dHCP Fourth Data Release (May 2024)

dHCP Citations

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 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.

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

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., Christiaens, D., Hutter, J., Price, A. N., Cordero-Grande, L., Hughes, E., Bastiani, M., Sotiropoulos, S. N., Smith S. M., Rueckert, D., Counsell, S. J., Edwards, A. D., Hajnal, J. V. A data‐driven approach to optimising the encoding for multi‐shell diffusion MRI with application to neonatal imaging. NMR in Biomedicine. 2020; 33:e4348. doi: 10.1002/nbm.4348

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, S. J., Price A. N., and Hajnal J. V. Optimized Amplitude Modulated Multi-Band RF pulses In Proc ISMRM 2015: p2398

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

Cordero-Grande, L., Hughes, E. J., Hutter, J., Hutter, J., Price, A. N., and Hajnal, J. V. Three-Dimensional Motion Corrected Sensitivity Encoding Reconstruction for Multi-Shot Multi-Slice MRI: Application to Neonatal Brain Imaging Magnetic Resonance in Medicine (2018), 79(3): 1365–1376. DOI: 10.1002/mrm.26796

Diffusion MRI signal retrieval: Denoised diffusion images are obtained by:

Cordero-Grande, L., Christiaens, D., Hutter, J., Price, A. N., and Hajnal, J. V. Complex diffusion-weighted image estimation via matrix recovery under general noise models Neuroimage (2019), 200: 391–404. DOI: 10.1016/j.neuroimage.2019.06.039

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 paper was applied to all dHCP open access fMRI data.

Fitzgibbon, SP., Harrison, SJ., Jenkinson, M., Baxter, L., Robinson, EC., Bastiani, M., Bozek, J., Karolis, V., Cordero Grande, L., Price, AN., Hughes, E., Makropoulos, A., Passerat-Palmbach, J., Schuh, A., Gao, J., Farahibozorg, S., O’Muircheartaigh, J., Ciarrusta, J., O’Keeffe, C., Brandon, J., Arichi, T., Rueckert, D., Hajnal, JV., Edwards, AD., Smith, SM., Duff, E., Andersson, J. The developing Human Connectome Project automated functional processing framework for neonates., NeuroImage (2020), 223: 117303, 2020. DOI: 10.1016/j.neuroimage.2020.117303 Authors contributed equally.

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 EDDY pipeline

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

Diffusion MRI SHARD pipeline

The second dMRI processing pipeline is described in:

Christiaens, D., Cordero-Grande, L., Pietsch, M., Hutter, J., Price, A.N., Hughes, E.J., Vecchiato, K., Deprez, M., Edwards, A.D., Hajnal, J.V., & Tournier, J-D. Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI NeuroImage (2021), 225: 117437. DOI: 10.1016/j.neuroimage.2020.117437

Additionally, inter-slice intensity inconsistencies were corrected with

Pietsch, M. and Christiaens, D. and Hajnal, J.V. & Tournier, J-D. dStripe: slice artefact correction in diffusion MRI via constrained neural network biorxiv (2020) DOI: 10.1101/2020.10.20.347518