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PREVENT-AD registered data
  • Creators: StoP-AD Center - Douglas Mental Health University Institute
  • Version: 2.1
  • Date Added: 2020-11-27
  • Date Updated: 2025-01-15
  • Modalities: MRI, demographics, neuropsychological tests, neurosensory assessments, general medical history, genetics, cerebrospinal fluid proteins levels
  • License: https://registeredpreventad.loris.ca/images/PREVENT-AD_Terms_of_Use.png
  • Files: 80500
  • Size: 556.0 GB
  • Subjects: 348
  • Formats: MINC, JSON, NIfTI, BIDS, CSV, TSV, TXT
ARK ID : https://n2t.net/ark:/70798/d7j9dvk4777m366kfb
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Keywords: prevention structural MRI functional MRI longitudinal cognition CSF protein levels genetics neurosensory
PREVENT-AD registered data
Creators: StoP-AD Center - Douglas Mental Health University Institute
Contact: Jennifer Tremblay-Mercier, jennifer.tremblay-mercier@douglas.mcgill.ca
Licenses: https://registeredpreventad.loris.ca/images/PREVENT-AD_Terms_of_Use.png
Version: 2.1
Modalities: MRI demographics neuropsychological tests neurosensory assessments general medical history genetics cerebrospinal fluid proteins levels
Formats: MINC JSON NIfTI BIDS CSV TSV TXT
Size: 556.0 GB
No of Files: 80500
No of Subjects: 348
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
Browse on GitHub: https://github.com/conpdatasets/preventad-registered
Project Landing Page: https://www.centre-stopad.com/en/
Metadata file: DATS.json
Dimensions: age at MRI, test language, handedness interpretation, handedness score, structural MRI, functional MRI, AD8 dementia screening interview, central auditory processing evaluation, clinical information, CSF protein levels, demographics, cardiovascular risk factors, aging, and incidence of dementia (CAIDE) dementia risk score, clinical dementia rating, Montreal cognitive assessment (MOCA), medical history, genetics, lab results, medication user, repeatable battery for the assessment of neuropsychological status (RBANS), smell identification
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: 2020-11-30 -- Last Update Date: 2021-02-11 -- Start Date: 2011-11-01 -- End Date: 2020-03-10
Description:
Longitudinal study of pre-symptomatic Alzheimer's Disease. Longitudinal data from 348 participants are available. This includes multi-modal MRI images, neuropsychological tests, neurosensory assessments, general medical history, genetics and cerebrospinal fluid proteins levels.

Dataset README information

README.md

PREVENT-AD Registered Dataset

Overview

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.

This PREVENT-AD dataset is available to researchers around the world 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 Registered PREVENT-AD (https://registeredpreventad.loris.ca).

Data organization

A DATS.json is present at the root of the dataset and describes the content of the dataset.

The dataset is organized in three different parts:

  • clinical-data: clinical data spreadsheets and data dictionary
  • images-in-BIDS: images orgnized according to the BIDS standard in the BIDS_dataset directory. Specifications of the BIDS standard are available here.
  • images-in-MINC: images provided in MINC format. In this portion of the dataset, candidate.json contains demographic information regarding the candidate in question; visit.json contains visit level information, handedness.json when present, contains results of the Handedness Edinburgh Inventory

Note: the two images folders contain the same modalities and number of subjects/visits.

preventad-registered
|__DATS.json
|__README.md
|__clinical-data
   |__AD8_Registered_PREVENTAD.csv
   |__APS_Registered_PREVENTAD.csv
   |__Auditory_processing_Registered_PREVENTAD.csv
   |__BP_Pulse_Weight_Registered_PREVENTAD.csv
   |__CSF_proteins_Registered_PREVENTAD.csv
   |__Data_Dictionary_Registered_PREVENTAD.csv
   |__Demographics_Registered_PREVENTAD.csv
   |__EL_CAIDE_Registered_PREVENTAD.csv
   |__EL_CDR_MoCA_Registered_PREVENTAD.csv
   |__EL_Medical_history_Registered_PREVENTAD.csv
   |__Genetics_Registered_PREVENTAD.csv
   |__Lab_Registered_PREVENTAD.csv
   |__List_of_participants_switched_back_to_cohort.txt
   |__List_of_participants_with_only_1_sibling.txt
   |__Med_categories.txt
   |__Med_use_Registered_PREVENTAD.csv
   |__RBANS_Registered_PREVENTAD.csv
   |__Smell_identification_Registered_PREVENTAD.csv
|__images-in-BIDS
   |__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
                           ...
|__images-in-MINC
   |__candidate_id
      |__candidate.json
      |__visit_label
         |__visit.json
         |__handedness.json
         |__images
            |__image_1.mnc
            |__image_2.mnc
            |__image_3.mnc
            ...

For more information:

Links to the open dataset of PREVENT-AD (basic demographics and MRIs):

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: 

datalad install https://github.com/CONP-PCNO/conp-dataset.git
2) Install the preventad-registered dataset

To install the preventad-registered 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: 

cd conp-dataset/projects
datalad install preventad-registered
3) Download data from the preventad-registered dataset

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

cd preventad-registered

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: 

datalad get <filepath>

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.