title: The minimal preprocessing pipelines for the Human Connectome Project.
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.
authors.affiliation: Department of Anatomy and Neurobiology, Washington University Medical School, 660 S. Euclid Avenue, St. Louis, MO 63110, USA. glasserm@wusm.wustl.edu
authors.forename: Matthew F
authors.initials: MF
authors.lastname: Glasser
authors.forename: Stamatios N
authors.initials: SN
authors.lastname: Sotiropoulos
authors.forename: J Anthony
authors.initials: JA
authors.lastname: Wilson
authors.forename: Timothy S
authors.initials: TS
authors.lastname: Coalson
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
authors.forename: Jesper L
authors.initials: JL
authors.lastname: Andersson
authors.forename: Junqian
authors.initials: J
authors.lastname: Xu
authors.forename: Saad
authors.initials: S
authors.lastname: Jbabdi
authors.forename: Matthew
authors.initials: M
authors.lastname: Webster
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Polimeni
authors.forename: David C
authors.initials: DC
authors.lastname: Van Essen
authors.forename: Mark
authors.initials: M
authors.lastname: Jenkinson
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology
keywords: CIFTI; Grayordinates; Human Connectome Project; Image analysis pipeline; Multi-modal data integration; Surface-based analysis