Elsevier

NeuroImage

Volume 80, 15 October 2013, Pages 105-124
NeuroImage

The minimal preprocessing pipelines for the Human Connectome Project

https://doi.org/10.1016/j.neuroimage.2013.04.127Get rights and content

Highlights

  • Multi-modal preprocessing pipelines for the Human Connectome Project

  • Description of CIFTI file format and grayordinate coordinate system

  • Combined surface and volume neuroimaging analysis

Abstract

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 grayordinate 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 to the pipelines.

Section snippets

Introduction and rationale

The Washington University–University of Minnesota Human Connectome Project Consortium (WU–Minn HCP) (D. Van Essen et al., 2012) is charged with bringing data from the major MRI neuroimaging modalities, structural, functional, and diffusion, together into a cohesive framework to enable cross-subject comparisons and multi-modal analysis of brain architecture, connectivity, and function. Specifically, the imaging modalities include T1-weighted (T1w) and T2-weighted (T2w) structural scans,

The CIFTI file format and grayordinates, a combined cortical surface and subcortical volume coordinate system

Standard volume-based neuroimaging analyses will be easy to carry out using the outputs of the minimal preprocessing pipelines (e.g. Barch et al., 2013--this issue). However, we note that such analyses will waste many of the potential benefits offered by the high resolution HCP data for greater accuracy in spatial localization, both within individuals and across subject groups. It is now well established (Anticevic et al., 2008, D.C. Van Essen et al., 2012, Fischl et al., 2008, Frost and

HCP structural acquisitions

The HCP structural acquisitions include high resolution T1-weighted (T1w) and T2-weighted (T2w) images (0.7 mm isotropic) for the purpose of creating more accurate cortical surfaces and myelin maps than are attainable with lower resolution data (Glasser and Van Essen, 2011). While high spatial resolution was a primary goal, we also optimized the contrast parameters (flip angle (FA) and inversion time (TI) for the T1w scans, and echo time (TE) for the T2w scans). The major change from Glasser and

HCP functional and diffusion acquisitions

The functional and diffusion acquisitions are described in detail in other papers of this special issue (Ugurbil et al., this issue, Smith et al., this issue, Sotiropoulos et al., this issue), but a number of points relevant to the HCP pipelines are noted here as well. The functional data are acquired at 2 mm isotropic, which is of unusually high spatial resolution for whole-brain coverage at 3 T. Although the spatial specificity of BOLD fluctuations measured at 3 T is around 4 mm FWHM (Parkes et

HCP pipelines: minimum acquisition requirements

A number of investigators have asked questions such as: “How do I acquire data like the HCP?” and “What data do I need in order to use the HCP pipelines?” Though many studies will not require, or be in a position to acquire, data using protocols identical to those in the HCP, it is worth describing the minimum acquisition requirements for the HCP pipelines, along with suggestions for best acquisition practices. For all of the following, a 32-channel head coil will be very beneficial in

HCP pipelines

As shown in Fig. 7, the six minimal preprocessing pipelines include three structural pipelines (PreFreeSurfer, FreeSurfer, and PostFreeSurfer), two functional pipelines (fMRIVolume and fMRISurface), and a Diffusion Preprocessing pipeline. Fig. 7 also shows the overall workflow for preprocessing and data analysis in the HCP. We provide a brief high-level description of all the pipelines, followed by a detailed description of each, including the rationale for choices made in the pipelines and

HCP pipelines: overview

The main goals of the first structural pipeline, PreFreeSurfer, are to produce an undistorted “native” structural volume space for each subject, align the T1w and T2w images, perform a B1 (bias field) correction, and register the subject's native structural volume space to MNI space. Thus, there are two volume spaces in HCP data (overview in Fig. 8): 1) The subject's undistorted native volume space (rigidly “aligned” to the axes of MNI space), which is where volumes and areas of structures

Future directions for the HCP minimal preprocessing pipelines

There are several specific areas in which the minimal preprocessing pipelines might be improved. One would be to use the acquired B1− and B1+ fields to correct the T1w and T2w images for receive and transmit inhomogeneities prior to myelin map generation, rather than relying on the myelin map normalization algorithm described in PostFreeSurfer. Another area of improvement is subcortical segmentation. Although FreeSurfer's subcortical segmentation is highly reliable, in some areas it

Acknowledgments

We thank Mike Harms and Alan Anticevic for their helpful comments on a draft of the manuscript. Additionally, we thank Steve Smith and Avi Synder for helpful discussions related to the minimal preprocessing pipelines. We thank Krish Subramaniam for assistance with the gradient nonlinearity correction code. MFG was supported by an individual fellowship NIH F30 MH097312. The project was supported by the Human Connectome Project (1U54MH091657-01) from the 16 NIH Institutes and Centers that support

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