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Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging

Nature Protocols volume 20, pages 2652–2684 (2025)Cite this article

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Abstract

Connectional neuroanatomical maps can be generated in vivo by using diffusion-weighted magnetic resonance imaging (dMRI) data, and their representation as structural connectome (SC) atlases adopts network-based brain analysis methods. We explain the generation of high-quality SCs of brain connectivity by using recent advances for reconstructing long-range white matter connections such as local fiber orientation estimation on multi-shell dMRI data with constrained spherical deconvolution, which yields both increased sensitivity to detecting crossing fibers compared with competing methods and the ability to separate signal contributions from different macroscopic tissues, and improvements to streamline tractography such as anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms, which have increased the biological accuracy of SC creation. Here, we provide step-by-step instructions to creating SCs by using these methods. In addition, intermediate steps of our procedure can be adapted for related analyses, including region of interest-based tractography and quantification of local white matter properties. The associated software MRtrix3 implements the relevant tools for easy application of the protocol, with specific processing tasks deferred to components of the FSL software. The protocol is suitable for users with expertise in dMRI and neuroscience and requires between 2 h and 13 h to complete, depending on the available computational system.

Key points

  • The procedure enables the creation of structural connectomes from diffusion-weighted magnetic resonance imaging data processed by using state-of-the-art techniques such as constrained spherical deconvolution, anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms implemented by using MRtrix3 and FSL software.

  • The intermediate steps can be adapted for region-of-interest tractography and quantification of local white matter properties.

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Fig. 1: The five building blocks of SC construction.
Fig. 2: Effects of different pre-processing steps of dMRI as proposed herein.
Fig. 3: Exemplary terminal output from running dwifslpreproc within MRtrix3.
Fig. 4: Exemplary results of the protocol.

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Data availability

The tutorial data (raw and derived) are freely available at Open Science Framework at https://osf.io/tm5x8/ (ref. 183).

Code availability

The complementary Docker image is available on Github at https://github.com/martahedl/SC-construction-using-CSD-in-MSMT-dMRI. The relevant code to perform the present protocol’s steps is provided within this manuscript and may be used freely. More code, for example, on quality assessment, is provided in the Supplementary Information.

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Acknowledgements

This work was supported by funding from the National Health and Medical Research Council of Australia. R.E.S. is supported by fellowship funding from the National Imaging Facility (NIF), an Australian Government National Collaborative Research Infrastructure Strategy (NCRIS) capability. J.-D.T. was supported with funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/20072013), ERC grant agreement no. 319456 (developing Human Connectome Project) and MRC strategic funds MR/K006355/1. J.-D.T. was also supported by the Wellcome/EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z) and by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. M.T. is supported by a Walter-Benjamin Postdoc Stipend from the Deutsche Forschungsgemeinschaft (DFG, TA 1902/1-1).

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Authors and Affiliations

  1. Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany

    Marlene Tahedl

  2. Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK

    J.-Donald Tournier

  3. Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK

    J.-Donald Tournier

  4. Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia

    Robert E. Smith

  5. Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia

    Robert E. Smith

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  1. Marlene Tahedl
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  2. J.-Donald Tournier
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Correspondence to Marlene Tahedl.

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Key references

Tournier, J.-D. et al. Neuroimage 202, 116137 (2019): https://doi.org/10.1016/j.neuroimage.2019.116137

Smith, R. E. et al. Neuroimage 62, 1924–1938 (2012): https://doi.org/10.1016/j.neuroimage.2012.06.005

Smith, R. E. et al. Neuroimage 104, 253–265 (2015): https://doi.org/10.1016/j.neuroimage.2014.10.004

Smith, R. E. et al. Neuroimage 119, 338–351 (2015): https://doi.org/10.1016/j.neuroimage.2015.06.092

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Supplementary Information

Supplementary material providing additional instructions for quality assessment and visual inspection of the main protocol.

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Tahedl, M., Tournier, JD. & Smith, R.E. Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging. Nat Protoc 20, 2652–2684 (2025). https://doi.org/10.1038/s41596-024-01129-1

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