How to acknowledge HCP and cite HCP publications if you have used data provided by the WU-Minn HCP consortium.
As stipulated in the Data Use Terms, authors of publications or presentations that use WU-Minn HCP data should acknowledge the funding sources and cite relevant publications that describe key methods used by the HCP to acquire and process the data. This web page provides guidance on both fronts.
Scientific publications that make use of HCP data should follow the authorship guidelines of the Society for Neuroscience.
Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from HCP data should contain the following wording in the acknowledgments section:
"Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University."
The specific publications that are appropriate to cite will depend on what HCP 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, preprocessing, and analysis. As additional publications become available, this list will be updated to include those that it may be relevant to cite.
The publications in this section describe HCP 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. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis.
David C. Van Essen, Stephen M. Smith, Deanna M. Barch, Timothy E.J. Behrens, Essa Yacoub, Kamil Ugurbil, for the WU-Minn HCP Consortium. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage 80(2013):62-79.
MR pulse sequences for multi-band EPI data (diffusion and BOLD fMRI). The EPI data acquired by the WU-Minn HCP have benefited greatly from the use of multi-band pulse sequences. Accordingly, for any studies that make use of HCP functional and/or diffusion MRI data, it is appropriate to cite the following four references.
Multiband multislice EPI: This was the first paper to use multiband accelerated imaging for fMRI. It brought to light the potential advantages of using multiband to significantly reduce the repetition time (TR) in fMRI, which can become impractically long in high resolution studies.
Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, Harel N, and Ugurbil K. (2010). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med. 63(5):1144-1153.
Multiplexed EPI: This paper combined multiband imaging with simultaneous image refocusing in order to increase the number of slices for fMRI or diffusion imaging, compared to using either of the techniques separately, that could be acquired during a single EPI echo train. It was the first paper to demonstrate the advantages of this multiplexed imaging to improve the detection of resting state networks, achieving whole brain TRs of 300 msec.
Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Gunther M, Glasser MF, Miller KL, Ugurbil K, and Yacoub E. (2010). Multiplexed Echo Planar Imaging for sub-second whole brain fMRI and fast diffusion imaging.PLoS One 5:e15710.
Blipped-controlled aliasing for simultaneous multislice EPI: Controlled aliasing permits high acceleration factors because it reduces the g-factor related SNR losses; however, in EPI its use has been limited due to voxel tilting. This paper was the first to demonstrate that controlled aliasing could be used in multiband accelerated EPI without introducing a significant amount of voxel tilting.
Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, and Wald LL. (2012). Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med. 67(5):1210-1224.
Highly accelerated whole brain imaging: The use of balanced controlled aliasing for EPI to reduce voxel tilting can result in a non-zero gradient moment at the center of k-space, introducing phase errors that can corrupt multiband slice unaliasing when high accelerations are used. This work demonstrates that aligning the k-space center may reduce sensitivity to phase errors in higher slice accelerations. This sequence is the variant of the multiband sequence currently being used by the HCP.
Xu J, Moeller S, Strupp J, Auerbach E, Feinberg DA, Ugurbil K, and Yacoub E. (2012). Highly accelerated whole brain imaging using aligned-blipped-controlled-aliasing multiband EPI. Proc. Int. Soc. Mag. Reson. Med. 20:2306.
Diffusion imaging reconstruction. The reconstruction algorithm for open access diffusion scans is based on the following reference. This study demonstrates that Sum of Squares reconstruction introduces a noise floor into multi-channel diffusion MR data that causes substantial biases in subsequent data analyses. It proposes SENSE1 recon as an alternative reconstruction algorithm, and demonstrates that this approach overcomes these problems:
S.N. Sotiropoulos, S. Moeller, S. Jbabdi, J. Xu, J. L. Andersson, E. J. Auerbach, E. Yacoub, D. Feinberg, K. Setsompop, L.L. Wald, T. E. J. Behrens, K. Ugurbil, and C. Lenglet. (2013). Effects of Image Reconstruction on Fibre Orientation Mapping from Multichannel Diffusion MRI: Reducing the Noise Floor Using SENSE. Magnetic Resonance in Medicine 00:000–000 (2013)
Defacing algorithm for structural MRI. All open access HCP structural scans (T1w and T2w) were defaced using the algorithm reported in this study:
Milchenko M, and Marcus D. (2012). Obscuring surface anatomy in volumetric imaging data. Neuroinformatics 2012 Sep 12 [Epub ahead of print].
DICOM to NIFTI conversion. Conversion of DICOM files NIFTI format was carried out using the dcm2nii utility. This utility is a component of the MRIcron suite of tools developed by Chris Rorden ( http://www.nitrc.org/projects/mricron/).
The publications in this section describe methods that are relevant if you have downloaded and used any of the HCP pre-processed data involving one or more modalities.
General Preprocessing. The HCP MRI data pre-processing pipelines are primarily built using tools from FSL and FreeSurfer. The major references are:
Matthew F. Glasser, Stamatios N. Sotiropoulos, J. Anthony Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. Andersson, Junqian Xu, Saad Jbabdi, Matthew Webster, Jonathan R. Polimeni, David C. Van Essen, and Mark Jenkinson (2013).
The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80: 105-124.Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, and Smith SM. (2012). FSL. NeuroImage, 62:782-790.
Fischl B. (2012). FreeSurfer. NeuroImage, 62:774-781.
Jenkinson M, Bannister PR, Brady JM, and Smith SM. (2002). Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825-841.
Structural pre-processing.
Glasser MF, and Van Essen DC. (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci. 31:11597-11616
Van Essen DC, Glasser MF, Dierker DL, Harwell J, and Coalson T. (2012). Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22:2241-2262
Multimodal Surface Matching (MSM) Registration. Improved intersubject registration of functional cortical areas based on information on areal features derived from myelin, Resting State Network (RSN), and rfMRI visuotopic maps in a joint multi-modal registration as described in these papers:
Robinson EC, Jbabdi S, Glasser MF, Andersson J, Burgess GC, Harms MP, Smith SM, Van Essen DC, and Jenkinson M. (2014). MSM: a new flexible framework for Multimodal Surface Matching. Neuroimage 100:414-426
Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L., Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, and Rueckert D. (2018). Multimodal surface matching with higher-order smoothness constraints. Neuroimage 167:453-465
Task-fMRI processing. The HCP task-FMRI analysis uses FEAT (FMRIB’s Expert Analysis Tool), with the major references being the FSL overview paper (see above) and the following. This reference describes FEAT’s approach to multiple regression with autocorrelation modelling and prewhitening:
Woolrich MW, Ripley BD, Brady JM, and Smith SM. (2001). Temporal autocorrelation in univariate linear modelling of FMRI data. NeuroImage 14(6):1370-1386.
Diffusion imaging distortion correction. Techniques described in the next two references were applied to all HCP open access pre-processed diffusion data:
Andersson JL, Skare S, and Ashburner J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage. 20(2):870-888.
Andersson JL, Sotiropoulos SN (2015). Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes. Neuroimage. 2015 Nov 15;122:166-76.
Andersson JL, Sotiropoulos SN (2015). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2015 Oct 16. pii: S1053-8119(15)00920-9.
Neuroinformatics Platform. This paper provides an overview of the HCP neuroinformatics infrastructure, including the ConnectomeDB database and the Connectome Workbench visualization software.
Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, and Van Essen DC. (2011). Informatics and data mining: Tools and strategies for the Human Connectome Project. Frontiers in Neuroinformatics 5:4.