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

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

Posted 06 Mar 2019
bioRxiv DOI: 10.1101/569319 (published DOI: 10.1016/j.nicl.2020.102195)

Multi-contrast MRI captures information about brain macro-and micro-structure which can be combined in an inte-grated 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 characterize typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth. Significance Statement Preterm birth affects 15 million deliveries each year and is closely associated with intellectual disability, educational under-performance and psychiatric disorders. Imaging studies reveal a cerebral signature of preterm birth that includes alterations in brain structure and network connectivity, but there has not been a unified data-driven approach that incorporates all available information from MRI. We report that morphometric similarity networks (MSNs), which integrate information from structural MRI and diffusion MRI in a single model, accurately predict brain age. MSNs reveal the networks that characterize maturation and those that contribute to neuroanatomic variation associated with preterm birth. MSNs are extensible and offer a new approach for investigating early life origins of neurodevelopmental and mental health disorders

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