Developmental score of the infant brain: characterizing diffusion MRI in term- and preterm-born infants
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ORIGINAL ARTICLE
Developmental score of the infant brain: characterizing diffusion MRI in term‑ and preterm‑born infants Dan Wu1 · Linda Chang2,3 · Thomas M. Ernst4 · Brian S. Caffo5 · Kenichi Oishi6 Received: 10 April 2020 / Accepted: 10 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Large-scale longitudinal neuroimaging studies of the infant brain allow us to map the spatiotemporal development of the brain in its early phase. While the postmenstrual age (PMA) is commonly used as a time index to analyze longitudinal MRI data, the nonlinear relationship between PMA and MRI data imposes challenges for downstream analyses. We propose a mathematical model that provides a Developmental Score (DevS) as a data-driven time index to characterize the brain development based on MRI features. 319 diffusion tensor imaging (DTI) datasets were collected from 87 term-born and 66 preterm-born infants at multiple visits, which were automatically segmented based on the JHU neonatal atlas. The mean diffusivity (MD) and fractional anisotropy (FA) in 126 brain parcels were used in the model to derive DevS. We demonstrate that transforming the time index from PMA to DevS improves the linearity of the longitudinal changes in MD and FA in both gray and white matter structures. More importantly, regional developmental differences in DTI metrics between preterm- and term-born infants were identified more clearly using DevS, e.g. 79 structures showed significantly different regression patterns in MD between preterm- and term-born infants, compared to only 27 structures that showed group differences using PMA as the index. Therefore, the DevS model facilitates linear analyses of DTI metrics in the infant brain, and provides a useful tool to characterize altered brain development due to preterm-birth. Keywords Developmental score · Infant brain · Diffusion tensor imaging · Preterm-born · Longitudinal · Segmentation Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00429-020-02132-4) contains supplementary material, which is available to authorized users. * Kenichi Oishi [email protected] 1
Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
2
Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
3
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
4
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
5
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
6
Russell H. Morgan Department of Radiology, Johns Hopkins University School of Medicine, Traylor 217, 720 Rutland Ave, Baltimore, MD 21215, USA
Introduction Brain development is a highly dynamic and intricate process that lays the foundat
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