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Neuroimage Clin. 2020 Jan 23;25:102195. doi: 10.1016/j.nicl.2020.102195. [Epub ahead of print]

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

Author information

1
MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK. Electronic address: paola.galdi@ed.ac.uk.
2
MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK.
3
Department of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, UK.
4
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK; Edinburgh Imaging, University of Edinburgh, Edinburgh EH16 4SB, UK.
5
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.
6
MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.

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.

KEYWORDS:

Brain age; Developing brain; MRI; Morphometric similarity networks; Multi-modal data; Preterm

Conflict of interest statement

Declaration of Competing Interest Authors declare no conflict of interests.

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