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

Abstract

1Multi-contrast MRI captures information about brain macro-and micro-structure which can be combined in an inte-2 grated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional 3 similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor 4 metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity net-5 works (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned 6 between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression 7 model of age at the time of scanning and in a classification model to discriminate between preterm and term infant 8 brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70 ± 9 0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain 10 age accurately; and they provide a data-driven approach to identify networks that characterize typical maturation and 11 those that contribute most to neuroanatomic variation associated with preterm birth. 12 Significance Statement 13 Preterm birth affects 15 million deliveries each year and is closely associated with intellectual disability, educational 14under-performance and psychiatric disorders. Imaging studies reveal a cerebral signature of preterm birth that includes 15 alterations in brain structure and network connectivity, but there has not been a unified data-driven approach that 16 incorporates all available information from MRI. We report that morphometric similarity networks (MSNs), which 17 integrate information from structural MRI and diffusion MRI in a single model, accurately predict brain age. MSNs 18 reveal the networks that characterize maturation and those that contribute to neuroanatomic variation associated with 19 preterm birth. MSNs are extensible and offer a new approach for investigating early life origins of neurodevelopmental 20 and mental health disorders 21 1

Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain

“…2019 ). The only work we found to apply MSNs in the perinatal period is a recent study in neonates using a variant of MSNs, where authors were able to successfully predict postmenstrual age (PMA) at scan and differentiated infants born prematurely, with superior performance compared with using single predictive measures ( Galdi et al. 2020 ).…”

Hierarchical complexity of the macro-scale neonatal brain

“…Early exposure to extrauterine life due to preterm birth affects around 11% of births, and is closely associated with neurodevelopmental, cognitive and psychiatric impairment [2,3,4], and alterations to development [5] that are apparent using in vivo imaging techniques. At the macro scale, these alterations can be characterised by charting white matter connections between brain regions using diffusion MRI (dMRI) [6,7,8,9,10,11,12].…”

Maternal cortisol is associated with neonatal amygdala microstructure and connectivity in a sexually dimorphic manner

“…Structural and diffusion MRI (dMRI) have been used to characterise brain structural maturation and emerging network connectivity during the perinatal period, and to investigate pathways to atypical development (23,24). It is a suitable tool to investigate the impact of prenatal stress exposure on the amygdala because age-specific templates enable accurate parcellation of the amygdala and associated structures (25); and diffusion tensor imaging and neurite orientation and dispersion density imaging (NODDI) support inference about tissue microstructure and network connectivity, modelled by fractional anisotropy (FA), mean diffusivity (MD), orientation dispersion index (ODI) and neurite density index (NDI) (26).…”