How to acknowledge dHCP and cite dHCP publications if you have used data provided by the KCL-Imperial-Oxford developing HCP consortium.

As stipulated in the User Terms, authors of publications or presentations that use KCL-Imperial-Oxford developing Human Connectome Project (dHCP) data should acknowledge the funding sources and cite relevant publications that describe key methods used by the dHCP to acquire and process the data.  This web page provides guidance on both fronts.

Acknowledge the Funding Source

Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from dHCP data should contain the following wording in the acknowledgments section: 

“Data were provided by the developing Human Connectome Project, KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial.”

Cite Relevant Publications

The specific publications that are appropriate to cite will depend on what dHCP data you used in your study and the purposes for which you used the data.  Here is an annotated list of publications that can guide your choices. They are grouped into categories and subcategories that relate to different aspects of data acquisition, pre-processing, and analysis. As additional publications become available, this list will be updated to include those that it may be relevant to cite.

1. Publications relevant to dHCP primary datasets

The publications in this section describe dHCP data acquisition methods that have been used to generate both the ‘raw’ NIFTI format and the pre-processed datasets that are available for download.

Overview Publication:

Hughes, E. J., Winchman, T., Padormo, F., Teixeira, R., Wurie, J., Sharma, M.,  Fox, M.,  Hutter, J.,  Cordero‐Grande, L., Price, A. N.,  Allsop, J., Bueno‐Conde, J., Tusor, N.,  Arichi, T.,  Edwards, A. D.,  Rutherford, M. A., Counsell, S. J.,  and Hajnal, J. V. A dedicated neonatal brain imaging system. Magnetic Resonance Medicine (2017), 78(2): 794–804. DOI: 10.1002/mrm.26462

MR pulse sequences and reconstruction for anatomical and multi-band EPI data (diffusion and BOLD fMRI): For any studies that make use of dHCP functional and/or diffusion MRI data, it is appropriate to cite the following references.

• Anatomical MRI – Motion corrected reconstruction: All anatomical images for all dHCP subjects have had motion corrected reconstruction:

Cordero-Grande, L., Teixeira, R. P. A. G., Hughes, E. J., Hutter, J., Price, A. N., & Hajnal, J. V. Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction. IEEE Transactions on Computational Imaging (2016), 2(3): 266–280. DOI: 10.1109/TCI.2016.2557069

• Diffusion MRI data acquisition: All dHCP diffusion data was acquired with a purpose designed multiband EPI acquisition featuring optimised multi-shell diffusion sensitisation scheme, gradient demand optimisation, restart capability with adjustable time setback to provide overlapping data, and use of all 4 phase encode directions.

Hutter, J., Tournier, J.D., Price, A.N., Cordero-Grande, L., Hughes, E.J., Bastiani, M., Sotiropoulos, S.N., Jbabdi, S., Andersson, J., Edwards, A.D., & Hajnal, J.V. Time-efficient and flexible design of optimised multi-shell HARDI diffusion. Magnetic Resonance in Medicine (2018), 79 (3): 1276-1292. DOI: 10.1002/mrm.26765

Tournier, J. D., E. Hughes, N. Tusor, A.D. Edwards and J.V Hajnal. Optimisation of single-shell HARDI for neonatal imaging. Proc ISMRM 2015: p2895.

Tournier, J. D.  E. Hughes, N. Tusor, S.N. Sotiropoulos, S. Jbabdi, J. Andersson, D. Rueckert, A.D. Edwards, J.V Hajnal. Data-driven optimisation of multi-shell HARDI. ISMRM 2015: p2897.

• Resting state functional MRI data acquisition: All dHCP functional imaging acquisitions were obtained using an optimised multiband sequence tuned for neonatal, in particular by deploying a high multiband factor to achieve a repeat time short enough to avoid aliasing cardiac fluctuations into the fMRI signal. Phase optimised multiband pulses were used throughout.

Price A. N., Cordero-Grande L, Malik S. J., Abaei M., Arichi T., Hughes E. J., Rueckert D., Edwards A. D., Hajnal J. V. Accelerated Neonatal fMRI Using Multiband EPI. In Proc ISMRM 2015: p3911.

Malik, SJ, Price AN, and Hajnal JV. Optimized Amplitude Modulated Multi-Band RF pulses. In Proc ISMRM 2015: p2398

1. Publications relevant to the dHCP pre-processed data

The publications in this section describe methods that are relevant if you have downloaded and used any of the dHCP pre-processed data involving one or more modalities.

Structural MRI data processing:

• Automated processing pipeline: All dHCP subjects have been processed and cortical meshes have been generated using the following automated pipeline:

Makropoulos, A., Robinson, E.C., Schuh, A., Wright, R., Fitzgibbon, S.P., Bozek, J., Counsell, S.J., Steinweg, J., Vecchiato, K., Passerat-Palmbach, J., Lenz, G., Mortari, F., Tenev, T., Duff, E.P., Bastiani, M., Cordero-Grande, L., Hughes, E., Tusor, N., Tournier, J.-D., Hutter, J., Price, A.N., Teixeira, R.P.A.G., Murgasova, M., Victor, S., Kelly, C., Rutherford, M.A., Smith, S., Edwards, A.D., Hajnal, J.V., Jenkinson, M., Rueckert, D. The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction. NeuroImage (2018), 173: 88-112. DOI: 10.1016/j.neuroimage.2018.01.054

• Surface templates: Cortical surfaces atlases have been generated following the procedures described in this paper:

Bozek, J., Makropoulos, A., Schuh, A., Fitzgibbon, S., Wright, R., Glasser, M. F., Coalson, T. S., O’Muircheartaigh, J., Hutter, J., Price, A. N., Cordero-Grande, L., Teixeira, R. P. A. G., Hughes, E., Tusor, N., Pegoretti Baruteau, K., Rutherford, M. A.,  Edwards, A. D., Hajnal, J. V. Smith, S. M., Rueckert, D., Jenkinson, M., and Robinson, E. C. Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project. NeuroImage (2018), 179: 11-29. DOI: 10.1016/j.neuroimage.2018.06.018

Resting-fMRI data processing: 

• Automated processing pipeline:The pipeline described in the following conference abstract was applied to all dHCP open access fMRI data.  A full manuscript will be available soon and this page will be updated with that citation.

Fitzgibbon, S. P., Harrison, S.J., Jenkinson, M., Baxter, L., Robinson, E. C., Bastiani, M., Bozek, J., Karolis, V., Cordero Grande, L.,  Price, A. N., Hughes, E., Makropoulos, A., Passerat-Palmbach, J., Schuh, A., Gao, J., Farahibozorg, S-R., O’Muircheartaigh, J., Ciarrusta, J., O’Keeffe, C., Brandon, J., Arichi, T., Rueckert, D., Hajnal, J. V., Edwards, A. D., Smith, S. M., Duff, E., and Andersson, J. The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.  bioRxiv 766030; DOI: https://doi.org/10.1101/766030

• fMRI motion and distortion correction: Techniques described in the following papers were applied to all open access pre-processed fMRI data:

Andersson, J. L. R., Graham, M. S., Drobnjak, I., Zhang, H., and Campbell, J. Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. NeuroImage (2018), 171: 277–295. DOI: 10.1016/j.neuroimage.2017.12.040

Andersson, J. L. R., Graham, M. S., Drobnjak, I., Zhang, H., Filippini, N., and Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage (2017), 152: 450–466. DOI: 10.1016/j.neuroimage.2017.02.085

Andersson, J. L. R., Hutton, C., Ashburner, J., Turner, R., and Friston, K. Modeling Geometric Deformations in EPI Time Series. NeuroImage (2001), 13(5): 903–919. DOI: 10.1006/nimg.2001.0746

Andersson, J. L. R., Skare, S., and Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage (2003), 20(2): 870–888. DOI: 10.1016/S1053-8119(03)00336-7

Diffusion MRI data processing:

• Automated processing pipeline: The pipeline described in the following reference was applied to all dHCP open access diffusion data: 

Bastiani, M., Andersson, J.L.R., Cordero-Grande, L., Murgasova, M., Hutter, J., Price, A.N., Makropoulos, A., Fitzgibbon, S.P., Hughes, E., Rueckert, D., Suresh, V., Rutherford, M., Edwards, A.D., Smith, S., Tournier, J. D., Hajnal, J.V., Jbabdi, S., & Sotiropoulos, S.N. Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. NeuroImage (2018), 185: 750-763. DOI: 10.1016/j.neuroimage.2018.05.064

The pipeline can be downloaded from: 

https://git.fmrib.ox.ac.uk/matteob/dHCP_neo_dMRI_pipeline_release.

• Diffusion imaging distortion correction and quality control:Techniques described in the following references were applied to all dHCP open access pre-processed diffusion data; the last reference describes the automated quality control framework that was used to detect processing issues or inconsistencies: 

Andersson, J.L.R., Skare, S., and Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage (2003), 20: 870-888. DOI: 10.1016/S1053-8119(03)00336-7

Andersson, J.L.R., and Sotiropoulos, S.N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage (2016), 125: 1063-1078.  DOI: 10.1016/j.neuroimage.2015.10.019  

Andersson, J.L.R., Graham, M.S., Zsoldos, E., and Sotiropoulos, S.N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage (2016), 141: 556-572. DOI: 10.1016/j.neuroimage.2016.06.058

Andersson, J.L.R., Graham, M.S., Drobnjak, I., Zhang, H., Filippini, N., and Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage (2017), 152: 450-466. DOI: 10.1016/j.neuroimage.2017.02.085

Andersson, J.L.R., Graham, M.S., Drobnjak, I., Zhang, H., and Campbell, J. (2018). Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. NeuroImage (2018), 171: 277-295. DOI: 10.1016/j.neuroimage.2017.12.040

Bastiani, M., Cottaar, M., Fitzgibbon, S.P., Suri, S., Alfaro-Almagro, F., Sotiropoulos, S.N., Jbabdi, S., & Andersson, J.L.R. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. NeuroImage (2018), 184: 801-812. DOI: 10.1016/j.neuroimage.2018.09.073