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Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth

View ORCID ProfilePaola Galdi, View ORCID ProfileManuel Blesa, David Q. Stoye, Gemma Sullivan, Gillian J. Lamb, Alan J. Quigley, Michael J. Thrippleton, View ORCID ProfileMark E. Bastin, View ORCID ProfileJames P. Boardman
doi: https://doi.org/10.1101/569319
Paola Galdi
aMRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
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  • For correspondence: paola.galdi@ed.ac.uk
Manuel Blesa
aMRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
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David Q. Stoye
aMRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
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Gemma Sullivan
aMRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
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Gillian J. Lamb
aMRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
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Alan J. Quigley
bDepartment of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, UK
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Michael J. Thrippleton
cCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
dEdinburgh Imaging, University of Edinburgh, Edinburgh EH16 4SB, UK
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Mark E. Bastin
cCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
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  • ORCID record for Mark E. Bastin
James P. Boardman
aMRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
cCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
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Abstract

Multi-contrast MRI captures information about brain macro- and micro-structure which can be combined in an integrated model to obtain a detailed “fingerprint” of the anatomical properties of an individual’s brain. Inter-regional similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity networks (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression model of age at the time of scanning and in a classification model to discriminate between preterm and term infant brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70 ± 0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain age accurately; and they provide a data-driven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth.

Highlights

  1. Multiple MRI features are integrated in a single model to study brain maturation in newborns.

  2. Morphometric similarity networks (MSNs) provide a whole-brain description of the structural properties of neonatal brain.

  3. The information encoded in MSNs is predictive of chronological brain age in the perinatal period.

  4. MSNs provide a novel data-driven method for investigating neuroanatomic variation associated with preterm birth.

Footnotes

  • Additional experiments and expanded discussion.

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-NC-ND 4.0 International license.
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Posted March 12, 2019.
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Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth
Paola Galdi, Manuel Blesa, David Q. Stoye, Gemma Sullivan, Gillian J. Lamb, Alan J. Quigley, Michael J. Thrippleton, Mark E. Bastin, James P. Boardman
bioRxiv 569319; doi: https://doi.org/10.1101/569319
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Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth
Paola Galdi, Manuel Blesa, David Q. Stoye, Gemma Sullivan, Gillian J. Lamb, Alan J. Quigley, Michael J. Thrippleton, Mark E. Bastin, James P. Boardman
bioRxiv 569319; doi: https://doi.org/10.1101/569319

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