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23 Jan 20. Featured Paper

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

Link to paper on Elsevier, Neuroimage: Clinical

 

Authors

Paola Galdi, Manuel Blesa, David Q. Stoye, Gemma Sullivan, Gillian J. Lamb, Alan J. Quigley, Michael J. Thrippleton, Mark E. Bastin, James P. Boardman

 

Abstract

Multi-contrast MRI captures information about brain macro- & 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 & diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion & density imaging measures, can be modelled as morphometric similarity networks (MSNs).

Here, individual MSNs were derived from 105 neonates (59 preterm & 46 term) who were scanned between 38 & 45 weeks postmenstrual age (PMA).

Inter-regional similarities were used as predictors in a regression model of age at the time of scanning & in a classification model to discriminate between preterm & 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, & the classification model achieved 92% accuracy.

We conclude that MSNs predict chronological brain age accurately; & they provide a data-driven approach to identify networks that characterise typical maturation & those that contribute most to neuroanatomic variation associated with preterm birth.

 

Keywords