The minimal preprocessing pipelines for the Human Connectome Project

Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark (2013) The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80 . pp. 105-124. ISSN 1053-8119

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/718719
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine
Identification Number: https://doi.org/10.1016/j.neuroimage.2013.04.127
Depositing User: Eprints, Support
Date Deposited: 11 Jul 2018 10:40
Last Modified: 04 May 2020 16:39
URI: http://eprints.nottingham.ac.uk/id/eprint/52878

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