The Minimal Preprocessing Pipelines for the Human Connectome Project

By Matthew F. Glasser, Stamatios N. Sotiropoulos, J. A. Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. R. Andersson, Junqian Xu, Saâd Jbabdi, Matthew A. Webster, Jonathan R. Polimeni and 2 more. NeuroImage, 2013

5,317 citations recorded by the source corpus.

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinates spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP’s acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements for the pipelines.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b18ddb0b6b7518526bce6450cea66af5fde4d37e

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Citations in the corpus

Gradients of structure–function tethering across neocortex (2019, neutral): “4 y old) were scanned as part of the HCP Q3 release (84, 85).”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, neutral): “The importance of accounting for relevant confounds is exemplified in Fig 6e, which shows a strong apparent association between total white matter volume and fat-free body mass (one scatter point per subject) without de-confounding.”
Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, methodology): “Generation of surface meshes and associated shape features were carried out using dHCP Structural Pipelines (Makropoulos et al., 2017).”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, methodology): “The high-resolution T1 images were averaged together and used to generate hand-edited cortical surfaces using Freesurfer (Dale et al., 1999) and the resulting surfaces were registered into fs_LR_32k surface space as described in Glasser et al., 2013.”
Proportional thresholding in resting‐state fMRI functional connectivity networks and consequences for patient‐control connectome studies: Issues and recommendations (2017, positive): “Functional connectivity matrices were reconstructed for the Human Connectome Project (Glasser et al., 2013; Van Essen et al., 2012) (Q3 release, resting-state fMRI data of 466 healthy controls included, voxel-size 2mm isotropic, TR/TE 720/33.”
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project (2013, methodology): “Additionally, new cortical surfacebased and constrained subcortical volume-based preprocessing and analysis methods have been developed to maximize the spatial specificity of the rfMRI and tfMRI data generated (Barch et al., 2013; Glasser et al., 2013; Smith et al., 2013).”
Functional system and areal organization of a highly sampled individual human brain (2015, methodology): “Surface extraction and sampling of functional data to the brain surface followed procedures similar to those previously described in Glasser et al. (2013).”
neuromaps: structural and functional interpretation of brain maps (2022, methodology): “HCP processing pipeline All structural data were preprocessed using the HCP minimal preprocessing pipelines [27, 78].”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, methodology): “The downloaded data had been minimally preprocessed as described in Smith et al. (2013) and Glasser et al. (2013).”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “The minimal preprocessing pipelines developed by the Human Connectome Project (HCP) (Glasser et al., 2013) are good examples of pipelines optimised for the speciﬁc needs of a project with very special characteristics.”
Denoising of diffusion MRI using random matrix theory (2016, methodology): “Other examples of targeted image processing tools are the Gibbs correction framework of Kellner et al. (2015) or FSL’s TOPUP and EDDY for EPI and eddy current distortion corrections, respectively (Smith et al., 2004; Sotiropoulos et al., 2013; Glasser et al., 2013).”