Abstract
Neuroimage analysis pipelines rely on parcellated atlases generated from healthy individuals to provide anatomic context to structural and diffusion MRI data. Atlases constructed using adult data introduce bias into studies of early brain development. We aimed to create a neonatal brain atlas of healthy subjects that can be applied to multi-modal MRI data. Structural and diffusion 3T MRI scans were acquired soon after birth from 33 typically developing neonates born at term (mean postmenstrual age at birth 39+5 weeks, range 37+2–41+6). An adult brain atlas (SRI24/TZO) was propagated to the neonatal data using temporal registration via childhood templates with dense temporal samples (NIH Pediatric Database), with the final atlas (Edinburgh Neonatal Atlas, ENA33) constructed using the Symmetric Group Normalization (SyGN) method. After this step, the computed final transformations were applied to T2-weighted data, and fractional anisotropy, mean diffusivity, and tissue segmentations to provide a multi-modal atlas with 107 anatomical regions; a symmetric version was also created to facilitate studies of laterality. Volumes of each region of interest were measured to provide reference data from normal subjects. Because this atlas is generated from step-wise propagation of adult labels through intermediate time points in childhood, it may serve as a useful starting point for modeling brain growth during development.
Highlights
Labeled atlases provide anatomic information to a range of structural and diffusion MRI analysis tasks including structural connectivity mapping and spatio-temporal modeling
Using MRI data from 33 healthy newborn infants, we created a neonatal brain atlas that parcellates the brain into 107 anatomical regions that can be applied to T1w, T2w, dMRI (FA and Mean Diffusivity (MD)), and tissue probability maps; it contains a symmetric version of all templates
The framework for atlas creation was based on temporal propagation of a labeled adult brain atlas (SRI24/TZO) via a sequence of MRI templates from childhood to early infancy, which may make it suitable for modeling human brain growth using a consistent set of labels over time
Summary
Labeled atlases provide anatomic information to a range of structural and diffusion MRI (sMRI, dMRI) analysis tasks including structural connectivity mapping and spatio-temporal modeling. The majority of human brain atlases have been developed using adult data (for review see Evans et al, 2012), and their use for studying the brain during early life may not be valid due to differences in adult and newborn anatomy and image properties (Muzik et al, 2000; Wilke et al, 2003; Kazemi et al, 2007; Yoon et al, 2009; Kuklisova-Murgasova et al, 2011) The latter include marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes (Matsuzawa et al, 2001; Paus et al, 2001; Lenroot and Giedd, 2006; Knickmeyer et al, 2008; Serag et al, 2011; Vardhan et al, 2014). While such atlases describe anatomical detail well (Gilmore et al, 2007; Kabdebon et al, 2014; Alexander et al, 2015), they may not capture population diversity adequately (Evans et al, 2012), are time-consuming to generate and are susceptible to inter- and intra-rater variability
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