Abstract The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 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. Highlights An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data Includes integrated dynamic distortion and slice-to-volume motion correction A robust multimodal registration approach which includes custom neonatal templates Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection Data analysis of 538 infants imaged at 26-45 weeks post-menstrual age

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants