TY - JOUR
T1 - The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants
AU - Fitzgibbon, Sean P.
AU - Harrison, Samuel J.
AU - Jenkinson, Mark
AU - Baxter, Luke
AU - Robinson, Emma C.
AU - Bastiani, Matteo
AU - Bozek, Jelena
AU - Karolis, Vyacheslav
AU - Cordero Grande, Lucilio
AU - Price, Anthony N.
AU - Hughes, Emer
AU - Makropoulos, Antonios
AU - Passerat-Palmbach, Jonathan
AU - Schuh, Andreas
AU - Gao, Jianliang
AU - Farahibozorg, Seyedeh Rezvan
AU - O'Muircheartaigh, Jonathan
AU - Ciarrusta, Judit
AU - O'Keeffe, Camilla
AU - Brandon, Jakki
AU - Arichi, Tomoki
AU - Rueckert, Daniel
AU - Hajnal, Joseph V.
AU - Edwards, A. David
AU - Smith, Stephen M.
AU - Duff, Eugene
AU - Andersson, Jesper
PY - 2020/12
Y1 - 2020/12
N2 - The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.
AB - The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.
KW - Connectome
KW - Developing Human Connectome Project
KW - Functional MRI
KW - Neonate
KW - Pipeline
KW - Quality control
UR - http://www.scopus.com/inward/record.url?scp=85090748045&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2020.117303
DO - 10.1016/j.neuroimage.2020.117303
M3 - Article
C2 - 32866666
AN - SCOPUS:85090748045
SN - 1053-8119
VL - 223
JO - NeuroImage
JF - NeuroImage
M1 - 117303
ER -