-02-12 -- Start Date: 2011-11-01 -- End Date: 2017-11-27
Description:
Longitudinal study of pre-symptomatic Alzheimer's Disease

Dataset README information

README.md

PREVENT-AD open Dataset organized according to the BIDS standard

Overview

This is a derived dataset of preventad-open where all the images are organized according to the BIDS standard. Conversion to NIfTI was performed from the original MINC files available in the preventad-open dataset.

The PREVENT-AD (Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease) cohort is composed of cognitively healthy participants over 55 years old, at risk of developing Alzheimer Disease (AD) as their parents and/or siblings were/are affected by the disease. These ‘at-risk’ participants have been followed for a naturalistic study of the presymptomatic phase of AD since 2011 using multimodal measurements of various disease indicators. One clinical trial intended to test a pharmaco-preventive agent has also been conducted.

The PREVENT-AD research group is now releasing data openly with the intention to contribute to the community’s growing understanding of AD pathogenesis.

More detailed information about the study design can be found in the LORIS instance of Open PREVENT-AD (https://openpreventad.loris.ca).

Data organization

Data are organized according to the BIDS standard in the BIDS_dataset directory. Specifications of the BIDS standard are available here.

preventad-open
|__DATS.json
|__BIDS_dataset
   |__dataset_description.json
   |__participants.json
   |__participants.tsv
   |__README
   |__sub-<candidate_id>
      |__ses-<visit_label>
            |__sub-<candidate_id>_ses-<visit_label>_scans.json
            |__sub-<candidate_id>_ses-<visit_label>_scans.tsv
                  |__anat
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.nii.gz
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.json
                        ...
                  |__asl
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.nii.gz
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.json
                        ...
                  |__dwi
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.nii.gz
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.json
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.bval
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.bvec
                        ...
                  |__fmap
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.nii.gz
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.json
                        ...
                  |__func
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.nii.gz
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>.json
                        |__ sub-<candidate_id>_ses-<visit_label>_run-<number>_<BIDS_scan_type>_events.txt
                        ...
  • DATS.json is a JSON file that describes the content of the dataset

For more information:

Download Using DataLad

CircleCI status

The following instructions require a basic understanding of UNIX/LINUX command lines. A subset of open datasets on the Portal are also available through a browser-based download button. The instructions below regard dataset download with the use of DataLad. To install DataLad on your system, please refer to the install section of the DataLad Handbook .

Note: For maximum compatibility with conp-dataset, the CONP recommends versions 3.12+ of Python, 10.20241202+ of git-annex, and 1.1.4+ of datalad.

1) Initiate the CONP dataset

Run the following command in the directory where you want the CONP dataset (conp-dataset) to be installed:

2) Install the preventad-open-bids dataset

To install the preventad-open-bids dataset, run the following commands to move into the "projects" subdirectory under the "conp-dataset" directory (created in the previous step) and run datalad install:

3) Download data from the preventad-open-bids dataset

Now that the dataset has been installed, go into the preventad-open-bids dataset directory.

The files visible after installing the dataset but before downloading (in the next step) are symbolic links and need to be downloaded manually using the datalad get command:

If you run datalad get * command, all the files available in the dataset directory will be downloaded.


For more information on how DataLad works, please visit the DataLad Handbook documentation.