Citation
Cieslak, Matthew, Philip A Cook, Xiaosong He, Fang-Cheng Yeh, Thijs Dhollander, Azeez Adebimpe, Geoffrey K Aguirre, et al. 2021. “QSIPrep: An Integrative Platform for Preprocessing and Reconstructing Diffusion Mri Data.” Nature Methods 18 (7). Nature Publishing Group US New York: 775–78. https://doi.org/10.1038/s41592-021-01185-5.
More citations (Click to expand/minimize)
Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” Frontiers in Neuroinformatics 8. https://doi.org/10.3389/fninf.2014.00014.

Dhollander, Thijs, David Raffelt, and Alan Connelly. 2016. “Unsupervised 3-Tissue Response Function Estimation from Single-Shell or Multi-Shell Diffusion Mr Data Without a Co-Registered T1 Image.” In ISMRM Workshop on Breaking the Barriers of Diffusion Mri, 5:5.

Dhollander, T, R Mito, D Raffelt, and A Connelly. 2019. “Improved White Matter Response Function Estimation for 3-Tissue Constrained Spherical Deconvolution.” In Proc. Intl. Soc. Mag. Reson. Med, 555.

Garyfallidis, Eleftherios, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stefan Van Der Walt, Maxime Descoteaux, and Ian Nimmo-Smith. 2014. “Dipy, a Library for the Analysis of Diffusion Mri Data.” Frontiers in Neuroinformatics 8. Frontiers: 8.

Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5: 13. https://doi.org/10.3389/fninf.2011.00013.

Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” Software. Zenodo. https://doi.org/10.5281/zenodo.596855.

J-Donald, Robert Smith, David Raffelt, Rami Tabbara, Thijs Dhollander, Maximilian Pietsch, Daan Christiaens, Ben Jeurissen, Chun-Hung Yeh, and Alan" Connelly. 2019. “MRtrix3: A Fast, Flexible and Open Software Framework for Medical Image Processing and Visualisation.” NeuroImage 202: 116137.

Raffelt, David, Thijs Dhollander, J-Donald Tournier, Rami Tabbara, Robert E Smith, Eric Pierre, and Alan Connelly. 2017. “Bias Field Correction and Intensity Normalisation for Quantitative Analysis of Apparent Fibre Density.” In Proc. Intl. Soc. Mag. Reson. Med, 25:3541.

Tournier, J-Donald, Fernando Calamante, David G Gadian, and Alan Connelly. 2004. “Direct Estimation of the Fiber Orientation Density Function from Diffusion-Weighted Mri Data Using Spherical Deconvolution.” NeuroImage 23 (3). Elsevier: 1176–85.

Tournier, J-Donald, Chun-Hung Yeh, Fernando Calamante, Kuan-Hung Cho, Alan Connelly, and Ching-Po Lin. 2008. “Resolving Crossing Fibres Using Constrained Spherical Deconvolution: Validation Using Diffusion-Weighted Imaging Phantom Data.” Neuroimage 42 (2). Elsevier: 617–25.

Yeh, Fang-Cheng. 2020. “Shape Analysis of the Human Association Pathways.” Neuroimage 223. Elsevier: 117329.

Yeh, Fang-Cheng, Van Jay Wedeen, and Wen-Yih Isaac Tseng. 2010. “Generalized q-Sampling Imaging.” IEEE Transactions on Medical Imaging 29 (9). IEEE: 1626–35.


Preprocessed diffusion data (QSIPrep outputs) were further analyzed using QSIRecon, which executes reconstruction workflows defined in a recon-spec YAML file. This file specifies reconstruction steps, parameter settings, post-processing tools, and software dependencies. We used a recon-spec file for AutoTrack bundle reconstruction and bundle-wise statistics (Bundle Stats).

Bundle Stats

Diffusion tensors were estimated with TORTOISE, and diffusion orientation distribution functions (ODFs) were reconstructed using two approaches: generalized q-sampling imaging (GQI) in DSI Studio and single-shell three-tissue constrained spherical deconvolution (SS3T-CSD) in MRtrix3. AutoTrack tractography was applied to both GQI and SS3T reconstructions to delineate major white matter tracks. For each method, bundle-wise mean values were computed for GQI and tensor scalars.

Boilerplate