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

摘要: 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 multi-modal MRI data from over 1000 in- and ex-utero subjects combined with development optimised pre-processing pipelines. In this paper we present an automated robust pipeline minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, low failure rates high quality-assurance. has been designed specifically address challenges that presents including variable contrast levels head motion. We provide description evaluation which includes integrated slice-to-volume motion correction dynamic susceptibility distortion correction, multimodal registration approach, bespoke ICA-based denoising, QC framework. assess these components on large cohort dHCP demonstrate processing refinements into substantial reduction in movement related distortions, resulting significant improvements SNR, detection quality RSNs neonates.