Welcome to Mind Data Hub
Mind Data Hub is a centralized platform for accessing and exploring curated brain-related datasets across multiple modalities.
We aim to accelerate neuroscience and mental health research by offering high-quality imaging data and transparent preprocessing pipelines.
📂 Currently Available Datasets
We currently provide T1-weighted, T2-weighted, DTI, and functional MRI (fMRI) data, along with rich metadata, from the following studies:
📂 Coming Soon
We are working to integrate the following datasets as part of upcoming data releases:
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and dozens of additional datasets currently under review or preprocessing...
These additions will support large-scale, multimodal research and enable the development of clinically meaningful and generalizable AI models.
📂 Preprocessing Documentation
📂 Request Data Access (Stanford Collaborators Only)
Please review the following requirements before submitting a request. If you meet the policy requirements, please proceed to complete the internal request form.
- Applicants must already have official access approval for each requested source dataset (e.g., ADNI, HCP, OpenNeuro).
- Proof of dataset access approval is required and must be uploaded for every dataset requested.
- Requests without valid approval documentation will not be reviewed or processed.
Requests will be reviewed by our team, and access will be granted in accordance with project goals and data use policies.
🔒 Access is currently limited to Stanford-affiliated users.
🌍 Public access is not possible in this phase of the project!
📂 How to Cite:
If you use these pipelines in your research, please cite them as follows:
1 - Structural MRI Processing Pipeline
Abbasi, M.H., & Adeli, E. (2025). sMRI Processing Pipeline: A lightweight, end-to-end framework for structural MRI preprocessing and quality control.
Stanford Translational AI Lab, Stanford University. Zenodo.
https://doi.org/10.5281/zenodo.17503175
@software{abbasi2025smri,
author = {Mohammad Hassan Abbasi and Ehsan Adeli},
title = {s{MRI} {P}rocessing {P}ipeline: A lightweight, end-to-end workflow for structural brain {MRI} preprocessing and quality control},
year = {2025},
institution = {Stanford Translational AI Lab, Stanford University},
publisher = {Zenodo},
doi = {10.5281/zenodo.17503175},
url = {https://doi.org/10.5281/zenodo.17503175},
note = {Zenodo, doi: 10.5281/zenodo.17503175},
}2 - Functional MRI Pre-Processing Pipeline
Abbasi, M.H., & Adeli, E. (2025). fMRI Processing Pipeline: A lightweight, end-to-end framework for BIDS-compatible fMRI preprocessing and quality control.
Stanford Translational AI Lab, Stanford University. Zenodo.
https://doi.org/10.5281/zenodo.17516604
@software{abbasi2025fmri,
author = {Mohammad Hassan Abbasi and Ehsan Adeli},
title = {f{MRI} Processing Pipeline: A lightweight, end-to-end framework for {BIDS}-compatible f{MRI} preprocessing and quality control},
year = {2025},
institution = {Stanford Translational AI Lab, Stanford University},
publisher = {Zenodo},
doi = {10.5281/zenodo.17516368},
url = {https://doi.org/10.5281/zenodo.17516604},
note = {Zenodo, doi: 10.5281/zenodo.17516604},
}3 - Diffusion Tensor Imaging (DTI) Pre-Processing Pipeline
Abbasi, M.H., Chau, G., & Adeli, E. (2025). DTI Processing Pipeline: A reproducible framework for diffusion MRI preprocessing, and quality control.
Stanford Translational AI Lab, Stanford University. Zenodo.
https://doi.org/10.5281/zenodo.17503171
@software{abbasi2025dti,
author = {Mohammad Hassan Abbasi and Gustavo Chau and Ehsan Adeli},
title = {{DTI} {P}rocessing {P}ipeline: A reproducible framework for diffusion {MRI} preprocessing and quality control},
year = {2025},
institution = {Stanford Translational AI Lab, Stanford University},
publisher = {Zenodo},
doi = {10.5281/zenodo.17503171},
url = {https://doi.org/10.5281/zenodo.17503171},
note = {Zenodo, doi: 10.5281/zenodo.17503171},
}📂 Vision
Mind Data Hub is designed to support the next generation of neuroscience research, bridging data from imaging, genetics, cognition, and environment into a unified platform for discovery and innovation.