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
Preterm birth is closely associated with increased risk of neurodevelopmen3 tal, cognitive and psychiatric impairment that extends across the life course 4 (Nosarti et al, 2012; Anderson, 2014; Mathewson et al, 2017; Van Lieshout 5 et al, 2018)
We conclude that morphometric similarity networks (MSNs) predict chronological brain age accurately; and they provide a datadriven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm
Structural and diffusion MRI support the con6 ceptualisation of atypical brain growth after preterm birth as a process charac7 terised by micro-structural alteration of connective pathways due to impaired 8 myelination and neuronal dysmaturation (Boardman et al, 2006; Anjari et al, 9 2007; Counsell et al, 2008; Ball et al, 2013b; Back and Miller, 2014; Van Den Heuvel et al, 2015; Eaton-Rosen et al, 2015; Thompson et al, 2016; Batalle et al, 2017; Telford et al, 2017; Batalle et al, 2018); this leads to a “dysconnec12 tivity phenotype” that could form the basis for long term functional impairment (Boardman et al, 2010; Caldinelli et al, 2017; Keunen et al, 2017; Cao et al, 2017; Batalle et al, 2018b)
Summary
Preterm birth is closely associated with increased risk of neurodevelopmen tal, cognitive and psychiatric impairment that extends across the life course 4 (Nosarti et al, 2012; Anderson, 2014; Mathewson et al, 2017; Van Lieshout 5 et al, 2018). Structural and diffusion MRI (sMRI and dMRI) support the con ceptualisation of atypical brain growth after preterm birth as a process charac terised by micro-structural alteration of connective pathways due to impaired 8 myelination and neuronal dysmaturation (Boardman et al, 2006; Anjari et al, 9 2007; Counsell et al, 2008; Ball et al, 2013b; Back and Miller, 2014; Van Den Heuvel et al, 2015; Eaton-Rosen et al, 2015; Thompson et al, 2016; Batalle et al, 2017; Telford et al, 2017; Batalle et al, 2018); this leads to a “dysconnec tivity phenotype” that could form the basis for long term functional impairment (Boardman et al, 2010; Caldinelli et al, 2017; Keunen et al, 2017; Cao et al, 2017; Batalle et al, 2018b). Other approaches have constructed subjectspecific SCNs (Li et al, 2017; Mahjoub et al, 2018) or higher order morphological networks to model the relationship between ROIs across different views (Soussia and Rekik, 2018), but these techniques have been restricted to the use of morphometric variables available through standard structural T1-weighted
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