NeuroImage (Dec 2020)
The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants
- Sean P. Fitzgibbon,
- Samuel J. Harrison,
- Mark Jenkinson,
- Luke Baxter,
- Emma C. Robinson,
- Matteo Bastiani,
- Jelena Bozek,
- Vyacheslav Karolis,
- Lucilio Cordero Grande,
- Anthony N. Price,
- Emer Hughes,
- Antonios Makropoulos,
- Jonathan Passerat-Palmbach,
- Andreas Schuh,
- Jianliang Gao,
- Seyedeh-Rezvan Farahibozorg,
- Jonathan O'Muircheartaigh,
- Judit Ciarrusta,
- Camilla O'Keeffe,
- Jakki Brandon,
- Tomoki Arichi,
- Daniel Rueckert,
- Joseph V. Hajnal,
- A. David Edwards,
- Stephen M. Smith,
- Eugene Duff,
- Jesper Andersson
Affiliations
- Sean P. Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Corresponding author.
- Samuel J. Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Switzerland
- Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Luke Baxter
- Paediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
- Emma C. Robinson
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK
- Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
- Vyacheslav Karolis
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Lucilio Cordero Grande
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Anthony N. Price
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Emer Hughes
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Jonathan O'Muircheartaigh
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Judit Ciarrusta
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Camilla O'Keeffe
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Jakki Brandon
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Tomoki Arichi
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Department of Bioengineering, Imperial College London, UK; Children's Neurosciences, Evelina London Children's Hospital, King's Health Partners, London, UK
- Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- Joseph V. Hajnal
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- A. David Edwards
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Journal volume & issue
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Vol. 223
p. 117303
Abstract
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.
