Contribute

Help us grow our multi-site database by sharing your data.

Quality Control

Methods to protect subject privacy and evaluate image quality.

2ndary Datasets

Use our resources to process your image data and receive neuroimaging metrics associated with dyslexia.

Download Datasets

Use our multi-site data for discovery and to replicate your findings.

Questions ?

Website and Server Security: All website information is shared through the HTTPS protocol. User passwords are hashed and never saved in raw format. The web server and data processing server are hosted in a highly secure Clemson University academic network and can be accessed only through the Clemson University virtual private network. In addition, all user logs are inspected for unusual activity.

Privacy and Directed Attacks: Open-access data are de-identified to significantly reduce the risk for re-identification. You should not upload any data with personal health identifiers and we recommend using our Deidentification Toolbox to deidentify your data. In addition, your imaging data is examined using an AI approach to confirm that voxels representing participant faces have been removed. This approach protects subject identify by preventing searching for participants in photo repositories using face renderings of the MRI scans.

You choose whether your data is to be designated as open access and available to the user community. To access any data, users must be approved by the administrator and can download only the data labeled as open access and/or the data they contributed.

All T1-weighted images are currently evaluated by examining the degree to which the covary with a gray matter template image in MNI space. This voxel-by-voxel correlation analysis provides a similarity metric that can be used to identify noisy or atypical images. Images with relatively low similarity estimates are also inspected by our personnel to identify problematic images.

Each T1-weighted image is automatically processed when uploaded. This processing includes rigid co-registration to MNI space and CAT Toolbox procedures to create normalized segmented, cortical thickness, and Jacobian determinant images. You can download these images to address your experimental questions.

In addition to providing secondary processed images (e.g., cortical thickness), we also make available metrics that have been observed to relate to dyslexia (e.g., superior temporal sulcus gray matter volume; doi.org/10.1523/ENEURO.0103-15.2015 ). Your images will also be automatically processed with a classifier to determine the likelihood that a T1-weighted image is from someone with reading disability.

There is a significant and increasing amount of available real data. We are also working to provide synthetic data based on the entire dataset. Users will be able to select demographic and behavioral parameters of interest, based on their planned data analysis model, which will guide generation of the synthetic data.

You can access data once you sign up and your registration is approved by the administrator. For access to real data, you must also sign a data use agreement form and agree that you will not share the data or attempt to reidentify the dataset.

Contact

Email
dyslexia.consortium@gmail.com

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