Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Correspondence
  • Published:

DSI Studio: an integrated tractography platform and fiber data hub for accelerating brain research

Nature Methods volume 22, pages 1617–1619 (2025)Cite this article

Subjects

Access through your institution
Buy or subscribe

Tractography is a fundamental neuroimaging technique used to map and analyze white matter tracts, offering valuable insights into the brain’s structural connectivity. By reconstructing axonal fiber pathways, tractography enables researchers to investigate the complex networks underlying cognitive functions and behaviors1. Over the past two decades, it has evolved into a widely used tool in neuroscience, neurology and psychiatry. This technique has substantially deepened our understanding of brain connectivity, elucidating processes related to development, neural plasticity and the pathology of various neurological disorders.

The latest release of DSI Studio, now integrated with a cloud-based fiber data hub, tackles core challenges in tractography by providing preprocessed datasets that allow researchers to bypass tedious preprocessing and begin directly with fiber orientation data and diffusion metrics (Fig. 1b). The hub hosts over 37,000 preprocessed fiber datasets from major repositories (Supplementary Fig. 1), including the Human Connectome Project (HCP) Lifespan studies4, the Adolescent Brain Cognitive Development (ABCD) study5 and OpenNeuro6, with ongoing expansions as new datasets become available. Each dataset has undergone preprocessing (Supplementary Note 1) using high-performance computing resources to generate quality-controlled fiber data stored using BIDS-compatible filenames for immediate tractography and connectome analysis. The preprocessed data — including local fiber orientation and diffusion metrics — are reduced in size by approximately 100-fold compared to raw diffusion-weighted imaging data. This compression is achieved through 8-bit storage conversion, background masking and selective data retention, allowing compact storage while maintaining compatibility with tractography analysis (Supplementary Figs. 2 and 3). This approach substantially reduces storage requirements, download times and preprocessing efforts while addressing common diffusion MRI challenges, such as variability in gradient tables, phase encoding and file structures, thereby streamlining workflows for researchers. More importantly, the fiber data hub is accessible via direct download links, web portals (for example, https://brain.labsolver.org) or REST (Representational State Transfer) API, enabling potential integration with tools such as MRtrix7, DIPY8, TRACULA9, SlicerDMRI10 or cloud-computing platforms such as brainlife.io2 and NiiVue. Researchers can leverage these tools alongside the fiber data hub for quality control, denoising, Gibbs ringing correction, tract analysis and data harmonization, enhancing accessibility, consistency and efficiency in tractography research.

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Tractography data processing with DSI Studio’s and the Fiber Data Hub workflow.

Data availability

All 37,486 preprocessed brain fiber datasets in this study are publicly available on the fiber data hub, accessible through an integrated graphical user interface in DSI Studio. Alternatively, the hub includes multiple independent decentralized storage locations on GitHub repositories. A web portal is available at https://brain.labsolver.org for access to the hub’s resources outside DSI Studio. These datasets include derived fiber data from major studies such as the HCP Lifespan Project, the ABCD study, OpenNeuro repositories and other studies detailed in the Supplementary Note 1. The datasets provide diffusion metrics and voxel-level fiber orientations. Each dataset on the data hub is accessible via https links, enabling direct downloads.

The redistribution of datasets follows the agreements of the source studies:

• HCP Lifespan and ABCD studies: the derived fiber data were shared under an agreement with the National Institute of Mental Health (NIMH) Data Archive (NDA). The redistribution of fiber data was confirmed with the NDA Help Desk.

• OpenNeuro repositories: the derived fiber data are shared under the CC0 license.

• Other studies (CamCAN, HBN, NKI-Rockland and others): the derived fiber data are distributed in accordance with the original agreements of each dataset, allowing public sharing of derived data.

Code availability

The source code for DSI Studio is publicly available at https://github.com/frankyeh/DSI-Studio/ and https://doi.org/10.5281/zenodo.4764264. It is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), which governs its use and redistribution. This repository contains all components of DSI Studio, including the core algorithms for diffusion modeling, fiber tracking and connectometry analysis. In addition to DSI Studio, the TIPL (Template Image Processing Library) library — an essential dependency for the software — is also available as open-source code. TIPL provides optimized routines for image processing tasks such as tensor computation and transformation, facilitating high-performance diffusion MRI analysis. The TIPL library can be accessed at https://github.com/frankyeh/TIPL.

References

  1. Jbabdi, S. & Johansen-Berg, H. Brain Connect. 1, 169–183 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hayashi, S. et al. Nat. Methods 21, 809–813 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gorgolewski, K. et al. Sci. Data 3, 160044 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Somerville, L. H. et al. Neuroimage 183, 456–468 (2018).

    Article  PubMed  Google Scholar 

  5. Casey, B. J. et al. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Markiewicz, C. J. et al. eLife 10, e71774 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tournier, J. D. et al. Neuroimage 202, 116137 (2019).

    Article  PubMed  Google Scholar 

  8. Garyfallidis, E. et al. Front. Neuroinform. 8, 8 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yendiki, A. et al. Front. Neuroinform. 5, 23 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhang, F. et al. JCO Clin. Cancer Inform. 4, 299–309 (2020).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The author was supported by NIH grant R01 NS120954. During the preparation of this work, the author used ChatGPT 4o (OpenAI) to revise the manuscript. After using this tool and service, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

Author information

Authors and Affiliations

  1. Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Fang-Cheng Yeh

  2. Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Fang-Cheng Yeh

Authors
  1. Fang-Cheng Yeh
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Fang-Cheng Yeh.

Ethics declarations

Competing interests

The author declares no competing interests.

Peer review

Peer review information

Nature Methods thanks Maxime Descoteaux and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Notes 1 and 2

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yeh, FC. DSI Studio: an integrated tractography platform and fiber data hub for accelerating brain research. Nat Methods 22, 1617–1619 (2025). https://doi.org/10.1038/s41592-025-02762-8

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41592-025-02762-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics