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

View ORCID ProfileSean 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
doi: https://doi.org/10.1101/766030
Sean P. Fitzgibbon
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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  • ORCID record for Sean P. Fitzgibbon
  • For correspondence: sean.fitzgibbon@ndcn.ox.ac.uk
Samuel J. Harrison
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
kTranslational Neuromodeling Unit, University of Zurich & ETH Zurich
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Mark Jenkinson
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Luke Baxter
ePaediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
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Emma C. Robinson
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Matteo Bastiani
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
fSir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
lNIHR Biomedical Research Centre, University of Nottingham, UK
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Jelena Bozek
dFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
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Vyacheslav Karolis
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Lucilio Cordero Grande
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Anthony N. Price
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Emer Hughes
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Antonios Makropoulos
bBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Jonathan Passerat-Palmbach
bBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Andreas Schuh
bBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Jianliang Gao
bBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Seyedeh-Rezvan Farahibozorg
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Jonathan O’Muircheartaigh
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
gForensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
hMRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
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Judit Ciarrusta
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Camilla O’Keeffe
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Jakki Brandon
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Tomoki Arichi
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
iDepartment of Bioengineering, Imperial College London, UK
jChildren’s Neurosciences, Evelina London Children’s Hospital, King’s Health Partners, London, UK
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Daniel Rueckert
bBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Joseph V. Hajnal
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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A. David Edwards
cCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
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Stephen M. Smith
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Eugene Duff
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
ePaediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
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Jesper Andersson
aWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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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

  1. An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data

  2. Includes integrated dynamic distortion and slice-to-volume motion correction

  3. A robust multimodal registration approach which includes custom neonatal templates

  4. Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection

  5. Data analysis of 538 infants imaged at 26-45 weeks post-menstrual age

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Updated to incorporate comments made by peer-review.

  • http://www.developingconnectome.org/second-data-release/

  • https://git.fmrib.ox.ac.uk/seanf/dhcp-neonatal-fmri-pipeline

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 12, 2020.
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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
bioRxiv 766030; doi: https://doi.org/10.1101/766030
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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
bioRxiv 766030; doi: https://doi.org/10.1101/766030

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